2023 Annual report"Hyper-adaptability for overcoming body-brain dysfunction:
Integrated empirical and system theoretical approaches"
Program Director: Jun Ota (The University of Tokyo)

Grant-in-Aid for Scientific Research on Innovative Areas(Research in a proposed research area), supported by Japan Society for the
Promotion Science (JSPS), and The Ministry of Education, Culture, Sports,
Science and Technology (MEXT), Japan
Area No. 8102
Fiscal Year: 2019-2023
https://www.hyper-adapt.org/

Contents
Program Overview and Activities of Steering Committee
Principal investigator, Jun OTA (Professor, The University of Tokyo)
Annual report of “Financial support for International Collaboration and Human Resource
Development”
Principal investigator, Jun OTA (Professor, The University of Tokyo)
Activities of Group A (Experiment and Analysis)
Leader of Group A, Tadashi ISA (Professor, Kyoto University)
Annual report of research project A01
Principal investigator, Tadashi ISA (Professor, Kyoto University)
Annual report of research project A02
Principal investigator, Kazuhiko SEKI (Director, NCNP)
Annual report of research project A03
Principal investigator, Hiroshi IMAMIZU (Professor, The University of Tokyo)
Annual report of research project A04
Principal investigator, Kaoru TAKAKUSAKI (Professor, Asahikawa Medical University)
Annual report of research project A05-1
Principal Investigator, Yuichi TAKEUCHI (Associate Professor, Hokkaido University)
Annual report of research project A05-2
Principal Investigator, Shinichi IZUMI (Professor, Tohoku University)
Annual report of research project A05-3
Principal Investigator, Rie KIMURA (Specially Appointed Assistant Professor,
The University of Tokyo)
Annual report of research project A05-4
Principal Investigator, Akihiro FUNAMIZU (Lecturer, The University of Tokyo)
Annual report of research project A05-5
Principal Investigator, Yoko Yazaki-Sugiyama (Associate Professor, The Okinawa Instituteof Science and Technology Graduate University / Project Associate Professor,
The University of Tokyo)
Annual report of research project A05-7
Principal Investigator, Riki MATSUMOTO (Professor, Kobe University)

Annual report of research project A05-8
Principal Investigator, Hiroyuki MIYAWAKI (Lecturer, Osaka
Metropolitan University)
Annual report of research project A05-9
Principal Investigator, Takaki MAEDA (Lecturer, Keio University)
Annual report of research project A05-10
Principal Investigator, Tomohiko TAKEI (Associate Professor, Brain Science Institute,Tamagawa University)
Annual report of research project A05-11
Principal Investigator, Hironobu OSAKI (Program-Specific Associate Professor,
Doshisha University)
Annual report of research project A05-12
Principal Investigator, Rieko OSU (Professor, Waseda University)
Annual report of research project A05-13
Principal Investigator, Kosei TAKEUCHI (Professor, Aichi Medical University)
Annual report of research project A05-14
Principal Investigator, Tatsuya MIMA (Professor, Ritsumeikan University)
Annual report of research project A05-15
Principal Investigator, Atsushi NAMBU (Professor, National Institute for Physiological
Sciences)
Annual report of research project A05-16
Principal Investigator, Noriyuki HIGO (Group Leader, National Institute of Advanced
Industrial Science and Technology)
Annual report of research project A05-17
Principal Investigator, Osamu YOKOYAMA (Senior Researcher, Tokyo Metropolitan
Institute of Medical Science)
Activities of Group B (Mathematical Model)
Leader of Group B, Toshiyuki KONDO(Professor, Tokyo University of Agriculture and Technology)
Annual report of research project B01
Principal investigator, Toshiyuki KONDO(Professor, Tokyo University of Agriculture and Technology)
Annual report of research project B02
Principal investigator, Yasuharu KOIKE (Professor, Tokyo Institute of Technology)

Annual report of research project B03
Principal investigator, Hajime ASAMA (Professor, The University of Tokyo)
Annual report of research project B04
Principal investigator, Jun OTA (Professor, The University of Tokyo)
Annual report of research project B05-1
Principal Investigator, Mitsuhiro HAYASHIBE (Professor, Tohoku University)
Annual report of research project B05-2
Principal Investigator, Naomichi OGIHARA (Professor, The University of Tokyo)
Annual report of research project B05-3
Principal Investigator, Hoshinori KANAZAWA (Research Assistant Professor,
The University of Tokyo)
Annual report of research project B05-4
Principal Investigator, Isao NAMBU(Associate Professor, Nagaoka University of Technology)
Annual report of research project B05-5
Principal Investigator, Yuichi KOBAYASHI (Associate Professor, Shizuoka University)
Annual report of research project B05-6
Principal Investigator, Michiteru KITAZAKI (Professor, Toyohashi University ofTechnology)
Annual report of research project B05-7
Principal Investigator, Taishin NOMURA (Professor, Osaka University)
Annual report of research project B05-8
Principal Investigator, Kazuhiro SAKAMOTO(Associate Professor, Tohoku Medical and Pharmaceutical University)
Annual report of research project B05-9
Principal Investigator, Takeshi SAKURADA (Associate Professor, Seikei University)
Annual report of research project B05-10
Principal Investigator, Yuki UEYAMA(Associate Professor, National Defense Academy of Japan)
List of Publications
Member List

X00 Program Overview and
Activities of Steering Committee
Jun Ota

Research into Artifacts, Center for Engineering (RACE), School of Engineering, the University of Tokyo

I. PURPOSE OF THE RESEARCH PROJECT
In Japan, where the population is rapidly aging at an
unprecedented pace, brain and motor dysfunction, such as
stroke and spinal cord injuries and frailty, which is decline ofbodily and neurological functions, are rapidly increasing. Here,
there is a common source where we ourselves cannot adapt
well to these changes in the body-brain system.
The human body has a high degree of redundancy. For
example, “when a hand is paralyzed by a spinal cord injury,
the ipsilateral motor cortex immediately joins its control byreactivating its pre-existing neural pathway, which is normallysuppressed and preserved in the course of development” (Isa,2019).
In light of such facts, we believe that clarifying the brain’s“hyper-adaptability” may resolve the abovementioned issues.
The goal of our research project is to elucidate the neuraland computational principles of hyper-adaptability in which
the brain manages impairment of brain functions by linking
neuroscience with systems engineering in order tocomprehensively understand acute/chronic impairments anddisorders, and the principle of frailty.
II. CONTENT OF THE RESEARCH PROJECT
When a person experiences acute/chronic impairment or
disorder due to aging, the brain reorganizes neural networks
by disinhibiting pre-existing neural network that is normallysuppressed and searching for latent but available network thathas long been unutilized through course of evolution anddevelopment. We call this process of functional compensation
as “reconstruction of neural structure”, i.e. a neural entity that
achieves hyper-adaptability. In order to implement practical
functions to this reconstituted neural network, the network
should acquire a new control policy of motor effectors basedon precise recognition of the present states of the brain andthe body. Here, the brain has to activate the new network by
repeatedly performing neural computations and updates the
network based on prediction error. We call this learning cycle
in a new control space as “reconstitution of sensorimotor
control rules”, i.e. neural computation principle that enables
hyper-adaptability.
In order to verify the hypotheses described above,
knowledge of neuroscience is essential. However, with only
the “bottom-up” approach relying on experiments andanalyses, it would be difficult to clarify hyper-adaptability

that is manifested by systematic behavior of a neural network.
Therefore, we apply an interdisciplinary approach that
integrates the mathematical modeling technology of systems
engineering with neuroscience (Fig. 1). We adopt two new
analytical
approaches:(a)
Robotic-interventional
neuroscience, i.e. combinatory use of well-controlled robotic
technologies and biological approaches of viral vector,
optogenetics, chemogenetics and brain stimulation. This
allows verification of cause-effect relationship of neuralactivity and its generated functions and behaviors. (b)Function-oriented neural encoding, which constitutes a modelthat may incorporate any knowledge of brain functions intogray-box modeling or hypothesizes the structure of a model
based on statistical methods.
III. ACTIVITIESFollowing events were held by management group.
A. Activities organized by the project- The 2nd international symposium HypAd2023
Date: October 28, 2023
October 29, 2023
Place: Kyoto University Centennial Clock Tower Memorial
Hall
Contents: the 2nd International Symposium (HypAd2023)
was held with full local participation. The symposium invited
Prof. Andrea d'Avella (University of Messina, Italy), Prof.
Steven Cramer (University of California, Los Angeles, USA),
Prof. Grégoire Courtine and Prof. Jocelyne Bloch (Swiss
Federal Institute of Technology, Lausanne, Switzerland), andDiego Torricelli of the Cajal Institute, Spain to give invitedlectures at the site. In addition, 14 members of the planninggroup and some members of the subscribed research groups
presented their research results. There were also 60 poster
presentations from inside and outside of the project, andactive discussions took place. Total number of participants
was 120.
- 12th management meeting
Date: October 28, 2023, 12:15 - 1:00 p.m.
Place: Kyoto University Clock Tower Conference RoomContent: members of the management group discussed about
the management of the project, plenary conference, public
symposiums, and other meetings.
- 2nd evaluation committee meeting
Date: October 29, 2023, 17:00-18:00

Place: Kyoto University Clock Tower Conference RoomContents: under the participation of evaluation committee
members Prof. Ugawa and Prof. Kitazawa, and external
evaluation committee member Prof. d'Avella, the status andresults of project activities were explained, evaluated by each
evaluation committee member, and opinions were exchanged.
- 6th plenary conference
Date: March 1 - March 2, 2024
Place: Hiroshima University, Kasumi Campus
Contents: summary of the research in the year was presented
by project representative and researchers in the project.
- 13th management meeting
Date: March 1, 2024, 12:00-13:00
Location: Hiroshima University, Kasumi Campus
Content: members of the management group discussed about
the management of the project, about the public symposium inthe next year, etc.
B. Organized Session- OS: IEEE-EMBC 2023 (45th Annual International
Conference of the IEEE Engineering in Medicine and Biology
Society)
Date: July 27, 2023
Location: International Convention Centre, Sydney, Australia.
Contents: A Mini-Symposium was held at IEEE-EMBC 2023,
and five researchers were presented, including four members
from the project and one invited researcher.
- OS: The 60th Annual Conference of the Japanese
Association of Rehabilitation Medicine
Date: July 30, 2023
Place: Fukuoka International Congress Center, Fukuoka,
Japan
Contents: A joint symposium "Hyper-adaptation in
Rehabilitation" was held on June 30. Five researcherspresented and a panel discussion was held.
- OS: The 46th Annual Meeting of the Japan Neuroscience
Society
Date: August 1, 2023
Place: Sendai International Center, Miyagi Prefecture
Contents: A symposium, "New Horizons of Hyperadaptation," was held on August 1st and five researcherspresented their research.
- Research Meeting: 37th Basal Ganglia Research Meeting
Location: Art Hotel Asahikawa, Hokkaido
Date: August 19 and August 20, 2023
Conents: on August 19 and 20, a research meeting about basal
ganglia research was held, and two special lectures and a
symposium were held.
- OS: MHS 2023 (34th International Symposium on MicroNanoMechatronics and Human Science)
Date: November 20, 2023

Place: Noyori Conference Hall, Nagoya University
Contents: symposium "Hyper-Adaptability" was held. One
plenary talk by B05-5 Kobayashi, and 5 talks including one
keynote talk were presented.
- OS: The 36th Symposium on Autonomous Decentralized
Systems
Date: February 16 - 17, 2024
Place: Tokyo University of Agriculture and Technology,
Koganei Campus
Contents: OS "Hyper-adaptation" was held and presentations
from project members were made.
IV. ACTIVITIES BY YOUNG RESEARCHERS
The Young Scientists' Group, chaired by Dr. Qi An (The
University of Tokyo), has been organized to promote theactivities of young scientists in this field. This year's activities
are listed below.
A. Workshop and Organized Session
A special session was held on July 27, 2023 at IEEEEMBC2023 (Sydney, Australia). Prof. Jun Ota, Prof. Hiroshi
Imamizu, Prof. Victor Barradas, Prof. Qi An, Prof. Lingbin
Bian attended the session and the results were presented anddiscussed actively.
On November 01, 2023, a workshop was held jointlywith Moonshot Goal 3 (“Development of Awareness AI
Robot System to Promote Proactive Behavior Change”) at the
University of Tokyo. Prof. Jun Ota, Prof. Arito Yozu, Prof.
Qi An, and Dr. Kohei Kaminishi attended the workshop.
Presentations and discussions were held on diagnostic
techniques and modeling of Parkinson's disease.
Organized session about hyper-adaptability was held on
February 16-17, 2024 at The 36th Symposium on Distributed
Autonomous Systems (Tokyo University of Agriculture andTechnology). There were 12 presentations, mainly fromwithin the field, and research results were presented andactively discussed.
B. Research Meeting
On October 27, 2023, a meeting of the Young Researchers
was held at Kyoto University. About 20 young researchers
participated in the meeting. They presented their research anddiscussed about important factors to promote phenomena ofhyper-adaptability.
In addition, on February 29, 2024, a meeting of theYoung Researchers was held in Hiroshima, and many young
researchers participated and exchanged their views on hyperadaptability phenomena.
V. FUTURE PERSPECTIVE
The third public symposium is scheduled to be held inTokyo in September 2024.

Annual report of “Financial support for International
Collaboration and Human Resource Development”
Jun Ota

Research into Artifacts, Center for Engineering (RACE), School of Engineering, the University of Tokyo

I. INTRODUCTION
The "Financial support for International Collaboration and
Human Resource Development" program for hyperadaptability provided support for activities in line with thefollowing objectives.
a. To improve the level of research in the field of hyperadaptation and increase its global presence through discussions
and joint research by inviting prominent overseas researchers
or by staying at overseas research institutes of the project
members.
To enable members of the hyper-adaptability project tointeract with world-class researchers so that they can maketheir research widely known to the public. Through these
activities, we aim to form the basis for building aninternational network. In addition, we will support the writingof jointly authored papers through collaborative research.
Spread the research content of the project throughout the world.
Activities aimed at getting members to invite others to theirconferences, etc.
b. Develop hyper-adaptability human resources throughmutual exchange between the Neuroscience and Systems
Engineering laboratories.
Aiming to develop human resources with knowledge inboth neuroscience and systems engineering, researchers ineach area will spend a short period of time in the laboratory of
the other area to gain an understanding of its contents.
II. SUPPORT FOR ACTIVITIES IN FY2023
In FY2023, support was provided for the following three
activities.
Type: Invitation of overseas researchers
Applicant: B03 Qi An (Graduate School of Frontier Sciences,
The University of Tokyo)
Description: Dr. Juan Moreno of the Caja Institute (Spain)
was invited as the international support. Dr. Moreno is aleading researcher in the field of neurorehabilitation in Europe,and has a great experience in functional electrical stimulationand exoskeletal robotic rehabilitation. During his stay in Japan,

we exchanged opinions on diagnostic techniques in daily life
space, which is particularly important for Parkinson's disease.
As a result, we have devised a joint research project on
diagnostic technology using a shoe-type force sensor
developed by our laboratory, which will be carried out in the
future.
Type: Invitation of overseas researchers
Applicant: B02 Yasuharu Koike (Institute of Innovative
Research, Tokyo Institute of Technology)
Description: Dr. BIAN LINGBIN from Shanghai Universityof Science and Technology was invited to IEEE EMBC 2023
held in Sydney from July 24 to 27 to give a talk on "Bayesiancommunity detection for brain states and transitions using
functional He discussed the latest results of fMRI analysis,
and also discussed the analysis of hyperadaptive brain activity.
We also invited Dr. Nicolas Schwighofer of the University ofSouthern California to Tokyo Tech to collaborate on a modelof brain motor control and learning. We discussed
computational models that reproduce the results ofpsychophysical experiments on arm movements.
Type: Invitation of overseas researchers
Applicant: B03 Jun Izawa (Institute of Systems andInformation Engineering, University of Tsukuba)Contents: From 2023/10/22 to 10/30, we invited Dr. Lucas
Dal'Bello, a young researcher from Fondazione Santa Lucia in
Italy, to conduct joint research discussions at the University ofTsukuba. This was followed by a presentation and discussion
at the 2nd International Symposium Hyper-Adaptability 2023,
hosted by Kyoto University. He presented the results of ourcollaboration about hyper-adaptability in the symposium.

Group A: Neuroscience
Tadashi Isa, Professor, Kyoto University
isa.tadashi.7u@kyoto-u.ac.jp
I. AIM OF THE GROUP
The traditional motor control research field has worked on theadaptation mechanism of the neural systems. “Hyperadaptation” operates as the biological responses to the severe
acute insults such as brain and spinal cord injury or chronic
dysfunctions of the brain and spinal cord caused by aging andfrailty, far beyond the ordinary adaptation. The Group A(Neuroscience) will aim at clarifying the mechanism of“Reconstruction of neural structures” and “Reconstruction of
sensorimotor control rules” associated with Hyper-adaptation
through experimental studies mostly in the field of
neurosciences. However, just looking at the experimental data,
it is difficult to get insight into the principles of the neuralsystem operation underlying the obtained data. Therefore, theGroup A will promote research in collaboration with theGroup B (Systems engineering) from the start of designing theexperiments.
II. MEMBERS
Group A01 (Isa, Naito, Aizawa, Asada, Nakano) aimed atrevealing the global “disinhibition” associated with therecovery from brain and spinal cord injury (experiments on
nonhuman primates), or aging (experiments on humans) and at
elucidating its neuronal mechanisms in rodents. Isa group
showed that decrease of inhibitory connectives among brain
areas induced the disinhibition associated with recovery after
spinal cord injury in monkeys. Naito group conductedvisualization of trainability and plasticity of interhemisphericinhibition between left and right motor cortices. Aizawa grouptested dynamics of acetylcholine released into the extracellularspace when the hyperadaptive reorganization of the nervous
structure upon large-scale brain injury.
Group A02 (Seki) In the final fiscal year of 2023, we
advanced the publication of research results on muscle
relocation surgery obtained up to the previous year andsubmitted them to academic journals, but they have not yet
been accepted. Currently, based on comments from reviewers,
the manuscript is being thoroughly revised, enabling newinterpretations of the experimental results. Furthermore, thisfiscal year has seen progress in research on rehabilitation forstroke, a concrete example of these adaptive changes in clinicalsettings.
Group A03 (Imamizu, Tsutsui) In the human study, weclarified the formation process of "sense of agency" in motor
learning that is so novel that the outcome cannot be predicted,
which is assumed in hyper-adaptation. In addition, we found
the right hemispheric dominance in the induction of the senseof agency by brain stimulation. In the monkey study, we
identified the emotional signal flowing from the cingulate to
MI cortex before the onset of task-related hand movement.
Group A04 (Takakusaki, Hanakawa) In the 5 years of
research, animal experiments by the Takakusaki group revealed

that dopamine (DA) facilitated the actions of the cholinergic
system that regulated the activity of the brainstem-spinal
postural control systems. This mechanism may enhance α-band
activity that promotes task-relevant neural circuit generation.
Through clinical studies, the Hanakawa group advanced
technology to examine the state of DA in human brains,
revealing that the dynamics of brain circuits may be altered inassociation with DA-reduced states in patients with Parkinson's
disease, dementia with Lewy bodies, and their prodromal stage.
Report of A05 group
Yuichi Takeuchi “A closed-loop brain stimulation forreinforcing hyper-adaptability” This project achieved control ofpathological fear of rat PTSD model, control of depression-likebehavior of rats, and improvement of cognitive function of
Alzheimer’s disease model mice by using novel time-targeted
brain stimulation technologies.
Shinichi Izumi “Comprehensive understanding of themechanisms of adaptive changes in brain, body consciousness
and arm use underlying upper limb recovery in stroke patients”
We found that different brain regions and nerve fibers wereassociated with arm use and functional recovery in stroke
patients. Furthermore, we found that facilitation of imitative
movement in patients with chronic hemiplegia triggered byillusory ownership.
Rie Kimura “Elucidation of neural circuits for optimal
adaptation to the external environment.” I aim to understandthe neural basis of optimal adaptation to the external
environment by examining the visual responses in
multidimensional brain systems. This year, I prepared toanalyze the neural basis of the differences in adaptation to thediversity of visual information by stress loading dependent on
the difficulty of tasks or by producing an animal model ofautism by administration of valproic acid.
Akihiro Funamizu “Neural substrate of unified learningtheory for adaptive behavior” This study hypothesizes that thebrain has one learning rule to achieve various behavioral
strategies. We investigated the neural substrate of learning ruleby modeling the choice behavior of mice with artificial neuralnetwork. We also recorded the dopamine changes in nucleus
accumbens with fiber bundle imaging during a task.
Yoko Yazaki-Sugiyama “Enhanced neuronal circuits for
vocal learning with extensive experiences during
development” We have submitted our manuscript reporting
the finding of developmentally transient neuronal projections
from zebra finch auditory to motor cortex (in revision). We
further developed our study to understand how this transient
projection alters its timing of disconnections based on early
auditory experiences.
Riki Matsumoto “Mechanism of Hyper-Adaptability of thehuman premotor area: electrophysiological connectomes
analysis with electrocorticogram” We created anelectrophysiological connectome library from comprehensive
CCEP measurements, visualized the brain networks involved

in speech of apraxia, and analyzed the CCEP connectome forlatent circuits during recovery, suggesting the possibility ofhyperadaptation by the premotor cortex. We also revealed
alterations in the connectome in epilepsy pathology, andsuggested that decreased CCEP network density is associatedwith poor postoperative outcome in epilepsy surgery.
Hiroyuki Miyawaki, “Regulatory mechanisms of interregional network changes underlying hyper-adaptation frommal-adaptation state caused by fear memory.” By using multiregional, large-scale electrophysiological recording techniques,
we analyzed the network dynamics that support the extinction
learning of fear memories and found that local activity
patterns coactivating with patterns in other brain regions tend
to be maintained after extinction learning.
Takaki Maeda “Research on developing a cognitive
rehabilitation method for neurological and psychiatric diseases
thorough improving precision on the sense of agency”
Aberrant sense of agency (SoA) in patients with schizophrenia
has been considered due to temporal delays in sensory-motor
prediction signals. We demonstrated this hypothesis via
computational modeling using a recurrent neural network
expressing the sensory-motor prediction process. (Okimura etal., 2023). Moreover, we have developed methods forcognitive rehabilitation of SoA in order to tune up precision of
SoA. We showed different patterns of learning of internal
prediction models for agency judgment between normal
controls and patients with schizophrenia (in preparation).
Tomohiko Takei “Adaptive mechanisms for perturbations in
the neural manifold” We recorded and analyzed the lowdimensional structure of the fronto-parietal cortical activity ina monkey performing a context-dependent adaptive feedback
motor task and suggested that the activity in the dorsal
premotor cortex may be responsible for the adaptive switchingof low dimensional neural subspaces.
Hironobu
Osaki, “Spatio-temporal modulation ofdisinhibition for super-recovery from motor dysfunction in thechronic phase of stroke" We used a rodent infarct model andmonitored a motor function and neural activity before andafter motor cortex infarction. We also monitored the effect of
optogenetic motor cortex inhibition on motor function. An
increase of the falling time of forelimb and a change injoint
angle after the infarction were successfully observed.
Rieko Osu “Hyper adaptive changes in spatial recognition
Using Augmented reality (AR)” Using augmented reality(AR) technology, we developed a system that projects theentire visual space, including the left visual space, which isoften missed by patients with hemispatial neglect, in theirright visual field. The effectiveness of the system wasconfirmed through feasibility tests on patients withhemispatial neglect. In addition, experiments with healthy
participants showed that self-generated movement enhanced
attention to the direction of movement. These results lead tothe development of attention promotion training in hemispatial
neglect.
Kosei Takeuchi “Creation of hyper-adaptability by syntheticorganizers and micro-environmental control of neuralreconstruction” We conclusively demonstrated, using a rodent

model, that synthetic synaptic connectors facilitate functional
recovery even in the chronic phase following spinal cord
injury. Additionally, we identified the impact of novel nextgeneration synaptic connectors, including inhibitory synapses
or neurotrophic factor connectors, on post-injury physiological
functions. The findings highlight the potential of theseinterventions as new tools for promoting hyper-adaptation
beyond injury recovery, showcasing their applicability inmotor function analysis.
Tatsuya Mima “Brain reorganization in stroke patients withhyper-recovery by measuring EEG modulation induced bystatic and dynamic magnetic fields.” We discovered that tSMS,
one of the noninvasive brain stimulation methods clinicallyapplied in humans, can induce intrinsic plasticity mediated by
Cl- channels. This contributes to the elucidation of thephysiological mechanisms of adaptability and hyperadaptability phenomena. The results of TMS/tSMS-EEG in
healthy subjects to measure functional neural networks withgood temporal resolution are in submission. Clinical research
is underway to apply this method to hyper-recovered stroke
survivors.
Atsushi Nambu “Brain adaptation after limb loss” Weobserved how a somatotopic representation in the cerebral
cortex was reorganized in macaque monkeys who lost theirforelimbs accidentally. In the motor cortices, the area
previously representing a lost distal forelimb shrunk, whilethat in the somatosensory cortex was rather preserved. This
discrepancy between the motor and somatosensory cortices
might be the basis of phantom limb, the sensation that an
amputated limb is still attached.
Noriyuki Higo “Adaptive mechanism occurring in bothhemispheres after unilateral brain damage” We examined themolecular and brain activity changes that occur during motor
recovery in macaques with focal infarcts in the unilateralinternal capsule. At the period of motor recovery, there was anincrease in excitatory synapse markers and a change inhemodynamics during grasping in the ventral premotor cortex.
Osamu Yokoyama “Functional reorganization of motor andsomatosensory cortices during recovery of motor functions
after the loss of peripheral sensory inputs” During therecovery of motor function in monkeys whose upper limb
somatosensory input was impaired by dorsal root transectionof the cervical spinal cord, we found frequency-banddependent changes in the activity levels of the primary motorand somatosensory cortices, and in the flow of information
between these regions.
III. ACTIVITIESTo summarize the results, Group A meeting was held on
January 17 (Wed) in 2024 at center for information and neuralnetworks.
IV. FUTURE PLAN
We will summarize our results and focus on publishing papers.
At the same time, we will continue to facilitate thecollaboration with the members of Group B.

A01 Annual report of research project
Tadashi Isa, Professor, Kyoto University
Eiichi Naito, General manager, CiNet
Hidenori Aizawa, Professor, Hiroshima University
Minoru Asada, Professor, Osaka University
Hideki Nakano, Associate professor, Kyoto Tachibana University
Abstract— We have demonstrated the polarity of theconnectivity associated with disinhibition after spinal cord injury,
visualization of trainability and plasticity of interhemisphericinhibition between left and right motor cortices and cellularmechanism underlying the interhemispheric inhibition in mice.

I. INTRODUCTION
The A01 Group will examine the hypothesis that disinhibition
across the large-scaled network of the brain is the basis of
hyper-adaptation by “reconstruction of biostructures” by fusionand further development of our current researches. Our
previous researches have shown that the brain is equipped withthe global disinhibition mechanisms and in case of spinal cord
injury, the mechanism is triggered to recruit the latent circuit
for functional recovery. However, details of the underlying
neural mechanism are still elusive. On the other hand, humanstudies suggested that such disinhibition mechanism is declined
as aging, which may be related to the difficulty in recovery forthe aged people. This research group will study these issues
and wish to propose the strategies to prevent the decline inlatent adaptive capacity.
II. AIM OF THE GROUP
The A01 Group will aim at clarifying the mechanism of
disinhibition through experiments on rodents, nonhumanprimates and humans, and proposing the effective strategies topromote functional recovery to overcome frailty in the aged
people. Isa Group will clarify the mechanism of disinhibitionin brain network associated with recovery from the spinal cord
injury or lesion of the primary visual cortex in macaquemonkeys. In rodents, Aizawa Group will perform the activity
measurement and optogenetical stimulation of monoaminergic
systems including dopaminergic and serotonergic neurons, tosupply the information about the global disinhibition in thecortex. In humans, Naito Group will capture the chronic
disinhibitory state of the aged people by fMRI and propose theeffective training methods to improve the brain functions
using the disinhibitory state as a measure of progression of
aging-related frailty.
III. RESEARCH TOPICS
A. Cortical connectivity associated with recovery from spinal
cord injury by statistical causal search with LiNGAM (Isa)
Our previous study showed that drastic motor recovery wasobserved after subhemisection spinal cord injury in macaques

that received a combination of intensive training and corticalelectrical stimulation. We also found that disinhibition
occurred globally and nonspecifically across wide corticalareas in both hemispheres, and effect of interhemisphericinteraction from ipsilesional premotor cortex (PM) tocontralesional primary cortex (M1) became inhibition tofacilitation by using a conditioning paradigm. According tothese results, we hypothesized that the change of excitationinhibition balance is considered as neural basis of plasticchanges which can improve the recover from severe
impairment. On the other hand, Granger causality can assessthe directionality of the connectivity between each brain area,polarity of the connectivity among brain regions associatedwith excitation-inhibition balance is still unclear. The aim of
this study is to reveal the polarity of the connectivity
associated with the motor recovery after spinal cord injury byusing a novel causal inference model based on a statisticalcausal discovery method (linear non-Gaussian acyclic model,
LiNGAM). First, we conducted the physiological validation to
the connectivity predicted by the LiNGAM. In this validation,
we blocked interhemispheric pathway unidirectionally andreversibly that is known to work mainly as inhibition andanalyzed brain activity using LiNGAM. In fact, we confirmedthat only inhibitory connectivity was significantly decreased
after the blockade, and polarity of connectivity predicted by
LiNGAM was demonstrated to reflect physiological relevance.
Next, we tested the polarity change of connectivity in cortical
network associated with recovery from spinal cord injury. In
the early stage of recovery, there were no significant changes
in excitatory connectivity. On the other hand, intensity of
inhibitory connectivity in α band significantly decreased in thepathway from ipsilesional PM to ipsilesional M1, recurrent
circuit of ipsilesional PM, and recurrent circuit ofcontralesional M1. These results suggested that global
disinhibition associated with recovery was induced mainly by
the reduction of inhibitory connectivity.
[1] Sasaki R, Ohta Y, Onoe H, Yamaguchi R, Miyamoto T, Tokuda T, Tamaki
Y, Isa K, Takahashi J, Kobayashi K, Ohta J, Isa T. Balancing risk-return
decisions by manipulating the mesofrontal circuits in primates. Science 383,
55-61. DOI:10.1126/science.adj6645, 2024
[2] Yamaguchi R, Ueno S, Kawasaki T, Chao Z C, Mitsuhashi M, Isa K, Takei
T, Kobayashi K, Takahashi J, Onoe H, Isa T. Global disinhibition andcorticospinal plasticity for drastic recovery after spinal cord injury. bioRxiv,
2023.2003.2015.532773. DOI:10.1101/2023.03.15.532773, 2023
[3] Mitsuhashi, M., Yamaguchi, R., Kawasaki, T., Ueno, S., Sun, Y., Isa, K.,
Takahashi, J., Kobayashi, K., Onoe, H., Takahashi R, Isa T. State-dependentrole of interhemispheric pathway for motor recovery in primates. bioRxiv,
2023.2004.2023.538021. DOI:10.1101/2023.04.23.538021, 2023

B. Visualization of trainability and plasticity ofinterhemispheric inhibition between left and right motor
cortices and verification of the effects of
excitation/inhibition balance on network structure andfunction (Naito, Asada, Nakano)
Using MRI, Naito group (NICT, CiNet) discovered a hyperadaptation phenomenon in the brain of a top wheelchair racing
Paralympian in which interhemispheric facilitation, rather than
the interhemispheric inhibition typically seen in typicallydeveloping individuals, developed through long-termbimanually synchronized upper-limb training for wheelchairracing since childhood (Morita et al. 2023). Based on thisfinding, in collaboration with Nakano group (Kyoto Tachibana
Univ), we found that training one hand to move passively insynchronization with the voluntary movement of the other
hand can effectively induce the muscle activity of the passive
hand. In addition, using right finger simple and complex motor
tasks, we found that young adults exhibit interhemisphericinhibition during a simple task, but during a complex task,
mainly the ipsilateral premotor cortex complements complex
movement by increasing functional coupling with thecontralateral sensorimotor cortices. On the other hand, older
adults chronically disinhibit interhemispheric inhibition evenduring the simple task, and are unable to adopt strategies forinterhemispheric functional coupling and complementation
during the complex task (Miura et al., in prep). Asada group(Osaka Univ) tested the effects of the disturbance in thebalance between excitation and inhibition (E/I balance) of thenervous system (which can be assumed in older brains) on
general network structure and function, using a spiking neuralnetwork model. Increasing the E/I ratio locally strengthened
excitatory coupling throughout the network, reduced thecomplexity of neural activity, and decreased informationtransfer between groups of neurons in response to an external
input (Park et al. 2023). The higher E/I ratio due to chronic
disinhibition in older adults may decline informationprocessing capacity in their aging brains.
[4] Morita T, Takemura H, and Naito E Functional and structural properties
of interhemispheric interaction between bilateral precentral hand motor
regions in a top wheelchair racing Paralympian. Brain Sciences 13(5), 715;
https://doi.org/10.3390/brainsci13050715, 2023.
[5] Park JH, Kawai Y, and Asada M Spike timing-dependent plasticity underimbalanced excitation and inhibition reduces the complexity of neural activity.
Frontiers in Computational Neuroscience, 17:1169288, 2023.

C. Neural circuit mechanism underlying interhemisphericinhibition and its modulation using mouse model (Aizawa)
Aizawa group (Hiroshima University) addressed a mechanism
underlying interhemispheric inhibition and its alteration as aneural basis of hyperadaptability using mouse motor cortex.
Upon activation of the unilateral motor cortex via optogenetic
stimulation, the extracellular field response in the contralateralmotor cortex turned out to reflect an extent ofinterhemispheric inhibition. This approach enabled the groupto unravel a novel role of acetylcholine to modulate theinterhemispheric inhibition so far. Based on this finding, thegroup addressed 1. interhemispheric interaction of spiking

activities across hemisphere of mice engaging in forelimbreaching task and 2. an effect of pharmacological activationand inhibition of the cholinergic transmission on thisinteraction. Results showed that correlative activity of neurons
across hemispheres emerged upon use of unilateral forelimbfor reaching movement. Specifically, excitatory neurons in the
motor cortex contralateral to the moving forelimb started to becorrelated negatively with the excitatory neurons in theipsilateral motor cortex, while they exhibited positive
correlation with the inhibitory neurons in the ipsilateral motor
cortex. Since such correlative activities in the pairs of neurons
across hemisphere was not observed during resting state, it issuggested to be involved with sharpening the unilateral
activation of the cortical hemisphere. Interestingly, positive
correlation of spiking activity between the contralateral
excitatory neurons and ipsilateral inhibitory neurons were upand down-regulated by the antagonist and agonist for themuscarinic cholinergic transmission, respectively. In line with
this, gene and protein expression analyses revealed that M2
muscarinic acetylcholine receptor was specifically expressedin the parvalbumin-expression inhibitory neuron in the deep
cortical layer. The group published a paper showing a novel
method examining those cells in the three dimensional spacein the brain (Kasaragod and Aizawa, 2023) and submitted anarticle summarizing the physiological results on cholinergic
modulation of the interhemispheric interaction between
murine motor cortices (Handa et al., 2024).
[6] Kasaragod DK, Aizawa H. Deep ultraviolet fluorescence microscopy ofthree-dimensional structures in the mouse brain. Sci Rep 26;13(1):8553;
https://doi.org/10.1038/s41598-023-35650-2, 2023
[7] Handa T, Zhang Q, Aizawa H. Cholinergic modulation ofinterhemispheric inhibition in the mouse motor cortex. bioRxiv
https://doi.org/10.1101/2024.02.05.579044, 2024

IV. FUTURE PERSPECTIVE
As for the monkey experiment, we will publish the papers forthe mechanism of disinhibition associated with functional
recovery and connectivity analysis using LiNGAM. In
addition, we will clarify the molecular system for theconnectivity predicted by LiNGAM collaborating with
Aizawa group. Focusing mainly on interhemispheric inhibition
between the left and right motor cortices, we have visualized
hyper-adaptation phenomena associated with special training,
flexible interhemispheric functional complementation inyounger brains, and chronic disinhibition and difficulty ininterhemispheric functional complementation in aging brains.
We also showed the possibility that higher E/I ratio due tochronic disinhibition may decline information-processing
capacity in aging brains. In the future, we will verify theeffectiveness of the new training developed based on thesefindings in rehabilitation settings. For the mouse study, we
will reveal dynamics of acetylcholine released into theextracellular space when the hyperadaptive reorganization of
the nervous structure upon large-scale brain injury. Thisapproach leads to a proposal of a novel method to induce
hyperadaptation
via
modulationofcholinergic
neurotransmission.

A02 Annual report of research project
Kazuhiko SEKI
National Institute of Neuroscience, NCNP

Abstract—In the FY2023, the aim of our research group was
mainly to summarize the experimental and modeling result. In
addition, we made significant progress in research on
rehabilitation for stroke, a concrete example of hyper-adaptivechanges in clinical settings.

I. INTRODUCTION
From the cradle to the grave, the musculoskeletal structure of
the human body changes continuously. It can change with aprolonged time constant in a predictive manner, following a
biological process of development and aging. It also couldchange immediately without any prediction, like a traffic
accident and some disease. Notably, we can spend our daily life
by using this changing body structure, with the aid of practice orrehabilitation therapy occasionally. This is a clear example that
our central nervous system (CNS) could adapt and keep
communicating with our body by making adaptive changes
corresponding to the change of bodily structure predictably or
unpredictably. So far, however, it is not established how the
CNS adapts to the continuously changing body and what is thetrigger of its adaptation.
II.

AIM OF THE GROUP

We will address the CNS mechanism of “hyper-adaptation”
corresponding to the ever-changing musculoskeletal structureby establishing novel animal models and cutting-edge
technology. By developing the muscle relocation model, where
the association of muscle activity and its physical action will besurgically or ontogenetically manipulated, we will investigate
how the CNS acquire a control strategy of their body de novo.
We will implement novel neurophysiological tools forassessing the hyper-adaptation occurring in the multiple levelsof the CNS, from the spinal cord, brain stem, and cerebral
cortex.
III.

RESEARCH RESULTS

In the final fiscal year of 2023, we advanced the publicationof research results on muscle relocation surgery obtained up to
the previous year and submitted them to academic journals, butthey have not yet been accepted. Currently, based on commentsfrom reviewers, the manuscript is being thoroughly revised,enabling new interpretations of the experimental results.
Furthermore, this fiscal year has seen progress in research on
rehabilitation for stroke, a concrete example of these adaptivechanges in clinical settings. Below, we report these researchoutcomes.
1. Development of Primate Model Animals of Stroke
1) Creation of a Cerebral Cortex Injury Model in Monkeys

We have used the photothrombosis method to locally induce
ischemic cerebral cortex injuries in the motor areas of the brain
in common marmosets. After all experiments were completed,the brain injury sites for each of the four animals wereconfirmed by perfusion-fixing the animals and performing
immunostaining on brain slices. As a result, cortical injuries
were observed in motor-related areas such as the primary motor
cortex, pre-motor cortex, and supplementary motor area in all
animals.
2)Recovery of Motor Function after Cerebral Cortex Injury
The overall body movement within the cage and the feeding
behavior using the paralyzed limb were recorded with a video
camera from before the cerebral cortex injury until 8 weeks
after the injury. The overall body movement was evaluated byscoring specific items. The feeding behavior was assessed byinstalling an acrylic board in the animal's cage and placing feed
at a fixed point on the board, requiring the paralyzed limb toperform upper limb reaching and grasping movements.
Moreover, reaching and grasping movements were evaluatedseparately based on kinematic indicators used for stroke
patients with hemiplegia. The indicators for grasping
movements included the grasping time, i.e., the time from thestart of finger flexion until the feed was grasped, and the
maximum distance between the thumb and index finger at
maximum extension, i.e., the maximum joint distance between
the thumb and index finger before finger flexion began.
Consistent motor function impairments were confirmed
among the animals. Flaccid paralysis was observed in all
animals the day after cortical injury induction. Feeding
behavior with the paralyzed limb was impossible until 2 weeks
after the cortical injury. Afterward, recovery of motor function
was confirmed in all individuals, and feeding behavior with theparalyzed limb became possible at 4 weeks after the injury.
However, indicators of reaching and grasping movements weresignificantly worsened in all animals compared to before theinjury. The animal model we developed excels in
reproducibility and similarity to humans in terms of the degreeof motor function impairment. Furthermore, recovery of most
motor functions was observed within 4 weeks after the corticalinjury, similar to the most dramatic recovery of motor function
occurring within a month in stroke patients with hemiplegia.
The subsequent recovery process showed consistent recovery
in overall body movement and grasping movement among
animals. However, a different recovery process was observed
for reaching movement, differing from overall body movement
and grasping movement. The angular error in movement
direction immediately after movement onset, an indicator of
feedforward control, recovered in some individuals from 4 to 8
weeks after the injury, while others showed no change or evenworsened. Similarly, trends were observed in the peak numberof movement direction speeds, an indicator of feedback control.
3)Neural Plasticity Changes Underlying Motor Function
Recovery
The factors that cause different recovery processes dependingon the type of movement and animal relate to the coordinationof muscle activity and the structure of the central nervous
system. For example, grasping strength requires thecoordination of muscles by interneurons in the spinal cord andmainly arises from the contribution of subcortical areas,
whereas upper limb reaching movements based on visual
information depend on the cerebral cortex and the frontalparietal lobe network. Therefore, it is considered that therecovery of movements involving subcortical regions occurs
quickly after cortical injury, while recovery of cortexdependent movements is slow. This indicates that the recovery
process varies depending on how the injury site and changes inneural plasticity affect motor control.
2. Reevaluation of the Method for Assessing the Effects ofStroke Rehabilitation Using Muscle Synergy Analysis
The Fugl-Meyer Assessment (FMA) is a representativeevaluation of motor function impairment recovery after stroke.
In the FMA, patients perform various daily activities, andmedical professionals score the completion of each activity.
Although FMA is widely used in clinical settings due to its
effectiveness, the movements used for evaluation areempirically selected by experts, and there has been little
research verifying the validity of FMA itself from theperspective of recovery mechanisms such as changes in neuralactivity. Therefore, this study analyzed patients' muscle activity
during FMA evaluation movements and assessed the changes
in muscle activity accompanying recovery from the perspectiveof muscle synergy.
1)Muscle Synergy Constituting FMA: Standard Synergy
The muscle activity of 41 sites was measured in stroke
patients (20) and healthy individuals (7) during 37 upper body
FMA evaluation movements, and muscle synergy analysis was
conducted. Initially, the muscle synergy of healthy individuals
was estimated, and 13 types of muscle synergies common to the
7 subjects were identified. This indicates that the muscle
activity during the 37 FMA evaluation movements is composedof combinations of these 13 types of muscle synergy (hereafterreferred to as standard synergy). Upon examining the muscles
with high weights in each muscle synergy, it was found thateach standard synergy is composed of muscle groups fromvarious body parts, such as the upper arm, forearm, fingers,
chest, abdomen, and posterior trunk.
When investigating which standard synergy each of the 37
tasks is composed of based on muscle activity during each task,
it was observed that the standard synergies related to the upper
arm, forearm, and fingers sequentially activate, and the standard
synergy corresponding to the posterior trunk activates in all
movements.
Looking at the activity of the posterior trunk in stroke patients,
it was found that mild patients had increased activity in thestandard synergy of the posterior trunk compared to healthy

individuals, whereas severe patients had decreased activity.
These results suggest that activity in the posterior trunk isimportant in all upper body movements, and severe patients
who could not perform tasks well might have been unable touse their trunk effectively.
2)Changes in Muscle Synergy in Stroke Patients
Next, to examine the correlation between FMA scores andchanges in muscle synergy, the correlation between thestandard synergy and each patient's muscle synergy wascalculated. It was found that mild patients had a one-to-one
correspondence between the standard synergy and the patient's
muscle synergy, but as severity increased, this correspondence
broke down, and each patient's muscle synergy was composedof multiple fused standard synergies. Therefore, to examine therelationship between the fusion of standard synergy and theseverity of the patient, the degree of fusion of standard synergy
was plotted against FMA scores, and it was found that patientswith lower FMA scores (higher severity) had a higher degree offusion. In severe patients with significant functional decline,
unnecessary muscle synergies activated simultaneously (fused)instead of being given individual motor commands for eachstandard synergy, reflecting this property of the nervous system
in FMA scores.
3)Simplification of Tasks by Reevaluating FMA
The relationship between the degree of synergy fusion andpatient severity (FMA scores) is derived from a series of muscle
activities in all FMA tasks, but by dividing muscle activity bytask, a similar relationship can be found for each of the 37 FMA
tasks. By examining whether a significant relationship between
synergy fusion degree and severity is observed in each task (i.e.,
muscle activity), it is possible to assess whether each task
reflects the relationship between patient severity and synergychange. Analysis showed that 26 out of 37 tasks had asignificant relationship between severity and synergy fusion
degree (P<0.01), whereas the remaining 11 movements had arelatively low relationship (P>0.01). This indicates that fromthe perspective of synergy, there are effective and ineffective
tasks in using FMA to evaluate patient recovery, suggesting thepossibility of simplifying the number of FMA tasks to about 26.
IV.

FUTURE PERSPECTIVE

The analysis of the muscle relocation model has made it
possible to quantitatively demonstrate the hyper-adaptivefunction inherent in the central nervous system of primates.
Going forward, it will be necessary to evaluate the neuralplasticity at the cellular level that underlies this phenomenon.
REFERENCES[1] Funato T, Hattori N, Yozu A, An Q, Oya T, Shirafuji S, Jino A, Miura K,
Martino G, Berger D, Miyai I, Ota J, Ivanenko Y, d'Avella A, Seki K: Muscle
synergy analysis yields an efficient and physiologically relevant method ofassessing stroke. Brain Communications 4(4), 2022, fcac200.
[2] Kosugi A, Saga Y, Kudo M, Koizumi M, Umeda T, Seki K: Time course ofrecovery of different motor functions following a reproducible cortical
infarction in non-human primates. Front Neurol. 2023; 14: 1094774

A03 Annual report of research project
Hiroshi Imamizu1 and Ken-Iichiro Tsutsui2

1

Graduate school of Humanities and Sociology, The University of Tokyo
2
Graduate school of Life Sciences, Tohoku University

Abstract— Our research project aims to elucidate the neuralmechanisms by which body cognition and positive emotions, suchas motivation, facilitate motor learning in challenging situations("hyper-adaptation"). Our main achievements during the year
were: 1) Psychological experiments revealed a formation processof a sense of agency (a sense that "I am the one causing an action,"
which is an aspect of body cognition) in line with the acquisition of
an internal model for motor apparatus (Imamizu). 2) Non-invasive
brain stimulation of frontoparietal connections in the left and righthemispheres revealed a right hemispheric dominance in theformation of the sense of agency (Imamizu). 3) Through electroencephalogram (EEG) recording experiments in monkeys, we
identified the neural activity that may be reflecting the process bywhich emotion and motivation influence motor function (Tsutsui).
4) Through neurotracing experiments in monkeys, we identified
parallelly organized subsystems linking different part of ACC andsubcortical areas, such as amygdala, nucleus accumbens, andperiaqueduct gray (PAG), which may be differentially involved in
the control of mood and emotion. (Tsutsui).

I. INTRODUCTION
Previous studies in neuroscience and psychology haveinvestigated how feedback from the external world (such as
motor error and reward prediction error) contributes to motor
learning. By contrast, many researchers have recently beeninterested in the contribution of internal information, such as
motivation and body cognition, to motor learning. For instance,a patient's motivation affects the recovery of motor functions
after the spinal cord injury and the sense of agency increases
during motor learning. However, little is known about thetheoretical framework and neurophysiological mechanisms inwhich motivation and body cognition facilitate motor learning.
We expect that investigation of such mechanisms leads todeveloping methods for inducing efficient motor learning evenin challenging situations.

A. The formation process of agency in novel motor learning
The comparator model proposes that the difference between"prediction of action-outcome" and "sensory feedback"(prediction error) is important in determining the sense ofagency. However, it is difficult to predict the action outcome inthe case which is assumed in hyper-adaptation. The principal
investigators (Takumi Tanaka and Imamizu) applied principal
component analysis to multiple joint sensor values measured
with a data glove. They examined changes in the sense ofagency in a novel learning task in which the first and second
components manipulate the cursor position on a screen (Fig.
1A). During the course of learning, the sense of agency was
examined under various conditions. The results revealed that 1)
before learning the "internal model" for predicting the action
outcome, synchronization between the cursor and own motion
is important to gain a sense of agency over the cursor, and 2) as
the internal model is acquired, the difference between thepredicted and actual motion direction of the cursor (prediction
error) is important for the formation of a sense of agency (Fig.
1B). In addition, the principal investigators (Chiyohara andImamizu) clarified the role of short-term memory of
proprioceptive sensation, which is important for bodyperception, in motor learning [1].

II. AIM OF THE GROUP
Our aims are 1) understanding mechanisms in which motivation
and body cognition facilitate motor learning in challenging
situations and 2) developing methods for facilitating motor
learning through artificial control of motivation and bodycognition. Our research activity will contribute tounderstanding the "hyper-adaptability" and future developmentof the methods for inducing and facilitating it.
III. RESEARCH TOPICS

Fig. 1 (A) A novel learning task. We measured various hand shapes and applied
principal component analysis to the sensor values. Participants manipulated the
cursor with the first and second components. (B) Changes in the sense of agency
examined under four different conditions during the learning process. The blackarrows indicate the difference between the cases in which there is synchrony
between the cursor and hand movements (conditions 1 and 2) and those in which
there is not (conditions 3 and 4). The white arrows indicate the difference between
the cases with (1) and without (2) spatial bias. In the early stages of learning, thepresence or absence of synchrony causes differences in the sense of agency, but
in the later stages, spatial misalignment (prediction error) begins to affect theagency.

B. Hemispheric dominance in the neural basis for sense ofagencyHiromitsu, Asai, and Imamizu have previously shown that therelationship between prediction error and sense of agency
changes when brain stimulation (transcranial anti-synchronous
AC stimulation) is applied to the connection between theinferior parietal lobule and inferior frontal gyrus in the righthemisphere. To investigate left-right differences in the neuralbasis of the sense of agency based on prediction error, weapplied the same type of brain stimulation to the left hemisphere.
We examined how the prediction error-sense-of-agency
correlations changed from the baseline condition without
stimulation. The results showed that the relationship between
prediction error and sense of agency changed less than when theright hemisphere connections were stimulated (Fig. 2). This
result indicates that the mechanism by which the sense ofagency is formed based on prediction error is right-hemisphere
dominant.

Fig. 2 Effects of interventions in the inferior parietal lobule-inferior frontal
gyrus of the left and right hemispheres on the relationship between
prediction error and sense of agency. Correlation values show the change
from no stimulation to anti-synchronous stimulation．**: P = 0.0064, t(40)= 2.88.

C. Neural processes through which the emotion and motivation
affect the motor function
Tsutsui and his colleagues have shown that the medial frontal
cortex and dorsolateral prefrontal cortex play an important rolein controlling mood, emotion, and motivation. However, itremains unclear how such information related to internal states
influences motor output. We conducted an experiment in whichmonkeys performed a Pavlovian probabilistic conditioning task
in which the same movement (the movement of releasing thehand from a hold key) resulted in a different outcome (juice orsaline) in each trial. Prior to the “go” cue for the movement, acue stimulus indicating the possible outcome of the movement
was presented. There were five progressive conditions in which
either favorable or unfavorable outcome was delivered(favorable outcome: unfavorable outcome = 100%: 0%, 75%:
25%,
50%:
50%,
25%:
75%,
0%:
100%).
Electroencephalogram (EEG) was recorded during the task
performance. We found differential neural signals preceding
the onset of the movement, in the midline (Fz, Cz) and in thecontralateral side (C3) of the hand use, preferentially greater for
either favorable or unfavorable outcome. Furthermore, thefunctional connectivity of the midline and lateral electrodes
became significantly higher at this period. As the sources of thesignals observed in the midline and lateral electrodes can be
estimated as the medial frontal cortex and the lateral motor
cortices, the observed neural activity may reflect the neuralprocess of emotional and motivational information influencing
the motor function.
D. Organization of the neural circuits connecting Anterior
Cingulate Cortex (ACC), amygdala, nucleus accumbens,
and periaqueductal gray (PAG)
The anterior cingulate cortex (ACC), which is a part of themedial frontal cortex and surrounds the corpus callosum, isknown to have various functions related to cognition andemotion. It can be subdivided into several areas, such as
pregenual, subgenual, and dorsal anterior cingulate cortex (pg,sg, and d-ACC), however their functional segregations largely
remain unknown. Here we conducted neural tracing
experiments to examine projections from these regions to theamygdala, nucleus accumbens, and periaqueductal gray (PAG).
When we locally injected adeno-associated virus (AAV), an
anterograde viral tracer, into the dACC, pgACC, and sgACC,
we found that they all project to the amygdala, nucleus
accumbens, and PAG, but to their different subregions. Thus, it
was indicated that the neural circuits connecting the ACC,
amygdala, nucleus accumbens, and PAG are separately andparallelly organized, presumably having different contributionsin the regulation of mood an emotion, as well as theirintegration with cognition.
IV. FUTURE PERSPECTIVE
In the first stage of hyper-adaptation (reconstruction of neural
structure), the synchrony between motor commands and theirconsequences is important, and in the latter stage(reorganization of sensorimotor control rules), the prediction
error is important. The right hemisphere stimulation was foundto be a key factor in the manipulation of the sense of agency bybrain stimulation. These results are expected to clarify themechanism of the formation process of the sense of agency inmotor learning and to contribute to the efficient manipulationof the sense of agency. Regarding the relationship between
motivation and motor function, it was suggested that input fromthe medial frontal cortex to the motor cortex may be involved
in regulating motor function according to emotion. Additionally,
the ACC, which belongs to the medial frontal cortex, has been
indicated to be a part of multiple parallel circuits connecting its
subregions to the amygdala, nucleus accumbens, and PAG,
each of which has a different role in regulating mood, emotion,and motivation. By investigating the interactions between theseareas, we will be able to uncover the mechanism by which
motivation promotes motor function and motor learning.
REFERENCES[1]

Chiyohara, S., Furukawa, J., Noda, T., Morimoto, J. & Imamizu, H.
(2023). Proprioceptive short-term memory in passive motor learning.
Scientific Reports, 13(1), e20826.

A04 Annual report of research project
Kaoru Takakusaki1, Takashi Hanakawa2

Department of physiology, Division of Neuroscience, Asahikawa Medical University (AMU)
2
Kyoto University Graduate School of Medicine

1

Abstract— Our research projects aimed at testing the hypothesisthat the alteration of neural dynamics following abnormal DA or
ACh neurotransmissions may lead to the change of the “rule ofthe conduct” as one of the mechanisms of “hyper-adaptation.” In
the fifth year, we performed animal experiments and humanclinical studies. Takakusaki and colleagues have examined therole of midbrain DA-ACh interaction acting on the posture-gait
control system in the brainstem-spinal cord in the cat. Hanakawa
and colleagues have been developing simultaneous EEG-fMRI toevaluate a dynamic profile of functional connectivity between
distinct neural networks in elders relating to DA content and Aβ
accumulation. These studies will clarify the relationship acrosscognitive functions, neural network dynamics, and neurotransmitters underlying hyper-adaptation generation.

I. INTRODUCTION
Age-related decreases in neurotransmitters such as
dopamine (DA) and acetylcholine (ACh) in the brain areassociated with the pathogenesis of Parkinson's disease (PD)and Alzheimer's disease (AD), which impair motor and higher
brain functions. [1, 2]. To overcome the decline in the brain
and bodily functions of these diseases, it is necessary tounderstand how the above neurotransmitters control motor andhigher brain functions. It is also required to elucidate themechanism of hyper-adaptation that works under the reductionof these neurotransmitters.
The mission of A04 item is to examine the mechanisms ofalteration of dynamic brain activities accompanying changes inthe dynamics of DA and ACh. The 5th year (2023) worked onanimal experiments where the role of ACh-DA interactions in
the regulation of brainstem-spinal cord pathways' activity inpostural control and on human studies where the measurementof brain network dynamics related to DA decrease and Aβ
accumulation. These findings further assisted in developing
mathematical models.
II. AIM OF THE GROUP
Takakusaki group conducted animal experiments indecerebrate cats to investigate the role of the interaction of the
DA and ACh systems between the substantia nigra pars
compacta (SNc) and the pedunculopontine nucleus (PPN) at
the midbrain in the control of brainstem-spinal cord posturalcontrol systems.
Hanakawa group has been conducting clinical studies. They
aim at discovering relationship among brain functions anddynamics of brain activity-connectivity in association withsenescence. To this end, the Hanakawa lab will takeadvantages of the PADNI cohort, which is a longitudinal study
involving healthy elderly people as well as patients with PD

and AD. They are also involved in a cohort study about
isolated REM behavioral disorder (iRBD), which is regarded as
prodromal PD accompanying subclinical DA deficiency. The
Hanakawa group is also working on improving DA transporter(DAT) SPECT technology for evaluating the activity of the DA
system in humans.
III. RESEARCH TOPICS
The following points are the major achievements.
A. Role of the DA-ACh interaction in the control ofbrainstem-spinal pathways (Takakusaki; Asahikawa)
The role of DA projections in the PPN area was elucidated
in decerebrate cats of how micro-injection DA into the PPN
modulated the activities of the reticulo- and vestibulo-spinal
tracts. The PPN, pontomedullary reticular formation (PMRF),
and lateral vestibular nucleus (LVN) were stimulated (Fig.1A).
The stimuli activated the reticulospinal and vestibulospinal
tracts to elicit a mixture of excitatory (blue) and inhibitory(red) effects on motoneurons as monitored by the ventral root
potentials (VRPs) (upper row in Fig.1B). Injecting a small
amount of DA into the PPN amplified both the excitatory andinhibitory effects by each stimulus (bottom row in Fig.1B).
Observation of an extended period showed 10-14Hz (a-band)
oscillation time-locked to the PMRF stimulation after DA
injection (Fig.1B, upper and lower right). These results indicatethat the DA projection to the PPN-ACh neurons activates
postural control systems to generate rhythmic α-band activity

Figure 1. Effects of PPN-DA injection on brainstem-spinal cord pathways

in motoneurons. The PPN-ACh neurons exhibit α-band activity(Fig.1A inset), which maintains the whole brain activity in the
α-band (α-wave in EEG and physiological tremor, Fig.2).
The DA systemmay, thus, enhance the PPNACh system's
activity to entrain neuralactivity of diverse areas into
the α band, facilitating plastic
brain network
generation.
Figure 2. Role of DA-ACh interaction

B. Non-invasice multimodal measurement of dynamicchanges of brain activity and connectivity and DA imaging
in humans (Hanakawa, Kyoto)
In patients with PD and iRBD who have DA deficiency,
Hanakawa and colleagues used neuroimaging technology such
as fMRI to investigate dynamic adaptive changes of thecortico-basal ganglia circuits under the strong influence of DA.
They also develop DAT SPECT technology for evaluating theactivity of the DA system in patients. This year, the groupused DAT SPECT data collected at four clinical centers fromhealthy aged persons and patients with PD and examined theeffects of inter-site differences in striatal DAT uptake in theform of specific binding ratio. The group observed substantial
inter-site differences in SBR. Although a statistical method
called ComBat was able to remove the inter-site differences,
this method showed limitation in terms of quantitativity.
Hence, the study recommended the calibration across the
SPECT scanners using a phantom and standardization ofoperations whenever possible [3]. A study analyzed a clinicaldataset from prospective PADNI and found pronounced mild
behavioral impairment in patients with both the reduction of
DA and the deposition of Aβ [4]. PADNI consists of bothprospective and retrospective cohorts. Some patients with PD
in the retrospective PADI underwent a motion analysis during
stance and gait. Such data were used for a simulation study by
the B04 group [5]. This simulation analysis will use DAT data
to link between a parameter in the simulation and whereaboutsof the DA deficiency in the brain.
The J-PPMI is a longitudinal cohort study that recruits
patients with iRBD. iRBD draws recent attention as aprodromal stage of α-synucleinopathy such as PD anddementia with Lewy bodies [6]. The group computed
functional connectivity from resting-state fMRI data collectedfrom iRBD patients in the J-PPMI cohort and healthy aged
controls and then tested whether FC carried information todiscriminate between iRBD and controls. They retrieved
8646-dimensional FC, used the random forest for dimensional
reduction and weight computation, and made a classifier using
either the logistic regression or the support vector machine.
the results showed that the classifier with FC was able todifferentiate iRBD and HC at accuracy around 70% (areaunder the curve of receiver-operator characteristic curve).
Furthermore, the FCs adapted by the classifier were correlatedwith measures of motor disturbance and cognitive impairment
in iRBD [7]. The group went on analyzing dynamic FC andperforming topology analysis based on a graph theory. The
group also reported that macrostructural neuroplasticity of thecerebellum underlies motor recovery after stroke [8].
IV. SUMMARY OF THE PROJECTSIn the past 5 years of research, the Takakusaki group foundthat the ACh system in the brainstem regulated the activity ofthe brainstem-spinal cord postural control systems in which
the excitability was facilitated by the DA system. This DAACh interaction may enhance α-band activity in the posturalcontrol system which promotes task-relevant neural circuit
generation. Hanakawa group developed methods to assess theintegrity of DA system in humans. They performed manystudies about dynamics of the brain with DA reduction andidentified network properties that may serve imaging
biomarkers of DA depleted conditions.
REFERENCES
[1]
[2]
[3]

[4]

[5]

[6]

[7]

[8]

Takakusaki K, Takahasi M, Noguchi T, Chiba R. Neurophysiologicalmechanisms of gait disturbance in advanced Parkinson’s disease patients
Neurol Clin Neurosci 2022. DOI: 10.1111/ncn3.12683
Takakusaki K. Gait control by the frontal lobe. Chapter 5. Motor System
Neuromuscular Disorders, Vol 1. in Handbook of Clinical Neurology 3rd
Series. 2023 (in press)
Wakasugi N, Takano H, Abe M, Sawamoto N, Murai T, Mizuno T,
Matsuoka T, Yabe H, Matsuda H, Hanakawa T, PADNI: Harmonizationof Dopamine Transporter SPECT Imaging Improves Segregation
between Patients with Parkinson’s disease and Healthy Elderlies inMulticenter Cohort Studies. Front Neurol (in press)
Matsuoka T, Oya N, Narumoto J, Morii-Kitani F, Niwa F, Mizuno T,
Akazawa K, Yamada K, Abe M, Takano H, Wakasugi N, Shima A,
Sawamoto N, Ito H, Toda W, Hanakawa T, Parkinson’s and Alzheimer’s
disease Dimensional Neuroimaging Initiative: Contribution of
Alzheimer’s disease and Lewy body disease pathologies on the mild
behavioral impairment. Int J Geriatr Psychiatry 38(9):e5993, 2023.
Omura Y, Togo H, Kaminishi K, Hasegawa T, Chiba R, Yozu A,
Takakusaki K, Abe M, Takahashi Y, Hanakawa T, Ota J: Analysis of
Stooped Posture in Patients with Parkinson’s Disease Using a
Computational Model Considering Muscle Tones. Front Comp Neurosci
17:1218707, 2023.
Nishikawa N, Murata M, Hatano T, Mukai Y, Saito Y, Sakamoto T,
Hanakawa T, Kamei Y, Tatsumori H Hatano K, Matsuda H, Taruno Y,
Sawamoto N, Kajiyama Y, Ikenaka K, Kawabata K, Nakamura T, Iwaki
H, Kadotani H, Sumi Y, Inoue Y, Hayashi T, Ikeuchi T, Shimo Y,
Mochizuki H, Watanabe H, Hattori N, Takahashi Y, Takahashi R, theJapan Parkinson’s Progression Markers Initiative (J-PPMI) study group:
An observational study on idiopathic rapid eye movement sleep behavior
disorder in Japan. Parkinson Relat Disord 103: 129-135, 2022.
Matsushima T, Yoshinaga K, Wakasugi N, Togo H, Hanakawa T, Japan
Parkinson's Progression Markers Initiative (J-PPMI) study group.
Functional connectivity-based classification of rapid eye movement
sleep behavior disorder. Sleep Med 115:5-13, 2024.
Hanakawa T, Hotta F, Nakamura T, Shindo K, Ushiba N, Hirosawa M,
Yamazaki Y, Sato Y, Takai S, Mizuno K, Liu M: Cerebellar
neuroplasticity correlated with motor recovery after stroke. Neurorehab
Neural Repair 37(11-12):775-785, 2023.

A05-1 Annual report of research project
Yuichi Takeuchi

Faculty of Pharmaceutical Sciences, Hokkaido University
Abstract— We develop a brain stimulation technology thatreinforces a target brain state by precisely timed stimulation of
the brain reward system toward beneficial reorganization of theneural circuit in the brain to control brain disorders. Toward thegoal, the following three research milestones have been achieved
during this fiscal year: A, Control of pathological fear memory ofrats by time targeted stimulation of the brain reward system; B,
Control of depression-like behaviors in rats; C, Improvement of
Alzheimer’s disease model mice.

I. INTRODUCTION
Neurological diseases such as epilepsy and Alzheimer's disease
and psychiatric disorders such as depression and post-traumaticstress disorder (PTSD) are often drug resistant. Induction of
network reorganization in the brain by deep brain stimulation
may be effective in controlling such drug-resistant brain
diseases. [1] However, when the brain network reorganization
is properly induced, it may work beneficially, such as recovery
of disorders, whereas when it is improperly induced, it may
produce undesirable results, such as no recovery of disorders or
a condition like phantom limb pain, for example. However, few
methods are known to properly reinforce and induce network
reorganization in the brain. Therefore, it is necessary todevelop a new technology that appropriately guides brain
network reorganization via time targeted stimulation of thebrain rewarding system by the combination of our timetargeted brain stimulation technology [2] and stimulation of the
reward system in the brain [3] (Fig. 1). [4]depression-like symptoms, and to recover cognitive deficits indementia animal models such as Alzheimer’s disease [5].
III. RESEARCH TOPICS
A. Control of pathological fear memory of rats by timetargeted stimulation of the brain reward system
Rats that had been fear conditioned with sound and plantar
electrical stimulation (CS+ US+) were trained to erase theirfear memories in a sound-only environment (CS+ US-). The
brain rewarding system of those rats were then stimulated bytimings triggered by hippocampal Sharp Wave / Ripples(SWRs) during the sleep after the extinction training. The
group that received SWR-triggered stimulation of the brain
reward system decreased freezing responses (fear) significantly
more efficiently than the no-stimulus group (Fig. 2). [6]Fig. 1. A closed-loop brain stimulation for reinforcing hyper-adaptability [4]II. AIM OF THE GROUP
Therefore, the aim of this group was set as "Development of amethod to enhance brain network reorganization by timetargeted stimulation the reward system in the brain".
Specifically, we aim to control disease-like symptoms inanimal models of epilepsy, Alzheimer's disease, depression,and PTSD by reinforcing specific brain states between
ongoing brain activities. In this fiscal year, we aimed todevelop a technology to control PTSD-like symptoms andFig. 2. Hippocampal Sharp Wave-Ripple-triggered stimulation of the reward
system in the brain during the sleep after extinction training reinforces
extinction of a fear memory. [6] A: Experimental schedule and a recording
and stimulation systems of the hippocampus and the medial forebrain bundle.
B: A closed-loop medial forebrain bundle stimulation system. C: Days for
80% remission of freezing behaviors.

B. Control of depression-like behaviors by time-targeted deepbrain stimulation
Olfactory bulbectomized rats have been used as a model ofdepression because they show depression-like symptoms suchas decreased performance in sucrose preference tests (reflecting
anhedonia). However, it is unclear whether brain damage,inflammation, or changes in brain activity associated witholfactory bulbectomy produce these depression-like symptoms.
Therefore, we here implemented a real-time electricalstimulation system of the bilateral piriform cortices withwaveforms of gamma frequency oscillation patterns in theolfactory bulb in real time to bidirectionally manipulate thefunctional coupling int the gamma band frequency between theolfactory blub and the piriform cortices. We found that the
gamma frequency coupling between the olfactory bulb andpiriform cortices was related to the maintenance of positivemood as measured by the performance on a sucrose preference
test. Furthermore, we have successfully alleviated depressionlike symptoms by time targeted stimulation of the piriform
cortices via increasing their gamma frequency coupling withthe olfactory bulb. [7]C. Alleviation of deficit of cognitive functions of Alzheimer’s
model mice by time targeted stimulation of the brain
reward system
We have experimentally addressed the hypothesis thatcognitive functions impaired by Alzheimer's disease or otherdiseases can be improved by enhancing γ-frequency brain
activity in the cerebral cortex and hippocampus, which havebeen reported to be involved in cognitive functions. We
employed humanized amyloid precursor protein knock-in mice,
which carries mutations derived from a familial Alzheimer's
disease patient. [5] First, we confirmed that γ-frequency brain
activities in the cerebral cortex and hippocampus decreased in
the model mice with a decrease in cognitive function asassessed by the Y-maze test. In the model mice, we stimulated
the rewarding brain system triggered by γ-frequency brain
activity. As a result, γ-frequency brain activity in the cortex
and hippocampus of APP mice was increased and theirperformance in the Y-maze test was improved (n = 2).
IV. FUTURE PERSPECTIVE
We have achieved control of pathological fear memory
and improvement of cognitive functions of rodents by timetargeted stimulation technologies of the brain reward system.
We have also alleviated depression-like behaviors of rodents
by time-targeted deep brain stimulation. Regarding theimprovement of cognitive functions by time-targeted
stimulation of the brain rewarding system, there is still roomfor further investigation of more efficient stimulation
conditions, etc. Thus, further research is needed.
REFERENCES
[1]
[2]
[3]
[4]

Fig. 3. Gamma frequency functional couplings between the olfactory bulb
and the piriform cortex mainteins hednic mood in rats. [7] A, B: A closedloop electrical stimulation system of the piriform cortex in real-time gamma
band oscillatory activity patterns in the olfactory bulb. C: Experimental
schedule. D: Performances of the sucrose preference test. E: Locomotion. F–
H: Bilateral modulation of gamma oscillatory activities in the piriform cortex
by the closed-loop stimulation.

[5]

[6]
[7]

Y. Takeuchi, A. J. Nagy, L. Barcsai, et al., “The medial septum as apotential target for treating brain disorders associated withoscillopathies,” Front. Neural Circuits, vol. 15, pp. 701080, July 2021.
Y. Takeuchi and A. Berényi, “Oscillotherapeutics – Time-targetedinterventions in epilepsy and beyond,” Neurosci. Res., vol. 152, pp. 87107, March 2020.
H. Norimoto and Y. Ikegaya, “Visual cortical prosthesis with ageomagnetic compass restores spatial navigation in blind rats ,” Curr.
Biol., vol 25, pp. 1091-1095, April 2015.
K. Hara and Y. Takeuchi, ” Time-targeted stimulation of the brain
rewarding system as a novel brain disorder treatment,” Medical Science
Digest, in press.
T. Saito, Y. Matsuba, N. Mihira, et al., “Single App knock-in mousemodels of Alzheimer’s disease,” Nat. Neurosci., vol. 17, pp. 661-663,
May 2014.
R. O. Sierra, L. K. Pedraza, L. Barcsai, et al., “Closed-loop brain
stimulation augments fear extinction in male rats,” Nat. Commun., vol.
14, 3972, July 2023.
Q. Li†, Y. Takeuchi†, J. Wang, et al., “Reinstating olfactory bulbderived limbic gamma oscillations alleviates depression-like behavioral
deficits in rodents,” Neuron, vol. 111, pp. 2065-2075, July 2023.

A05-2 Annual report of research projectShin-Ichi Izumi

Graduate School of Biomedical Engineering, Tohoku University
Abstract—This study aims to comprehensively understand themechanism of adaptive changes in brain, real-world arm use andbody consciousness underlying upper limb (UL) recovery in stroke
patients. In this study, we longitudinally investigate plasticchanges in function and structure of the brain in stroke patientsusing fMRI and DTI. In addition, we measure real-world arm useand UL function by using accelerometers and Fugl-Meyer
Assessment (FMA), respectively, to elucidate the relationshipamong them. We expect to investigate the neural basis of hyperadaptation in the rehabilitation of stroke UL paralysis and tocontribute to the elucidation of the relationship between
behavioral execution and neural reorganization. The results
revealed that different brain regions and neural pathways wereassociated with the recovery of use behavior and function. Thesefindings provide new insights into the relationship between use
behavior and brain function and structure, and may contribute to
the development of rehabilitation strategies to promote real-worldarm use after stroke．Furthermore, we examined the effect of asense of body ownership, one of body consciousness, on the motor
imitation training in stroke patients, and found that the increase
in sense of body ownership facilitated the effect of imitation
training. We believe that these results contribute to a multifacetedunderstanding of the relationship between body consciousness andmotor function in stroke patients. We have also made steadyprogress in the publication of the above research results.

I. INTRODUCTION
The most common disability after stroke is UL paralysis
occurring on the contralateral side of unilateral cerebral
hemisphere injury, and more than 80% of stroke patients
experience this condition in the acute phase and more than 40%
have residual disability in the chronic phase. In order to establish
effective rehabilitation for UL paralysis, various treatment
techniques based on plastic changes in the central nervous
system have been developed so far. However, thepathophysiology and recovery process of stroke hemiplegia arediverse, and the therapeutic effects vary widely amongindividuals, reflecting this. No standard has been established toindicate which treatment technique should be applied to eachindividual patient. The combination of various therapeutictechniques has also been studied, but the optimal type and timingof combination is not clear. In order to overcome these problems,
we have been working to understand the adaptive mechanismsof the neural basis that mediates between the brain and the bodyand to develop rehabilitation treatment based on body
consciousness. In this study, we developed a method to quantify
body-specific attention as a marker of body consciousness, andfound that body-specific attention was lower in chronic stroke
patients with longer time since stroke onset and lower handfunction [1]. This is the first finding to measure learned non-use
in chronic stroke patients from the aspect of body consciousness.

Furthermore, the relationship between body-specific attention,
real-world arm use, and UL function from subacute to thechronic phase was clarified [2]. We also successfully quantified
body-specific attention in the lower limb as well as the upper
limb, and reported the relationship between the different roles oflimb functions and body-specific attention [3]. In addition, tobetter understand the relationship between limb use and bodyawareness in the physically disabled, we measured bodyspecific attention to prosthetic legs in lower limb amputees andrevealed a use-dependent increase in body-specific attention
during the gait acquisition process [4]. These studies haveprovided important insights into the relationship between bodyspecific attention to limbs and the amount of limb use. However,
it is not clear the relationship between the real-world arm use
and brain function and structure. In addition, the relationshipbetween limb use and body-specific attention in the physicallydisabled and the effects of enhance of a sense of body ownership,
one of body consciousness, on upper limb motor function havenot been fully investigated.
II. AIM OF THE GROUP
This study aims to comprehensively understand themechanism of adaptive changes in the brain, real-world arm use
and body consciousness underlying upper limb (UL) recovery
in stroke patients. 1) We longitudinally investigate plasticchanges in function and structure of the brain in stroke patientsusing fMRI and DTI. In addition, we measure real-world arm
use and UL function by using accelerometers and Fugl-Meyer
Assessment (FMA), respectively, to elucidate the relationshipamong them. 2) Furthermore, we elucidate the effect ofenhance of body ownership, on the effect of motor imitation
training in stroke patients, and investigate that the increase in
sense of body ownership facilitated the effect of imitation
training.
III. RESEARCH TOPICS
A. Relationship between the real-world arm use and plasticchanges in the brain in stroke patients: A longitudinal study.
We conducted a longitudinal observational study of 25
patients with first-ever subacute stroke．Measurements weretaken at baseline, 1 month, 2 months, and 6 months after
enrollment. Real-world arm use was measured using
accelerometers on both wrists. UL function was measured using
the Fugl‒Meyer Assessment (FMA). Brain function wasmeasured by functional magnetic resonance imaging (fMRI) to
determine brain activity during paretic hand movements, anddiffusion tensor imaging (DTI) to determine fractional

anisotropy (FA) values for brain structures, and the relationshipbetween changes in each index was examined. Results showedthat real-world arm use and UL function improved up to 6
months. The amount of change in arm use during the period ofgreatest improvement was correlated with activity in theposterior parietal lobe. Furthermore, the ratio of FA values of the
injured to the uninjured side of the superior longitudinalfasciculus (FA ratio) was correlated with the final (6 months)
real-world arm use. Final UL function was correlated withactivity of the injured primary motor cortex and FA ratio of thecorticospinal tract. The posterior parietal lobe and connecting
nerve pathway in the frontal and parietal lobes were identified
as brain regions associated with arm use during UL recovery
after stroke, revealing that different brain regions and neuralpathways are associated with use behavior and functional
recovery. These results provide new insights into therelationship among arm use and brain functions and structures.
B. Effectof a sense of body ownership on motor imitation
practice in stroke patients: Virtual hand illusion (VHI) using ahead-mounted display
In post stroke rehabilitation, encouraging the use of theparetic limb in daily life is one of the vital issues. The settled a
sense of body ownership toward the paralyzed body part isconsidered to promote increased arm use of the paretic limb andto prevent learned non-use. Therefore, in addition to traditional
methods, there is a growing need for novel interventions using
neurorehabilitation techniques that induce self-bodyrecognition. Present study aimed to investigate whether theillusory experience of the patients’ ownership alterations fortheir paretic hand causes facilitation in the motor output ofsucceeding imitation movements, and an experiment
combining a modified version of the rubber hand illusion (RHI)
with imitation training by presenting a pre-recorded video
stream through a head-mounted display was conducted in 13
patients with chronic hemiplegia. A larger imitation movementof the paretic hand was observed in the illusion induced
condition, indicating that the feeling of ownership toward theobserving limb is conducive to the induction of intrinsic
potential for motor performance. This type of training, which
utilizes subjective experience to enhance the sense of bodyownership toward the observing body part, may contribute to
the development of new rehabilitation methods in post stroke
rehabilitation.
IV. FUTURE PERSPECTIVE
In the recovery process of paretic UL, we found that different
brain regions and neural pathways are associated with use
behavior and functional recovery in stroke patients. These
results provide new insights into the relationship between use

behavior and brain, and may contribute to the development of
new rehabilitation strategies to promote real-world arm use
after stroke. Furthermore, we examined the effect of sense ofbody ownership on the motor imitation training, and found thatan increase in body ownership in the paretic hand facilitated theeffectiveness of imitation movements. We believe that thesefindings will contribute to the construction of rehabilitation
strategies to enhance body awareness and increase motor
function and arm use after stroke. In addition, our previous
studies have revealed a use-dependent increase in body-specificattention during the process of gait acquisition with prostheses
after lower limb amputation. We believe that these results
contribute to a multifaceted understanding of the relationshipbetween body awareness, function, and behavior in thephysically disabled, using the findings of stroke patients andamputees as a clue.
In the future, we will investigate the neural basis of bodyspecific attention and use-behavior in more detail, and clarify
the long-term changes in the brain functional and structural
networks during the recovery process of stroke patients.
REFERENCES[1]

N. Aizu, Y. Oouchida, and S. Izumi, “Time-dependent decline ofbody-specific attention to the paretic limb in chronic stroke
patients,” Neurology, vol. 91, pp. e751-758, 2018, doi:
10.1212/WNL.0000000000006030.

[2]

R. Otaki, Y. Oouchida, N Aizu, T Sudo, H Sasahara, Y Saito, S
Takemura, S. Izumi et al., “Relationship Between Body-specificattention to a Paretic Limb and Real-World Arm Use in Stroke
Patients: A Longitudinal Study,” Front. Syst. Neurosci., vol. 15,
2022, doi: 10.3389/fnsys.2021.806257.

[3]

N. Aizu, R. Otaki, K. Nishii, T. Kito, R Yao, K Uemura, S. Izumi,
K. Yamada et al., “Body-Specific Attention to the Hands and Feetin Healthy Adults,” Front. Syst. Neurosci., vol. 15, Jan. 2022, doi:
10.3389/FNSYS.2021.805746/FULL.

[4]

N. Aizu, Y. Oouchida, K. Yamada, K. Nishii, and I. S. Izumi, “Usedependent increase in attention to the prosthetic foot in patients withlower limb amputation,” Sci. Rep., vol. 12, no. 1, p. 12624, Dec.
2022, doi: 10.1038/S41598-022-16732-Z.

[5]

K. Ataka, T. Sudo, R. Otaki, E. Suzuki, and S. Izumi, “Decreased
Tactile Sensitivity Induced by Disownership: An Observational
Study Utilizing the Rubber Hand Illusion,” Front. Syst. Neurosci.,
vol. 15, Jan. 2022, doi: 10.3389/FNSYS.2021.802148/FULL.

[6]

N. Aizu, T. Sudo, Y. Oouchida, and S. Izumi, “Facilitation ofimitative movement in patients with chronic hemiplegia triggered byillusory ownership,” Sci. Rep., vol. 13, no. 1, Dec. 2023, doi:
10.1038/S41598-023-43410-5.

A05-3 Annual report of research project
Rie KimuraInternational Research Center for Neurointelligence, Institutes for Advanced Study, The University of Tokyo
Abstract— Blindly over-adapting or failing to adapt to theexternal environment often leads to mental and physical illnesses
and, in many cases, a decline in our quality of life (QOL).
Optimal adaptation is thought to be essential to improving QOL.
Using rats performing visual discrimination tasks, we aim toclarify how the adaptation to changes in visual stimulation is
influenced by mental stress and developmental disorders. By
examining the visual responses in the multidimensional brain
systems across multiple regions, we aim to understand the neuralbasis of optimal adaptation to the external environment.

I. INTRODUCTION
To adapt and live with stress in a flood of information,
people occasionally eliminate unpleasant information, whether
consciously or unconsciously. This is also true for sensory
perception; people may adapt and perceive unpleasant sensory
information by attenuating it. Over-adaptation can result in
psychogenic sensory impairments. Despite the inability todetect organic abnormalities, in the case of vision, psychogenic
visual impairment can result in reduced visual acuity and visual
field defects due to psychological causes. In contrast, in autism
spectrum disorders, which are characterized largely by
impaired social skills and limited behavior and interests,
sensory hypersensitivity or hypoesthesia is often observed. In
the case of visual hypersensitivity, people with autism perceive
sunlight as dazzling and blank in front of their eyes. It isthought that the brain is unable to regulate and adapt its neuralactivity to allow for comfortable perception. In both instances,
they have problems adapting to the external environment.
Over-adaptation to the external environment is a problem, but
so is failing to adapt. Moderate adaptation to the external
environment is thought to be important for improving qualityof life. Filtering in the thalamus [1] is thought to play animportant role in the representation of sensory information
during adaptation to stress [2]. During acute stress, the brain isthought to increase sensory responses and represent more
information from the external environment. However, whenstress is repeatedly applied over a long period of time, theprefrontal cortex and other areas are activated by anticipating
the stress event, and conversely, sensory responses are thought
to be attenuated [3]. On the other hand, it has been reported
that the effects of inhibition against excitation are weakened
within the neural circuits in autism [4, 5] and that additional
weight is given to bottom-up input rather than top-down
prediction [6]. In addition, neural connections within theanterior and dorsomedial nuclei of the thalamus, which
performs an important function during the aforementioned
stress adaptation, are reduced [7, 8]. This area is known toproject sensory and association areas such as the primary visualcortex and prefrontal cortex [9]. In contrast, long-range
connections among these regions are increased [7, 8].

Consequently, different brain regions become active
simultaneously, increasing excitability and causing sensory
hypersensitivity [7].
We previously trained head-restrained rats to discriminate
between vertical and horizontal gratings in a visual
discrimination task. After achieving high correct percentages,
lower-contrast stimuli were also presented. We performed
multiple single-unit recordings from deep layers of the primaryvisual cortex of trained rats during this task. We found that the
number of low contrast–preferring neurons, which responded
more strongly to low-contrast stimuli than high-contrast stimuli,
increased after learning. Even the primary sensory cortex,
which is thought to represent the external world faithfully, canrepresent low-contrast information well enough as a population
by creating a new function that fires strongly with weak input.
Adaptation to changes in the visual contrast has been observed[10]. However, there is less systematic understanding of how
neural circuits and sensory responses in the brain adapt
depending on the degree of adaptation at the perceptual level.
We have yet to understand the neural basis of optimal
adaptation to the external environment.
II. AIM OF THE GROUP
In this study, we will determine how the brain adapts tochanges in the external environment, especially whenpresented with a variety of different visual stimuli, and how
this adaptation mechanism is modified by different levels ofadaptation, stress load, and developmental characteristics in
autism. We aim to understand how changes in neural activity
across multiple dimensions within single neurons, neuronalpairs in single brain regions, multiple neurons, and multiple
brain regions result in optimal adaptations to the external
environment at the perceptual level.
III. RESEARCH TOPICS
This year, we first examined how visual discrimination isaffected by stress loading depending on the difficulty of tasks.
Using the touchscreen operant platform, freely moving rats
were trained to discriminate between vertical and horizontal
gratings. The stimuli were then rotated while maintaining the
90-degree difference in tilt between the two gratings. Twoblocks were switched at regular intervals: an easy block with asmall rotation angle and a low-stress condition, and a difficult
block with a large rotation angle and a high-stress condition.
The easy and difficult blocks were explicitly distinguished bypresenting a pure tone of a different frequency simultaneouslywith the visual stimuli. We observed that the percentage ofcorrect responses differed between the easy and difficult
blocks, even when the task was of the same difficulty level.
We hypothesized that the balance of various influences, such

as attention, stress, novelty, degree of boredom, experience ofreference trials, and satiety, would change depending on the
difficulty of tasks, thereby affecting the correct response rate
for visual discrimination. This may be related to thecharacteristics of psychogenic visual impairment, for example,
difficulty in reading a question text during an exam ordifficulty in seeing when trying to concentrate. In the future,
we would like to understand the neural basis that influences
visual discrimination by collecting the number of data andanalyzing the various effects separately.
Next, to investigate the effects of developmental
characteristics of autism, which is known to cause sensory
hypersensitivity problems in many cases, on visual
discrimination, we created a rat model of autism by injecting
valproic acid, an antiepileptic drug, into pregnant rats. Objects
and new rats were placed in two of the three compartments,
and the contact time was measured. The control group showed
long contact with the new rat. On the other hand, the valproic
acid-treated group stayed in the middle compartment more
frequently, without much contact with either the rats or theobjects. Thus, as in many previous reports, a decrease insociality was observed in the valproic acid-treated rats. In the
future, we would like to perform a visual discrimination taskusing these rats and record neural activities.
The brain makes predictions based on past experience
when processing input sensory information and outputting
behavior. It is known that the brain initially faithfully
represents information from the external world, but as it learns,
it builds internal predictive models about the input informationand increases top-down inputs related to prediction,
expectation, and attention from higher brain regions [11].
Therefore, we first examined the involvement of top-down
inputs related to visual perception using normal rats. The rats
were repeatedly exposed to a discrimination task using theorientation of the gratings after switching from a black screen
to a gray Cue screen. At Cue, some neurons in the primaryvisual cortex (V1) showed gradually increasing activity, i.e., apredictive signal. In contrast to previous reports, this wasobserved to the same extent before and after learning in highcontrast−preferring neurons. In contrast, the predictive signal
was almost nonexistent in the anesthetized rats, suggestingthat the predictive signal is generated by top-down modulation,
as in previous reports. We believe that the increased
attentional state of the rats makes a lower-order prediction of
the upcoming presentation of the gratings even before learning.
However, the coherence between the local field potential(LFP) in the secondary motor cortex (M2) [12], considered as
one of the top-down sources, and the LFP in V1 during thegrating presentation was increased by learning. Furthermore,
we calculated Granger causality of the interaction between M2
and V1: top-down causality from the LFP of M2 to V1
became higher during Cue presentation, both before and after
learning. However, after learning, causality was again stronger
during the presentation of the low-contrast gratings. Next,
bottom-up causality from V1 to M2 remained low before
learning, but increased after learning during the presentationof the low-contrast gratings. In summary, after learning, thetop-down
modulationfrom M2 to V1
and the bottomup modulationfrom V1 to M2
were enhancedinboth
directions
duringlowcontrast
andlow-visibility
stimuli. After
Fig. 1. Schematic diagram of optimal adaptation
learning,
theto the external environment.
top-down andbottom-up interactions between higher-order brain regions andlower-order sensory cortices were altered in response toexternal input, leading to more refined predictions.
IV. FUTURE PERSPECTIVE
Although we were unable to understand the neural basis ofmoderate adaptation during this year, we are now ready todevelop experiments of stress load and developmental
characteristics of autism. In the future, we will use theseexperimental systems to confirm differences in adaptation to avariety of visual stimuli, record neural activity, and discussmoderate adaptation.
REFERENCES[1]

M. M. Halassa, and S. Kastner, “Thalamic functions in distributed cognitive
control,” Nat Neurosci, vol. 20, no. 12, pp. 1669-1679, Dec, 2017.
[2] 岡本 泰昌, “ストレスを感じる前頭前野―ストレス適応破綻の脳内機構―,” 日本薬理学雑誌, vol. 126, no. 3, pp. 194-198, 2005.
[3] K. Onoda, Y. Okamoto, K. Shishida, A. Hashizume, K. Ueda, A. Kinoshita, H.
Yamashita, and S. Yamawaki, “Anticipation of affective image modulates visualevoked magnetic fields (VEF),” Exp Brain Res, vol. 175, no. 3, pp. 536-43, Nov,
2006.
[4] C. E. Robertson, and S. Baron-Cohen, “Sensory perception in autism,” Nat Rev
Neurosci, vol. 18, no. 11, pp. 671-684, Nov, 2017.
[5] O. Yizhar, L. E. Fenno, M. Prigge, F. Schneider, T. J. Davidson, D. J. O'Shea, V. S.
Sohal, I. Goshen, J. Finkelstein, J. T. Paz, K. Stehfest, R. Fudim, C. Ramakrishnan,
J. R. Huguenard, P. Hegemann, and K. Deisseroth, “Neocorticalexcitation/inhibition balance in information processing and social dysfunction,”
Nature, vol. 477, no. 7363, pp. 171-8, Jul 27, 2011.
[6] E. Pellicano, and D. Burr, “When the world becomes 'too real': a Bayesian
explanation of autistic perception,” Trends Cogn Sci, vol. 16, no. 10, pp. 504-10,
Oct, 2012.
[7] C. Gernert, P. Falkai, and C. M. Falter-Wagner, “The Generalized Adaptation
Account of Autism,” Front Neurosci, vol. 14, pp. 534218, 2020.
[8] D. Tomasi, and N. D. Volkow, “Reduced Local and Increased Long-Range
Functional Connectivity of the Thalamus in Autism Spectrum Disorder,” Cereb
Cortex, vol. 29, no. 2, pp. 573-585, Feb 1, 2019.
[9] T. E. Behrens, H. Johansen-Berg, M. W. Woolrich, S. M. Smith, C. A. WheelerKingshott, P. A. Boulby, G. J. Barker, E. L. Sillery, K. Sheehan, O. Ciccarelli, A. J.
Thompson, J. M. Brady, and P. M. Matthews, “Non-invasive mapping of
connections between human thalamus and cortex using diffusion imaging,” Nat
Neurosci, vol. 6, no. 7, pp. 750-7, Jul, 2003.
[10] R. Kimura, and Y. Yoshimura, “The contribution of low contrast-preferring
neurons to information representation in the primary visual cortex after learning,”
Sci Adv, vol. 7, no. 48, pp. eabj9976, Nov 26, 2021.
[11] H. Makino, and T. Komiyama, “Learning enhances the relative impact of top-down
processing in the visual cortex,” Nat Neurosci, vol. 18, no. 8, pp. 1116-22, Aug,2015.
[12] S. Manita, T. Suzuki, C. Homma, T. Matsumoto, M. Odagawa, K. Yamada, K. Ota,
C. Matsubara, A. Inutsuka, M. Sato, M. Ohkura, A. Yamanaka, Y. Yanagawa, J.
Nakai, Y. Hayashi, M. E. Larkum, and M. Murayama, “A Top-Down Cortical
Circuit for Accurate Sensory Perception,” Neuron, vol. 86, no. 5, pp. 1304-16, Jun
3, 2015.

A05-4 Neural substrate of unified learning theory foradaptive behavior
Akihiro Funamizu
Institute for Quantitative Biosciences, the University of Tokyo
Abstract— Our brain has multiple strategies and selects thestrategy in a context dependent manner. Previous studies
investigate the neural mechanism of multiple strategies inhumans and find parallel neural circuits for multiple strategies.
However, the neural circuits of multiple strategies are still
unclear at least in a single neuron level, as precise neuralrecording and manipulations are required with animal
experiments. Here we perform a multi-strategy task in head-fixed
mice to investigate the neural substrate of model-free andinference-based strategy. We propose an AI (artificial
intelligence) -based approach to model the relationship between
the algorithms of brain and AI. During the task, we record the
dopamine changes in nucleus accumbens with fiber bundle
imaging. Our study potentially proposes an idea that the brain
learns multiple behavioral strategies with one learning rule andswitches the strategy with a non-linear neuronal circuit.

I. INTRODUCTION
The brain has multiple behavioral strategies in response to
one sensory stimulus and selects the strategy depending on acontext. In control theory, behavioral strategies are categorized
into a model-free (habit), which is based on direct experiencesof choices and outcomes, and an inference-based (or modelbased), which infers a hidden context from sensory inputs torealize flexible behavior.
Previous neuroscience studies often focus on the modelfree strategy, primarily due to the simple behavior tasks andmodeling [Ref.1]. Conversely, the inference strategy is often
tested in human experiment [Ref.2]. Through sophisticatedexperimental designs, human studies find the parallel neuralpathways for model-free and inference-based strategies [Ref.3,4].
However, due to the limitation of neural manipulations andrecording in humans, the detail neural circuits for inferencebased strategies are still unclear.
Recently, rodent studies investigated the brain regions
essential for inference behavior [Ref.5-7]. These studies used theoptogenetics and pharmacological tools for neural silencing
and found that the hippocampus and prefrontal cortex arenecessity for inference-based strategy. Nonetheless, the neuralrepresentation of inference-based strategy and of strategy
switching are still unknown. The learning phase of inference
strategy remains unclear.
Here we propose a behavioral task that required switching
the behavioral strategies in head-fixed mice. We model themouse behavior with an artificial neural network to predict thecircuits executing multiple strategies. At the same time, werecord the dopamine activity of mice during the task. Due tothe current research phase, we did write the detail methods andresults on this abstract.
II. AIM OF THE GROUP
We propose a unified learning theory for model-free andinference-based strategies and investigate their neural
implementations in mice. We build an artificial neural network(ANN) with the behavioral data to model the strategy
switching in the brain. We then compare the neural activity ofmice with the activity of ANN.
III. RESEARCH TOPICS
We summarize the two achievements in this year as
follows:
A. Modeling mouse choice behavior with recurrent neuralnetwork
Last year, we performed a "multi-strategy behavioral task"that segregates habitual and inference-based behavior. The taskpresented sound stimuli with various frequencies to head-fixed
mice. The mice received a water reward by selecting either theleft or right spout, depending on the low or high frequency (L,H) of sound. The task switched the sound stimulus (L or H)
with a transition probability of p [Ref.8]. We set the probability at
p=0.2 or p=0.8 (repeating or alternating condition) todifferentiate between the habitual or inference-based behaviors,
respectively. Mice properly biased the choices based on thetransition probability in both the conditions. The acquisitions ofproper choice biases were faster in the repeating than in thealternating conditions.
We continued to perform the behavioral task in mice since
our different research projects. In this year, we modeled the
choice behavior of mice in the repeating and alternating
conditions with a Continuous-time Recurrent Neural Network(CT-RNN) (Fig. 1a) [Ref.9]. The learning rule of the RNN was
the Advantage Actor-Critic (A2C) in a reinforcement learning.
The RNN consisted of 100 nodes. The inputs of the RNN were
the sound frequency in current trial, the choice and outcome inprevious trial. The output of RNN was the choice in the current
trial.
We first trained the RNN with simulated data solving themulti-strategy task. The RNN achieved the 100% success rate
in both the repeating and alternating conditions. These results
indicate that the RNN was properly trained in this study. When
we added a noise in the activity of each unit, the RNN biased
the outputs (choices) depending on the transition probabilities

Fig. 1. Behavioral modeling and dopamine-activity recording during multi-strategy task in head-fixed mice.
a. Scheme of continuous-time recurrent neural network (CT-RNN). The inputs of RNN were the current-trial sound inputs,
previous outcomes, and previous choices. The outputs of RNN were the left or right choice in the current trial.
b. Simulation of choice behavior with RNN. RNN succeeded to model the proper choice biases in both the repeating andalternating conditions depending on the transition probability of sound category.
c. Imaging of dopamine activity. Right: the dopamine activity was aligned at sound onset and averaged across trials in
different sounds and choices. Left: Z-scored df/f in each trial. Colormap was scaled between 5% and 95% percentile of df/f.
of repeating and alternating conditions (Fig. 1b). Additionally,
by adjusting some parameters within the RNN (the details of
which are not written in this abstract), the RNN was able toreplicate the difference in learning speed of the mice between
the two task conditions.
B. Imaging of dopamine activity during task
We measured the dopamine activity during the task with afiber bundle imaging. This method, using GCaMP [Ref.9] or Gprotein-coupled receptor-activation-based (GRAB) sensors[Ref.10], enables imaging of neuronal activity or changes inneuromodulators. This study used the dopamine sensor, dLight(AAV5-CAG-dLight1.1), to measure the dopamine activity
from the Orbitofrontal Cortex (OFC), Nucleus Accumbens(NAcc), and Ventral Tegmental Area (VTA). We now mainly
focus on the NAcc for dopamine measurement (Fig. 1c).
This study imaged the dopamine activity in the NAcc while
the mice gradually achieved the proper choice biases in themulti-strategy task. We found that the traces of dopamine in the
NAcc depended on the bias-acquisition phase.
IV. FUTURE PERSPECTIVE
We summarize the achievements in this fiscal year andbriefly explain the plan for the next year.
This year, we performed the “multi-strategy task” forhead-fixed mice that we had previously constructed. We
modeled the mice choice behavior using a continuous-time
Recurrent Neural Network (CT-RNN). First, the RNN solved
the behavioral task with 100% accuracy, indicating that thetraining of the RNN in this study was appropriate. In addition,by (i) adding noise to the activity of the RNN units and (ii)adjusting other parameters, the RNN reproduced the choicebiases in the repeating and alternating conditions of mice andthe difference in learning speed between the two task
conditions.
During the multi-strategy task, we measured the
dopamine activity in the NAcc with fiber bundle imaging. We
found that the dopamine activity was dynamically changed
during the acquisition phase of choice biases.

Next year, we will continue measuring the dopamine
activity of NAcc with the fiber bundle imaging and analyze
the data. We will investigate the relationship between theactivity of RNN units and the dopamine activity of mice. We
will submit the results obtained in this study to aninternational journal.
REFERENCES[1]

W. Schultz, P. Dayan, and P.R. Montague, “A neural substrate ofprediction and reward,” Science, vol. 275(5306), pp. 1593-9, Mar 1997.
[2] A.N. Hampton, P. Bossarerts, and J.P. O’Doherty, “The role of theventromedial prefrontal cortex in abstract state-based inference during
decision making in humans,” J Neurosci, vol. 36(32), pp. 8360-7, Aug
2006.
[3] N.D. Daw, S.J. Gershman, B. Seymour, P. Dayan, R.J. Dolan, “Modelbased influences on humans' choices and striatal prediction errors,”
Neuron, vol. 69(6), pp. 1204-15, Mar 2011.
[4] J. Glascher, N.D. Daw, P. Dayan, J.P. O’Doherty, “States versus
rewards: dissociable neural prediction error signals underlying modelbased and model-free reinforcement learning,” Neuron, vol. 66(4), pp.
585-95, May 2010.
[5] K.J. Miller, M.M. Botvinick, C.D. Brody, “Dorsal hippocampus
contributes to model-based planning,” Nat Neurosci, vol. 20(9), pp.
1269-76, Sep 2017.
[6] P. Vertechi, E. Lottem, D. Sarra, B. Godinho, I. Treves, T. Quendera,
M.N.O. Lohuis, Z.F. Mainen, “Inference-Based Decisions in a Hidden
State Foraging Task: Differential Contributions of Prefrontal CorticalAreas,” Neuron, vol. 106(1), pp. 166-76.e6, Apr 2020.
[7] T. Akam, I. Rodrigues-Vaz, I. Marcelo, X. Zhang, M. Pereira, R.F.
Oliveira, P. Dayan, R.M. Costa, “The anterior cingulate cortex predicts
future states to mediate model-based action selection,” Neuron, vol.
109(1), pp. 149-163.e7, Jan 2021.
[8] A. Hermoso-Mendizabal, A. Hyafil, P.E. Rueda-Orozco, S. Jaramillo, D.
Robbe, J. de la Rocha, “Response outcomes gate the impact of
expectations on perceptual decisions,” Nat Commu, vol. 11(1), 1057,
Feb 2020.
[9] G.R. Yang, X.J. Wang, “Artificial Neural Networks for Neuroscientists:
A Primer”, Neuron, vol.107(6), pp.1048-70, Sep 2020.
[10] T.L. Daigle, L. Madisen, T.A. Hage, M.T. Valley, U. Knoblich, R.S.
Larsen, et al., “A Suite of Transgenic Driver and Reporter Mouse Lines
with Enhanced Brain-Cell-Type Targeting and Functionality,” Cell, vol.
174(2), pp. 465-80, Jul 2018.
[11] T. Patriarchi, J.R. Cho, K. Merten, M.W. Howe, A. Marley, W.H. Xiong,
et al., “Ultrafast neuronal imaging of dopamine dynamics with designed
genetically encoded sensors,” Science, vol. 360(6396), eaat4422, Jun
2018.

A05-5 Annual report of research project
Yoko Yazaki-Sugiyama

Neuronal Mechanism for Critical Period Unit, OIST Graduate University
WPI-IRCN, the University of Tokyo
Abstract— Like humans learning to speak, male zebra finches
learn to sing by memorizing a tutor’s song (TS) then vocallymatching it in sequentially well-orchestrated auditory thensensorimotor developmental learning periods. Previous studies
identified TS memory ensembles in the caudal nidopallium(NCM) which transiently project into the motor control region,
HVC. Excessive song learning with sequential song learning fromtwo tutors of different species yielded distinct neuronal ensembles
for two song memories in the NCM, both of which retained
connectivity to HVC into adulthood. Here we try to identify theaxonal retraction timeline which support timely song learning.
We further study the neuronal mechanism for persistence ofneuronal connection with enriched experiences in juveniles.
Increased neuronal circuit capacity may give rise a possibility or
relearning in adulthood.

I. INTRODUCTION
Like humans learning to speak, male zebra finches learn tosing by memorizing a tutor’s song then vocally matching it insequentially well-orchestrated auditory then sensorimotor
developmental learning periods. Recently, we reported that asubset of neurons in the auditory phase is stored in the caudal
nidopallium (NCM) exhibit highly-selective auditory responses
to the playback of TS within some days after tutoring
experiences (1,2). We found a transient neuronal projection
into the motor control region, HVC, from TS responding NCM
neurons during developmental song learning period. Moreover,enriched song learning experiences in juvenile period resulted
in the retention of NCM projections in HVC into adulthood.
Here in this FY, we identified the timeline of axonal retraction
which are suggested from the transient NCM-HVC projections.
We also investigated what time of song experiences regulate
the maturation of NCM-HVC neuronal circuits and its
modulations. Increase the capacity of neuronal circuits inadulthood with enriched experiences are suggested by humanbilingual condition (3, 4), implicating a possibility ofrecovering learning ability in adults.

more exactly when axon retraction takes place especiallyregarding song learning. Zebra finches injected with the AAVcFOS-TetOn-EYFP-PEST at 70, 80 or 90 DPH were exposed
to TS playback concurrent with DOX in drinking water toinduce EYFP expression in TS-responsive neurons. We foundprojection within HVC from the TS-responsive NCM neurons
in 70 DPH juveniles were significantly higher than those in 80
or 90 DHP juveniles (Fig. 1). As zebra finch juveniles change

Fig. 1: Axonal projections in the song motor region, HVC from TSresponsive NCM neurons
The amount of axonal projection detected as GFP positive area,
comparing to adjacent Shelf region, was significantly higher in thejuveniles of 70DPH than those in juveniles of 80 or 90 DPH.

their songs by motor learning and gradually crystalize theirsongs by adults, we further analyzed the amount of songchanges during the 10 days before sacrificing for thehistological analysis by measuring the song similarity in
between. Juveniles of 90 DPH barely changed their songs in
the last 10 days as indicated in higher similarity between songs

II. RESEARCH TOPICS
A. The timeline of NCM axonal disconnection and its relation
with song maturation
In our recent research we found that NCM TS responding
neurons project to the song motor control region, HVC in
juveniles at 60 days post hatch (DPH) which were song vocal
learning period, while not in the older juveniles which were at
the end of song learning period (90 DPH), suggesting axon
retraction happening between these period. Here we identified

Fig. 2: NCM axonal projections in HVC correlate with songmaturation
Sound spectrographs of songs at sacrificed day for histological analysis(right) and 10 days before (left). The amount of axonal projection in HVCshows negative correlation with the song similarity between songs in thelast 10 days (linear regression, r2 = 0.71).

recorded across 10 days, while juveniles of 70 DPH altered
their songs. Interestingly, we found the negative correlation
between the amount of song changes in the last 10 days and theamount of NCM projections in HVC, suggesting NCM-HVC
projections are involved in song maturation (Fig 2).
B. Early experience dependent NCM-HVC circuit maturation
Our recent study revealed retained axonal projection in
HVC from TS-responsive NCM neurons in adults which hadenriched song learning experiences. To further investigatedwhat kinds of and which time of song experiences regulate
NCM-HVC neuronal circuit maturation and its modulations,
we raised zebra finch juveniles with two tutors sequentially.
They were raised by the first tutor (T1) and isolated withvarious timing and durations, then were exposed to the second
tutor for two hours as indicated in Figure 3. The projections
from the neurons in the NCM which were activated by the
playback of the T1 songs and T2 singing of the second tutor(T2) were examined by using AAV (AAV-CFOS-TetOn-GFP &CFOS-TetOff-RFP) techniques.
Interestingly, with the song learning experiences from theT1, only a couple of hours exposure to T2 yielded axonal
projections into HVC from the NCM activated by hearing ofT2 singings, while those projections were not observed in thejuveniles which were exposed to the T1 only the first 10 daysof hatching and did not learn from them (Fig 3). Further
investigation would tell us how NCM-HVC neuronal circuit
maturation is determined by experiences and ultimately
regulate song learning capacity.

III. FUTURE PERSPECTIVE
Neuronal circuit reshaping with experiences during
development has been well reported. Our studies, revealingtransient neuronal projections subserving developmental
auditory memory guided vocal learning, suggest novel conceptof neuronal circuit rewiring for balancing acquiring new vocal
patterns, while ensuring consistent motor patterns by limiting
the temporal window of sensory-guided motor learning. We
further suggest here that the early experiences might
accelerate additional neuronal circuit wiring and enhanceneuronal circuit capacity. Our future experiments in coming
years would tell us how early experiences regulate interareal
auditory-motor neuronal circuit maturate and underlaying
molecular machinery.
Our research might help us to understand theneuronal mechanism for adult language learning of bilingual
kids and to develop a new rehabilitation tools by using
increased neuronal circuit capacities with training in early
period.
REFERENCES
[1]

[2]
[3]
[4]

Fig. 3: Song learning experiences regulate NCM-HVC projections
Sequential song learning from two tutors with various timing anddurations (top), and the amount of projections in HVC from the NCM
neurons activated by the playback of the first tutor song (T1) and hearingof the second tutor singing (T2).

Yanagihara S. and Yazaki-Sugiyama Y. (2016) Auditory experience
dependent cortical circuit shaping for memory formation in bird songlearning. Nat. Commun, doi: 10.1038/NCOMMS11946. (featured
article)Katic K., Morohashi Y. and Yazaki-Sugiyama Y. Neural Circuit forSocial Authentication in Song Learning. Nat. Commun (2022)
13(1):4442. doi: 10.1038/s41467-022-32207-1
Pan, L., Ke, H. & Styles, S.J. Early linguistic experience shapes
bilingual adults’ hearing for phonemes in both languages. Sci Rep 12:4703, DOI: 10.1038/s41598-022-08557-7, (2022).
Oh, J.S., Jun, S.A., Knightly, L.M. & Au T.K. Holding on to childhood
language memory. Cognition 86: B53-64, (2003).

A05-7 Annual report of research project
Riki Matsumoto

Division of Neurology, Kobe University Graduate School of Medicine
Abstract—To
elucidatetheHyper-Adaptability
mechanism of motor function under aging andpathological conditions, it is essential to understand thehyper-adaptability of the premotor cortex that integrates
the information top-down from the prefrontal cortex andbottom-up from the parietal lobe. In this fiscal year, inorder to understand the brain network associated withmotor control and hyper-adaptive reorganization, we
continued to construct an electrophysiological connectomeusing cortico-cortical evoked potentials (CCEPs) as anindex of effective connectivity obtained by systemic
evaluation of the whole implanted electrodes. We clarified
the distinct CCEP connectivity patterns from each
subregion of the medial parietal cortex. We also elucidated
that the CCEP late-latency potentials (N2 responses) wellcorrelated with the resting-state fMRI functional
connectivity in the HCP database. From clinical point ofview, we elucidated the CCEP connectivity changes
associated with the epileptic network and those with
supplementary motor area syndrome. We proceed with
EEG or ECoG network analysis using a time-varying
graphical lasso for sleep stating and understand the
network dynamics during reach-to-grasp movement.
I. INTRODUCTION

In order to elucidate the Hyper-Adaptability mechanism of
motor function under aging and pathological condition, it
is essential to understand hyper-adaptability of the
premotor cortex that integrates the information top-downfrom the prefrontal cortex and bottom-up from the parietal
lobe. For epilepsy surgery, it is crucial to fully resect theepileptic focus to cure the disease. At the same time, it isalso important to preserve brain functions. As a part ofpresurgical evaluations for intractable focal epilepsy,
patients undergo chronic implantation of subduralelectrodes when the focus is not well determined by noninvasive evaluations or the focus is located around theimportant functional cortices. For functional mapping, weusually record neural activities (e.g., ERPs, high gamma
activities) while patients complete a task, and then locate
the cortex responsible for a particular task by delineating
functional impairment during high-frequency electrical
cortical stimulation (ECS). Although we apply various
methods in epilepsy surgery, we still have difficulties in
predicting the functional disabilities or recovery after
resection surgery.

In the present research group “Hyper-Adaptability,” forinvestigating the acute and subacute (hyper) adaptation at
the network level, we aimed to investigate the impact of
the resection of the premotor area by simulating the virtual
lesion using the electrophysiological connectome andcomparing it with the movement disability and its recovery
after surgery. This fiscal year, we studied the relationshipbetween the effective connectivity of CCEP and theresting-state functional connectivity of the Human
Connectome Project (HCP) database and modification of
the brain network in epilepsy patients. Additionally, weclarified the network pathophysiology of supplementary
motor area (SMA) syndrome by longitudinal evaluation of
the resting-state functional connectivity.
II. AIM OF THE GROUP/METHODS

Subjects are patients with intractable partial epilepsy who
underwent chronic subdural electrode implantation in thefrontal & parietal areas for presurgical evaluations andgave written consent to the research protocols IRB#C533,
443, 1062, B230150, B230128.
In order to understand the brain network associated withmotor control, we make an electrophysiologicalconnectome by using CCEP as an index of effective
connectivity, which were obtained by systemic stimulationand evaluation of the whole implanted electrodes. We havestudied the differences in connectivity in the medial
parietal lobe by CCEP, the association between effective
connectivity of CCEP and resting-state functional
connectivity in the Human Connectome Project (HCP)
database, and the association between network alterations
due to epilepsy pathology and seizure outcomes after focal
resection. We also promote inter-group collaborative
researches on the Time-Varying Graphical Lasso (TVGL)
for analysis of intracranial and scalp EEG recordings withGroup B. We published papers on the CCEP connectivity
differences in the medial parietal lobe, and the dynamics ofcortical interactions in visual recognition of object
category (living vs. non-living) using ECoG high gamma
activities.

III. RESEARCH TOPICS

We have carried out the following three research projects.
A. Comparison of effctive connectivity by CCEP withfunctional connectivity in the HCP database andexploration of brain connectivity alternation inpatients with epilepsy
Using the CCEP, we showed differences in connectivity
within the medial parietal lobe, with the anterior precuneushaving connection with the premotor cortex, the posterior
precuneus with the occipital lobe, and the posterior
cingulate gyrus with the medial frontal lobe. Thesefindings suggest that these regions are differentiallyinvolved in motor and visual functions [1]. We also
clarified dynamics of the cortical interactions in visual
recognition of object category - living vs. non-living.
Specifically, we showed that the information processing
within the posterior area of the basolateral temporal lobe islarger for living objects than non-living objects at around
250 ms after visual presentation [2]. Comparison between
CCEP connectivity and HCP functional connectivity
database revealed a strong correlation between CCEP N2
potentials and functional connectivity [3]. With regards toepileptogenicity, network alterations were observed inepileptic foci and areas with strong epileptogenicity(highly irritative area), but not in areas with weak
epileptogenicity (less irritative area). [4].
B. Longitudinal functional connectivity assessment ofmotor compensatory mechanisms by humanmotor cortical network
In brain tumor neurosurgery, we experience patients whodevelop paresis despite preservation of motor evoked
potentials originating from the primary motor cortex (M1)after removal of lesions in the premotor cortex(supplemental motor area (SMA) syndrome). The network
alteration patterns underling SMA syndrome were
investigated by longitudinal fMRI evaluation before andafter surgery. We found that preserved anatomic
connectivity but altered functional connectivity among
cortical motor areas is involved in the development of
SMA syndrome. Future studies are aimed at elucidating thehyperadaptation of cortical motor networks that play a rolein the recovery process of SMA syndrome.
C. Application of new network analyses for evaluationof network dynamics using intracranial EEG andscalp EEG
Together with the Kondo and Nambu Groups of Group B,
we are applying the TVGL technique to scalp EEG during
sleep and intracranial EEG during reaching and grasping
movements to analyze the brain network at each sleep
stage and during reaching and grasping movements. Sleep
staging is possible based on the network dynamics in thebrain. We also elucidated network dynamics in the frontal
and parietal lobes during reaching and grasping
movements.
In addition, we developed a novel dimensionality
compression algorithm (representational similarity
learning) for multidimensional, graded representations, andclarified the gradients and dynamism of semantic
representations in the human anterior temporal lobe [6].

We have clarified the electrophysiological network
between cortices by measuring stimulus-evoked brain
potentials using intracranial electrodes. For comprehensive
understanding of the brain connectome, it is also very
important to study how the white matter fibers connect tothe cortices. To establish electrical tracing of the whitematter fibers, we have refined the methods and reported thecharacteristics of the evoked waveforms in two
international journals [7][8].
IV. FUTURE PERSPECTIVE

In this research project, we employed the large retrospective
CCEP data and some prospective data to constructelectrophysiological or CCEP connectomes related to thepremotor area to elucidate the network characteristics atboth individual and group levels. We investigated the effectof high-frequency electrical cortical stimulation based on
the CCEP connectivity, its correlation with functional
connectivity by fMRI, and its alteration by epileptic
pathology. The electro-physiological CCEP connectome
can contribute to individual patients for surgical strategy. At
the group level, it is regarded as one of the gold standard
references to validate the mathematical and analytic models
and non-invasive connectome for the understanding hyperadaptability of the human brain and its clinical application.
References
[1]

[2]

[3]

[4]
[5]

[6]

[7]

[8]

Togo M, Matsumoto R, Usami K, et al. Distinct connectivity patterns inhuman medial parietal cortices: evidence from standardized
connectivity map using cortico-cortical evoked potential. Neuroimage
2022;263:119639 DOI: 10.1016/j.neuroimage.2022.119639
Usami K, Matsumoto R, Korzeniewska A, et al. The dynamics ofcortical interactions in visual recognition of object category: living vs
non-living. Cereb Cortex. 2022 Nov 20:bhac456. doi:
10.1093/cercor/bhac456.
十河 正弥, 林 拓也, 麻生 俊彦ら. 皮質皮質間誘発電位(CCEP)の N2
電位は安静時 fMRI 機能的結合性と相関する：Human Connectome
Project（HCP）データベースとの比較研究. 第 52 回臨床神経生理学会.2022/11/24-26. (口演)
Togo M, Matsumoto R, Kobayashi K, et al. Network modification in
irritative zones of epilepsy patients: a comparison of CCEP with HCP
database. 第 64 回日本神経学会, 2023/5/31-6/3.
Yamao, Sawamoto N, Kuieda T, et al. Changes in distributed motor
network connectivity correlates with functional outcome after surgicalresection of brain tumors. Neurosurgery open 4(1):e00028, March 2023.
| DOI: 10.1227/neuprac.0000000000000028.
Cox CR, , C.R., Rogers, T.T., Shimotake, A., et al. Representational
similarity learning reveals a graded multidimensional semantic space inthe human anterior temporal cortex. Imaging Neuroscience, 2024,
Advance Publication. https://doi.org/10.1162/imag_a_00093
Rossel O, Schlosser-Perrin F, Duffau H, et al. Short-range axonocortical evoked-potentials in brain tumor surgery: Waveformcharacteristics as markers of direct connectivity. Clin Neurophysiol.
2023 Sep;153:189-201. doi: 10.1016/j.clinph.2023.05.011.
Schlosser-Perrin F, Rossel O, Duffau H, et al. The orientation of thestimulating bipolar probe modulates axono-cortical evoked potentials.
Brain Stimul. 16:1009-1011, 2023. doi: 10.1016/j.brs.2023.06.008.
Epub 2023 Jun 15

A05-8 Annual report of research project
Hiroyuki Miyawaki

Osaka Metropolitan University
Abstract—Animals that have undergone traumatic
experiences develop maladaptive states, characterized by aninability to exhibit adaptive behavior. Subsequent fear-extinction
learning can induce a hyper-adaptive state that allows theseanimals to recover their capacity for adaptive behaviors.
However, the neural mechanisms underlying these processes are
not well understood. This study aimed to elucidate the changes in
the inter-regional network during the transition to maladaptive
states and into the hyper-adaptive states. Employing multiregional large-scale electrophysiological recording techniques, we
tracked the activity of neuronal cell ensembles in the ventral
hippocampus, basolateral amygdala, and prefrontal cortex
during fear conditioning and extinction learning. We found thatthe composition of coactivated pairs changed through extinction
learning, although the fraction of significantly coactivatedensemble pairs remained similar. Furthermore, ensembles
participating in inter-regional coactivation were more likely to be
active in subsequent retention-of-extinction sessions. These
results suggest a correlation between participation in interregional coactivity and the stability of cell ensembles. However,the causal relationship between the stability of cell ensembles andinter-regional coactivity remains to be elucidated. This aspectrequires further exploration, particularly through themanipulation of inter-regional coactivity in future studies.

I. INTRODUCTION
Aversion and fear responses to situations associated withharmful stimuli are crucial for the survival of animals.
However, when the memory of fear is too strong, maladaptive
behaviors such as showing fear responses even in safe
environments can occur. In humans, such maladaptive states
are known as Post-Traumatic Stress Disorder (PTSD), which
impairs quality of daily life. The development and treatment of
PTSD have been actively studied through animal models offear conditioning and its extinction learning. Previous researchsuggests that the extinction of fear memories is not forgetting
but rather a process of recovering adaptive behavior while
retaining the memory of fear [1], which can be considered as a
hyper-adaptation process. It has been clarified that brain
regions such as the amygdala, ventral hippocampus, andprefrontal cortex play a crucial role in fear conditioning andextinction learning [2]. However, it remains unclear how the
network between these brain regions changes during thetransition from a maladaptive state caused by fear memory to a
hyper-adaptive state and how these changes are controlled.
Clarifying these points is expected to advance theunderstanding of the pathology and recovery processes ofdisorders related to memory and emotions such as PTSD andanxiety disorders, leading to insights for the development ofeffective treatments of these disorders.

II. AIM OF THE GROUP
This research aims to elucidate the changes that occur in thebrain during the transition to a maladaptive state caused by fear
memory and to clarify how these changes are overcame inprocess of hyper-adaptation. The representation of informationin the brain is thought to be carried out by "cell ensemble,"which are relatively small groups of cells that aresynchronously activated, and the presence of cell ensembles
has been reported in various brain regions such as the
hippocampus, amygdala, and cerebral cortex [3]. Furthermore,this research project has revealed that the transition to amaladaptive state due to fear conditioning leads to interregional coactivation of cell ensemble [4]. In the previous
year's research, it was revealed that new cell ensemble, areformed through the hyper-adaptation process (i.e. extinction
learning), extinction learning. Building on this result, this year
we focused on the changes in inter-regional coactivation of cell
ensemble through extinction learning. The results imply thatinter-regional coactivation may stabilize participating cell
ensembles in the local circuits.
III. RESEARCH TOPICS
A. Analysis of inter-regional coactivation of cell ensembles
identified during extinction learning
Using a multi-regional large-scale electrophysiologicalrecording technique, which allows us to record activities ofmultiple neurons in various brain regions simultaneously fromfreely behaving rats, we recorded the activities of neurons in
the basolateral amygdala (BLA), ventral hippocampus CA1
region (vCA1), and layer 5 of the prelimbic cortex (PL5).
During these recordings, fear conditioning learning, extinction
learning, and retention of extinction tests were conducted [4].
Furthermore, independent component analysis (ICA) was used
to identify cell ensemble active during each of the behavioral
session in each brain region. Previous studies have shown thatamong cell ensembles identified during fear conditioning, theproportion of inter-regional pairs which significantly
coactivated were larger in post-learning sleep than pre-learning
sleep [4]. The same analysis was performed on cell ensembles
identified during extinction learning, but no significant change
in the proportion of coactivated ensemble pairs was detected
through extinction learning. (Fig.1; p > 0.64, Fisher's exacttest). This suggests that, unlike fear conditioning learning,
extinction learning does not enhance inter-regional coactivation.

regions separately. These results suggest that inter-regional
coactivation may contribute to the stabilization of cell
ensembles.

Fig. 1. The proportion of ensemble pairs showing significant coactivation
during sleep before and after extinction learning.

Next, the pairs of cell ensembles that showed significant
coactivation during post-extinction learning sleep wereclassified into two groups, those that had coactivated during
pre-extinction learning sleep (preserved pairs) and those that
had not (generated pairs). Although the proportion varied
between region pairs, considerable fractions of pairs (25.8% to75.0%) were categorized as the generated pairs (Fig. 2). Thissuggests that extinction learning causes a recombination ofcoactivated cell ensembles.

Fig. 2. The proportion of genereated and preserved pairs among coactivatedensemble pairs during sleep after extinction learning.

B. Relationship between coactivation observed after
extinction learning and the maintenance of cell ensembles
Next, we examined the possibility that inter-regional
coactivation of cell ensembles during post-extinction sleep may
be related to whether the cell ensembles were maintained until
the retention of extinction sessions. Cell ensembles detectedduring extinction learning were classified based on whether
they showed synchronous activity with ensembles in otherregions during post-extinction sleep, and the proportion ofensembles maintained until the retention of extinction sessions
was calculated for each group (Fig. 3). The cell ensembles
involved in coactivation were maintained more than those that
did not have coactivation partner (p=0.03, Fisher's exact test).
Similar trends were observed when examining individual brain

Fig. 3. The proportion of ensembles active during the extinction retentiontask, categorized by whether they showed synchronous activity during sleep
after extinction learning.

IV. FUTURE PERSPECTIVE
This study revealed that although there was no change inthe strength of inter-regional coactivation through theextinction learning of fear memory, there were changes in thecomponents of the coactivations. Additionally, our results
suggest that participation in inter-regional coactivation isrelated to the stability of cell ensembles. However, it is notclear whether stable cell ensembles are more likely toparticipate in synchronous activity or inter-regional
coactivation stabilizes cell ensembles. In the future, we would
like to clarify this point through intervention experiments onsynchronous activity.
REFERENCES

[1]
[2]
[3]
[4]

Furini, C., J. Myskiw, and I. Izquierdo, The learning of fear extinction.
Neurosci. Biobehav. Rev., 2014. 47: p. 670-83.
Tovote, P., J.P. Fadok, and A. Luthi, Neuronal circuits for fear andanxiety. Nat. Rev. Neurosci., 2015. 16(6): p. 317-31.
Tonegawa, S., Liu, X., Ramirez, S., and Redondo. R., Memory engram
cells have come of age. Neuron, 2015. 87(5): p 918-31
Miyawaki, H., and Mizuseki, K., De novo inter-regional coactivations ofpreconfigured local ensembles support memory. Nat. Commun. 2022.
13(1): 1272.

A05-9 Annual report of research project
Takaki Maeda, Oi Hiroki, Tsukasa Okimuta(Department of Neuropsychiatry, Keio University School of Medicine)

Yuichi Yamashita(Department of Information Medicine,National Institute of Neuroscience, National Center of Neurology and Psychiatry)
Our research project aims to reveal neural mechanisms of senseof agency (SoA). Moreover, we intend to study pathophysiologyof neurological and psychiatric illnesses which show abnormal
SoA. Then, we try to establish the method to recover from thoseillness through reorganization of neural systems on the SoA. We
have studied methodology to intervene learning (updating)mechanisms of the internal prediction model for the SoA using
original digital device and applications. Our approach is basedon the basic premise that controllability is rewarding. Actually,
we have developed experimental settings where temporal or
spatial biases are introduced in order to make controllability
ambiguous. Moreover, we designed experiments which evaluatehierarchical level (bottom-up ～ top-down process) of SoA
emergence, and we have shown that pathophysiology ofschizophrenia would be in the level of bottom up process.

in schizophrenia. Several neurophysiological and theoreticalstudies have suggested that aberrancy may be due to temporaldelays (TDs) in sensory-motor prediction signals. Here, we
examined this hypothesis via computational modeling using arecurrent neural network (RNN) expressing the sensory-motor
prediction process. The proposed model successfully
reproduced the behavioral features of SoA in healthy controls.
In addition, simulation of delayed prediction signals
reproduced the bidirectional schizophrenia-pattern SoA,
whereas three control experiments (random noise addition,
TDs in outputs, and TDs in inputs) demonstrated noschizophrenia-pattern SoA. These results support the TD
hypothesis and provide a mechanistic understanding of thepathology underlying aberrant SoA in schizophrenia.

I. INTRODUCTION
We have studied pathophysiology of neurological andpsychiatric illnesses from the stand of the sense ofagency(SoA). Our approach is based on the basic premise that
controllability is rewarding. Actually, we have developed
experimental settings where temporal or spatial biases are
introduced in order to make controllability ambiguous. We
have studied pathophysiology of mental illnesses including
schizophrenia from the standpoint of hierarchical level(bottom-up～top-down process) of SoA emergence.
II. AIM OF THE GROUP
Our research project aims to reveal neural mechanisms of
SoA. Moreover, we intend to study pathophysiology ofneurological and psychiatric illnesses which show abnormal
SoA. We have studied methodology to intervene learning(updating) mechanisms of the internal prediction model for the
SoA using original digital device and applications.
III. RESEARCH TOPICS
A. Aberrant sense of agency induced by delayed prediction
signals in schizophrenia: a computational modelingstudy [1]Aberrant sense of agency (SoA, a feeling of control over
one’s own actions and their subsequent events) has been
considered key to understanding the pathology ofschizophrenia. Behavioral studies have demonstrated that abidirectional (i.e., excessive and diminished) SoA is observed

B. Hierarchical Components of Sense of Agency in
Schizophrenia: From Motor Control to Self-Attribution[2]
The sense of agency refers to the feeling of initiating andcontrolling one’s actions and their resulting effects on theexternal environment. Abnormal sense of agency has beenreported in patients with schizophrenia. Previous studies haveuncovered behavioural evidence of excessive self-attribution
and, conversely, a reduction in sense of agency. This apparent
paradox has yet to be fully resolved. In the current study, we
employed three behavioural tasks utilising the same stimuli
and experimental design to systematically evaluate multiple
factors that influence sense of agency. These tasks included
motor control, sensorimotor processing, and self-attribution. In
all three tasks, participants’ real-time mouse movements werecombined with prerecorded other’s motions with an angularbias of either 0 or 90 degrees. In the reaching task, participants
were asked to move a dot to touch a target on the screen as
frequently as possible. The control detection task required
participants to identify one target dot among three moving
dots whose motion incorporated a certain level of real-time
mouse movement. In the control judgement task, participants
made a binary yes/no response to indicate whether they felt
they had control over the direction of one moving dot on thescreen. The results indicated that patients with schizophrenia
performed significantly worse on reaching and control
detection tasks than healthy controls. However, their selfattribution judgement in the control judgement task wascomparable to that of the healthy controls. The patient groupseemed to retain the ability to evaluate the contingency
between sensory and motor information; however, theircapacity to use this sensorimotor information as a cue to detect
control in a noisy environment was compromised. Subsequentcluster analysis revealed that the combined performance
results of the three tasks accurately distinguished patients fromhealthy controls. To understand the interplay between thehierarchical 3 components of sense of agency, we propose thatboth top-down and bottom-up sensorimotor signals processing
is involved. Patients with schizophrenia show notably stronger
dysfunction in bottom-up processes than in top-down
processes.
Fig.1. Motor Control Tasks

accuracy. This suggests that participants’ sense of control isshaped by actively altering action plans, viewed as generating
causal evidence through intervention. Further, participants
proactively expanded the diversity of their action plans, asmeasured by the dimensionality of the action plan distribution.
It facilitates the exploration of the available action plan
options while concurrently accumulating causal evidence forthe inference process. Contrarily, patients with schizophrenia
exhibited reduced action plan diversity, suggesting limited
active control inference and impaired detection of self-relevant
cues. Yet, their judgment responses closely matchedpredictions from the geometrical and dynamical aspects of
their action plans, indicating that they adapt a proper decisionboundary to optimise their active inference about their causal
role in environmental changes. In conclusion, these findings
expand our knowledge, offering a more comprehensive
understanding of the sense of agency, deeply rooted in theprocess of active causal inference.
Fig.2. Motion trajectory during motor control tasks

Control detection task

C. The sense of agency from active causal inference [3]This study investigates the active component of the sense ofagency (SoA) which is often overlooked in the literature, andposits that SoA is fundamentally an outcome of active causal
inference regarding one’s own actions and their impact on theenvironment. Participants engaged in tasks requiring control
over visual objects via a computer mouse, with tasks designed
to test their ability to judge control or detect controlled objects
under varying noise conditions. Our findings reveal thatparticipants actively formed high-level, low-dimensional
action policies (plans) that were idiosyncratic across but
consistent within individuals to effectively infer their degreeof control in a noisy environment. Employing transformerLSTM-based autoencoders, we captured these lowdimensional action plans and demonstrated that thegeometrical and dynamical properties of these action plans
could predict behavioural profiles in the tasks with remarkable

IV. FUTURE PERSPECTIVE
We have shown that pathophysiology of schizophrenia would
be in the level of bottom up process. Then, we would like tointervene learning (updating) mechanisms of the internal
prediction model for the SoA in the level of bottom up process.
REFERENCES
[1]
[2]
[3]

Tsukasa Okimura et al. (2023). Aberrant sense of agency induced bydelayed prediction signals in schizophrenia: a computational modelingstudy. Schizophrenia ;9(1):72.
https://osf.io/preprints/psyarxiv/y3aes
https://www.biorxiv.org/content/10.1101/2024.01.29.577723

A05-10 Annual report of research project
Tomohiko Takei, Akihiro Masaoka

Brain Science Institute, Tamagawa University
Abstract—"Hyper-Adaptability" is the extraordinary
plasticity that animals show at a recovery from a large-scale
restructure of the central nervous system (e.g., spinal cord injury)
or musculoskeletal systems (e.g., tendon transfer). To elucidate theneural mechanisms for the hyper-adaptability, it is necessary to
identify the flexibility and constraints of the adaptation in thecentral nervous system. This research project aims to identify lowdimensional spaces of a fronto-parietal cortical neural activity,
which is called the “neural manifold”, in monkeys performing aflexible feedback motor task to reveal the neural dynamics foradaptive motor behavior. Monkeys were trained to perform acontext-dependent feedback motor control task, and frontoparietal cortical neural activity was recorded withelectrocorticogram (ECoG). The results showed that the contextdependent neural components are sustained over preparatory andresponse periods, while the response-dependent components
emerged after the perturbation applied. Moreover, the context andresponse dependent components are separately represented in
premotor cortex and motor-parietal network, respectively. These
results suggests that context-dependent activity in premotor cortex
switch the neural manifolds represented in motor-parietal
network to achieve flexible feedback motor control.

I. INTRODUCTION
In the mammalian central nervous system, acute and chronic
injuries cause a large-scale neural network restructure, or"Hyper-Adaptability," that is beyond the extent of normal
adaptation. For example, in monkeys hemiparalysed due tospinal cord injury, the ipsilateral motor cortex, which is normallyinactive in healthy condition, is activated, and bilateral motor
cortex takes place in a motor control of paralyzed hand that isdifferent from the normal contralateral motor control [1,2]. It isalso known that the visual cortex, which is normally inactivatedby tactile stimulation, is activated when a visually impaired

person performs Braille tactile reading [3]. What has become
clear from the observation of these series of "HyperAdaptability" phenomena is that these large-scale plasticchanges do not occur randomly in the central nervous system,
but under certain constraints. For example, in the example ofspinal cord injury, bilateral primary motor cortex is active in theearly stages of recovery, but in the later stages, activity ofprimary motor cortex is again confined to the contralateral side
[1].
Recent studies have shown that the coactivation pattern ofneuronal populations in the motor cortex (primary motor cortex
and premotor cortex) remains largely unchanged when monkeys
are subjected to standard motor learning tasks such as
visuomotor rotation and force field adaptation tasks [4]. The
coactivation pattern of neuronal population is called the “neuralmanifold” and has been shown to be stable over a long term foryears [5] and across different behaviours [6]. This neuralmanifold constrains the adaptability of the central nervous
system. Using a brain-computer interface (BCI), monkeys weretrained to move a cursor by either activating motor cortex
neurons with a different coactivation pattern than original
manifold (out of manifold) or coactivating them within theoriginal manifold (within manifold). The results showed that thewithin manifold condition can be learned in a few hours of
training, whereas it requires a longer period of learning over
several days for the out of manifold condition [7]. These findings
suggest that (1) a stable neural manifold exists in the motor
system, which enables stable but flexible motor control andmotor learning, and (2) adaptation to deviate from this neuralmanifold requires a longer period of training to induce
significant neural circuit restructure. Based on these findings, wehypothesized that "normal adaptation" occurs in existing neuralmanifolds, whereas "Hyper-Adaptability" requires restructuringof new neural manifolds".
II. AIM OF THE GROUP
The purpose of this research program is to establish a method
for recording fronto-parietal cortical neural activity in monkeys
to identify neural manifolds, as well as to understand neuraldynamics during adaptive behavior based on neural manifolds.
III. RESEARCH TOPICS

Figure 1 Context-dependent flexible feedback motor
control task (INOUT task)

A. Establishment of a technique for recording fronto-parietal
cortical activity in monkeys performing a flexible feedback
motor task
Two macaque monkeys were trained to perform a flexible
feedback motor task in which motor response to a perturbationis needed to be changed in a context-dependent manner (INOUT
task, Figure 1). By attaching an exoskeleton robot (KINARM)

to the monkeys' arm, the kinematics of the shoulders and elbows
were measured and the hand position was fed back online on acomputer screen. The animal’s feedback response was evaluatedby applying a torque perturbation to the arm during the task. In
each trial, a target was first presented as a contextual cue at oneof the UP and DOWN locations. After a delay period, themonkey's arm was perturbed in one of the two directions (UP or
DOWN). With the presentation of the perturbation, the initial
target disappeared, and the monkey was required to make aquick reaching to the target in the presence of the perturbation.
When the target direction and the perturbation directions arecongruent, the monkey does not need to actively exert force
because the monkey's arms are pushed toward the target
direction (IN condition). On the other hand, when the target
direction and the perturbation direction are opposite, the monkey
needs to exert more force because it needs to reach the target
against the perturbation (OUT condition). Thus, this task
requires varying the response to the disturbance depending on
the context (target location). Two monkeys that had completed
training were implanted with electrodes of a 32-channel corticalelectroencephalogram (ECoG). With these techniques, we
succeeded in simultaneously recording the cortical activity of
dorsal premotor (PMd), primary motor (M1), primarysomatosensory (S1), and parietal area 5 (A5) during the task.
B. Identification of fronto-parietal cortical neural dynamics
during a flexible feedback motor task
We applied frequency decomposition analysis to ECoG
signals recorded from the monkey's fronto-parietal cortex during
the INOUT task to examine the neural dynamics during motor
preparation and during motor response. First, the results showedthat the β-band (15-35 Hz) power of the PMd prominently

changed in response to the target position during the preparation
period after the target presentation (Fig. 2). Then, we further
performed a low-dimensional decomposition analysis (demixed
principal component analysis [8]), in which signal components
dependending on the task parameters (target position and motor
response) were extracted, and found that the target-dependent
componts emerged during the motor preparation period andsustained to the response period, while the response-dependent
component emerged after the perturbation was presented.
Furthermore, the target-dependent components were mainly
encoded in the PMd, whereas the response-dependent
components involved the M1 and posterior parietal cortex(PPC). These results suggest that target-dependent corticalactivity in the PMd switches the neural manifold of the M1-PPC
to achieve the context-dependent feedback motor control.
IV. FUTURE PERSPECTIVE
We trained a flexible feedback response task in two macaquemonkeys and recorded fronto-parietal cortical activity (ECoG).
The results showed that the target-dependent component mainly
encoded in PMd sustained from preparatory and response
periods, while the response-dependent components encoded
inM1-PPC are emerged after the perturbation. These results
suggest that motor responses to the sensory input (perturbation)
are adaptively selected by switching neural manifolds in theneural activity space according to context, and are expected toprovide fundamental insights for understanding the neuralmechanisms of Hyper-Adaptability.
[1]
[2]
[3]
[4]
[5]
[6]
[7]

Figure 2 Neural dynamics of the fronto-parietal network
during the INOUT task. PMd: dorsal premotor cortex; M1:
primary motor cortex; S1: primary somatosensory cortex;
A5: parietal area 5.

[8]Nishimura Y, Onoe H, Morichika Y, Perfiliev S, Tsukada H, Isa T (2007)
Time-dependent central compensatory mechanisms of finger dexterity
after spinal cord injury. Science (New York, NY) 318:1150–1155.
Isa T (2016) Dexterous Hand Movements and Their Recovery After
Central Nervous System Injury. Annu Rev Neurosci 42:1–21.
Sadato N, Okada T, Honda M, Yonekura Y (2002) Critical Period for
Cross-Modal Plasticity in Blind Humans: A Functional MRI Study.
Neuroimage 16:389–400.
Perich MG, Gallego JA, Miller LE (2018) A Neural Population
Mechanism for Rapid Learning. Neuron 100:964-976.e7.
Gallego JA, Perich MG, Chowdhury RH, Solla SA, Miller LE (2020)
Long-term stability of cortical population dynamics underlying consistentbehavior. Nature neuroscience 23:260–270.
Gallego JA, Perich MG, Naufel SN, Ethier C, Solla SA, Miller LE (2018)
Cortical population activity within a preserved neural manifold underlies
multiple motor behaviors. Nature communications:1–13.
Sadtler PT, Quick KM, Golub MD, Chase SM, Ryu SI, Tyler-Kabara EC,
Yu BM, Batista AP (2014) Neural constraints on learning. Nature
512:423–426.
Kobak D, Brendel W, Constantinidis C, Feierstein CE, Kepecs A, Mainen
ZF, Qi X-L, Romo R, Uchida N, Machens CK (2016) Demixed principal
component analysis of neural population data. Elife 5:e10989.

A05-11 Annual report of research project
Hironobu Osaki

Doshisha University
Abstract— Following cortical infarction, the activity of theremaining areas of the brain is upregulated. Such up-regulation
is often considered necessary for functional recovery, but it does
not always facilitate it. To investigate which areas aid infunctional recovery and which do not, we analyzed the changes incortical activity following cortical infarction. To study the change
in cortical activity after cortical infarction, we have developed amacro Ca2+ imaging system to observe neural activity during alimb movement task in rodents. The movement of the forelimb
was monitored and analyzed to reconstruct the estimation of the
3D position. The neural activity related to forelimb movement
was successfully monitored. Optical stimulation controlled by agalvo-scanning system was used to modify the movement of aforelimb by optogenetically stimulating a forelimb region of the
motor cortex. Furthermore, we investigated cognitive deficits inaddition to motor function after cortical infarction. Our aim is todevelop an effective modulation of brain activity after infarction
by combining these strategies.

I. INTRODUCTION
Functional recovery following cerebral cortex infarction is
involved in compensatory mechanisms in the remaining neuralcircuits. For instance, when the primary motor cortex (M1) isdamaged, the activity of the ipsilateral higher motor [1] andsomatosensory [2] areas is increased. Furthermore, in cases oflarge infarctions, increased activity is often observed in thecontralateral (healthy) hemisphere of the injured side.
Stimulating neuronal activity by a transcranial magnetic oran electrical stimulation of the injured hemisphere has beenfound to be effective, and optogenetics has shown promise inexperimentally restoring function. The relationship between theincrease in neural activity across multiple regions andfunctional recovery has not been fully explained. Studies using
animal models of cortical infarction are needed to clarify thisrelationship. However, the relationship between the increase inneural activity in these different regions and functional
recovery has not been fully elucidated. For example, in the
primary motor cortex (M1), infarction causes a release of
inhibition in the directly connected primary somatosensory
cortex, resulting in a transient decrease in tactile information
processing [2] and a decrease in sensory function. Thus, suchnon-specific post-infarct hyperactivity is not necessarily aneffective change for functional recovery.
The results that optogenetic activation of peri-infarct regionis effective for functional recovery [3], whereas activation of
the healthy side prevents functional recovery of the damaged
side [4], suggest that region-selective neuromodulation isbeneficial for functional recovery after infarction. In addition tosuch region-selective activity control, it is expected that more

effective methods for functional recovery will be found whenstimulus timing and schedule are also investigated.
II. AIM OF THE GROUP
The aim of the group is to develop a method to promotefunctional recovery after cortical infarction by controlling
specific neural circuits, focusing on functional changes inneural circuits from the acute to the chronic phase of stroke.
Using an animal model of cortical infarction, we will performfunctional imaging of neural activity during a ladder-rung taskthat requires fine motor skills, observe how M1 changes
before and after motor cortex infarction. Based on theseobservation we will develop a new strategy to promotefunctional recovery after stroke.
III. RESEARCH TOPICS
A. Long-term Ca2+ imaging before and after infarction
We have developed an in vivo Ca2+ imaging system during
the animal is walking on the ladder-rung. Using this system, we
measured the behavior of an animal model of photothrombotic
cortical infarction and performed macro-imaging of thesurroundingarea,
including
M1.
Specifically,aphotothrombotic infarction was created with Rose Bengal in
M1 [Fig. 1], and activity during locomotion of the ipsilateraland contralateral M1 of the animal was measured by Ca2+
imaging during a ladder-rung task. The Ca2+ sensor,
GCaMP6f, was expressed in M1 by injecting an adenoassociated virus vector into the target region, and theendogenous signal and other noise affecting the Ca2+ signal
were corrected. In order to observe the changes in activity over
several months, image registration was performed using thevessel pattern.

Fig. 1. Photothrombosis using Rose Bengal and green LED. SFA, sensory
forelimb area; CFA, caudal forelimb area; RFA, rostoral forelimb area.

To quantitatively assess motor dysfunction after infarction,pose estimation was performed using DeepLabCut [5] and 3D
limb position was reconstructed [Fig. 2]. Infarction in the M1
forelimb region resulted in falling of contralateral forelimb andreduction in joint angle.

direction, we created damage by photothrombosis andexamined the recovery process to investigate the relationshipbetween the position of damage and recovery. We found thatthe degree of recovery tended to be slower in the rostral AGm[7, under review]. In the present study, mice were asked tochoose the direction of arms of 8 maze to estimate unilateral
spatial neglect-like symptoms. Ishii and colleagues also
summarized other methods for measuring spatial cognition inrodent models of unilateral neglect [8].
IV. FUTURE PERSPECTIVE
We summarize the results of the project. This year, we wereable to quantify the recovery process after infarction in termsof motor and cognitive deficits using the M1 and AGm animalmodels of cortical infarction, respectively. Each analysis isautomated using a machine learning combined with
proprietary algorithms. This allows for many validations to be
performed in a short period of time without human error.
Furthermore, the multiple validations will be useful in thedevelopment of more effective interventions for functional
recovery in the future.

Fig. 2. Reconstruction of 3D positions from DeepLabCut

B. Optogenetic neural modulation during behavioral task
The system mentioned above can also control activity in
specific brain regions by optogenetics controlled through X-Y
Galvano-mirrors. We have confirmed that this system cancontrol activity in motor-related regions and influence motor
activity. We are currently using this system to identify brain
regions that influence the long-term recovery process and toanalyze the spatiotemporal stimulation patterns that are
effective for recovery.

C. Recovery process of attention disorder (collaboration withgroup B04)
It has been suggested that the degree of functional
recovery varies according to the location of the infarction. We
investigated not only motor deficits but also cognitive deficits.
Our group collaborated with group B04 (Ishii, Ibaraki
Prefectural University of Health Sciences; Yozu, The
University of Tokyo) by providing an analysis system, andinvestigated the relationship between the site of injury and theprocess of recovery from infarction.
The medial agranular cortex (AGm) is a region associatedwith several sensory areas and related to attention, and we haveshown in rodent mice that damage to this region causes
unilateral spatial neglect-like symptoms [6]. Because the AGm
is a region with a longitudinal shape in the rostro-caudal

REFERENCES

[1]

[2]

[3]

[4]

[5]
[6]

[7]
[8]

Y. Murata, N. Higo, T. Hayashi, Y. Nishimura, Y. Sugiyama, T. Oishi,
H. Tsukada, T. Isa, H. Onoe, Temporal Plasticity Involved in Recovery
from Manual Dexterity Deficit after Motor Cortex Lesion in Macaque
Monkeys, J. Neurosci. 35 (2015) 84–95.
A. Fukui, H. Osaki, Y. Ueta, K. Kobayashi, Y. Muragaki, T. Kawamata,
M. Miyata, Layer-specific sensory processing impairment in the primarysomatosensory cortex after motor cortex infarction., Sci Rep-Uk 10(2020) 3771.
M.Y. Cheng, E.H. Wang, W.J. Woodson, S. Wang, G. Sun, A.G. Lee, A.
Arac, L.E. Fenno, K. Deisseroth, G.K. Steinberg, Optogenetic neuronalstimulation promotes functional recovery after stroke, Proceedings of the
National Academy of Sciences 111 (2014) 12913–12918.
A.R. Bice, Q. Xiao, J. Kong, P. Yan, Z.P. Rosenthal, A.W. Kraft, K.P.
Smith, T. Wieloch, J.-M. Lee, J.P. Culver, A.Q. Bauer, Homotopic
contralesional excitation suppresses spontaneous circuit repair andglobal network reconnections following ischemic stroke, ELife 11(2022) e68852.
A. Mathis, P. Mamidanna, K.M. Cury, T. Abe, V.N. Murthy, M. Mathis,M. Bethge, DeepLabCut: markerless pose estimation of user-defined
body parts with deep learning, Nat Neurosci 21 (2018) 1281–1289.
D. Ishii, H. Osaki, A. Yozu, K. Ishibashi, K. Kawamura, S. Yamamoto,
M. Miyata, Y. Kohno, Ipsilesional spatial bias after a focal cerebral
infarction in the medial agranular cortex: A mouse model of unilateral
spatial neglect, Behav Brain Res 401 (2021) 113097.
D. Ishii, H. Osaki, A. Yozu, T. Yamamoto, S. Yamamoto, M. Miyata, Y.
Kohno, Role of the medial agranular cortex in unilateral spatial neglect,
BioRxiv (2023) 2023.11.24.568203.
D. Ishii, H. Osaki, A. Yozu, S. Yamamoto, Y. Kohno, The rodent modelof unilateral spatial neglect and its clinical applications, J. Rehabilitation
Neurosci. (2024) 241201.

A05-12 Annual report of research project
Hyper adaptive changes in spatial recognition
Rieko Osu

Faculty of Human Sciences, Waseda University
Kento Hirayama, Yuki Ito
Graduate school of Human Sciences, Waseda University
Taiki Yoshida
Faculty of Rehabilitation, School of Health Sciences, Fujita Health University
David W Franklin
Department of Sport and Health Sciences, Technical University of Munich
Abstract—When the right hemisphere is damaged by stroke,hemispatial neglect, in which the person ignores the visual spaceon the left side of the body, appears with relatively highfrequency, even if the vision is normal. It often improves within a
few months, but in long-term residuals, the 'not noticing' of
objects in the left space can interfere with daily life. Various
interventions have been tried, but there is currently no definitive
method. This study aims to gain insight into the methodology andneural mechanisms of spatial attention to help improve
hemispatial neglect through hyper-adaptive interventions. Using
augmented reality (AR) technology, we developed a system thatprojects the entire visual space, including the left visual space,which is often missed by patients with hemispatial neglect, in
their right visual field. The effectiveness of the system wasconfirmed through feasibility tests on patients with hemispatial
neglect. In addition, experiments with healthy participants
showed that self-generated movement enhanced attention to thedirection of movement. These results lead to the development ofattention promotion training in hemispatial neglect.Introduction

I.

AIM OF THE GROUP

A. AR-based intervention systems for hemispatial neglect
Prism adaptation therapy is a well-established clinicalintervention for addressing spatial neglect in patients who havesuffered right hemisphere brain damage due to a stroke orsimilar conditions. This therapy involves the use of prism
glasses to shift the visual field 10 to 15 degrees to the right,
thereby assisting patients in adapting to this altered visualperspective. It has been observed that such adaptation can leadto significant improvements in symptoms of spatial neglect
once the glasses are removed [1]. Moreover, virtual reality(VR) systems have been developed to augment rehabilitationby promoting attention to the left side of space, demonstrating
the potential for technology-assisted interventions [2].
However, while prism glasses directly modify the perception ofreal space, they are constrained by optical limitations. Similarly,
VR's applicability to real-world environments is limited, as it
primarily functions within a virtual domain. In contrast,
augmented reality (AR) technology, which has seen substantial
advancements in resolution and real-time processing
capabilities, offers a promising alternative. AR has thepotential to transform the treatment of spatial neglect byshifting the entire real space into the patient's right visual field,
thus enabling the real-time display and interaction within an
adjusted spatial context. This novel approach could allow
patients to hyper-adapt to a modified environment where real
space is perceived predominantly in the right visual field,
potentially facilitating the use of AR glasses as a daily aid forcorrecting spatial perception. Furthermore, AR technology
could be leveraged for rehabilitation exercises that
incrementally reintroduce stimuli to the left visual field,
thereby training the patient's attention to shift leftward.
B. Inducing attention through movement
It has been reported that in healthy participants, combining
self-generated movements with visual search reduces reaction
time during visual search [3]. [4] compared the case in whichpressing a button causes the circle on the display to move withthe case in which the user only sees the circle move, andshowed that the former, in which the user has a sense ofmanipulating the circle, shows enhancement of attention
toward the direction of the circle's movement. On the other
hand, it is not clear whether the same effect can be obtained incontinuous movements such as those performed in daily life.
Therefore, we examined the effects of continuous movementson spatial attention to the intended direction of movements inhealthy participants. We also varied the sense of agency of themanipulated object and examined its effects on attention.
II.

RESULTS

A. AR-based intervention systems for hemispatial neglect
A presentation system was developed and tested in threepatients with hemispatial neglect. Fig. 1 shows the actual
images presented in the goggles and the eye movement during
the presentation. In both presentations, most of the gaze wereconcentrated in the right from the center of the visual field. In
other words, when the entire real space was presented in theright visual field, it was suggested that the gaze was also
allocated to the left space in the real world. When patients wereasked to response to the randomly presented target, theprobability of finding the target was increased when the real

space was presented in the right visual field than when it waspresented in the full-visual field in the severely ill patient cases.
The patient with mild neglect who already adapted to daily life
performed well in both presentation conditions.
a

An ANOVA for reaction time in visual attention tasks with
6 target positions and 4 motor task conditions revealed that
self-generated movements promoted attention to the spaces that
do not coincide with the movement directions, compared topassive motion pursuit. It was also suggested that attention to
the movement direction may be promoted when the sense ofagency is low.

bcFig. 1 The image in the goggles and the gaze
Left: Condition in which the entire visual space is presented in thefull visual field. Right: condition in which the whole visual space
was presented in the right visual field. a: images in the goggles. b, c:
the gaze position in relation to the images in the goggles and its heatmap during a whack-a-mole game.

One patient with severe neglect conducted a whack-a-molegame with wearing the goggle. It was observed that the patient
was able to find and hit more moles in the left real space when
the real space was presented in the right visual field. We plan
to quantify and validate these performances in the future.
B. Inducing attention through movement
Participants performed a motor task in different conditions
followed by a visual attention task. participants controlled a red
circle on the screen by a computer mouse, while the sense ofagency was manipulated to be high in the High condition, andlow in the Low condition [5]. In the Passive condition, theparticipants watched the red circle movement of others
recorded in advance. Participants also performed a control
condition in which they did not perform any motor task
(Attention condition). In the visual attention task, participants
were instructed to quickly click the mouse (right click for rightgap, left click for left gap) according to the gap of the target
Landolt ring after 6 rings appeared (Fig. 2). Target positions
were differentiated between trials in which the direction of
movement in the preceding motor task and the target position
coincided (D) and trials in which they did not (od), and labeled
1-3 in order of proximity of the target position from thefixation point.
motor task

Fig.3 Comparison of reaction times between task conditions foreach target location

III. FUTURE PERSPECTIVE
We conducted feasibility tests of the AR system and basic
experiments on self-generated movement and attention. We
plan to apply these to the rehabilitation of spatial neglect in the
future.
REFERENCES
[1]
[2]

[3]

visual attention task
[4]
[5]

Fig.2 Task sequence

Y. Rossetti, G. Rode, L. Pisella, A. Farné, L. Li, D. Boisson, and M.-T.
Perenin, "Prism adaptation to a rightward optical deviation rehabilitatesleft hemispatial neglect," Nature, 395(6698), pp. 166-169, 1998.
K. Yasuda, D. Muroi, M. Ohira, and H. Iwata, "Validation of an
immersive virtual reality system for training near and far space neglect
in individuals with stroke: a pilot study," Topics in Stroke Rehabilitation,
24(7), pp. 533-538, 2017.
Y. Tamura, M. Egawa, S. Yano, T. Maeda, M. Kato, and H. Asama,"Activeness Improves Cognitive Performance in Human-MachineInteraction," Journal of Advanced Computational Intelligence andIntelligent Informatics, 17(3), pp. 425-432, 2013.
R. Nakashima, "Beyond one's body parts: Remote object movement withsense of agency involuntarily biases spatial attention," Psychon Bull Rev,
26(2), pp. 576-582, 2019.
W. Wen, N. Shimazaki, R. Ohata, A. Yamashita, H. Asama, and H.
Imamizu, "Categorical Perception of Control," eNeuro, 7(5), 2020

A05-13 Annual report of reseach project
Kosei Takeuchi

Dept. of Medical Cell Biol., Sch. of Med., Aichi Medical University
Abstract— Spinal cord injury (SCI) causes permanent
dysfunction in the body movement and sensation. It is wellknown that recovery becomes more difficult as time passes frominjury. Even with the power of stem cell transplant, it is still hardto cure the late phase of SCI. Despite the difficulty, majority ofpatients suffering from SCI are clinically late phase. Thus,
overcoming the late phase of SCI is an important mission. CPTX
is the structurally guided synthetic peptide that joins the presynaptic neurexin and the post-synaptic AMPA receptors. We
previously showed that CPTX recovered the early phase of SCI
in mouse model. CPTX restored the behavioral deficiency ofAtaxia and Alzheimer’s mouse model as well. Therefore, CPTX is
expected to be a promising medical agent to cure degenerated or
injured CNS with novel concept.

I. INTRODUCTION
Central nervous system injuries, especially spinal cord
injuries, lack fundamental treatment methods. Currently,
various regenerative medical treatments, including induced
pluripotent stem cell (iPS) transplantation, are being
attempted. However, the path towards therapeuticinterventions through drug discovery remains challenging[1]. While advancements, particularly in Brain(Spine)Computer(Machine) Interface, show promise in processing
information from nerves and the spinal cord to guiderecovery [2], numerous challenges persist. These include
determining which neural circuits to intervene in,addressing issues such as allodynia that arise from sensory
reception impairment in spinal cord injuries, andovercoming various obstacles.
Efforts towards clinical applications and attempts at
different treatments raise questions about which circuits
require regeneration enhancement and what artificial
interventions, including rehabilitation, are both possible
and effective. Understanding these aspects is a significant
ongoing theme. Considering analyses and research in thiscontext, exploring the possibility of achieving hyperadaptation in physiological function recovery after spinal
cord injuries is a major focus.
Spinal cord injury models prove to be highly effective
analysis systems for exploring neural circuit reorganizationand hyper-adaptive phenomena. Based on the regeneration
and reorganization of neural circuits, our goal is toinvestigate hyper-adaptive mechanisms.
Inspired by synaptic organizers controlling synaptic
formation, we have demonstrated spinal cord injury
treatment and recovery through the creation and applicationof artificial synaptic connectors connecting excitatory
synapses. Additionally, we are progressing in constructing
a gene-regulation system to suppress the expression of
nerve regeneration inhibitory factors and accelerate neuralreorganization. Combining these efforts, we aim to build
model systems that far surpass the physiological recovery
speed and functional improvement of existing spinal cord
injury mouse and rat models. We also strive to elucidate thecorrelation between adaptive interventions (such as
rehabilitation) and circuit reorganization in the functional
recovery process, along with understanding the mechanismoffunctional
recoverythroughthecontrolofinhibitory neurons.
Alongside the rapid
recoverymodel
creation, we haveestablished an analysis system incorporating AI motion
capture for the analysis of the movement recovery process.
This system helps identify distinctive behavioral changes
and extract elements corresponding to neural reorganization
and regeneration.
II. AIM OF THE GROUP
In this research project, our goals are to further explore theregenerative function of synaptic connector factors andpropose a regenerative model from the subacute to chronic
phase, traditionally considered difficult to treat. We also
aim to progress with interventions like rehabilitation,correlating them with the physiological recovery process.
The overarching objective is to advance a versatile
physiological function recovery evaluation system andutilize AI machine learning to extract essential elements.
III. RESEARCH TOPICS
A. Application of the Synthetic Synaptic Connector CPTX

to Subacute and Chronic Spinal Cord Injuries

CPTX possesses rapid and potent synaptic formation
capabilities by bridging presynaptic molecules (Neuroligin)and postsynaptic molecules (AMPA receptors) (Science.
2020) [3]. (Refer to Fig.1 )Using CPTX, we have observed recovery in a mouse spinal
cord injury model, even in the extremely challenging
conditions of the subacute and chronic phases. This
represents a groundbreaking recovery effect, especiallyconsidering the limited effectiveness seen in various
previous treatment strategies. Furthermore, this effectpersists even at the point of entry into the chronic phase, as
evidenced in the mouse model six weeks post-injury(equivalent to approximately six months in human spinal
cord injury timelines), where significant efficacy has been
consistently demonstrated.

When considering the application of the recovery processduring the chronic and subacute phases, we thoroughly
analyzed issues prevalent in human spinal cord injuries,
such as the presence of allodynia (abnormal pain
sensitivity). As a result, it was found that the expression ofallodynia induced by CPTX administration did not occur.
However, the recovery of tactile sensation in the paw
following CPTX administration showed a clear trend of
sensory improvement when compared to other drug
administrations or natural recovery processes.
The remarkable recovery facilitated by CPTX after spinal
cord injury is not only attributed to motor neurons and theirfunctions but also considered a significant factor in therestoration of sensory functions in the paw. Looking aheadto the analysis, application, and development of "hyperadaptation," it is suggested that beyond the analysis of
motor physiological functions, understanding and assessingchanges in the sensory functions (such as tactile stimulus
perception in limbs and its recognition) is a crucial andimportant element. Connecting these analyses and changes
to applications is essential for the comprehensive
advancement of this field.
B. Motor Physiological Function Analysis from theHyper-Recovery
Model
SystemthroughtheConstruction of an AI Tracing System
In the hyper-recovery process following spinal cord injury,
we are advancing thorough analyses of quantitative motor
function and the extraction of its factors using AIintegrated walking function analysis to gain correlations
with neural reorganization.
In particular, within the physiological function recovery
process, we have been extracting distinctive recovery
patterns through CPTX administration to enhance treatment
effects. Furthermore, we have analyzed the application ofthis to chronic phase treatment and the effects ofrehabilitation. The construction of an AI-driven automatic

analysis system has successfully extracted free-walking
data of mice in dark conditions. Utilizing this system, we
have successfully extracted elements of recovery through
CPTX, introduced them into mice's voluntary rehabilitation,
and are in the process of further enhancing recovery fromthe chronic phase.

III. FUTURE PERSPECTIVE
Regarding the application of the synaptic connector CPTX
in spinal cord injuries, we have achieved notable results,
particularly in its adaptation to the chronic phase and its
correlation with sensory reception. By tracing AI throughthis recovery process, we have successfully extracted
essential elements for physiological function recovery anddeveloped the capability to analyze their correlation withconnected circuits.
Furthermore, our analyses are indicating the increasing
importance of "sensory reception recovery" in improving
physiological functions [5]. Additionally, we are advancing
the application of novel synaptic connectors and their use
in neurological functional recovery, such as spinal cord
injuries. With these novel synaptic connectors, we haveestablished a system that, along with behavioral extraction
using the AI machine learning system constructed in thisstudy, allows us to explore how synaptic interventions
impact the recovery process and how they connect withvarious physiological functions. Based on the correlation
analyses with physiological functions, we plan to continue
advancing artificial interventions and their analyses
towards hyper-adaptation in the future.
REFERENCES
[1]
[2]
[3]
[4]
[5]

Christopher S. et.al., Traumatic spinal cord injury. Nature
Reviews Disease Primers 3,: 17018 (2017)
Lorach H et.al., Walking naturally after spinal cord injury usinga brain–spine interface. Nature 618, 126–133 (2023)
Suzuki K, Elegheert J, Song I, Sasakura H,et.al., A synthetic
synaptic organizer protein restores glutamatergic neuronal
circuits. Science 369 ;6507 : eabb4853 (2020)
Patricia C. Salinas, Restoring neuron connections, Science. 369;
6507, 1052-1053.(2020)
Habuchi H. et.al., Bone marrow derived mast cells injected into
the osteoarthritic knee joints of mice induced spontaneous pain.,
PLoS One;16(6):e0252590(2022A05-14 Brain reorganization in stroke patients withhyper-recovery by measuring EEG modulationinduced by static and dynamic magnetic fields
Tatsuya MIMA, M.D., Ph.D.
Ritsumeikan University

Abstract— To promote research on hyper-adaptability, thisstudy aims to measure functional neural networks using the
TMS/tSMS-EEG method, which is a combination of noninvasive
brain stimulation (NIBS) and EEG measurement, to elucidate thephysiological mechanisms of NIBS and the specificity of neuralnetwork reconstruction in hyper-adaptive individuals. In FY2023,
we discovered that tSMS, one of the NIBS clinically applied inhumans, can induce intrinsic plasticity mediated by Cl- channels.
This contributes to the elucidation of the physiologicalmechanisms of adaptability and hyper-adaptability phenomena.
The results of TMS/tSMS-EEG in healthy subjects are in
submission. Clinical research is underway to apply this method
to hyper-recovered stroke survivors.

I. INTRODUCTION
In this research project, we will treat the phenomenon that has
been treated as a "miraculous healing" episode from stroke as a
dramatic expression of hyper-adaptation, that is areconfiguration of the brain's potential adaptive capacity. Then,from this perspective, we will conduct a clinical study of some
exceptional stroke recoveries ("hyper-recovery"), focusing on
the redundancy of neural networks. As a result, we aim toscientificallyelucidatetheso-called"miracles".
In this study, transcranial magnetic stimulation (TMS) will begiven to measure functional neural networks in humans, andelectroencephalography (EEG) will be recorded simultaneously.
In addition to the TMS-EEG method, which measures EEG
responses in real time to the application of dynamic
electromagnetic field pulses, we will measure changes in EEG
oscillatory responses to the application of a continuous
magnetic field by transcranial static magnetic field stimulation(tSMS) in real time manner.
The main results in FY2023 are as follows.
(1) The physiological mechanism of transcranial static
magnetic field stimulation (tSMS) was elucidated in an animalmodel [1].
(2) The results of clinical studies on stroke rehabilitation andParkinson's disease were published [2, 3, 4].
(3) We showed the "N-of-1 study" as a method to elucidate
individual differences in hyper-adaptability [5, 6, 7].
(4) The TMS-EEG method was studied on healthy subjects [8].

(5) We are doing a cross-disciplinary collaboration between
medicine and engineering to develop a new method to use
neurofeedback methods for rehabilitation of sensory disorders,with B05-09 (Prof. Sakurada) and a joint research on
simultaneous measurement of tSMS with A05-12 (Prof. Osu).
II. AIM OF THE GROUP
The purpose of this study is to measure functional neuralnetworks by TMS/tSMS-EEG to elucidate the physiologicalmechanisms of non-invasive brain stimulation, to reveal the
specificity of neural network reconstruction in hyperrecovering individuals, and to explore neural connections thatare latent in healthy individuals but are activated during theprocess of hyper-adaptability.
III. RESEARCH TOPICS
Two major specific results for FY2023 are listed below andoutlined respectively.
A. Physiological Mechanism of tSMS
By placing a powerful neodymium magnet on the human headsurface, tSMS can produce functional inhibition of the cortex
directly below it. It has attracted attention as one of the safest
noninvasive brain stimulation methods with no risk of
epilepsy. However, the mechanism of its biological action was
not known.
We showed that tSMS changes the intrinsic electricalproperties of neurones (intrinsic or non-synaptic plasticity) byaltering the activity of chloride (Cl−) channels in neurones.
Our whole-cell current-clamp experiments show that exposureof mouse pyramidal neurones to a static magnetic field at astrength similar to human tSMS temporarily decreased their
excitability and induced transient neuronal swelling, which
was caused by a temporary increase in the membrane Cl−
conductance acting as a shunt to offset membrane excitation.
The Cl− channel blocker 4,4 ′ -Diisothiocyano-2,2 ′ stilbenedisulfonic acid abolished the effects of tSMS; however,another blocker, 5-Nitro-2-(3-phenylpropylamino) benzoic
acid, failed. A more selective blocker GlyH101, when applied,
reversed the effects of tSMS. In addition, voltage clamp
recordings revealed voltage dependence similar to SLC26A11
protein mediated currents.

interhemispheric inhibition (IHI). The increase in N1
amplitude in the right M1 region is thought to reflect a
mediated effect through the corpus callosum.
Based on the results of the study in healthy subjects,
recordings were made in two hyper-recovering stroke
survivors and are now being analyzed. So far, the results showthat on the affected side, P1 (P30) has disappeared and theamplitude of P45 is also reduced. These results suggest adecrease in cortical excitability.
IV. FUTURE PERSPECTIVE
These findings provide experimental evidence for the
essential role of a specific Cl− channel in the neural effects oftSMS and advance our mechanistic understanding of noninvasive human neuromodulation.
Noninvasive brain stimulation methods in clinical applicationin humans are thought to induce synaptic plasticity by amechanism similar to LTP/LTD. In contrast, we found thattSMS is based on intrinsic plasticity mediated by Cl- channels.
This could be an important contribution to the understandingof the physiological mechanisms of adaptive and hyperadaptive phenomena.
B. Establishment of TMS-EEG recording analysis method
To evaluate the functional neural network in humans noninvasively and quantitatively, TMS was given at the left
primary motor xortex (M1) while multi-channel EEG
recordings were continuously performed. The TMS-evoked
potential (TEP), which is the change in the brain electric field
induced by the eddy current pulse induced in the brain by themagnetic pulse, was recorded and analyzed using an EEG
amplifier (BrainAmp) that can handle TMS artifacts andartifact elimination software (TESA).
In a study in healthy subjects, we investigated theneuromodulation elicited by tSMS using transcranial magnetic
stimulation (TMS) in combination with simultaneouselectroencephalography(EEG)
toclarifytheneurophysiological basis of tSMS [8]. 15 healthy subjects
were tested with tSMS in the left primary motor cortex (M1)or sham stimulation was applied for 20 min. Single-shot TMS
was given to left M1 before and after the intervention while
EEG was recorded. 120 additions were made to record TEP.
The amplitude around P30 of the TEP in the left primarysensory-motor cortex (SM1) was significantly decreased after
real TMS, while the amplitude around N60 of the TEP in theright SM1 was significantly increased after real TMS.
Furthermore, the alpha power of the TMS-evoked oscillatory
response (IOR) in left and right SM1 significantly decreased
after real TMS.
TMS-EEG is a powerful tool for studying local and global
cortical reactivity to external stimuli with high spatiotemporal
resolution. P1 in the left M1 region has been reported tocorrelate positively with motor evoked potentials (MEPs)(Mäki, 2010), and the decrease in P1 amplitude on thestimulus side may reflect cortical inhibition by tSMS. The
TEP obtained on the contralateral M1 is said to reflect

In FY2023, the results of clinical research on the
rehabilitation application of noninvasive brain stimulation
methods (TMS, tSMS) to enhance recovery from stroke were
published [2, 3, 4]. Especially for tSMS, a double-blinded
prospective study revealed its clinical usefulness [3].
As a physiological mechanism of hyper-adaptability,
plasticity at the level of networks and systems, rather than
individual neurons or regions, is assumed to be important. To
elucidate this, we first established a method for analyzing
TMS-EEG recordings in healthy subjects, which is being
submitted for publication in FY2023 [8]. We are currently
analyzing the neural network of stroke survivors with hyperrecovery by TMS/tSMS-EEG methods.
REFERENCES
[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

Sinha, A.*, Shibata, S.*, Takamatsu, Y.*, Akita,, T., Fukuda, A., Mima,
T. (2024) Static magnetic field stimulation enhances shunting inhibition
via a SLC26 family Cl− channel, inducing intrinsic plasticity. Journal of
Neuroscience, in press.
Nojima I, Horiba M, Sahashi K, Koganemaru S, Murakami S, Aoyama
K, Matsukawa N, Ono Y, Mima T, Ueki Y, Gait-combined closed-loop
brain stimulation can improve walking dynamics in Parkinsonian gait
disturbances: a randomised-control trial. J Neurol Neurosurg Psychiatry
94(11): 938-944.
Shimomura R, Shibata S, Koganemaru S, Minakuchi M, Ichimura S,
Itoh A, Shimotake K, Mima T, (2023) Transcranial static magnetic fieldstimulation (tSMS) can induce functional recovery in patients withsubacute stroke. Brain Stimulation 16(3): 933-935.
Yukawa, Y., Shibata,S., Koganemaru, S., Minakuchi,M., Shimomura, R.,
Nakamura K., Mima, T. (2023) Low-frequency repetitive transcranial
magnetic stimulation can alleviate spasticity and induce functional
recovery in patients with severe chronic stroke: A prospective, noncontrolled, pilot study. Heliyon 9: e15564
Yamaoka, T., Takagi, Y., Shimomura, R., Murata, Y., Shimotake, K.,
Itoh, A., Mima, T., & Koganemaru, S. (2023). N-of-1 Trial of ElectricalSensory Stimulation Therapy on the Tibial Innervated Area during Gait
in a Case of Post-stroke Sensory Disturbance. Progress in Rehabilitation
Medicine 8: 20230018.
Ogawa, A., Koganemaru, S., Takahashi, T., Takemura, Y., Irisawa, H.,
Goto, K., Matsuhashi, M., Mima, T., Mizushima, T., Kansaku, K. (2023)
Swallow-related Brain Activity in Post-total Laryngectomy Patients: A
Case Series Study. Progress in Rehabilitation Medicine 8: 20230026.
Mima, T. (2023) The Personal can be Scientific: N-of-1 approach to
hyper-adaptability. The 46th Annual Meeting of the Japan Neuroscience
Society, Symposium: New horizon of Hyper-Adaptability 1st Aug.,
2023. Sendai, Japan.
Shibata, S., Onishi, H., Mima, T. (in submission) TMS-EEG signaturesof the effects of transcranial static magnetic field stimulation (tSMS) on
cortical
excitability.
Scientific
Report.

A05-15 Annual report of research projectAtsushi Nambu, Resercher, National Institute for Physiological Sciences
Nobuhiko Hatanaka, Assistant Professor, National Institute for Physiological Sciences
Satomi Chiken, Assistant Professor, National Institute for Physiological Sciences
Abstract— Our brain, especially somatomotor cortices,shows a somatotopic representation, a point-to-point
correspondence of each body part to a specific brain
region. The aim of this study is to investigate how suchsomatotopy is affected when the subjects lose their bodyparts. We perform physiological mappings of these corticalareas of adult Japanese monkeys in the awake state, wholost their left distal forelimbs accidentally in theirchildhood.

I. INTRODUCTION
Somatotopy is a point-to-point correspondence of a part ofthe body to a specific region of the brain. In the cerebral
cortex, there are several sensorimotor cortices, and they havetheir own somatotopic maps. The primary motor cortex (M1)
is located in the precentral gyrus and the anterior bank of thecentral sulcus (CS) in humans and nonhuman primates.
Hindlimb, trunk, upperlimb, and orofacial regions arearranged in the mediolateral direction. The intracortical
microstimulation (ICMS) mapping revealed the finesomatotopic arrangement of the upper limb. Digit
representations are surrounded by more proximal
representations in the anterior bank of the CS and theprecentral gyrus.
Much attention has been paid to how such somatotopy isaffected when the subjects lose their body parts. Several lines
of evidence have demonstrated small to large changes insomatotopy of the sensorimotor cortices. Clinical studies inhumans using transcranial magnetic stimulation (TMS) andfunctional magnetic resonance imaging (fMRI) indicated thatthe face region expanded to the lost hand region in the M1.
However, such expansion of the face region was not observedin the ICMS mapping on nonhuman primates. Studies onsomatotopic differences between the healthy and affectedsides after loss of the forelimb or hindlimb in the monkey M1found that neuronal activity in the lost body part coded the
movements of the stump, a remaining part of the limb. The
ICMS threshold to induce muscle movements was similar
between the amputated and healthy sides or between theamputated and normal monkeys.

II. AIM OF THE GROUP
The aim of this group is how such somatotopy is affected
when the subjects lost their body parts. Previous studies were
acute and long (10-20 hours) experiments under generalanesthesia with ketamine and xylazine or urethane.
Application of general anesthesia increases the ICMS
thresholds. Therefore, in the present study, we mappedsomatotopy in chronic setup, recording neuronal activity underawake states without general anesthesia.
The supplementary motor area (SMA) is located on the
medial wall anterior to the M1 leg region, and its somatotopy
from face to leg is arranged antero-posteriorly. The distalproximal segregation in the SMA was not clear. However,there are no reports about effects of the loss of body parts on
the SMA somatotopy using electrophysiological approach. To
address this question, we also investigated somatotopic
changes after loss of the forelimb in the SMA as well as in the
M1.
The somatosensory cortex (S1) located in the postcentral
gyrus. It is well known that this cortical area plays a role in thesomatosensory processing and sensorimotor integration.
Further, recent physiological studies revealed that the S1
received efference copy from the M1 just before sensory
feedbacks from the spinothalamic system. The S1 also showeda reorganization of the somatotopy; the face representation
expanded into the former region of the amputated forelimb.
In this study, we tried to investigate differences ofsomatotopic arrangement in the M1, SMA, and S1 between
healthy and affected sides by ICMS-evoked movement andsomatosensory inputs under awake states using two Japanesemonkeys who lost their left distal forelimbs accidentally intheir childhood..
III. RESEARCH TOPICS
Somatotopic changes in the M1, SI, and SMA
The M1 occupies the surface area of the precentral gyrus
and the anterior bank of the central sulcus. In the healthy
cortical side, the somatotopic map of the M1 seems to be thesame as that in normal monkeys. Somatotopy of the M1 wassimilarly represented in the both cortical sides of normalmonkeys. On the other hand, in the affected cortical side, thecortical region that was supposed to represent the distal
forelimb was lost, and instead represented the stump (Fig. 1):

Figure 1. Somatotopic maps of the M1 and S1 in the healthy (left) and affected (right) hemispheres. Upper, M1 motor map
representing body part movements evoked by ICMS. Lower, S1 somatosensory map representing body parts whose
somatosensory stimulation evoked neuronal activity. Letters represent the following body parts: A, ankle; D, forelimb digits; d,hindlimb digits; El, elbow; FA, forearm; H, hip; J, jaw; K, knee; L, lip; Le, leg; No, nose; Sh, shoulder; St, stump; T, trunk; To,
Tongue; Wr, wrist; X, no evoked movement or no somatosensory response.
forelimb region in the healthy side, while the total forelimb
regions were similar between the affected and healthy sides.
Left hand

Right hand

Amputated

Left cortex

Right cortex

Anterior

CS

Affected side

Healthy side
DistalDistalNeurons in this region responded to the palpation of thestump, and ICMS in this region induced movements of thestump at threshold < 10 µA (core region, Fig. 1, orange).
Therefore, the distal forelimb region in the M1 is considered
to be substituted by the stump region. Other generalsomatotopic map of the M1, such as orofacial, proximal
forelimb, trunk, and hindlimb regions, seems to be the same asthat in the healthy sides. The core region in the affected side
was smaller than that in the healthy side.
The S1 is located posteriorly to the CS. In the healthy side,
the somatotopic arrangement of the S1 was similar to thatreported previously. Somatotopy of the S1 was similarly
represented in the both sides of normal monkeys. On the other
hand, in the affected side, the stump region occupied someregion in the forelimb region (Fig. 1). Therefore, the area that
had been dedicated to the distal forelimb region wasconsidered to be partly dedicated to the stump. Other bodyparts, such as orofacial, trunk, and hindlimb, are similarly
represented as in the healthy side. The areas of the distal
forelimb and total forelimb regions were compared between
the affected and healthy sides. The distal forelimb region in
the affected side was smaller than that in the healthy side,
while the total forelimb regions showed inconsistent changes.
In the SMA, it is known that the orofacial, forelimb, trunk,and hindlimb regions are presented rostro-caudal direction in
the mesial wall. In the healthy side, both motor andsomatosensory examination identified the forelimb region
between the orofacial and hindlimb/trunk regions. On theother hand, in the affected side, motor and somatosensory
examination identified the forelimb regions, and most of theforelimb region represented the proximal forelimb, not thestump. The distal/stump and total forelimb regions in the SMA
were compared between the affected and healthy sides. The
stump region in the affected side was smaller than the distal

SMA

Forelimb

M1

Forelimb
Core

Core

DistalDistal

Hand

Hand

S1

Forelimb

Figure 2. Summary diagram of the present study
IV. FUTURE PERSPECTIVE
We observed how a somatotopic representation in the
cerebral cortex was reorganized in macaque monkeys who losttheir forelimbs accidentally. In the motor cortices, the area
previously representing a lost distal forelimb shrunk, whilethat in the somatosensory cortex was rather preserved (Fig. 2).
This discrepancy between the motor and somatosensory
cortices might be the basis of phantom limb, the sensation thatan amputated limb is still attached. The knowledge on the
reorganization of motor and somatosensory cortices is also
important to develop a brain machine interface, especially a
prosthetic hand controlled by brain activity.

A05-16 Annual report of research project
Noriyuki Higo

Human Informatics and Interaction Research Institute,
National Institute of Advanced Industrial Science and Technology (AIST)
Abstract— There are reports on compensatory plastic changes
in the remaining intact brain which are involved in motor
functional recovery following stroke. Functional brain imaging
has revealed compensatory activation during movements
following recovery; our brain imaging using functional nearinfrared spectroscopy (fNIRS) suggested compensatory increaseof brain activity in the rostral ventral premotor cortex (PMv)during voluntary movements after motor recovery. However, themechanism by which structural plastic changes underlie
functional reorganization is unclear. In this project, therefore, weinvestigated the structural plastic changes underlying functionalchanges together with voxel-based morphometry (VBM) analysis
of magnetic resonance imaging data and immunohistochemical
analysis using SMI-32 antibody in the macaque model of internal
capsular infarcts. We observed significant increases in the graymatter volume (GMV) and the dendritic arborization of layer V
pyramidal neurons in the contralesional rostral PMv.
Immunohistochemical analysis also revealed increased
immunoreactivity of brain-derived neurotrophic factor (BDNF)
and vesicular glutamate transporter (VGLUT1) in the area.
These results indicated that compensatory structural changes
occur in the contralesional rostral PMv during motor recovery
following internal capsular infarcts, and the dendritic growthand synaptic proliferation of pyramidal neurons may underlie
the observed GMV increase. In addition, Our fNIRS data showedthat response of oxyhemoglobin in the contralesional rostral PMv
had changed in both strength and timing; the peak occurredsignificantly earlier after recovery from infarction than before
infarction. Although it is not clear why these changes in response
occurred, this result suggests that changes in strength and timingof oxyhemoglobin response as well as the activated area may be
used as an indicator of functional compensations in the motor
cortex after stroke.

I. INTRODUCTION
Many previous reports have suggested that structural andfunctional changes in the brain in the residual regions are
responsible for the recovery of brain function after brain
damage. These changes are more dynamic than those in thehealthy brain, and are a typical example of the "hyper-adaptivechanges" in the brain. Elucidating these changes will lead toinnovative neurorehabilitation technologies to assess andappropriately guide the brain to appropriate changes. The
principal investigator of this project has been studying theadaptive changes that occur in the process of functional
recovery after brain damage using macaques, which have
similar brain and musculoskeletal structures to humans, as amodel animal. We have elucidated that the recovery of"precision grasp" is accelerated by rehabilitation training [1]and that a change in brain activity compensates for the functionof the damaged area [2]. Clinically, however, cerebral

hemorrhages and infarcts that occur subcortically are often aproblem.
II. AIM OF THE GROUP
By utilizing the macaque internal capsular infarction model,
which has a clinical-like pathology, we aim to elucidate thefunctional and structural adaptation mechanisms of the brain
during the process of motor function recovery, and to obtainknowledge that can be seamlessly applied to the rehabilitationof stroke patients. Using the obtained knowledge, we aim todevelop a system to evaluate changes in brain activity during
the rehabilitation process after stroke.
III. RESEARCH TOPICS
A. Molecular chnages during motor recovery after focal
infarction of the macaque internal capsule
We established a technique to measure macaque motor
cortex activity using functional near-infrared spectroscopy(fNIRS) [3] and measured motor cortex activity during agrasping task. The results showed that before the infarction,
there was increased activity in the hand area of the primary
motor cortex with the performance of grasping movements,
whereas during functional recovery, there was increased
activity in the rostral part of the ventral premotor cortex(PMv) [4]. We performed voxel-based morphometry (VBM)
analysis using T1-weighted MRI and immunohistochemical
staining to examine the structural changes underlying thefunctional changes. The results of VBM analysis suggested anincrease in gray matter (GMV) in the rostral part of thecontralateral PMv of the infarct during the period of motor
recovery after cerebral infarction [5].
Immunohistochemical staining using SMI-32 antibody,which stains pyramidal cells, revealed increased dendritic
branching in pyramidal cells in layer V of the infarct
contralateral rostral PMv. Expression of brain-derived
neurotrophic factor (BDNF), a neurotrophic factor involved inneuroplasticity, and vesicular glutamate transporter(VGLUT1), a marker of excitatory synapses, were also
increased in the ventral part of the contralateral PMv of theinfarct (Fig. 1). These experimental results suggest thatcompensatory structural changes in motor output cells in the
rostral part of the contralateral PMv are important for motor
function recovery after infarction and that structural changes

in dendrites and excitatory synapses underlie the GMVchanges observed in the VBM analysis.

collaboration with Ibaraki Prefectural University of Health
Science, a clinical institution, and a related company (Fig. 2).

Fig. 2. Response of oxyhemoglobin (Oxy-Hb) in the contralesional rostral
PMv had changed in both strength and timing; the peak occurred significantly
earlier after recovery from infarction than before infarction. Based on thesefindings, we are developing a system to extract brain signal features that
correlate with the degree of recovery and to predict recovery based on changes
in these features over time. The right panel show a prototype system forverification in stroke patients.

IV. FUTURE PERSPECTIVE

Fig. 1. Immunohistochemical analysis revealed increased immunoreactivityof brain-derived neurotrophic factor (BDNF) and vesicular glutamate
transporter (VGLUT1), but not vesicular GABA transporter (VGAT) in
pyramidal neurons in the contralesional rostral PMv..

B. fNIRS waveform changes and clinical application during
the recovery process after internal capsular infarction
In the rostral PMv, where functional compensation by
fNIRS was observed, characteristic changes in hemodynamics
were observed. The oxyhemoglobin and deoxyhemoglobinchanges seen during exercise before injury had a single peak or
trough, and both had the same arrival time of about 5 seconds,
whereas the arrival time of oxyhemoglobin was accelerated bymore than 2 seconds in PMv after functional recovery, while
the arrival time of deoxyhemoglobin was not accelerated. The
opposite trends of oxy- and deoxyhemoglobin was lost (Fig. 2).
At this point, it is not possible to rationally explain why thehemodynamic changes specific to compensatory areas occur
based on conventional neuroscience findings, but these results
suggest the possibility of using changes in fNIRS waveforms
as well as activation areas as indicators of functional
compensations. Based on these findings, we are developing asystem to extract brain signal features that correlate with thedegree of recovery, and to predict recovery based on changes
in these features over time. A prototype system for verification
has been completed and is currently being tested on patients in

The results of this year's study revealed functionaland structural changes that occur in the rostral part of theventral premotor cortex of the contralesional hemisphere
during motor function recovery after internal capsular
infarction. In addition to aiming for clinical application of ourfindings, we believe that analysis of the functional andstructural changes in the neural network centered on the
cerebral cortex is a future challenge for a more detailed
understanding of the mechanism.
REFERENCES
[1]

[2]

[3]

[4]

[5]

Y. Murata, N. Higo, T. Oishi, A. Yamashita, K. Matsuda, M. Hayashi, S.
Yamane, "Effects of motor training on the recovery of manual dexterity
after primary motor cortex lesion in macaque monkeys", J Neurophysiol
99, pp. 773-786, 2008
Y. Murata, N. Higo, T. Hayashi, Y. Nishimura, Y. Sugiyama, T. Oishi,
H. Tsukada, T. Isa, H. Onoe, "Temporal plasticity involved in recovery
from manual dexterity deficit after motor cortex lesion in macaquemonkeys", J Neurosci 35, pp. 84-95, 2015
T. Yamada, H. Kawaguchi, J. Kato, K. Matsuda, N. Higo, “Functional
near-infrared spectroscopy for monitoring macaque cerebral motor
activity during voluntary movements without head fixation”. Scientific
Reports 8: 11941, 2018
J. Kato, T. Yamada, H. Kawaguchi, K. Matsuda, N. Higo, “Functional
near-infrared-spectroscopy-based measurement of changes in corticalactivity in macaques during post-infarct recovery of manual dexterity.“,
Scientific Reports, 10 (1), Article number: 6458, 1-12, 2020
K. Matsuda, K. Nagasaka, J. Kato, I. Takashima, N. Higo, “Structural
plasticity of motor cortices assessed by voxel-based morphometry andimmunohistochemical analysis following internal capsular infarcts in
macaque monkeys”, Cerebral Cortex Communications 3 (4), tgac046,
2022

A05-17 Annual report of research project
Osamu Yokoyama

Tokyo Metropolitan Institute of Medical Science
Abstract— Somatosensory information plays an essential rolein motor control. In this study, we induced somatosensory
impairment in monkeys by transecting the dorsal roots of the
spinal cord, which carry peripheral somatosensory information
to the central nervous system. Motor function and neural activity
in the cerebral motor and somatosensory cortices werelongitudinally recorded and analyzed to elucidate the adaptive
mechanisms of cortical activity that support functional recovery
from motor impairments caused by a deficit of somatosensory
input to the central nervous system. Immediately after thetransection of the dorsal root in the cervical spinal cord, motor
performance of the upper limb was impaired, and movementrelated high-gamma and beta-band activity in the primary motor
and primary somatosensory cortices and the flow of informationin the beta band between these areas were significantly altered.
Then, over a period of about two weeks, motor performance
gradually recovered to a similar level as before the lesion, andover a similar time course, high gamma activity in the primary
motor cortex and primary somatosensory cortex and the flow of
information in the beta-band returned to a similar state as before
the lesion. These changes in cerebral activity might contribute to
the recovery of motor function. The results of this study reveal a
process by which the cerebrum adapts to a deficit insomatosensory input from the periphery by dynamicallyreorganizing its activity.

I. INTRODUCTION
Somatosensory information plays an essential role not onlyin the sensory function itself, which perceives the state of one's
own body and the external world, but also in the motor control,
for at least two reasons. First, when the primary motor cortex
generates motor commands, it must use somatosensory
information such as the position of body parts, joint angles andoutput force. Second, during motor execution, spinal motor
neurons receive sensory signals from peripheral sensory
receptors in addition to descending motor commands from thecerebrum and integrate them to generate muscle activity [1,2].
Indeed, patients unable to receive somatosensory information
due to peripheral sensory neuropathy have motor dysfunctionin addition to somatosensory impairment [3-5]. Similarly, amonkey model of somatosensory impairment, in which the
input of somatosensory information from the upper limb to thecentral nervous system is impaired by transection of the dorsal
root of the cervical spinal cord, showed motor impairment of
the upper limb [6]. The motor deficits in this somatosensory
deficit model monkey recover over a period of weeks tomonths [6]. During this process of motor recovery, the central
nervous system is assumed to adapt to the loss ofsomatosensory input. However, the details of this adaptation
mechanism have remained unknown.

II. AIM OF THE GROUP
The aim of this study was to elucidate the adaptation
process of cortical activity that supports recovery of motor
function by creating a monkey model of upper limb
somatosensory impairment in which the input ofsomatosensory information from the upper limb to the central
nervous system was selectively impaired by transection of thedorsal root of the spinal cord, and by longitudinally recording
and analyzing the activity of the cerebral motor-related andsomatosensory cortices during recovery of motor function.
III. RESEARCH TOPICS
After training two Japanese macaques in a reaching andgrasping task and implanting a multi-electrode array on thesurface of the upper limb region of the primary motor (M1),premotor and primary somatosensory (S1) areas of the left
hemisphere of the brain, the dorsal roots in the right lower
cervical cord (C6-8), the input pathway of somatosensory
signals from the right upper limb to the spinal cord, wassurgically transected (Fig. 1 left). We confirmed that the
response of S1 (somatosensory evoked potentials) toperipheral (right upper limb) electrical stimulation underanesthesia was attenuated after the lesion compared to before
the lesion (Fig. 1, right).

Fig. 1. (Left) Schematic representation of the somatosensory and motor nerve
pathways that transmit somatosensory information and motor commands,
respectively, between the periphery of the body and the central nervous
system and the location of the dorsal root transection. (Right) The response of
the primary somatosensory cortex (somatosensory evoked potentials) tostimulation of the adductor internus muscle under anesthesia was attenuatedafter transection of the dorsal roots (C6-C8) entering the lower cervical spinal
cord.

The day after the dorsal root transection, the animals wereable to perform the reaching and grasping task with the rightupper limb, but the time required to complete the motor task

was prolonged. Over the following days, the time required tocomplete the task gradually decreased, returning to a similarlevel as before the lesion approximately 14 days after thelesion (Fig. 2). When motor performance had recovered,somatosensory evoked potentials to peripheral somatosensory
stimuli had not yet recovered. These results suggest that motor
performance was recovered by adaptive changes in the central
nervous system in response to the loss of somatosensory input
associated with voluntary movements due to dorsal roottransection.
We next analyzed electrocorticogram (ECoG) signals
during the motor task, recorded from the motor-related andsomatosensory cortices during recovery of motor performance.
The results showed that on the day after the lesion, highgamma (80-120 Hz) activity in S1 and M1 immediately before
the onset of reaching movements was increased comparedwith to pre-lesion levels. Furthermore, we used Granger
causality analysis to examine the flow of information between
brain regions and found that the flow of information in thebeta band (15-30 Hz) from S1 to M1 during motor preparationand from M1 to S1 during motor initiation was reduced.
Subsequently, it was found that the high gamma activity levelsof S1 and M1 and the flow of information through the beta
band from M1 to S1 gradually returned to similar levels as
before the dorsal root transection as motor performancerecovered (Fig. 2) [7]. The increase in cortical high gamma
activity during motor initiation observed immediately after
dorsal root transection might be a compensatory mechanism
for reduced spinal reflexes to drive muscle activity. The flowof information from M1 to S1 during movement initiation,
which was reduced by the dorsal root transection, mightreflect efference copy, and the gradual re-establishment of theinternal feed-forward model as motor function was restored
and predictions were gradually made again. These adaptivechanges in cortical activity are thought to provide the neuralbasis for the recovery of motor function from the motor
impairment induced by dorsal root lesion.

IV. FUTURE PERSPECTIVE
In the present study, we generated monkeys with impairment
in somatosensation in the upper limb by transection of thedorsal root of the spinal cord of the Japanese macaque, andexamined longitudinal changes in activity in the cerebral
motor and somatosensory cortices immediately after dorsal
root transection and during recovery of motor function, toclarify changes in activity levels and information flow
between brain regions during recovery of motor function. The
results revealed changes in activity levels and information
flows between brain regions associated with motor recovery.
These results may reflect a process of rapid, large-scale
adaptive change by the cerebrum to the new state created by
the dorsal root transection, followed by a gradual reacquisitionof predictive and control functions.
In the present study, the activity of only the motor andsomatosensory cortices of the hemisphere ipsilateral to theinjury was recorded and analyzed. Similar to the mechanismof motor recovery in the monkey model of spinal cord injury
due to lateral corticospinal tract injury [8], the motor andsomatosensory cortices of the hemisphere contralateral to theinjury may also be involved in motor recovery. Reorganizationof activity may also occur in other brain regions andsubcortical structures such as the medulla and thalamus.
Further clarification of the whole aspect of cross-talkbetween motor and somatosensory neural circuits, including
under normal conditions, is expected to lead to thedevelopment of new rehabilitation methods andneuroprosthetic methods to facilitate functional recovery after
central nervous system injuries such as spinal cord injury andstroke.
REFERENCES
[1]
[2]
[3]
[4]

[5]
[6]
[7]

Fig. 2. Summary of changes in the magnitude of activity (bar graphs) andinformation flow (arrows) associated with voluntary movement in the primary
motor cortex (M1) and primary somatosensory cortex (S1) before and after
dorsal root transection and during recovery of motor function[8]

T. Umeda, T. Isa, and Y. Nishimura, “Temporal dynamics of thesensorimotor convergence underlying voluntary limb movement," Proc.
Natl. Acad. Sci. USA, vol. 119 (48), e2208353119, 2022.
T. Umeda, O. Yokoyama, M. Suzuki, M. Kaneshige, T. Isa, and Y.
Nishimura, “Future spinal reflex is embedded in primary motor cortex
output,” bioRxiv [preprint], 2023. doi: 10.1101/2023.06.19.545500
J.C. Rothwell, M.M. Traub, B.L. Day, J.A. Obeso, P.K. Thomas, andC.D. Marsden, “Manual motor performance in a deafferented man,”Brain, vol. 105, pp. 515-542, 1982.
N. Teasdale, R. Forget, C. Bard, J. Paillard, M. Fleury, and Y. Lamarre,“The role of proprioceptive information for the production of isometricforces and for handwriting tasks,” Acta Psychol (Amst), vol. 82, pp.
179-191, 1993.
R. Forget, and Y. Lamarre, “Rapid elbow flexion in the absence of
proprioceptive and cutaneous feedback,” Hum. Neurobiol., vol. 6, pp.
27-37, 1987.
C. Darian-Smith, and M.M. Ciferri, “Loss and recovery of voluntary
hand movements in the macaque following a cervical dorsal rhizotomy,”
J. Com. Neuro., vol. 491 (1), pp. 27-45, 2005.
O. Yokoyama. Adaptive changes in cortical activity during recovery of
motor function after transection of the dorsal roots in monkeys. The 53rd
Annual Meeting of the Japanese Society of Clinical Neurophysilogy, 1st
December 2023.
Y. Nishimura, H. Onoe, Y. Morichika, S. Perfiliev, H. Tsukada, and T.
Isa, “Time-dependent central compensatory mechanisms of finger
dexterity after spinal cord injury, ” Science, 318 (5853), pp. 1150-1155,
2007

Group B: Systems engineering
Toshiyuki Kondo
Tokyo University of Agriculture and Technology

I. O BJECTIVE
Group B aims to understand the phenomenon of hyperadaptability through computational modeling. In the research
project, it has been expected that the systems engineeringgroup mainly plays the following three roles:• Promotion of understanding through computational modeling• Development of intervention technology/strategies toclarify the causal relationship• Proposal of novel research hypotheses to be investigated
To model the phenomenon observed in neuroscience and/or
behavioral science, there are three types of modeling approaches; 1) white-box model, in which we hypothesize amathematical expression according to the observation; 2)
black-box model, where we assume a functional approximatorsuch as artificial neural networks, and optimize its parameters
via machine learning algorithms; and 3) gray-box model, amixture of white and black box modelings. Especially in thehyper-adaptability project, we try to develop the methodology for function-oriented neural encoding using the gray-box
modeling approach.
An important thing for understanding the phenomenon of
hyper-adaptability is to try to investigate the causal relationshipof neural activity and its generated functions and behaviors.
For this aim, we propose robotic-interventional neuroscience,
which realizes a model-based intervention using not only
robotic technology, but also opto/chemo-genetics and brain
stimulation technologies. In addition, the constructed model
can be used to predict the behavior of target systems, and it isexpected to offer novel research hypotheses to be investigated.
II. M EMBERS
To achieve the above mentioned research objective, weorganized four planned and 10 proposed research projects.
Please refer to the report of each research project for theirresearch outcomes in the fiscal year.
Planned research projects
B01 Systems modelling of hyper-adaptation mechanism forreconstruction of neural structure
Principal investigator: Toshiyuki Kondo (TUAT), Funded
co-investigators: Ryosuke Chiba (Asahikawa Med Univ),
Megumi Miyashita (TUAT)This research group aims to realize systems modeling of
hyper-adaptability mechanism with functional dis-inhibition
observed in the impaired brain, especially from the viewpointof reconstruction of neural structure. To clarify the underlying

adaptability mechanism of a large-scale and complex network
system such as the brain, the constructive approach is indispensable, in which a phenomenon can be modeled with theminimum degrees of freedom, and behavior of the model isverified by computer simulations.
In this year, 1) they worked on the estimation of timevarying structure of latent variables embedded in brain activities (sleep-EEG and multi-unit recording data). 2) They
proposed a method to search for parameters under conditions
that facilitate gait by increasing the stiffness of each joint andsearch the parameters using an optimization method. 3) Theydeveloped a ring-shaped device to measure the finger usage
in daily life of stroke patients, revealed that the correlation
between the finger-usage rate and general clinical indices suchas FMA-UE.
B02 Modeling of ultra-adaptive to body change
Principal investigator: Yasuharu Koike (Tokyo Tech),
Funded co-investigator: Tetsuro Funato (UEC)
This research group aims at the modeling of the mechanism underlying the recovery of the motor function after thealteration of the body, especially from the viewpoint of thereconstruction of a neural structure in hyper-adaptability.
In this year, they obtained the following three results:
1) regression analysis of muscle activity and brain activity
showed brain activity involved in muscle activity changes after
tendon transfer. 2) Simulation of the monkey musculoskeletal
system using reinforcement learning reproduced the learning
process for motor acquisition. 3) They proposed a theoreticalframework to explain differences in learning rates of inverse
models with respect to learning of reaching movements.
B03 Systematic understanding and realization of hyperadaptive phenomena focusing on cognition and emotion
Principal investigator: Hajime Asama (U Tokyo), Funded
co-investigators: Jun Izawa (U Tsukuba), Wen Wen (RikkyoU), and An Qi (U Tokyo)
This research group focuses on rehabilitation, the mechanism of hyper-adaptability is investigated from a systematical
approach, and new rehabilitation methods are developed basedon the investigation of effect of cognition and emotion on
behavior adaptation and motor learning.
The achievements are summarized as follows: 1) Investigation of changes in the sense of agency in Parkinson’s disease
patients and compensatory mechanisms in the brain of patients with spinocerebellar degeneration, 2) Construction anddemonstration of the computational theory of meta-learning,3) Development of a predictive model for the sense of agency

based on motion data using deep learning, 4) Constructionof a movement function evaluation system to enhance user
motivation.
B04 Modeling of hyper adaptability in human postural control
considering the role of neurotransmitters
Principal investigator: Jun Ota (U Tokyo), Funded coinvestigator: Arito Yozu (U Tokyo)
The research group aims to verify the following hypothesis
from the viewpoint of reconstitution of sensorimotor control
rules of the hyper-adaptation functions: Neurotransmitters(such as dopamine; DA), whose levels are reduced in patientswith neurodegenerative disorders, adjust the activity levels invarious brain areas and coupling strength between neuronal
circuits as well as control the multitasking function.
In this year, 1) they analyzed the abnormal posture ofParkinson’s disease patients using the standing posture control
model developed in the previous year. 2) They analyzed DATSPECT images, and analyzed motor and neural activities undervoluntary movements of Parkinson’s disease patients.
Proposed research projects
B05-1: Motor learning of modularity in musculoskeletal models toward the emergernce of muscle synergy
Principal investigator: Mitsuhiro Hayashibe (Tohoku U)
A neural network model that can self-organize the automatic switching mechanism of human posture control mode
according to the environment was constructed.
B05-2: A neural network model for hyper-adaptability of
bipedal locomotion
Principal investigator: Naomichi Ogihara (U Tokyo)
To clarify the hyper-adaptability mechanism in bipedal
locomotion to the alteration of the body structure, a gait
simulation was constructed combining a 3D precise musculoskeletal model and a neural network model.
B05-3: Hyper-adaptation of bodily and neural sensorimotor
information structures in early developmental stage
Principal investigator: Hoshinori Kanazawa (U Tokyo)
A motion generation model that can reproduce the actual
motions of newborns and infants was proposed and used toshow the sensory-motor information structure can be generated
only through embodiment.
B05-4: Low-dimensional functional connectivity across bilateral motor-related areas for hyper-adaptability
Principal investigator: Isao Nambu (Nagaoka U of Tech)
A low-dimensional spatial identification method was applied
to cortical EEG to investigate the low-dimensional dynamics
of brain functional connectivity.
B05-5: Application of motor learning model for partial relationship reuse to reconstruction of muscle synergy
Principal investigator: Yuichi Kobayashi (Shizuoka U)
A mathematical model that can explain the ”reuse of neuralcircuits” was investigated in the framework of reinforcement
learning.

B05-6: Hierarchical understanging of adaptation to a new
relationship between the eye and the bodyPrincipal investigator: Michiteru Kitazaki (Toyohashi U
Tech)
Adaptation to visuomotor disruption was investigated using
immersive VR technology, found that the motor learning occurred in strategic and automatic levels after 10 days learning.
B05-7: Supraspinal mechanisms of the human upright posturalcontrol based on the EEG dynamics associated with microfalls
Principal investigator: Taishin Nomura (Osaka U)
Neural mechanisms of postural stabilization was investigated through the EEG responses to micro-falls during humanquiet stance.
B05-8: Higher brain functions as hyper-adaptability: an exploration of the principle of proactive outreach to an indefinite
environment
Principal investigator: Kazuhiro Sakamoto (Tohoku Med
and Pharma U)
A reinforcement learning model that dynamically and autonomously generates the probability or state space based on
the criteria of experience saturation and decision uniquenesswas developed.
B05-9: Individual differences in suitable neural circuits forattention control and its effect on motor control
Principal investigator: Takeshi Sakurada (Seikei U)
To realize neurofeedback training that takes into accountindividual differences in brain function, grouping subjects byattention level using SSVEP/SSSEP responses was investigated.
B05-10: Brain mechanisms for generating exploratory adaptation: Modeling the brain function based on meta-reinforcement
learning
Principal investigator: Yuki Ueyama (NDA Japan)
DCM analysis was used to identify the brain networks in
exploratory adaptation.
III. ACTIVITIES
Activities mainly organized by the members of Group B are
as follows:• November, 20th, 2023, HMS2023 Organized session(Nagoya University)
Prof. Kobayashi (B05-5) gave plenary talk. In the organized session, Prof. Nambu (B05-4) gave keynote talk and5 researchers in Group B presented their recent research
progress, and they deeply discussed.
• February 16-17rd, 2024, Organized session at the symposium on Distributed Autonomous Systems (Tokyo University of Agriculture and Technology), 13 oral presentations.

B01 Annual report of research project
Toshiyuki Kondo
Tokyo University of Agriculture and Technology
t_kondo@cc.tuat.ac.jp

Abstract— This research project aims to realize systems
modeling of hyper-adaptability mechanism with functional ”disinhibition” observed in the impaired brain, especially from theviewpoint of reconstruction of neural structure. For this aim,
we investigate (1) Tensor decomposition and probabilistic latentvariable model for analyzing long-term brain activity, (2) Integration of computational brain network and musculoskeletal
models, and (3) Motor learning experiments with VR/Robottechnologies enhancing hyper-adaptability.

I. I NTRODUCTION
When a person experiences acute/chronic impairment or
disorder due to aging, the brain reorganizes neural networks
by dis-inhibiting pre-existing neural network that is normallysuppressed and searching for latent but available network
has not been utilized through the course of evolution anddevelopment. We call this functional compensation process
as “reconstruction of neural structure”, i.e. a neural entitythat achieves hyper-adaptability.
In order to verify the hypotheses described above, knowledge in neuroscience is essential. However, with only the“bottom-up” approach relying on experiments and analyses,
it would be difficult to clarify hyper-adaptability that is
manifested by systematic behavior of a neural network.
Therefore, this research project takes an interdisciplinary approach that integrates the mathematical modeling technology
of systems engineering with neuroscience to understand thereconstruction of neural structure.
II. R ESEARCH OUTCOMES
A. Probabilistic latent variable model for analyzing longterm multi-modal data
Prof. Kondo (Tokyo University of Agriculture and Technology, TUAT), Dr. Miyashita (TUAT) and Dr. Yano (Toyota
Motor Cooperation) have developed a statistical techniqueto quantify the time-varying structural change in the brain
networks behind the hyper-adaptability. In this fiscal year,
they worked on the estimation of latent structure in brain
activity.
Specifically, the channel, frequency, and time components
were obtained as a tensor by performing time-frequency analysis (the short-time Fourier transform) on the multi-channel
time-series data of sleep EEG provided by Prof. Matsumoto(A05-7) [1]. After performing tensor decomposition (nonnegative CP decomposition), the tensor were expressed as
the sum of latent variables. The time component of the latentvariables was divided by a pre-detedmined time window.
For each time window, the correlation between the latentvariables was calculated, and the dynamic structure of thelatent variables was estimated by the Time-varying Graphical
Lasso (TVGL) method. We can extract the relationshipbetween latent variables as a graph structure specific to sleep
stages, and visualize the characteristics of the dynamics as a
combination of channels and frequency bands.
As shown in Fig. 1, we applied the method to multi-unit
recording data of mice in action, which were provided by
Prof. Aizawa (A01).
B. Integration of computational brain network and musculoskeletal models
Prof. Chiba (Asahikawa Medical University) and his colleagues is focusing on gait initiation to estimate differencesin the brain networks of young and elderly people. It has
been reported that the amount of movement of the Center ofPressure (CoP) and the time of movement of the CoP at the
gait initiation in elderly are both shorter than those of healthy
young adults. We hypothesize that this is due to "increased
muscle tone during standing posture maintenance and insufficient inhibition during gait initiation. Their immediate goal
is to estimate the factors that cause the difference between
young and elderly subjects, based on the differences in theparameters using a musculoskeletal model (Fig. 2).
In the previous year, the stiffness of the hip and hip joints
could not be completely set to zero in the musculoskeletal
model. In other words, the results were the gaits with virtualaids in the simulations. Therefore, it was difficult to makecomparisons with actual humans. In this fiscal year, we
proposed a design method that enables the stiffness of all
joints to be set to zero, and designed a controller whichenables gait for several seconds. Based on this gait, we
designed and analyzed a gait initiation motion.
We designed a gait and gait initiation motion for a bodymodel with 70 muscles and 15 joint degrees of freedom
in the lower limbs and trunk, a nerve transmission time of20[ms], and motor noise, which enable a comparison withactual human. It is very difficult to design a controller under
the above conditions and obtain a reasonable gait becauseof the large number of design parameters. We solved thisproblem by 1) assigning an feedforward (FF) controller and2) applying a stepwise optimization method: 1) designingan FF controller without time delay and 2) designing an
feedback (FB) controller with time delay to deal with noise.
2) In the design of each controller, optimization is done by
fixing some of the degrees of freedom. The gait control
parameters are obtained by using the optimal solution asFactor Matrices
Channel

#1

#4
#3

Time

#2

Spike data

TensorApply short time
Fourier transform

#6

#1
…

…

Frequency

Fig. 1.

#5#2Frequency
factors

Apply
CP decomposition

ChannelfactorsTimefactors

#7
#9 #8

…

time

Graph

Estimate dynamic graph structureby using time factors

Structural change in multi-unit recording data of mice in action identified by using Tensor decomposition and TVGL.

high correlation between activity on the DLPFC related to
executive function and the performance score.
Moreover, they developed a ring-shaped device and used
it to measure the amount of finger usage in daily life of 20
stroke hemiplegic patients [5]. They investigated the correlation between the rate of finger usage on the paralyzed sideand general clinical indeces (FMA-UE, ARAT, STEF, MAL).
As a result, they confirmed that the measure correlated with
the quantitative indices FMA-UE, ARAT, and STEF, but notwith the qualitative index MAL.
III. C ONCLUSIONS

Fig. 2.

Constructive modeling approach.

the initial values and releasing the fixed degrees of freedom
sequentially. As a result, we confirmed to obtain a gait,
although its gait is an outward pointing gait.
In addition, we designed a neural controller for gait initiation to enable the above gait from standing. We confirmedthat just switching from posture-maintaining control to gait
control alone would cause the model to fall down. Therefore,
we designed the target posture and the timing of the switch
in the gait start operation by optimization. As a result, we
obtained a solution that enables the transition to gait. The
analysis results showed that the CoP was moved to the
supporting leg after being moved to the swinging leg side.
This may be an anticipatory postural adjustment to the gait
initiation. However, the model did not reach a sufficientvelocity and fell at the fourth step. This means that the gait
initiation may require different motions not only in the firststep, but also after the second step [6].
C. Motor learning experiments with VR/Robot technologies
enhancing hyper-adaptability
Prof. Kondo and his colleagues investigated motor learning
experiments with VR and/or haptic robot technologies andmotor function evaluation with brain signal analysis [2], [3].
In this fiscal year, they analyzed NIRS signals under anattention task (continuous performance test) for quantitativeevaluation of human attention levels [4]. They found thatAs the final year of the project, we investigated usefulness of the proposed methodologies for modeling thehyper-adaptability in collaboration with neuroscience research groups.
In the future, we will continue to deepen the modelingmethodology, and we further apply the models to actual
neurophysiological data. Moreover, we will investigate themotor tasks that can induce ”reconstruction of neural structure” with dis-inhibition in the brain of elderly people under
the frailty state.
R EFERENCES[1] Megumi Miyashita, Toshiyuki Kondo, Analysis of data using tensor
decomposition and time-varying graphical lasso, The 2nd International
Symposium Hyper-adaptability, Kyoto, Japan, (10/28-29, 2023).
[2] Mizuki Miyazawa, Yoshikatsu Hayashi, Tamami Sudo, Megumi
Miyashita and Toshiyuki Kondo, Hand Velocity in Passive Motor
Experience affects Visuomotor Adaptation, The 45th Annual International Conference of the IEEE Engineering in Medicine and Biology
Society (EMBS), Sydney, Australia, (7/24-27, 2023).
[3] Ozge Ozlem Saracbasi, William Harwin, Toshiyuki Kondo andYoshikatsu Hayashi, Sequential Learning: A Pilot Hyperscanning
Study, The IEEE World Haptics 2023 conference, Delft, Netherlands,(7/10-13, 2023).
[4] Saki Niiyama, Rie Yoshida, Tamami Sudo, Megumi Miyashita andToshiyuki Kondo, Modulated Brain Networks via Motor Learning,2023 International Symposium on Micro-NanoMechatronics and Human Science (MHS2023), Nagoya, Japan (11/20, 2023).
[5] Naoya Yamamoto, Takato Matsumoto, Tamami Sudo, Megumi
Miyashita and Toshiyuki Kondo, Quantitative measurement of finger
usage in stroke hemiplegia using ring-shaped wearable devices, Journal of NeuroEngineering and Rehabilitation, 20, 73, 2023.
[6] Ryosuke Chiba, Hitohiro Etoh, Kaoru Takakusaki, Kohei Kaminishi,
Jun Ota, Generation of three-dimensional gait and gait initiation
motion using a musculoskeletal model with 70 muscles, The 2nd
International Symposium Hyper-adaptability, Kyoto, Japan, (10/28-29,2023).

B02 Annual report of research project
Yasuharu Koike∗, Tetsuro Funato†∗ Tokyo Institute of Technology,† The University of Electro-communications

Abstract—We obtained the following three results. 1. Regression analysis of muscle activity and brain activity showed
brain activity involved in muscle activity changes after tendon
transfer 2. Simulation of the monkey musculoskeletal systemusing reinforcement learning reproduced the learning process
for motor acquisition. 3.We propose a theoretical framework
to explain differences in learning rates of inverse models withrespect to learning of reaching movements.

I. I NTRODUCTION
In this study, we conduct a modeling study of the recovery
mechanism of the movement accompanying the body transformation.The nervous system adapts to the environment by
repeating the optimization and learning of the control system
in response to muscle tendon transformation. Here, humans
and animal experiments reported the existence of discontinuous changes by reconstruction of muscle synergies. However,conventional system engineering approach using optimizationand learning has paid little attention to the reconstruction ofsuch a discontinuous structure, and almost no research has
investigated its mechanism.
II. O BJECTIVE
In this research, we study the modeling of the recovery
mechanism of the movement accompanying the muscle tendon
transformation. In order to model this process, we perform 1)
construction of an experimental system to examine the effectsof long-term physical transformation of a person by virtualsurgery, 2) construction of a decoding method of brain andmuscle activity, 3) construction of a musculoskeletal modelthat can reproduce the transformation. Through these studies,
we will elucidate the mechanism of the hyperadaptive process
for body transformation.
In addition, we will construct an experimental systemthat virtually realizes changes in motor functions associatedwith human body transformation using Virtual Reality, anda dynamic simulation environment. Through this, an experimental system that obtains biological information associatedwith long-term / short-term physical transformation and an
information processing environment that handles the dynamic
process of physical transformation are established.
III. ACHIEVEMENTS
A. Analysis of Changes in Brain Activity in Monkeys after
Tendon Transfer
Our previous studies about tendon transfer in Monkeys haveshown stepwise changes of muscle usage in units of muscle
synergies. This year, we studied changes in brain activityA

15 days Before Tenton Transfer

59 Days After Tenton Transfer

Measured EMG
Reconstructed EMG From ECoG

B

15 days Before Tenton Transfer

59 Days After Tenton Transfer

Fig. 1. Reconstruction of muscle activity from brain activity. Results of
one trial each before and after tendon transfer are shown. A: Comparisonof EMG reconstructed from ECoG and measured EMG. B: Brain regions
within M1 used for reconstruction. Positive and negative contributions to thereconstruction are shown in red and blue, respectively.

after tendon transfer by investigating the relationship between
muscle activity and brain activity during this process [1].
We measured muscle activities (EMG) and brain activity(ECoG) during grasping movements in one macaque monkey
with tendon transfer, where the two muscles used to ﬂex andextend the ﬁngers were replaced. Regression analysis between
the measured EMG and ECoG was performed to determine
which combination of brain regions and frequencies of brain
activity constituted the muscle activity. In particular, regression
analysis using sparse regression was conducted to identify
factors that contribute to EMG above a certain level.
The results of sparse regression showed that muscle activity
was reconstructed with high accuracy by a total of about 20
to 30 brain regions at three main high frequencies (Fig. 1).
Furthermore, a comparison of the activity before and after
tendon transfer showed that the trend of brain activity changed
drastically approximately 3 months after tendon transfer aswell as imediately after tendon transfer. This indicates thatchanges in brain activity associated with recovery occurs in a
stepwise manner similar to muscle activity.

B. Reproduction of the motor reacquisition process using amonkey musculoskeletal model and reinforcement learning
To investigate motor recovery strategies after tendon transfer, we have constructed a monkey musculoskeletal muscle
model on the musculoskeletal simulation software MuJoCoand investigated changes in muscle activity and muscle synergy before and after tendon transfer on the model.
This year, in order to reproduce the motor reacquisition
process during recovery, we constructed a learning environment for reaching movements by reinforcement learning forthe monkey musculoskeletal muscle model. We constructeda learning system using Deep Deterministic Policy Gradient(DDPG), a deep reinforcement learning method that canhandle continuous state and action spaces, and trained it on amonkey musculoskeletal model constructed on MuJoCo. The
results showed that it could reproduce the reaching behaviorsof the monkey.

case in which the inverse model g −1 via a radial basis function(RBF) network. In the RBF network, the inverse model isgiven by:g −1 (f ) = W ϕ(f ) = m. where W is a matrix ofweights, ϕ is a vector of N 2 two-dimensional RBFs ϕ(f )evaluated at f
Learning in the isometric tasks aims to minimize the task
performance error given by the cost function J = 12 ||fd −
fr ||2 . The cost function can be approximated locally around
its minimum as a second order Taylor expansion J(w) =J(w∗ ) + 21 (w − w∗ )T H(w − w∗ ) where H is the Hessianof J.

C. Theoretical limits on the speed of learning inverse models
explain the rate of adaptation in arm reaching tasks
we use our theoretical framework to account for differencesin the speed of learning in two previous studies. First, weaddress the speed of learning in visuomotor rotation experiments with different number of targets Second, we study avirtual surgery task, a type of musculoskeletal transformationin which the pulling forces of muscles in a mapping between
muscle activations and end-point forces are changed, simulating tendon transfer surgeries.
Participants performed a reaching task using the trackpad ofa laptop computer. Each experimental session was conducted
on a different day.
Each trial began with the cursor at the center of the virtualenvironment. After a target appeared, participants quickly
moved the cursor to the target. Participants only received
visual feedback of the ﬁnal position of the cursor. At the end of
the trial, the cursor and the target disappeared, and participants
returned the cursor to the center of the virtual environment.
Only a circle indicating the distance of the cursor to the center
was visible during this stage.
An essential aspect of human motor learning is the formation of inverse models, which map desired actions to motor
commands. Inverse models can be learned by adjusting parameters in neural circuits to minimize errors in the performanceof motor tasks through gradient descent. However, the theoryof gradient descent establishes limits on the learning speed.
Speciﬁcally, the eigenvalues of the Hessian of the error surface
around a minimum determine the maximum speed of learning
in a task. Here, we use this theoretical framework to analyze
the speed of learning in different inverse model learning
architectures in a set of isometric arm-reaching tasks.
P represents the forward physics of the task, and m isan Nm -dimensional motor command vector produced by the
inverse model (joint torques, muscle activations or muscle
synergy activations). Because the task is isometric, forces canbe modeled as a linear function of the motor command m, or
the transformed motor command σ(m). We ﬁrst consider the

Fig. 2. Experimental and simulation results

Experimental and simulation results in the anisotropic scaling and anisotropic target distribution tasks. a. and c. Experimental learning rates in Experiment 1. e. Estimates of theslowest time constant of learning given by λHmin /λHmax forthe three scaling conditions in Experiment 1. f. Simulated
mean error in initial direction during learning in the threescaling conditions in Experiment 1 (mean of the 15 simulatedparticipants), and h. in Experiment 2.
In both simulated frameworks, the simulated learning curvesin the compatible and incompatible surgeries closely matchthe learning curves observed experimentally after selecting anappropriate learning rate for each simulated subject.
IV. C ONCLUSION
In the analysis of brain activity changes after muscle reconﬁguration, investigating brain activity related to muscle
activity generation through regression analysis has clariﬁed
the brain activity associated with changes in muscle activity
after reconﬁguration. In studies of monkey musculoskeletal
simulations, it has become possible to replicate the learning
process for reacquiring movement using reinforcement learning. Furthermore, a learning control model that replicates thelearning speed of motor adaptation has been proposed.
R EFERENCES[1] K. Himeji, T. Funato, R. Philipp, N. Ohta, T. Hara, K. Seki, Analysis ofBrain Activity Changes in Monkeys during Motor Recovery after Muscle
Reconﬁguration, 36th Symposium on Autonomous Distributed Systems,
1A2-5, 2024.
[2] Victor R. Barradas and Yasuharu Koike and Nicolas Schweighofer,
Theoretical limits on the speed of learning inverse models explain therate of adaptation in arm reaching tasks, Neural Networks, 170, 376-389,
2024

B03 Annual report of research projectHajime Asama

The University of Tokyo
Abstract— The aim of this research group is to elucidate theinfluence of body awareness and emotion on the acquisition
process of hyper-adaptation, and to construct and validate aquantitative mathematical model capable of predicting changes
in motor control. Additionally, we intend to propose and validatea model-based rehabilitation approach that facilitates function of
hyper-adaptation through body awareness and emotion based on
the mathematical model. The achievements of this year's study
are summarized as follows: 1) Investigation of changes in thesense of agency in Parkinson's disease patients and compensatory
mechanisms in the brain of patients with spinocerebellar
degeneration, 2) Construction and demonstration of thecomputational theory of meta-learning, 3) Development of apredictive model for the sense of agency based on motion datausing deep learning, 4) Construction of a movement function
evaluation system to enhance user motivation.

I. INTRODUCTION
The B03 project aims to propose new intervention methods
from cognitive aspects such as body awareness and emotion tofacilitate the acquisition of hyper-adaptation through bodyawareness and emotion. To quantitatively measure the hyperadaptive process and construct mathematical models, we willdevelop brain decoding techniques and establish methods toquantitatively measure body awareness and emotion. Next, tovalidate the model-based rehabilitation approach, we willdevelop a robotic intervention platform. Specifically, inindividuals with decreased motor function, we willquantitatively measure and model the hyper-adaptive process,propose and validate a model-based rehabilitation approachbased on the mathematical model of hyper-adaptation, using arobotic intervention platform.
II. AIM OF THE GROUP

III. RESEARCH TOPICS
A. Change in sense of agency and hyper-adaptation in
Patients with neurodegenerative disorders
In Asama’s group, we examine the relationship between
the altered sense of agency in individuals with Parkinson's
disease and the presence of micrographia. Forty-five patientswith Parkinson's disease were included, and based on thecharacteristics of handwriting, they were categorized into
groups: PM group (progressive micrographia), CM group(consistence micrographia), and without micrographia group.
Performance on a task assessing the regularity in motor
control contributing to the emergence of the sense of agency
was compared among these groups. The results revealed that,compared to without micrographia group, the PM group
showed a decrease in abilities related to regularity in motor
control (Fig. 1). Our findings suggest the presence of analtered sense of agency underlying micrographia.
Furthermore, Asama’s group investigated the changes involume by structural imaging to clarify how the cerebrum
compensates for the impaired motor control system observedin patients with spinocerebellar degeneration. Although ourprevious research has shown areas of increased cerebral
volume in groups of patients, it was unclear whether there wasa common pattern of increase in each area among patients.
Therefore, to clarify the existence of patterns in volume
increase, volumetric data of the cerebral cortex (68 regions)from 50 patients were subjected to principal component
analysis for dimensionality reduction. Cluster classification
based on the scores of each component was then performed.
The results identified four clusters, with significant differences

The specific aim of this research project is to construct amathematical model capable of representing thereorganization processes of body awareness, emotion, andmotor control laws during motor interventions, motor learning,
and long-term learning processes. Based on this model, we
aim to propose a model-based rehabilitation approach.
Specifically, we will target clinical cases involving
disorganization of body awareness and motor control, such asneurodegenerative diseases and schizophrenia, and constructmodels to investigate changes in the structure of the body andmotor control laws.

Fig. 1 Performance of each group in the task assessing theregularity of motor control

in motor function scores (SARA scores) observed between
two of them [1]. The observed differences in motor function
between these clusters suggest the presence of adaptive neuralactivity patterns or non-adaptive neural activity patterns
governed by the cerebral cortex.
B. The computational model of hyperadaptation as an
adaptation of hyperparameters of motor adaptation
If hyper-adaptation simply represents an excessive form ofadaptive behavior, it risks undermining a healthy adaptivefunctions, leading to maladaptation. Both humans and animals
possess an innate capability to adjust their adaptative processes
to optimize the efficiency of their adaptation mechanisms. This
capability, known as meta-learning, embodies the “learn-tolearn” process, often observed in our adaptive activities.
Regarding motor adaptation, it is well-established that
adaptation rates were altered in unstable environments,
influenced by signals of reward and punishment, yet acomputational model has not been proposed.
This year, we introduced a computational model for metalearning motor skills grounded in an optimization principle(Fig. 2) [2]. This model accounts for various hyper-adaptation
phenomena previously published. Moreover, we verified thatexisting models fail to interpret these phenomena. Our findings
were further examined through a novel motor meta-learningtask, wherein errors and rewards were presented in separatetask trials. It was demonstrated that human participants could
modulate both learning and retention by integrating rewards
and errors across different trials, thereby supporting our
computational model of motor meta-learning.
Previously, motor learning was conceptualized andmodeled as a simple mechanism of reducing prediction errors
in movement trajectories. The ecological rationale behind the
reduction of trajectory errors by humans and animals remainedvague. Our model, based on the optimization theory, prioritizes
the maximization of rewards and minimization of punishments.
This approach provides an ecological justification of the
trajectory error minimization theory only when error
minimization is linked to punishment minimization.

hidden layer after learning, they calculated the similarities anddifferences between the trajectories drawn by the participants
and those presented on the screen, thereby estimating theparticipants’ sense of movement agency with high accuracy [3].
Furthermore, this method accurately predicted the decline incontrol detection accuracy in patients with schizophrenia [4].
These results reveal that the reduction in the sense of agency inpatients with schizophrenia is not merely due to changes at theperceptual or judgment levels, but to problems in generating
behavioral strategies. Based on these findings, this study paves
the way for a novel research approach that involves extracting
and modeling information from behaviors that underlie theemergence of the sense of agency.
D. Evaluation System of Motor Function
With brain damage or aging, motor function declines,
inhibiting daily life activities. In response, rehabilitation anddaily exercise habits become crucial. Our research groupfocuses on human cognition and emotions, aiming to motivate
individuals to engage in rehabilitation by presenting them with
their own motor function.
This year, we targeted elderly individuals attending
daycare and developed a system to estimate their muscle
strength and balance capabilities using force sensors attached
to assistive devices. We confirmed the system's ability toaccurately estimate each parameter. By utilizing this system,
we can measure the longitudinal changes in motor function
without the need for sensor attachment and present thesechanges to users. This enables the convenient presentation oftraining effects in settings like daycare, potentially fostering
increased motivation.
IV. FUTURE PERSPECTIVE
In the B03 project this year, we focused on investigatingchanges in motor agency perception in patients withneurodegenerative disorders and developing methods to infer
these changes from movement data. Additionally, we
proposed a computational model for meta-learning in motor
tasks, constructing a new learning theory that surpasses
traditional error minimization approaches. Furthermore, we
proposed a system aimed at enhancing the motivation ofindividuals undergoing motor learning by developing astraightforward method for evaluating motor functions.
REFERENCES
[1]

[2]

Fig. 2 Mode structure of meta-learning
C. Verification of the active inference theory based on motion
analysis using deep learning
The Wen’s group has employed deep neural networks tolearn the motion trajectories of mice on a two-dimensional
plane, to analyze exploratory behaviors related to the sense ofagency. Based on the information from the neurons in the

[3]
[4]

Hamada, H., Kikuchi, Y., Wen, W., An Q., Yamashita, A., & Asama,
H “Characteristics of Structural Changes in the Cerebrum of Patientswith Spinocerebellar Degeneration,” Neuroscience 2023 (SfN2023),
Washington D.C. (USA), November 2023.
Sugiyama, Taisei, Nicolas Schweighofer, and Jun Izawa.
"Reinforcement learning establishes a minimal metacognitive process
to monitor and control motor learning performance." Nature
Communications 14.1 (2023): 3988.
A. Y. Chang, H. Oi, T. Maeda, and W. Wen, “The sense of agency
fromactivecausalinference,”
bioRxiv,
2024,
doi:
10.1101/2024.01.29.577723.
H. Oi, W. Wen, A. Chang, H. Uchida, T. Maeda, and K. Hospital,“Hierarchical components of sense of agency in schizophrenia: From
motor control to self-attribution,” PsyArXiv, 2023, doi:
10.31234/osf.io/yB04 Annual report of research project
Jun Ota

Research into Artifact, Center for Engineering (RACE), School of Engineering, The University of Tokyo

I. INTRODUCTION
The study in this project aims to verify the following
hypothesis for reconstitution of sensorimotor control rules of
the hyper-adaptation functions: Neurotransmitters (such as
dopamine; DA), whose levels are reduced in patients withneurodegenerative disorders, adjust the activity levels invarious brain areas and coupling strength between neuronal
circuits as well as control the multitasking function.
The term “multitasking function” denotes the ability to
execute multiple tasks smoothly and simultaneously. To
achieve this, we attempt to build a mathematical model thatconsiders the role of neurotransmitters in posture control in
co-operation with the A04 research group and other B01-B04
research groups. We address the study in three steps. 1)
Verification of the role of neurotransmitters in posture control.
The function necessary for multitasking is assumed to be
impaired in patients with neurodegenerative disorders, such as
Parkinson’s disease, and neuronal degeneration andabnormalities in neurotransmitters are thought to exist. To
verify the role of the neurotransmitters in multitasking, we
focus on neurotransmitters that may change in patients with
Parkinson’s disease. 2) Development of a multitasking
representation model that considers the role ofneurotransmitters in posture control. A mathematicalmultitasking model will be developed to integrate information
regarding neurotransmitters from a micro-viewpoint andinformation regarding behavioral and physiological reactions
from a macro-viewpoint that appear to result from information
processing. 3) Verification of this mathematical model using
data obtained from humans.
Members of B04 group consists of a principal
investigator (Ota), a funded co-investigator (Yozu), and 21 coinvestigators (Shirafuji, Kaminishi, Takamido, Hasegawa,
Kohno, Kishimoto, Yuine, Ishibashi, Hamada, Miyata,
Kanaya, Osaki, Kawano, Kanai, Omura, Hou, Fujiwara,
Makino, Nishizawa, Ishikawa).

posture with this control model, we analyzed abnormal postureof patients with Parkinson's disease: 1) Based on the postural
data of patients, calculate the muscle tone parameters thatallow the musculoskeletal model to stand; 2) After setting themuscle tone parameters, adjust the feedback control parametersand posture to reduce sway. The results showed that the muscle
tone parameters that realize a Parkinson's patient-like standing
posture were successfully estimated, and that a standing
posture more similar to the experimental results was realized
when the muscle tone was greater than the value equivalent tothat of a normal person [1]. In addition, the hypothesis that "thepatients exhibits abnormal posture because the sway in theabnormal posture is smaller than in the other postures inrelation to the increase in muscle tone" was supported.
We also analyzed Dopamine transporter single-photon
emission computed tomography (DAT-SPECT), which is used
for the diagnosis of Parkinson's disease, and examined therelationship between the model control parameters. The
relationship between DAT-SPECT and various motor
symptoms has not been sufficiently analyzed. Therefore, wedeveloped a system that captures 3D DAT-SPECT informationand examines the relationship between DAT-SPECT and each
motor symptom using deep learning technology. A model wasconstructed to regression analyze the scores associated witheach motor symptom using DAT-SPECT images as input, andit was visualized which parts of the image the model focusedon [2]. In the same way, it was shown that a neural networkmodel using DAT-SPECT images as input can estimate themagnitude of the muscle tone parameters [3]. In the estimation,
focus was placed on the area around the striatum, where DAT
is thought to accumulate (Fig. 1), which is consistent with theexisting findings. This provides a basis for describing therelationship between the state (quantity and distribution) of
neurotransmitters in the brain and the behavioral andphysiological responses that emerge. There is a possibility that
this method can be applied to the pathological examination of
diseases related to dopamine in the basal ganglia.

II. RESEARCH RESULTS
A. Analysis of abnormal posture in Parkinson's disease using
a computer model
Ota and colleagues build a mathematical model thatconsiders the role of neurotransmitters in posture control with
Prof. Takakusaki (A04) and Prof. Chiba (B01).
We have proposed a standing postural control model (withmuscle tone parameters and feedback control parameters) that
introduces control mimicking the reticulospinal andvestibulospinal tracts. In order to realize standing in abnormal

Fig. 1. Grad-CAM visualization of the estimated magnitude of the muscle tone
parameters. The closer to the red color, the more attention was paid.

To investigate the effects of dopamine on dual-task
performance, we examined task performance and brain activity
in Parkinson's disease patients in two states (ON and OFF).
The task was a dual task consisting of arithmetic task andstepping. We compare the data of ON with that of OFF states,
and also compare the data of single task with that of double
task. The results are under analysis.
Fig. 2. Using brain imaging, a standing postural control model, and a
musculoskeletal model, the behavioral-physiological responses that emerge fromthe neurotransmitter state are described.

B. Evaluation of the role of neurotransmitters in posturalcontrol while multitasking
Yozu et al. evaluated the role of neurotransmitters inpostural control while multitasking. To understand this subject,
we had conducted a review of prior research on the subject ofmultitasking [1]. Subsequently, we performed a preliminary
study to assess the impact of neurotransmitters on multitasking
in Parkinson’s disease patients with diurnal fluctuations, inwhich we used a multi-task paradigm of static standing (motortask) and calculation (cognitive task) [2]. Our research thus far
suggests that the impact of neurotransmitters on multitasking
depends on the type and difficulty of the task. Therefore, we
focused on voluntary sway and stepping as motor tasks, given
their versatility in terms of task difficulty. This year, we havesubmitted a basic study about voluntary sway on healthy
subjects [3] and conducted stepping study on the patient
subjects. Furthermore, as pain is often comorbid in Parkinson’s
disease, we submitted the basic study which investigated theeffects of pain on postural and motor performance in healthy
subjects [4]. We also conducted more basic study on
locomotion [5].
1) Analysis of cortical mechanisms during voluntary sway
Parkinson’s disease causes postural instability; thus, it isimportant to understand postural instability, as it could
potentially lead to patients falling. Patients with Parkinson’s
disease exhibits decreased forward center of pressure
movement speed from the early stages of the disease (Hoehn
and Yahr scale I~II). Therefore, we defined the task of moving
the center of pressure in the anterior-posterior direction while
adopting the standing posture as a voluntary sway and applied
it as a motor task. The difficulty level of this task can be widely
adjusted by increasing or decreasing the sway frequency. We
developed a measurement system with voluntary sway; andbefore analyzing Parkinson’s disease patients, we firstmeasured heathy subject to clarify the changes in movement[3] and brain activity [6] as a result of changes in sway
frequency. The postural analysis of voluntary sway in healthy
individuals revealed that the joint angles of the trunk and knees
varied depending on the speed of movement of the center ofpressure. We also measured cerebral blood flow during
voluntary sway using fNIRS. The results indicated that the
primary motor cortex and supplementary motor area were
continuously active during voluntary sway, whereas there wasa peak of activity at the beginning of the walking motion.
2) Effects of dopamine on postural control duringmultitasking3) Analysis of the effect of foot pain on posture during gait
Pain is one of the non-motor symptoms in patients with
Parkinson’s disease, and it adversely affects a patient’s qualityof life and activities of daily livings; thus, it is an important
symptom that should be addressed. Patients with Parkinson’s
disease often experience foot pain; however, the effect of painon these patients’ truncal posture during gait is unclear. Here,
we analyzed the effects of pain on truncal posture in healthy
subject. We induced pain during the stance phase, andmeasured data on trunk and lower limb joint angles, gait cycles,
and foot pressure distribution. As a result, the anterior tilt of thetrunk was observed. This study showed that experimentallyinduced plantar pain was associated with anterior trunk tilt [4].
When evaluating posture and movement in Parkinson’s disease
patients with comorbid pain, it might be necessary to consider
the possible influence of pain on the anterior tilt of the trunk.
4) Development of an experimental setup on the origin of
the multi task problems.
The problem of the multitasking is thought to be caused by
the combination of the higher motor function of bipedal
walking and the higher brain function of cognition. Therefore,
we conducted basic research on crawling in order to develop an
experimental system with more primitive postures andmovements [5].
REFERENCES
[1]
[2]

[3]
[4]
[5]

[6]
[7]

[8]
[9]

Y. Omura, K. Kaminishi, R. Chiba, K. Takakusaki and J. Ota, “A neuralcontroller model considering the vestibulospinal tract in human posturalcontrol,” Frontiers in Computational Neuroscience, vol. 16, 785099, 2022.
K. Kaminishi, D. Li, R. Chiba, K. Takakusaki, M. Mukaino, and J. Ota,“Characterization of Postural Control in Post-Stroke Patients by
Musculoskeletal Simulation,” Journal of Robotics and Mechatronics, vol.
34, no. 6, pp. 1451-1462, 2022.
Y. Omura, H. Togo, K. Kaminishi, T. Hasegawa, R. Chiba, A. Yozu, K.
Takakusaki, M. Abe, Y. Takahashi, T. Hanakaaw, and J. Ota.
Proceedings of the 36th SICE DAS Symposium, Tokyo, 2023.
A. Yozu, J. Ota, T. Hanakawa, K. Takakusaki. Studies on dual tasking:review and their relevance in rehabilitation. The journal of the JapaneseSociety of Human Care and Network. 20, 103-106, 2022.
A. Yozu, K. Kaminishi, D. Ishii, Y. Omura, A. Matsushita, Y. Kohno, R.
Chiba, J. Ota, Effects of medication and dual tasking on postural sway in
Parkinson’s disease: A pilot case study. Advanced Robotics, 35. 889897, 2021.
T. Hasegawa, T. Mori, K. Kaminishi, R. Chiba, J. Ota, A. Yozu, “Effectof Sway Frequency on the Joint Angle and Center of Pressure in
Voluntary Sway,” J Mot Behav., vol. 55, no. 4, pp. 373-383, 2023.
A. Yozu, K. Sonoda, T. Hasegawa, K. Kaminishi, M. Osumi,,
M. Sumitani, R. Chiba, J. Ota, “Effect of experimentally induced plantar
pain on trunk posture during gait,” J Phys Ther Sci., vol. 35, no. 9,
pp. 613-618, 2023.
A. Yozu, T. Hasegawa, N. Ogihara, J. Ota, “Peak vertical ground forceof hand-knee crawling in human adults,” J Phys Ther Sci., vol. 35, no. 4,
pp. 306-310, 2023..
N. Orihara, T. Hasegawa, K. Kaminishi, R. Chiba, J. Ota, A. Yozu.
Proceedings of the 35th SICE DAS Symposium. Osaka, 2023.

B05-1 Motor learning of modularity inmusculoskeletal models toward
the emergence of muscle synergy
Mitsuhiro HayashibeTohoku University
Abstract—In the first phase of the project, research was
conducted on the generation of synergy in redundant bodymodels. In the second phase, we went one step further and studied
learning that can self-organize the switching of synergy modules
as an extension of synergy.

I. I NTRODUCTION
To perform an energetically efficient motion as in humancontrol, the so-called mathematical optimization-based approach is a state-of-the-art approach for solving redundancy
problems. Such an optimization approach can provide an
optimal solution when the prior dynamics information of thearticulated body and environment is explicitly given. For themost of human daily task, the dynamics conditions are often
unknown and varying. It still remains an open problem on
how we can create human-like synergistic motion in a selforganized manner to switch synergy modality. This study
provides self-organized framework to induce adaptive motor
synergy alternation corresponding to the given environment
and the motion task.
II. P OSTURAL S YNERGY A LTERNATION
A self-organized phenomenon in postural coordination isessential for understanding the auto-switching mechanism ofin-phase and anti-phase postural coordination modes during
standing and related supra-postural activities. Previously, amodel-based approach was proposed to reproduce such selforganized phenomenon. However, if we set this problem including the process of how we establish the internal predictivemodel in our central nervous system, the learning process iscritical to be considered for establishing a neural network for
managing adaptive postural control. Particularly when bodycharacteristics may change due to growth or aging or are
initially unknown for infants, a learning capability can improve
the hyper-adaptivity of human motor control for maintaining
postural stability and saving energy in daily living. This study
attempted to generate a self-organizing neural network that canadaptively coordinate the postural mode without assuming aprior body model regarding body dynamics and kinematics.
Postural coordination modes are reproduced in head-target
tracking tasks through a deep reinforcement learning algorithm. The transitions between the postural coordination types,
i.e. in-phase and anti-phase coordination modes, could be
reproduced by changing the task condition of the head tracking
target, by changing the frequencies of the moving target. These

modes are considered emergent phenomena existing in humanhead tracking tasks. Various evaluation indices, such as correlation, and relative phase of hip and ankle joint, are analyzed
to verify the self-organizing neural network performance toproduce the postural coordination transition between the inphase and anti-phase modes. In addition, after learning, theneural network can also adapt to continuous task conditionchanges and even to unlearned body mass conditions keeping
consistent in-phase and anti-phase mode alternation [1].
III. WALKING S PEED G ENERALIZATION
We humans can walk efficiently, sometimes slowly, sometimes quickly and at various speeds, sometimes unconsciously.
However, there are still many unknowns about the mechanismsthat produce energy-saving walking at such diverse speeds.
Even for the same walking task, its muscle usage alters for
slow, normal and fast speed thus its synergistic usage issystematically to be modulated. However, the existing reflex
control modeling framework could not accurately change
walking speed.
Our research group has focused on the human nervous
system-based control known as reflex control and succeededin reproducing variable speed walking through simulations
using a musculoskeletal model that mimics humans [2]. We
also developed an optimisation algorithm that extends theleast-squares method and constructed a neural model that canrealise more energy-efficient gait over a wide speed range.
The analysis revealed that in reflex control, the neural circuits
controlling the muscles that promote and inhibit swinging
level of the swinging leg are an important factor in energysaving gait. Simulation of a human-like musculoskeletal model
varying its walking speed in response to an input target speed
can be confirmed at Fig.2. Muscle usage alternation could be
generated online depending on the given target speed.
IV. C ONCLUSION
It has been demonstrated that the self-organisation phenomenon can be reproduced solely by motor experience andreinforcement learning, without the need for previously required prior body dynamics information. As the frequency ofthe motor task increases, the in-phase mode changes to an
antiphase mode. This can be seen as a reproduction of thesynergistic transition process between different synergy types.

Screenshots of learning tracking-balancing tasks at motion
tracking frequency f of target point with regard to 0.15 [Hz]Evolution of correlation coefficient and relative phaseand 1.5 [Hz].
influenced by different motion tracking frequencies f of thetarget point.

Fig. 1. Self-Organizing Neural Network for Reproducing Human Postural Synergy Alternation through Deep Reinforcement Learning

Fig. 2. Results of adaptive walking across a wide range of velocities in reflex-based musculoskeletal systems.

R EFERENCES[1] K. Shen, G. Li, A. Chemori and M. Hayashibe, Self-Organizing Neural
Network for Reproducing Human Postural Mode Alternation throughDeep Reinforcement Learning, Scientific Reports, 13, 8966, 2023.
[2] S. Koseki, M. Hayashibe and D. Owaki, Identifying essential factors
for energy-efficient walking control across a wide range of velocities
in reflex-based musculoskeletal systems, PLOS Computational Biology,
20(1): e1011771, 2024.

B05-02: A neural network model for hyper-adaptability of
bipedal locomotion
Naomichi Ogihara

Department of Biological Science, Graduate School of Sciences, The University of TokyoI.

NTRODUCTION

Locomotion is one of the most important bodily functions
for survival and reproductive success of humans. It
encompasses actions such as evading predators, foraging forsustenance, and even seeking mates to propagate offspring.
Locomotion is accomplished by the mechanical interaction of
the musculoskeletal system with its inhabiting environment.
Therefore, the morphology of the musculoskeletal system of ananimal is shaped to adapt to its principal mode of locomotion.
Humans are no exception. The human body has evolved overmillions of years to adapt to the habitual bipedal locomotion.
Non-human primates can generally walk bipedally. Ourgroup has been investigating bipedal locomotion in Japanesemacaques to achieve a better understanding of the evolution ofhuman bipedalism [1-4]. The acquisition of bipedal locomotion
in an inherently quadrupedal primate could be regarded as amodern analogue for the evolution of bipedal walking, offering
a living model for clarifying and reconstructing the evolutionof bipedal locomotion. However, bipedal locomotion ofJapanese macaques is different from that observed in humans.
For example, the hip and knee joints are more flexedthroughout the gait cycle, and macaques do not generally
exhibit the characteristic double-peaked vertical groundreaction force (GRF) profile seen in humans. Such differences
in kinematics and kinetics of bipedal walking between humans
and nonhuman primates exist because of structural differencesin the musculoskeletal system.
Bipedal walking is a mechanical phenomenon that moves
the center of mass of the body from one place to the other byappropriately applying reaction forces acting from the groundto the feet. Therefore, the success or failure of bipedal walking
depends on how the GRF are appropriately controlled.
However, any changes of the musculoskeletal structure directly
affect the way the body mechanically interacts with the ground,and should drastically alter coordinated dynamics of bipedal
walking, more likely to disturb successful generation of stablebipedal locomotion. The fact that the selective pressure wasapplied to the human musculoskeletal structure during theevolution of human bipedal locomotion strongly indicates thatthe nervous system possesses an ability to spontaneously
reorganize itself in such a way to adaptively make use of themorphological change in the body structure to accomplish
more stable, robust and efficient bipedal locomotion. If theneuronal mechanism underlying such “hyper-adaptability” ofhuman locomotion can be elucidated, the findings will not only
contribute to clarifying the neural basis of the evolution ofhuman bipedal locomotion, but also provides implications for
effective therapeutic or rehabilitative interventions to restore
walking ability in old adults who suffer from decline of bodily
and neurological functions.
II. AIM OF THE GROUP
We aimed to clarify the neuronal mechanism underlying
the “hyper-adaptability” of human locomotion to the alterationof the body structure in a constructive approach using aforward dynamic simulation. Specifically, we attempt toconstruct a model of locomotor nervous system incorporating
the vestibulospinal tracts to realize stable, robust, and efficient
bipedal locomotion while adaptively coping with themorphological change in the human body structure.
III. METHODS
A. Musculoskeletal model
We constructed a 2D musculoskeletal model of the bipedal
Japanese macaque consisting of 9 links representing the HAT(head, arms, and trunk), thighs, shanks, and feet that are
represented by two parts: a tarsometatarsal part and aphalangeal part based on our recently constructed anatomically
based whole-body musculoskeletal model [5] (Fig. 1).
Dimensions and inertial parameters of the limb segments were
determined based on this 3D model. Here, we considered 10
principal muscle groups classified according to muscle
disposition. Each muscle was modeled as a string connecting
the origin and insertion points. The force generated by a muscle
was calculated as the sum of the force generated by thecontractile element due to the activation signal from thenervous system and the passive element parallel to thecontractile element.
B. Nervous model
Animal locomotion is generally accepted as being produced
by a rhythm-generating neuronal network in the spinal cord
known as the central pattern generator (CPG), with locomotion
evoked by stimulus input from the mesencephalic locomotor
region in the brain stem. Such a spinal rhythm-generatingneuronal network also seems to exist in primates and ishypothesized to contribute to the generation of actual
locomotion. The CPG consists of two layers: a rhythm
generation (RG) layer that generates oscillatory signals and apattern generation (PG) layer that generates muscle activity
patterns based on the phase signal from the RG layer.
Therefore, in the present study, a mathematical model of theCPG consisting of the RG and PG layers was constructed. The
RG layer was modeled by two phase oscillators corresponding
to the phase signals for the left and right legs. The PG layerthen generated the activation pattern of each muscle
represented by a combination of two Gaussian basis functionsof the phase signal. The RG layer in the CPG is known tomodulate its basic rhythm by producing phase shifts andrhythm resetting based on sensory information. To take this
into account, we reset the oscillator phase based on foot–ground contact information. To generate bipedal walking, anappropriate activation pattern was determined for each of the
10 muscles such as to minimize the gross metabolic cost oftransport estimated based on the mechanical work done by themuscles and basal metabolic energy by a genetic algorithm [6].
In addition, it is essential to incorporate the vestibulospinal
tract, which plays a crucial role in the vestibulospinal reflex
responsible for maintaining postural balance against external
perturbations. This reflex involves the vestibular organ sensing
the body's acceleration caused by external disturbances andadjusting the muscle tone in the limbs to preserve bodyequilibrium. In this study, we propose the hypothesis that thevestibular nucleus estimates the ground reaction force (GRF)
vector needed to control trunk orientation in response to
perturbations. This estimation is based on information fromvestibular input and involves converting it into muscle torques
and activations using the leg Jacobian matrix.
C. Whole-body angular momentum during bipedal walking
To elucidate how the locomotor nervous system stabilizes
trunk rotation during bipedal walking by generating
appropriate GRFs, we investigated the kinematics and GRFs
during bipedal walking in Japanese macaques and humans. We
estimated the position of the whole-body center of mass(COM) during walking and analyzed where the GRF vector
passes relative to the COM. Additionally, for humans, we
calculated changes in whole-body angular momentum aroundthe COM during bipedal walking and analyzed the contributionof the GRFs to the control of rotational stability of the gait.
IV. RESULTS AND DISCUSSIONSThe kinematics of the simulated bipedal locomotion in theJapanese macaque based on the CPG model generally agreed
with the measured data (Fig. 1C). The simulated gait also
captured the main features of the ground reaction force profiles
in the Japanese macaque [6]. When the foot morphology wasaltered, the vertical GRF profile shifted from a single- to adouble-peaked profile as in human walking, and the cost oftransport decreased compared with that of the intact condition.
Our results suggested that evolutionary changes in the footmorphology were important for the acquisition of human-likeefficient bipedal walking [6].
Analysis of bipedal walking has shown that the GRF vector
in Japanese macaques and humans does not pass through the
COM but consistently falls above it [7] (Fig. 2A). Thissuggests that during bipedal walking, continuously generating
active moments of forward or backward tilt around the COM
by directing the GRF vector away from it contributes to thecontrol of the whole-body angular momentum and,
consequently, the rotational stability of walking (Fig. 2B).
Based on the findings, we constructed a neurocontrol modelthat passes GRFs above the COM in response to trunk segment
angular acceleration, aiming to maintain the stability offorward and backward rotation of the body (Fig. 2C). Although
the realization of bipedal walking generation based on themodel of the vestibulospinal tract has not yet been achieved,
we aim to advance the understanding of the mechanism of"hyper-adaptation" associated with the alterations of the bodystructure by realizing the gait simulation based on thevestibulospinal tract.

A

B

C
1
2

6
3

7
8

4

5

9
10

Fig. 1. Musculoskeleta model of the Japanese macaque. A: 3D whole-bodymodel. B: 2D model used in the present study. C: Generated walking.

Fig. 2. Human gait kinematics and GRF (A), whole-body angular momentum(B), and the neurocontrol model of the vestibulospinal tract (C).

REFERENCES
[1]
[2]

[3]
[4]
[5]

[6]
[7]

Ogihara, N., Hirasaki, E., Kumakura, H., Nakatsukasa, M., 2007.
Ground-reaction-force profiles of bipedal walking in bipedally trained
Japanese monkeys. J. Hum. Evol. 53, 302–308.
Ogihara, N., Makishima, H., Nakatsukasa, M., 2010. Three-dimensional
musculoskeletal kinematics during bipedal locomotion in the Japanesemacaque reconstructed based on an anatomical model-matching method.
J. Hum. Evol. 58, 252–261.
Ogihara, N., Kikuchi, T., Ishiguro, Y., Makishima, H., Nakatsukasa, M.,
2012. Planar covariation of limb elevation angles during bipedal walking
in the Japanese macaque. J. R. Soc. Interface. 9, 2181–2190.
Ogihara, N., Hirasaki, E., Andrada, E., Blickhan, R., 2018. Bipedal gaitversatility in the Japanese macaque (Macaca fuscata). J. Hum. Evol. 125,
2-14.
Ogihara, N., Aoi, S., Sugimoto, Y., Tsuchiya, K., Nakatsukasa, M., 2011.
Forward dynamic simulation of bipedal walking in the Japanesemacaque: Investigation of causal relationships among limb kinematics,
speed, and energetics of bipedal locomotion in a nonhuman primate. Am.
J. Phys. Anthropol. 145, 568–580.
Oku, H., Ide, N., Ogihara, N., 2021. Forward dynamic simulation ofJapanese macaque bipedal locomotion demonstrates better energetic
economy in a virtualised plantigrade posture, Commun. Biol., 4, 308.
Negishi, T., Ogihara, N., 2023. Regulation of whole-body angularmomentum during human walking, Sci. Rep., 13, 8000.

B05-3 Hyper adaptability of sensorimotor information
structure in early human development
Hoshinori Kanazawa

Graduate School of Information Science and Technology, The University of Tokyo.
Abstract— It is well-known that neonates and infants exhibit avariety of spontaneous movements that are coordinated andstructured, even in the absence of external stimuli. Thesemovements, distinctive to early development, tend to disappear withthe onset of voluntary movements such as reaching. Spontaneous
movements and their developmental changes are believed to reflect
the activity and maturation process of the central nervous system.
Research involving the observation of spontaneous movements inhuman neonates and infants has investigated the patterns andcoordination of limb movements. Here, we proposed a method forgenerating movements that can replicate those of actual neonates
and infants. Furthermore, we demonstrated the sensorimotor
information structure that can be produced solely throughembodiment using this movement generation method. Our findings
also suggest that in actual sensorimotor development, the structure
derived from embodiment is influenced not only by neurologicalmaturation but also by higher functions such as intention andcuriosity.

I. INTRODUCTION
In cases of motor dysfunction or decline in higher brain
function due to brain or body damage or aging, there are
typically abnormal changes in neural activity and structure,
which may be utilized for motor and behavioral control.
Understanding the mechanisms underlying such changes isimportant. Adaptive processes involving function recovery orcompensation that cannot be observed in adults are often
reported in cases of physical impairment or neural damage
during early development. B05-03 group focuses on
sensorimotor responses and changes to dysfunctions occurring
during the neonatal to infancy period, in order to construct atheory regarding "hyper-adaptation" to the transformation of
the body and nervous system during early development.
In FY2023, to examine the influence of embodiment inbehavioral development, we utilized infant musculoskeletal
simulations to explore behaviors and sensorimotor information
structures that arise solely from embodiment. We thencompared these findings with actual neonates and infants.
II. AIM OF THE GROUP
The specific aim of this research is to examine the specificfunctional recovery and functional compensatory processes that
occur in early development. We therefore investigated thedevelopmental changes in sensorimotor interactions in
neonates and infants, constructing models to explain thesechanges, and examining the changes and adaptations caused by
dysfunction.

III. RESEARCH TOPICS
A. Generation of Spontaneous Movements in Early
Development
It is known that neonates and infants perform a variety of
coordinated and structured spontaneous movements in theabsence of external stimuli [1]. These movements, specific toearly development, tend to disappear with the onset ofvoluntary movements such as reaching. The central nervous
system's activity and maturation process are believed to bereflected in these spontaneous movements and theirdevelopmental changes, with studies observing the movement
patterns and coordination of limbs in human neonates andinfants [2,3].
Our goal was to generate random movements while
preserving the characteristic features of such specificspontaneous movements. Regarding the generation of random
joint angles, we used the joint angles of 12 joints with 26
degrees of freedom (hands, elbows, shoulders, hips, knees, andankles on both sides) from the whole-body movements ofinfants measured last year. We applied principal component
analysis to the joint angles collected and analyzed in last year's
study. By applying the surrogate method to the extracted
principal components, we created random data that preserved
the original data's frequency components and inter-joint
relationships [4].
Neonatal and infant movements are often evaluated fortheir inter-joint coordination. The movements created by thismethod preserve this coordination, as well as amplitude andfundamental frequency, making it difficult to distinguishbetween the original data and the randomly generated data.
This has potential applications in training for infant movement
observation and abnormality detection.
B. Extraction of Sensorimotor Information Structure
Dependent on Embodiment
The research group, led by the project leader, analyzed
movement data from neonates and infants up to three months
old who had not yet started voluntary movements in last year's
study, examining the structuring of sensorimotor interactions
occurring at that time [5]. This year, using the method ofgenerating random movements while preserving thecharacteristics of actual infant movement data achieved in
section A, we examined the role of embodiment in the
structuring of sensorimotor information.

Specifically, our verification process involved thefollowing steps: (1) Generating random joint angles using thesurrogate method based on joint movements measured inprevious studies, (2) Estimating proprioception and muscle
activities through inverse dynamics analysis and muscle
tension estimation using an infant musculoskeletal model, (3)Calculating the amount of information transmission between
proprioception and muscle activities, (4) Extracting
sensorimotor modules and calculating information transmission
density, (5) Comparing with actual infant data.

where spontaneous movements are task-free and without
rewards or goals, spatial structuring based on embodiment is
progressing [4].

The targeted sensorimotor modules are shown in Figure 1(left), and in this study, we focused solely on the information
density between the left and right arms and between the left
and right legs (as indicated within the red frame in Figure 1,
right).

Fig.1 extracted modules and information density
As a result, when examining the information transmission
density obtained, it was found that the surrogate data had an
information density of about 1/2 to 1/10 compared to the actual
movement data, but the general relative magnitude waspreserved (Figure 2, upper panel). To better understand thisrelationship, we calculated the rank correlation of sensorimotor
information density between surrogate and actual data (Figure
2, lower panel). We observed a certain degree of correlation,
especially a higher correlation in the legs (Bootstrap 95%CI,
arms: 0.22-0.44, legs: 0.53-0.67). However, there was no
significant correlation with developmental changes, i.e., thedifferences between neonates and infants in the actual data(arms: -0.28 to 0.09, legs: -0.25 to 0.06). The blue dots indicate
inter-limb information density, and the red dots indicate intralimb information density, suggesting that informationtransmission across different body parts is less likely to occur.
The generated joint movements do not include coordination
originating from the neural system, thus the calculated
sensorimotor information structure depends solely on
embodiment. The similarity of this information structure to thatof actual neonates and infants suggests the influence of
embodiment on the sensorimotor information structure during
spontaneous movements. On the other hand, the lack ofcorrelation with developmental changes implies that thedevelopment of sensorimotor information structure is
attributable to the maturation of the neural system andvoluntariness. It was found that even in early development,

Fig.2 comparison of sensorimotor information density

IV. CONCLUSION
This year, we proposed a method for generating movements
capable of replicating those of actual neonates and infants.
Furthermore, by utilizing this movement generation, we
demonstrated the sensorimotor information structure that canbe produced solely through embodiment. In addition, ourfindings suggest that in actual sensorimotor development, thestructure is influenced not only by embodiment but also by
neural maturation and higher functions such as intention andcuriosity.
REFERENCES
[1]
[2]
[3]
[4]
[5]

Hadders-Algra, M. (2018). Neural substrate and clinical significance ofgeneral movements: an update. Developmental Medicine & Child
Neurology, 60(1), 39-46.
De Vries, J. I., Visser, G. H., & Prechtl, H. F. (1982). The emergence of
fetal behaviour. I. Qualitative aspects. Early human development, 7(4),301-322.
Kanemaru, N., Watanabe, H., & Taga, G. (2012). Increasing selectivityof interlimb coordination during spontaneous movements in 2-to 4month-old infants. Experimental brain research, 218, 49-61.
金沢 星慶，國吉 康夫. 感覚運動発達における身体性の影響の検証.
日本赤ちゃん学会第 23 回学術集会，大阪，2023 年
Kanazawa, H., Yamada, Y., Tanaka, K., Kawai, M., Niwa, F., Iwanaga,
K., & Kuniyoshi, Y. (2023). Open-ended movements structureB05-4 Annual report of research project
Isao Nambu

Nagaoka University of Technology
Abstract—Functional connectivity is an important measure
to evaluate relationship among different brain regions.
The purpose of this study is to develop a method to identify
low-dimensional representations of the functional
connectivity and investigate time-varying network changes
in such the low-dimensional space. We used a method
using time-varying graphical lasso for electrocorticogramfor reaching task. We found the possibility to detect
dynamics related to the behavioral states in the lowdimensional subspace.
I. INTRODUCTION
There are various parts of the brain that realize perception
and behavior while exchanging information between theseregions. Functional connectivity is an index that quantifies thestatistical dependence of time-series data between such
multiple brain regions and investigates the relationshipbetween them [1]. However, since there are multiple brain
regions, it is difficult to interpret all relationships in a unified
manner.
In response to these problems, this study aims to elucidate
the brain state of "hyper-adaptivity" by developing a method
to identify brain activity involved in human movement asstate-space dynamics at the whole brain level.
In recent years, it has been clarified that the representationof physical movement in the brain depends on the position in
the low-dimensional state space (neural manifold) composed ofneural activity groups during motor planning and execution [2].
For example, it has been suggested that neural activity groups
may be represented in low-dimensional space in the motor
cortex [2, 3], but it is not clear how multiple brain regions
related to movement are related, and the relationships andinteractions between them are not clear as well. If lowdimensional dynamics at the whole-brain level involving
multiple brain regions are revealed, there is a possibility to gaina deeper understanding of human movement, adaptation, andlearning. Recently, studies have attempted to reduce thedimensionality of brain functional connectivity in human
functional magnetic resonance imaging data [4,5]. If we caninvestigate the dynamics of electroencephalogram (EEG) andelectrocorticogram (ECoG) with good temporal resolution, it
will be useful for elucidating the mechanism of brain functional
connectivity. It is also interesting to view the changes in the
dynamics of the bilateral motion-related region as arepresentation in low-dimensional space".

II. AIM OF THE GROUP
The purpose of this research is to develop a method for
identifying the spatial representation of brain states for the
elucidation of hyper-adaptation. In addition, we aim to detectchanges during hyper-adaptation by investigating the dynamics
in low-dimensional space, especially in the motion-related
regions on both sides, using the developed method. method) on
cortical EEG.
III. RESEARCH TOPICS
We examined two types of the methods as follows.
A. Development of the non-directed method to identifying low
dimensional subspace for EEG data
This year, we examined the analysis of low-dimensional spacevisualization using undirected graphical models, which wehave been working on continuously since last year, using
electrocorticogram. In this method, dynamically changing brain
functional coupling is described as Time-Varying Graphical
Lasso (TVGL), a probabilistic graphical model using a
Gaussian Markov probability field [7]. The distance matrix is
calculated using KL-divergence for the accuracy matrix(covariance matrix) identified as the network, and analysis
using multidimensional scaling is performed to represent timeseries with similar network patterns in low dimensions. Using
this analysis, we analyzed human ECoG data. The data wereobtained from the A05-3 group of Kobe University and werebased on ECoG during reaching movements. A total of two
subjects were analyzed. This study was conducted incollaboration with A05-3 Kobe University and B01 Tokyo
University of Agriculture and Technology.
The result of this analysis showed that there was a gradualtendency to change from pre-movement to the time of
execution in low-dimensional space (Fig.1). In the delta, theta,
and alpha bands, the three-dimensional trajectories wereobserved to move apart from resting time to movements, while
the trajectories tended to consolidate densely in the beta andlow gamma bands. When checking the data of the othersubject, it was confirmed that the trajectory was close to acircle in the high gamma band. These results suggest that the
low-dimensional space identification method using TVGL
may capture the low-dimensional dynamics of brain functional
connectivity that may exhibit behaviors. In the future, it isnecessary to search for parameters and study brain regions
corresponding to low-dimensional spaces.

IV. FUTURE PERSPECTIVE
In this study, we examined a low-dimensional spatial
identification method using TVGL on cortical EEG data inorder to clarify the low-dimensional spatial representation ofbrain functional connectivity. In addition, we clarified thepossibility that a data assimilation method for estimating E/Ibalance can be used for the analysis of motor data such as
cortical EEG. In the future, we aim to further investigate thepossibility of a method that can be applied to data of various
modalities and the low-dimensional dynamics of brain function
connectivity.
REFERENCES
Fig. 1. An example of three-dimensional states using TVGL-based models
for the reaching data. Each color represents different time points from rest
(white), cue (light blue), and exection (blue).

B. E/I balance estimation using data assimilation based
methodNext, in order to investigate disinhibition, which is one of
the important mechanisms for achieving hyper-adaptation, we
conducted an analysis using a method that can evaluate thebalance between excitation and inhibition (E/I balance).
Specifically, a method of estimating the activity of excitatoryneurons and inhibitory neurons using data assimilation basedon a neural mass model, thereby evaluating the E/I balance [8].
We investigated whether it is possible to determine the E/Ibalance from ECoG using this method. In this study, we
analyzed a total of two channels near the left and right motor
cortex, and we were able to observe different changes in the E/Ibalance in the left and right channels with the start of
movement, suggesting that it may be possible to estimate theE/I balance that cannot be observed directly from ECoG. On
the other hand, this study uses data assimilation of corticalbrain signals from 1 Hz to 30 Hz after pre-processing. This isbecause the limit indicated by the model is considered to be 30
Hz or less. In addition, at present, we are conducting local
analysis for each channel, and in the future, it is necessary to
perform E/I balance analysis as a network.

[1]
[2]
[3]

[4]

[5]

[6]

[7]
[8]

Makoto Fukushima, Functional Connectivity, Brain Science Dictionary
doi: 10.14931/bsd.9954 (2021)
J. A. Gallego, M. G. Perich, L. E. Miller and S. A. Solla, "Neuralmanifolds for the control of movement", Neuron, vol. 94, no. 5, pp. 978984, 2017.
K. V. Shenoy, M. T. Kaufman, M. Sahani and M. M. Churchland, "A
dynamical systems view of motor preparation: Implications for neural
prosthetic system design", Progr. Brain Res., vol. 192, pp. 33-58, Jan.
2011.
J. Rué-Queralt et al., "Decoding brain states on the intrinsic manifold of
human brain dynamics across wakefulness and sleep," Communications
Biology 2021 4:1, vol. 4, no. 1, pp. 1-11, 2021/7// 2021, doi:
10.1038/s42003-021-02369-7.
S. Gao, G. Mishne, and D. Scheinost, "Nonlinear manifold learning infunctional magnetic resonance imaging uncovers a low-dimensional
space of brain dynamics," Hum Brain Mapp, vol. 42, no. 14, pp. 45104524, Oct 1 2021, doi: 10.1002/hbm.25561.
Z. C. Chao, M. Sawada, T. Isa, and Y. Nishimura, "Dynamic
Reorganization of Motor Networks During Recovery from Partial Spinal
Cord Injury in Monkeys," Cereb Cortex, vol. 29, no. 7, pp. 3059-3073,
Jul 5 2019, doi: 10.1093/cercor/bhy172.
B. Cai et al., "Capturing Dynamic Connectivity From Resting StatefMRI Using Time-Varying Graphical Lasso," in IEEE Transactions on
Biomedical Engineering, vol. 66, no. 7, pp. 1852-1862, July 2019.
H. Yokoyama., and K. Kitajo, “A data assimilation method to track
excitation-inhibition balance change using scalp EEG”. Communications
Engineering, 2(1), 92, 2023.

B05-5 Extension of motor learning model
for reuse ofpartial relationship toward
regeneration of muscle synergy
Yuichi Kobayashi
Faculty of Engineering, Shizuoka University

Abstract—Aiming to construct a model that can explain theprocess of partial control knowledge reuse in the human motor
learning process, this study proposed a new method to model
the process of acquiring feedforward control control from a
motor control system that is mainly based on feedback control.
Taking advantage of the property that repetitive motion learningby humans follows relatively close trajectories, we proposed amethod to acquire feedforward control as a time series of signals using state-independent and time-dependent reinforcement
learning. In particular, we showed that motor learning can bemade more efﬁcient by progressively acquiring the time series ofcontrol signals. We set up an upper limb motor control task andconﬁrmed the effectiveness of the proposed feedforward control
based on state-independent reinforcement learning in two typesof paw tracking control: torque control and muscle drive control.

I. I NTRODUCTION
Human adaptability includes the ability to adaptively recover function in the event of partial dysfunction of the bodyor brain by reusing previously acquired neural circuits. Aiming
to explain this aspect of ’neural circuit re-use’ in motor control
adaptation, the research group has proposed motor learningmodels that can explain the process of re-using parts of a motor
control model once acquired, depending on the situation [1],[2]．The basis of these proposals is the automatic generation of
feedback control laws proposed in [3], where the information
to be reused corresponds to the feedback matrix in feedback
control (Ra,b in the upper left and upper right of Fig. 1).
However, the reuse of information in human motion control
can occur not only in the feedback control system but also in
the feedforward control component.
Therefore, in this study, the focus of attention in the humanmotor learning process is extended from the feedback control
system to the process of transition from the feedback control
system to the feedforward control system, as in the transition
process from the upper left to the lower left in Fig. 1. FF
control system can be regarded as a learning process that generates muscle synergy, and thus may contribute to modellingthe adaptation process (corresponding to the transition fromthe lower left to the lower right in the ﬁgure), including thereuse of muscle synergy in response to changes in the bodystructure. As a process for this, a motor learning model for
acquiring FF control while using the FB control system was
proposed and validated.

Fig. 1. Adaptation process to structural change of body through transition
from FB control to FF control.

II. ACHIEVEMENTS
Based on state-independent reinforcement learning (Blind
Action Sequence Learning with EM, BASLEM), which is notbased on the deviation between state observation and target,a motor learning model was proposed that can explain thegradual transition process from FB control to FF control in
repeated movements. It was shown that FB control can belengthened and control resources can be saved by allowing FF
control to be acquired in upper limb motor control, which ismainly based on FB control. To improve the learning efﬁciencyof the FF control acquisition method, we developed a learningmodel that can progressively change the FF learning interval
while combining FB and FF control.
As a speciﬁc control object, a two-degree-of-freedom manipulator that imitates a human upper limb is considered. Twotypes of drive systems are considered for this manipulator:
a model in which the joints are torque-controlled, and amodel in which artiﬁcial muscles are placed at the joints.
A progressive motion learning method was veriﬁed for thetorque-controlled model. The state-independent reinforcement
learning approach, on which the proposed method is based,
was veriﬁed in a musculoskeletal model with artiﬁcial muscles
attached to a 2-DOF manipulator (see Fig. 2).
Since BASLEM updates policy parameters simultaneouslylink0-0.25

joint1

Target Trajectory
FB Control
FF Control

-0.3

CPA1

CPA6

link1

y [m]

CPA5

CPA3
-0.35

CPA4

CPA2

-0.4

joint2

link2
-0.45
0.25

0.3

&
Fig. 2. Musculoskeletal arm with six muscles.

0.35
x [m]

0.4

0.45

Fig. 6. Trajectories of FB control and after acquiring FF control in a circular
trajectory tracking task with a musculoskeletal arm

recover by FB controller
try FF controller

5reference
trajectory5
FB Control
FF Control

learning
interval

Fig. 3. Concept of gradual acquisition method of FF control through use with
FB control system.

4

4

3

3

2

2

1

1

0

0

FB Control
FF Control

0

for the entire control time, it also updates parameters that havealready been optimised, which can lead to inefﬁciency. In thisstudy, we proposed a method to gradually change the learning
range of BASLEM by utilising the property that the trajectory
is constrained on the same trajectory by the FB control during
iterative learning. The idea is shown in Fig. 3.
The trajectories before and after the learning are shown in
Fig. 4 and Fig. 5, respectively. The FF control rate before
learning was 44.4%, whereas the FF control rate after learning
was 100%. Before learning, the reference trajectory could not
be followed by the control using the initial policy parameters
generated from the FB control when it entered the region
where the disturbance was added. However, the trajectory was
successfully followed after FF control was obtained.
The FF control acquisition learning of the musculoskeletalarm by BASLEM was veriﬁed by using a circular trajectory
tracking task Fig. 6 shows the trajectories after FF control acquisition. The red colour shows the target trajectory, the green
colour shows the trajectory in FB control before learning, andthe blue colour shows the trajectory in FF control after 1000
learning cycles. Fig. 7 and Fig. 8 show the time variation of the䣺䣻 䣶䣴䣣䣬䣧䣥䣶䣱䣴䣻

䢯䢲䢰䢴

䢯䢲䢰䢴䢷
䣻 䣝䣯䣟

䣻 䣝䣯䣟

䢯䢲䢰䢴䢷

䢯䢲䢰䢵
䣖䣣䣴䣩䣧䣶䢢䣖䣴䣣䣬䣧䣥䣶䣱䣴䣻䣈䣄䢢䣅䣱䣰䣶䣴䣱䣮䣈䣈䢢䣅䣱䣰䣶䣴䣱䣮

䢯䢲䢰䢵䢷

䢯䢲䢰䢶

䣺䣻䢢䣶䣴䣣䣬䣧䣥䣶䣱䣴䣻

䢯䢲䢰䢴

䢲䢰䢵

䢲䢰䢵䢷
䢲䢰䢶
䣺 䣝䣯䣟

䢲䢰䢶䢷

䢯䢲䢰䢵
䣖䣣䣴䣩䣧䣶䢢䣖䣴䣣䣬䣧䣥䣶䣱䣴䣻䣈䣈䢢䣅䣱䣰䣶䣴䣱䣮

䢯䢲䢰䢵䢷

䢯䢲䢰䢶

䢲䢰䢵

䢲䢰䢵䢷
䢲䢰䢶
䣺䢢䣝䣯䣟

䢲䢰䢶䢷

Fig. 4. Trajectory by FB control Fig. 5. Trajectory after FF control
before learning.
acquisition.

0.5

1

1.5

2

2.5

0

t [s]

0.5

1

1.5

2

2.5

t [s]

Fig. 7. Control input to CPA1.

Fig. 8. Control input to CPA3.

heating temperature of the muscles corresponding to CPA1 andCPA3 in Fig. 2, respectively. Overall, it can be seen that theheating temperature of the muscles was reduced by learning.
III. S UMMARY
To model the adaptation process including the acquisition ofmuscle synergy, we proposed a motor learning model in which
the feed-forward controller is acquired by state-independent
reinforcement learning through a combination with feedback
control. The simulation results of the proposed method on
a two-degree-of-freedom manipulator showed high tracking
performance. Using this model, it is expected to verify that
adaptive control is possible in humans through gradual acquisition to FF control. In application to musculoskeletal upperr
limb control, the proposed learning method has successfully
reduced the position error and the control input.
R EFERENCES[1] S. Nakamura and Y. Kobayashi, A Grid-Based Estimation of Transformation of Partial Dynamics using Genetic Algorithm for Motor
Learning, Proc. of The 32nd 2021 International Symposium on MicroNanoMechatronics and Human Science, MP2-2-3, 2021.
[2] S. Nakamura, Y. Kobayashi and T. Matsuura, Grid-Based Estimation ofTransformation Between Partial Relationships Using a Genetic Algorithm,
Journal of Robotics and Mechatronics, Vol. 34, No. 4, pp. 786-794, 2022.
[3] Y. Kobayashi, K. Harada and K. Takagi, Automatic Controller Generation
Based on Dependency Network of Multi-modal Sensor Variables for
Musculoskeletal Robotic Arm, Robotics and Autonomous Systems, Vol.
118, pp. 55-65, 2019.

B05-6 Annual report of research project
Michiteru Kitazaki

Toyohashi University of Technology
Abstract—The system for virtual eyes on the back of the head
was applied to test humans’ adaptability to it. We found that the
motor learning occurred in strategic and automatic levels after
10days learning. We identified temporal synchrony as a necessary
factor for the embodiment of a biomechanically impossible body.
We conducted experiments on shared-hand avatar. Finally, wedeveloped and fundamentally evaluated a system that can amplify
the direction of gaze with respect to head motion.

I. INTRODUCTION
Research on the sense of body ownership and enbodimentuses virtual reality (VR), which focuses on the transformation of
the body. By synchronizing body movements with those ofavatars, we can perceive and manipulate avatars as our own
bodies, even if they are different from our actual bodies. By
embodying such avatars, it has been shown that attitudes andbehaviors change according to the attributes of the avatar [1-3].
Systematically incorporating further transformations of bodyform and function is important for exploring the limits andplasticity of human body cognition.
II. AIM OF THE GROUP
We aim to modify the 'relationship between the eyes (visualsensors) and the body', intervene in the visual-motor
cooperative relationship and elucidate how this changes
people's perception and behavior. Normally, the human eyes arelocated in front of the head. This correspondence between bodyand sensory organs determines the hierarchical relationshipbetween perception and environment. We manipulate the 'eyebody relationship' to clarify how people's perception andbehavior change with long-term adaptation. Specifically, abody in which the relationship between the visual organ and the
body is manipulated, such as a body with eyes behind the head
and a body with eyes at the tips of the hands, is constructedusing VR, and three different levels ('level of conscious action
strategy', 'level of unconscious action' and 'level of perception')are focused on and clarified through psychophysicalexperiments and behavior analysis (Fig. 1).

Fig. 1 A body with eyes on the back of the head (left), and a body with eyeson the hand

III. RESEARCH TOPICS
A. A body with eyes on the back of the head
A body with eyes (virtual camera) behind the head was
implemented in a VR space and tested on six experimental
participants. The participants' body movements, including theirheads, were measured with a motion capture system (Optitrack
Primx22 12 camera). The viewpoint was set behind the head andvisual stimuli were presented on a head-mounted display (HTC
Vive Pro EYE) worn by the participants (Fig. 2). The experiment
was conducted over 12 days: on days 0 and 11, participants
performed two complex course walking tasks and a task where
they had to walk to reach two locations and return to their initial
location; on days 1-10, participants performed a daily task where
they had to avoid a colliding wall, a straight walking task and acircular walking task, as well as reaching two different objects.
Gaze

Walking

Fig. 2 Walking experiment with body with eyes behind the head.

Fig. 4 Scheme of the system for augmentation of visual field
Fig. 3 Data of the head angle relative to the trunk over a period of 10 days

For the participants' 10 days of circumferential walking, thewalking trajectories became smoother as the days progressed
and the walking time became shorter. When changes in the
direction of the head relative to the trunk were calculated, thedifference between the trunk and head directions tended to
become smaller and more stable as the days progressed. Thissuggests that the hierarchy of body movements is also acquiredby learning.
B. Adaptive learning for bodies with joints bent in opposite
directions
Experiments were performed by creating a body in which theelbow joints of the left and right arms were bent in(biomechanically impossible) opposite directions, andmanipulating the direction of joint bending and thesynchronization between the self-body movements and theavatar's movements. The results showed that adaptation did notoccur for temporal asynchrony and the sense of body ownership
was low, but for the joints that bend in the opposite direction, thebody became self-embodied even after 10 min of learning. The
findings were published in a journal [4].
C. Virtual augmentation of visual field
We have developed a system that can change the direction inwhich the viewer's gaze is directed depending on the amount ofhead rotation. By presenting images taken by a 360-degree
camera in the direction of a virtual gaze with any gain based on
the actual head direction, it is possible to observe directly behindthe person even if the head is turned only 90 degrees to the side.
With this system, it is possible to observe the entire environment
with little physical effort.

Fig. 5 A system that enables users to experience real images by attaching a
360-degree camera to an HMD (left). Actual use of the system (right).

IV. FUTURE PERSPECTIVE
We conducted an adaptation experiment to a system in which
the eyes were facing the opposite direction to the head andidentified changes in the level of implicit behavior in addition
to changes in the level of strategic behavior. Temporalsynchrony was identified as a requirement factor for theembodiment of the impossible body [4]. We also conducted
experiments on the shared body of the hand and publishedarticles [5,6]. Finally, we developed and fundamentally
evaluated a system that can amplify the direction of gaze in avariable manner with respect to head motion.
REFERENCES
[1]

[2]
[3]
[4]

[5]
[6]

Kondo, R., Sugimoto, M., Minamizawa, K., Hoshi, T., Inami, M., andKitazaki, M. (2018). Illusory body ownership of an invisible bodyinterpolated between virtual hands and feet via visual-motor
synchronicity, Scientific Reports, 8:7541
Kondo, R., Tani, Y., Sugimoto, M., Inami, M., and Kitazaki, M. (2020).
Scrambled body differentiates body part ownership from the full body
illusion. Scientific Reports, 10:5274
Hagiwara, T., Ganesh, Sugimoto, M., Inami, M., and Kitazaki, M. (2020)
Individuals prioritize the reach straightness and hand jerk of a sharedavatar over their own, iScience, 23(12): 101732
Hapuarachchi, H., Ishimoto, H., Kitazaki, M., Sugimoto, M., Inami, M.
(2023). Temporal visuomotor synchrony induces embodiment towards anavatar with biomechanically impossible arm movements, i-Perception,
14(6):20416695231211699
Katsumata, Y., Inoue, Y., Toriumi, S., Ishimoto, H., Hapuarachchi, H.,
and Kitazaki, M. (2023). Shared avatar for hand movement imitation:
Subjective and behavioral analyses. IEEE Access, 11, 96710-96717
勝俣安伸, 鳥海智志, 井上康之, Harin Hapuarachchi, 北崎充晃 (印刷中). 共有・結合した手による指リーチングにおける身体性と課題
成績, 日本バーチャルリアリティ学会論文誌, 29(1), 1-11.

B05-7 Neural mechanisms of postural stabilization
revealed by EEG responses to micro-falls
during human quiet stance
Taishin Nomura
Osaka University

Abstract— Postural sway during quiet stance often exhibits arepetition of micro-fall and the subsequent micro-recovery. The
classical view –that the quiet bipedal stance is stabilized by the
ankle joint stiffness– has been challenged by paradoxical nonspring-like behaviors of calf muscles: gastrocnemius muscles areshortened and then lengthened, respectively, during the microfall and the micro-recovery. Here, we examined EEG based brain
activity during quiet stance, and identified desynchronizationand synchronization of beta oscillations that were associated,
respectively, with the micro-fall and the micro-recovery. Basedon a widely accepted scenario for beta-band desynchronization
during movement and post-movement rebound in the control of
discrete voluntary movement, our results reveal that the beta
rebound can be considered as a manifestation of stop command
to punctuate the motor control for every fall-recovery cycle.
Namely, cortical interventions to the automatic posturalcontrol are discrete, rather than continuous modulations. The
finding is highly compatible with the intermittent control model,
rather than the stiffness control model.

I. INTRODUCTION
It has been believed that the human upright posture isstabilized by the high stiffness of the ankle joint caused by thesustained activity of antigravity muscles and the spinal stretch
reflex. Ten years ago, we challenged this dogma, andproposed the intermittent control model [1,2]. The intermittent
control model switches off the ankle joint active torqueintermittently in a state-dependent manner. It hypothesizes
that the upright posture is stabilized by turning off the active
control and exploits the passive posture recovery movement
during the OFF-control period. It has been shown that theintermittent control model exhibits a much higher accuracy inmodel fitting of experimental postural sway data during quiet
stance, compared to the conventional continuous stiffness
control model [3]. In this study, electromyography of ankle
muscles (EMG) and brain activity (EEG) related to neuralcontrol of posture were measured in addition to the posturesway during quiet stance. We aim to elucidate the brain
mechanism of the intermittent control of standing posture.
Elucidation of the functional meaning of the EEG ofsynchronous brain activity at beta-band (β-rebound) thatappears when a voluntary movement is terminated or whenmovement execution is inhibited is one of the major issues inupper extremity movement-related EEG research [4]. There
have been few studies related to β-rebound for automatic
movements including stabilization of upright posture.

II. AIM OF THE GROUP
The intermittent postural control hypothesis claims that the
upright posture is stabilized by inactivation (OFF) andactivation (ON) of the mediolateral gastrocnemius muscle at asequence of appropriate timings, based on the somatosensory
information related to the postural state, i.e., the tilt angle andangular velocity of the standing posture, affected by the neuraltransmission time delay. The OFF/ON switching of themediolateral gastrocnemius (MG) corresponds to the OFF/ON
switching of the active feedback controller. In the intermittent
control model, the timing of relaxation of the mediolateral
gastrocnemius plays an important role in postural stabilization.
In other words, in the intermittent control model, the upright
posture is stabilized using passive posture recovery motions
during the period when the active torque at the ankle joint isswitched OFF. Owing to the paradoxical property that theposture is stabilized by muscle relaxation rather than muscle
activation, the intermittent control model can generate posturalfluctuations with long-term correlation exhibited by healthy
young people. In other words, the essence of the intermittent
control hypothesis is to determine whether the brain region
that controls the MG muscle, i.e., the reticular formation in thebrainstem receiving projections from the peduncle tegmental
nucleus, is activated or inactivated depending on the posturalstate of the bodily mechanical system. Direct pathways (Go)and indirect pathways (No-Go) of the cortico-basal ganglia
loop has been considered performing such information
processing for voluntary movements.
In the first stage of this Research, we measured the standing
postural response to a support-surface perturbation that moves
a floor backward in a step function manner. It was clarified
that Event Related Synchronization (ERS) in the highfrequency β band appears in the EEG in the vicinity of parietal
association area with a long latency (about 1.5 seconds) andfor a long time (about 3 seconds) [5]. The β-ERS that appears
immediately after completion or cessation of voluntary
movement of the upper extremities or fingers is called βrebound or status quo [4]. It is also known that β-reboundappears at a timing similar to that of the Go response even forthe case of the No-Go response in Go/No-Go tasks. Such βrebound is thought to reflect motor control and brain activity
based on sensory re-afferent information [6]. The β-reboundduring perturbed stance that we have identified appeared when
the standing posture, tilted forward by the backward floormotion, moves back to the upright position without using theactive ankle joint torque. Since the muscle activation of themediolateral gastrocnemius was small during this process, thecorresponding postural recovery is determined passively
solely by the bodily mechanics. Such β-rebound may reflect
active postural monitoring associated with the ON/OFF
selection process of motor commands for controlling the MG
activity [4].
In the second stage of our Research, we investigatedwhether the β-rebound that appeared during the postural
recovery process in response to the perturbation also appears
during postural sway during quiet stance with no perturbation.
Postural sway during quiet stance can be regarded as astochastic process, in which a forward micro-fall followed bya micro-recovery repeat randomly. Micro-falls occur about
once every second. Our brain responds appropriately to eachmicro-fall and maintains our stance. Thus, healthy adults are
the experts in postural control who have been avoiding falls
tens of thousand times across the lifespan.
III. RESEARCH TOPICS
Fig. 1 shows the anteroposterior variation of Center of Mass
(CoM), which represents postural sway during quiet stance,
and the corresponding EMG of MG muscle. A large numberof vertical dotted lines in the figure represent the sequence ofoccurrences when the CoM toppling velocity attains a positive
peak for every micro-fall. Using the onset of the forward
micro-fall of the CoM, the micro-fall and subsequent microrecovery processes were extracted as epochs. Then, EMG andwavelet-transformed EEG time-frequency signals were

Fig. 1. Representative postural sway data in the anterior-posterior (AP)direction during stance with micro-falls. Top and middle panels show the
CoM-position and the CoM-velocity time-series, respectively. The CoMposition on the top trace was represented with respect to the mean ankle joint
position, i.e., the origin of the vertical axis was the mean ankle joint position.
Bottom panel shows the EMG of MG from the right leg. Vertical dotted lines
represent the micro-fall-peaks detected as the CoM-velocity positive peaks in
CoM-velocity. EEG analyses were performed only for fall-recovery cycles
associated with well-separated micro-fall-peaks that are represented by thevertical red dotted lines and the open black inverted triangles.

averaged to obtain Event-Related Spectral Perturbation(ERSP) as shown in Fig. 2. In Fig. 2, the MG activity
increased with the occurrence of the micro-fall (ON-period),
but when the CoM started to show the micro-recovery, theMG activity decreased and formed a plateau waveform, whichmight correspond to the OFF-period of the intermittent control.
ERD and ERS were confirmed in the β-band of ERSP,
respectively, during the ON- and OFF-periods of the control.
Decrease and increase in the ERSP power for ERD and ERS
were confirmed statistically.

Fig. 2. Event-locked averages of EEG-ERSP, EMG of MG, SOL, theCoM-position in AP direction, and the corresponding CoM-velocity for thefull fall-recovery cycle are shown in this order from the top to the bottom
traces. 0% and 100% of the cycle were the beginning of the micro-fall andthe end of the micro-recovery. The red dashed vertical line represents the
micro-fall-peak, followed by two black dashed vertical lines. The first
black dashed line represents the division between the micro-fall phase andthe micro-recovery phase. The second black dashed line represents the
micro-recovery-peaks defined by the negative peak of the CoM-velocity.

IV. FUTURE PERSPECTIVE
The β-band ERS in Fig. 2 is considered to be β-rebound thatreflects the active postural monitoring during the microrecovery process. It is likely that this beta-band activity isrelated to neural activities of the basal ganglia, as suggested
by previous studies that identified attenuated beta-band
desynchronization and synchronization in patients with
Parkinson's disease during upper limb motor tasks. Moreover,the basal ganglia-cortical loop plays an important role in theaction selection. Those evidences might support the idea thatthe beta-band desynchronization and synchronization we
found in this study reflect the information processing required
for the intermittent postural control.
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]

Bottaro, A.,et al. (2008) Bounded stability of the quiet standing posture:
An intermittent control model. Hum. Mov. Sci. 27, 473–495.
Asai, Y., et al. (2009) A Model of Postural Control in Quiet Standing:
Robust Compensation of Delay-Induced Instability Using Intermittent
Activation of Feedback Control. PLOS ONE 4, e6169.
Suzuki, Y., et al. (2020) Postural instability via a loss of intermittent
control in elderly and patients with Parkinson’s disease: A model-based
and data-driven approach. Chaos 30, 113140.
Engel, A. K., and Fries, P. (2010). Beta-band oscillations—signallingthe status quo? Curr. Opin. Neurobiol. 20, 156–165.
Nakamura, A., et al. (2021) Long- Lasting Event-Related Beta
Synchronizations of EEG Activity in Response to Support-Surface
Perturbations During Upright Stance. Front. Syst. Neurosci. 15:660434.
Takakusaki K. (2017). Functional Neuroanatomy for Posture and Gait
Control. J. Mov. Disord. 10, 1-17.

B05-8 Annual report of research project
Kazuhiro Sakamoto
Faculty of Medicine, Tohoku Medical and Pharmaceutical University
Abstract—We have previously proposed a model that learns
by adaptively generating states in an environment where even theprobability space is not determined. In this project, we proposeda model that not only enables learning of the action sequence taskthat has contributed to elucidating of functional differences
between premotor and supplementary motor areas, but also
provides computational interpretation of the neuronal activities.
In future research, we hope to develop a model that emerges
sequential actions as well, and build a computational theory ofhigher brain functions.

In the task, the monkeys first performed a visually-guided
task. They saw a signal that instructed them to perform one ofthree actions: Push, Pull, or Turn, and executed each instructedaction when a go signal was delivered. In a certain block oftrials, the same instruction signals were presented in the sameorder, and if the animals got seven consecutive trials correct, it
was shifted to the memory-guided task. After sevenconsecutive correct trials, the task returned to the visuallyguided task and animals were instructed to perform the actions
in a different order.

I. INTRODUCTION
In the real world, the unexpected events often occurs. An
environment in which even the probability space cannot be
defined is called an indefinite environment. To adapt to such anenvironment, it is necessary to actively engage the world(proactive outreach) and generate the framework for adaptation.
We have developed a reinforcement learning model thatdynamically and autonomously generates the probability orstate space based on the criteria of experience saturation anddecision uniqueness[1][2]. However, the model is halfway toreproducing the highly adaptive abilities of primates.

III. THE MODEL
The model generates different context-dependent learning
sets for the different displays of the task. Each training set
consists of a Sensory Information module and an Actionoutcome History module. They are very similar, but use
different cues in the dynamic generation of states, i.e.,
dynamically expanding the Q-table.

II. AIM OF THE GROUP
To this purpose, here we developed a model[2] that canlearn the action sequence task[3][4] (Fig. 1), which was used toelucidate the function of the monkey's higher motor cortices,
specifically, the functional differentiation between the
premotor and supplementary motor areas.

The Sensory Information module stores information about
the previous contexts or displays. For example, in the case of
the Go context, the current display, the previous Instruction
display, and so on. Different instruction displays were retained
as cues that distinguish different Instruction contexts (Fig. 2A).
In the other module, the histories of actions and outcomes
were used as cues for state generation (Fig. 2B). The outcomecues are error, reward (Rwd.), and success without reward. The
four action cues include Push, Pull, Turn, and Stay.
Within context

Action Sequence Task

7 consecutive rewarded trials

0.6

0.6

0.6

0.0

Possible actions
Parent
state

Rwd

New

0.5

0.5

0.5

0.5

Child
state

0.0

1.0

0.0

0.0

Uniquedecision

After learning

StatesStay Success Pull Success Stay Success Turn Success Stay Success Push Reward

States

Go

Stay

Push

Push

Go

Delay

Go

Push

0.5

0.5

0.5

0.5

Rwd

0.0

0.8

0.8

0.0

Rwd

0.5

0.5

0.5

0.5

Rwd

0.0

0.8

0.8

0.0

Turn

Go

Instr.(Push)

Go

Stay Success Turn Success Stay Success Pull Success Stay Success Push Reward

Fig. 1. Examples of trials in the action sequence task and block-wise changes.

Child
state

Push

Rwd

Push

Rwd

Parent
state

New

Child
state
Parent
state

New

0.5

0.5

0.5

0.5

Child
state

0.0

1.0

0.0

0.0

Uniquedecision

After learning

Instr.(Pull)

Parent
state

New

…

Go

Rwd

…
Turn

Visually-guided task
Instr.(Turn)

0.0

After learning

Stay Success Pull Success Stay Success Turn Success Stay Success Push Reward

7 consecutive rewarded trials

Stay

0.6

…

Push

Delay

Turn

0.6

…

Go

Pull

0.6

After learning

Memory-guided task
Delay

Push

…

Instr.(Push)

Turn

…

Go

Pull

…

Instr.(Turn)

Push

…

Go

B Action-outcome history

Possible actions

Visually-guided task
Instr.(Pull)- dependent learning set, dynamic Q table of…

A Sensory information

Fig. 2. Examples of extensions to the state or Q-table in the proposed model:
A. Sensory information submodule; B. Action-outcome history submodule.

The expansion of the state space or Q-table was based on
two criteria: Decision Uniqueness and Experience Saturation.
Decision Uniqueness is the tendency to prefer a state with asmall number of alternatives for action, specifically, threealternatives over four...one over two. Experience saturation isthe criterion for whether a state has been experienced
sufficiently, and actually monitors the change in the Q-table,
and if the change is below a threshold, the experience in thatstate is considered saturated. Both quantities were evaluated interms of Kullback-Leibler information.
IV. RESULTS
The action sequence task requires the participants toperform three moves in sequence. The model learned the taskquickly and showed a high rate of correct responses (Fig. 3A)
and smooth sequence switches (Fig. 3B) after having
previously learned a one-step and two-step tasks.
Physiological experiments have observed neuronal
activities that are selective to a specific order of actions, forexample, a cell that is active until the start of Push after the
Pull action, or a cell that is active only at the beginning of atrial in which the Pull-Turn-Push action is to be performed [4].
If such cell activity is to be understood within the framework of
the proposed model, it can be explained, for example, by the Q
value of the action Pull in the state "Turn→Push→Rwd", i.e.,"Turn was performed, followed by Push and Rwd was
obtained". Plotting the Q value from the time this condition
was identified to the time the Pull action was performed yields
a time profile that closely resembles the neural activity actuallyobserved in the supplementary motor area (figure not shown).

B

Sequence switch

Correct rate

A

Trial number after pre-training
Fig. 3. Performance of the model. A. Correct rate. Red line: total mean. B.
Switch of the sequence. The model has been pre-trained for 1,0000 trials foreach of the 1- and 2-step tasks.

V. FUTURE PERSPECTIVE
In this study, we constructed a model that can learn theaction sequence task used to elucidate functional differences
between the premotor and supplementary motor areas of thecerebral cortex by learning the task with a module that extends
the state based on sensory information and a module based on
action-outcome in parallel. Furthermore, the actual neuronal
activities were interpreted from the Q values of the model. The
model is not only adaptable to indefinite environments bygenerating states by itself, but also opens the door to acomputational understanding of the higher motor cortices.
For the two-target search task, which was the starting point
for the development of this model, we also developed anapparatus for mice and a VR system [7]. These enabled us toperform the two-target search task on multiple animal species
and to conduct a comprehensive experimental validation of the
dynamic state-space reinforcement learning model.
Sequential actions should be emerged, not just learned. We
have analyzed the prefrontal neuron activities involved in thisprocess[8]. In addition, we have worked on solving thetraveling salesman problem using the exclusive interaction
between oscillators, where a city is regarded as a single
oscillator [9-13]. In the latter, we showed by computer
experiments that the system exhibits the shortest path solution
in almost all cases if appropriate conditions are satisfied, andwe have also developed its theoretical basis.
In future research, we hope to integrate this learning modelwith the oscillator system to build a novel computational modelthat can generate sequential actions for problem solving basedon memory and experience and flexibly execute those actionsat arbitrary rhythms and timings.
REFERENCES[1]

T. Katakura, M. Yoshida, H. Hisano, H. Mushiake, K. Sakamoto,“Reinforcement learning model with dynamic state space tested ontarget search tasks for monkeys: self-determination of previous states
based on experience saturation and decision uniqueness,” Front.
Comput. Neurosci., Vol. 15, 784592, February 2022.
[2] K. Sakamoto, Kawaguchi, H. Mushiake, “Reinforcement Learning
Model With Dynamic State Space Tested on Target Search Tasks for
Monkeys: Extension to Learning Task Events,” Front. Behav. Neurosci.,
vol. 16, 750832, May 2022.
[3] H. Mushiake, M. Inase, and J. Tanji, Neuronal activity in the primate
premotor, supplementary, and precentral motor cortex during visually
guided and internally determined sequential movements, J.
Neurophysiol., vol.66, pp.705–718, 1991.
[4] J. Tanji, and K. Shima, Role for supplementary motor area cells inplanning several movements ahead, Nature, vol.371, pp.413–416, 1994.
[5] N. Matsumoto, N.M.Tamura, H. Mushiake, K. Sakamoto, "Significanceof single cell recording - Reverse engineering from supplementary motor
cortex neuronal activity to reinforcement learning model -," IEICE Tech.
Report,NC2023-25, 123(233) 1-5, Oct, 2023.
[6] K. Sakamoto, N. Matsumoto, N.M.Tamura, H. Mushiake, "A dynamic
state-space reinforcement learning model explaining neuronal activitiesof the supplementary motor area in the cerebral cortex," 46th Annu.
Meeting Jpn Neurosci. Soc., Aug 2, 2023.
[7] S. Zuguchi, K. Sakamoto, N. Katayama, H. Mushiake, "A high-speed
measurement system for treadmill spherical motion in virtual reality formice and a robust rotation axis estimation algorithm based on sphericalgeometry," IPSJ Trans. Bioinformatics, 16 1-12, Apr, 2023.
[8] K. Sakamoto, N. Saito, S. Yoshida, H. Mushiake, "A dynamic,
economical, and robust coding scheme in the lateral prefrontal neuronsof monkeys," Lec. Notes Comput. Sci., 13624 13-24, Apr, 2023.
[9] T. Kinugasa, F. Ono, K. Sakamoto, "Solving the traveling salesman
problem using oscillator interaction," IEICE Tech. Report NC2023-23,
123(90) 149-152, Jun, 2023.
[10] F. Ono, T. Kinugasa, K. Sakamoto, Analytical Solution for the
Exclusive Interactions between Three Kuramoto Oscillators," 33rd Annu.
Meeting Jpn Neu. Network Soc., Sep 4, 2023.
[11] K. Sakamoto, T. Kinugasa, F. Ono,"Solving the traveling salesman
problem using Kuramoto oscillators," 2023 Intl. Symp. Nonlinear
Theory and Its Applications, 362-365, Sep, 2023.
[12] Writer’s Handbook. Mill Valley, CA: University Science, 1989.

B05-9 Annual report of research project
Takeshi Sakurada

Faculty of Science and Technology, Seikei University
Abstract— This study focuses on the neurofeedback training
system for attention control. The achievements in this year are as
follows: A) we demonstrated the training effect based on SSVEP,
B) we optimized the vibrotactile stimulus for enhancing SSSEP
responses, and C) we found the relationship between SSVEP
responses and trainee fatigue.

I. INTRODUCTION
In this research project, we aim to develop a tailor-madetraining system based on neurofeedback technique using
sensory area activities, which is effective in improving
attention function in young and older adults. In conventional
neurofeedback training, a common goal (neural circuit to beobtained) is often set for the trainees [1]. However, in suchtraining protocols, individual differences in brain function [2][3]are not considered, which increases the possibility that thetraining effect may not be sufficiently obtained [4]. To
overcome these problems in neurofeedback training, we adopt
a goal-free approach that does not explicitly set a desired
activity for the brain region responsible for the targeted brain
function. We expect that this approach promotes the activationof the neural circuits that each trainee is most likely to acquire,and ultimately aims to improve attention function and motor
control ability efficiently.

EEG electrodes were placed around the left and right visualareas, and flicker stimulation was presented to both visual
fields from a high-frequency display. Participants were
instructed to keep their gaze position fixed at the center of thedisplay and direct their attention to the left visual field. During
training, in order to exclude the influence of habituation to a
stimulus with a specific frequency, the frequency of the flicker
stimulus presented to the left visual field was randomly
selected in the beta band. On the other hand, for the right visual
field, a frequency in the alpha band was randomly selected.
The results of the 5-day neurofeedback training revealed
that the participants were divided into two groups: four
participants had an amplified response in the right visual cortex,
while the others had a decreased response. Next, in the
GoNogo task, only participants who succeeded in neuralmodulation showed a decrease in reaction time.

II. AIM OF THE GROUP
The specific objective of this research project is to achieve
improvement in cognitive and motor functions by using aneurofeedback training system that presents an individual's
estimated attention state in real-time to promote the activationof the neural circuits. Regarding the estimation of attention
state, we focus on Steady-State Somatosensory Evoked
Potentials (SSSEP) in the somatosensory cortex or SteadyState Visual Evoked Potentials (SSVEP) in the visual cortex.
These rhythmic EEGs have the property of amplifying the
response by directing attention to sensory stimuli, so they areuseful signals for estimating the attentional state in real-time [5].
In addition, in order to successfully train the proposed system,
it is important to elicit SSSEP and SSVEP more robustly.
Therefore, we tried to identify the appropriate carrier
frequency for SSSEP response and the factors that influence
the SSVEP response.
III. RESEARCH TOPICS
A. Spatial attention training based on SSVEP
The attention directed toward a specific location in external
environments is called spatial attention. In order to train spatialattention, we conducted neurofeedback training using SSVEP.

Fig. 1. Relationship between SSVEP modulation and behavioral change.

B. Optimization of vibrotactile stimulation for SSSEPSSSEP is a rhythmic EEG elicited by vibrotactile
stimulation with a certain frequency. As a mechanical vibration
stimulus used to induce the SSSEP, we generated a low
frequency (10 to 30Hz) stimulus by amplitude modulating ahigh frequency (100-300Hz) vibration. Here, the former highfrequency component is called the carrier frequency, and thelatter low-frequency component is called the SSSEP frequency.
Previous research has shown that vibration stimulation in the
20Hz range is the band that most strongly induces SSSEP [6].
On the other hand, regarding carrier frequency, there is no
consistency in previous studies, and the relationship between
SSSEP response and carrier frequency is unclear.

Therefore, we set various carrier frequencies and evaluatedchanges in the SSSEP response. As a result, when the carrier
frequency was changed from 160Hz to 100Hz in 10Hz steps,
the same response strength was maintained in the 160-120Hz
band, even though the stimulus intensity weakened as thecarrier frequency was lowered. Then, the response became
significantly weaker at 110Hz/100Hz (p < 0.05, Fig. 2). Thissuggests that Meissner corpuscles, which are sensitive tofrequencies below 160Hz (low response threshold), areinvolved in the SSSEP response. In the future, it is expectedthat by adopting vibration stimulation that takes into accountthe frequency characteristics of mechanoreceptors, it will bepossible to establish a more robust neurofeedback training
system using SSSEP.
Fig. 3. Relationship between fatigue and SSVEP responses during
neurofeedback training.

IV. FUTURE PERSPECTIVE

Fig. 2. Influence of carrier frequency on SSSEP responses.

C. Individual difference factors in SSVEP response
There are various factors that affect SSVEP responses andtraining effects. For example, factors such as arousal level,
fatigue, and stress during training can have a large influence on
performance on various tasks. We assumed that these factors
cannot be ignored in neurofeedback training, we first attempted
to estimate fatigue based on brain waves. There are various
biological signals associated with fatigue and a relationshipwith brain waves has also been reported [7].
In the experiment, in parallel with the measurement of
SSVEP during neurofeedback training, we evaluated the powerin the α-θ band at C3/C4/F3/F4. As a result, it was confirmedthat the SSVEP power tends to be amplified in trainees who
had stronger alpha-theta band power (i.e., with higher fatigue
during neurofeedback training: p = 0.0582, Fig. 3). Althoughthese results seem contradictory, it is possible that the highfatigue level reflected a large amount of effort to theneurofeedback training.

In B05-9, in addition to verifying the effectiveness of theneurofeedback training system proposed this year, we alsobegan identifying the optimal stimulation format to morerobustly induce the relevant brain waves and identifying
factors that influence the training effect. Because we focusedon improving the robustness of the system, we have not beenable to obtain sufficient data (data from actual neurofeedback
training) to demonstrate the effectiveness of neurofeedback
training. We will continue to measure data including thecontrol group (Sham group). Finally, we will show that it iseffective for elderly people and stroke patients.
REFERENCES
[1]
[2]
[3]

[4]

[5]
[6]
[7]

Y. Bagherzadeh, D. Baldauf, D. Pantazis and R. Desimone, “Alphasynchrony and the neurofeedback control of spatial attention,” Neuron,105(3), pp. 577-587, 2020.
O.M. Bazanova and D. Vernon, “Interpreting EEG alpha activity,”
Neurosci Biobehav Rev, 44, pp. 94-110, 2013.
T. Sakurada, M. Yoshida and K. Nagai, “Individual optimal attentional
strategy in motor learning tasks characterized by steady-statesomatosensory and visual evoked potentials,” Front Hum Neurosci, 15:784292, 2022.
O. Alkoby, A. Abu-Rmileh, O. Shriki and D. Todder, “Can we predict
who will respond to neurofeedback? A review of the inefficacy problem
and existing predictors for successful EEG neurofeedback learning,”
Neuroscience, 378, pp. 155-164, 2018.
T. Sakurada, Patent No. 7353632.
S. Tobimatsu, Y.M. Zhang and M. Kato, “Steady-state vibration
somatosensory evoked potentials: physiological characteristics andtuning function,” Clinical Neurophysiology, 110, pp. 1953-58, 1999.
A. Craig, Y. Tran, N. Wijesuriya and H. Nguyen, “Regional brain wave
activity changes associated with fatigue,” Psychophysiology, 49, pp.
574-582, 2012.

B05-10 Annual report of research project
Yuki Ueyama
Department of Mechanical Engineering, National Defense Academy of Japan
Abstract— As we acquired skills or adapted to newenvironments, we were able to do so with limited information
through trial and error. In this study, we define the adaptation
process based on exploratory behavior as "exploratory
adaptation", which we hypothesize to be a computational
mechanism of brain function involved in hyper-adaptation. The
aim of this study is to elucidate the neural basis of the
exploratory adaptation. We approach both the computational
and the imaging analysis by building a model of exploratory
adaptation based on meta-reinforcement learning and by
performing fMRI experiments. This fiscal year, we identified
neural circuits that enable the exploratory adaptation based on
the fMRI data. In addition, we developed a method to facilitate
the exploration of motion planning as a method to support theacquisition of complex motor skills.

I. INTRODUCTION
We can efficiently adapt to the environment from theresulting limited information by adopting a trial-and-error
behavior in unknown environments. For example, when welearn a skill such as riding a bicycle or adapting to a newenvironment, we search for the best strategy based on thelimited information we receive as a result of our actions byrepeating the actions in a trial-and-error fashion. In this study,
we define "exploratory adaptation" as an adaptation mechanism
based on such exploratory behavior, consider it as acomputational mechanism of the brain that plays a role insuper-adaptation, and aim to elucidate the neural basis of such
exploratory adaptation. We model exploratory adaptation by analgorithm called meta-reinforcement learning [1], and approach
the task from both computational and functional brain imaging
perspectives by performing fMRI experiments using a simple
motor learning task.
II. AIM OF THE GROUP
The aim of this study is to elucidate the neural basis of
exploratory adaptation. In general, reward and sensory
prediction are thought to be used in adaptive processes such as
motor learning, but because many of the brain regions
associated with them are common, their independent brain
mechanisms and interactions remain unclear [2]. In this study,
we used fMRI to measure brain activity during a rotational
coordinate transformation task in visuomotor learning.
Exploratory adaptation to tasks is modeled as metareinforcement learning, and brain activity is analyzed based on
the model's estimation results to identify the brain network thatenables exploratory adaptation.
This fiscal year, we identified neural circuits that enable theexploratory adaptation based on the fMRI data. In addition, wedeveloped a method to facilitate the exploration of motion(a)Target+5˚Cursor rotation(invisible)×-5˚

Feedback（visible）

Shoot

+5˚

Hit:
Fail:

(b)

Fig. 1 fMRI study. (a) Experimental task. The fixation cross was presented at
the start position. The timing of the target presentation (white filled circle)
was randomized across trials between 1 and 13 s with respect to thepresentation of the fixation cross. Participants were asked to move a stylus
pen tip (dashed circle, invisible) from the start position to the center of thevisual target within 1 s after target presentation. When the pen hit the target,
participants received reward feedback (a filled white box explosion).
Unbeknownst to the participants, the reward zone was shifted by ±5° in someof the trials. (b) Result of the Dynamic Causal Modeling analisys.

planning through augmented reality (AR) technology as amethod to support the acquisition of complex skills in dart
throwing performance.
III. RESEARCH TOPICS
A. Neural connectivity for exploratory adaptation
We built a computational model to perform thereinforcement learning task and identified brain regions
involved in changes in exploratory level and motor memory
updating. In addition, we used the Dynamic Causal Modeling(DCM) analysis to identify the brain networks involved in
exploratory adaptation.
The experimental task in this study was a reaching motion
task in which the user manipulates the cursor on the screen bymanipulating the stylus pen on the tablet and moves it in thepresented target direction (Fig. 1a). In this task, participantscannot know the visual position of the cursor and perform the(a)z t  [ xt , yt ]ntExecution noise

(

Plan

According to the DCM analysis, the memory update andexplanation level were found to affect the left and right anterior
insula (aIN) and posterior parietal cortex (PPC), respectively(Fig. 1b).

We assumed that the acquisition of motor skills during dart
throwing follows a computational model called the Planned
Aim Point Correction (PAPC) model [3]. According to the
PAPC model (Fig. 2a), the position xt aimed at the target
position xd is affected by the motion noise represented by theplanning noise rt and the execution noise nt, and its action isdetermined by zt as follows:

Planning noise((b)

z t  xt  rt  n t To improve the performance, the error of zt with respect to xd
is reduced, and the error correction is performed on the motion
plan, which is the sum of the aimed position and the motion
noise, by updating the aimed position xt+1 for the next trial
using the follows:

xt 1  xt  rt  b  (x d  z t ) 



w

 r2    n22ryxx d Target

Aim

xt 1

(c)
*

100

1

*

0.5

50

Pre Exp Post

0

Pre/Post Exp

Fig. 2 Dart experiment. (a) Schematic illustration of the planned aim point
correction (PAPC) model and the coordinate system. (b) Dart throwing
accuracy shown as the mean error. The dots represent the data of individual
participants, and the vertical bars are the 95% confidential intervals. (c) Motornoise fraction.

has been actively seeking to update the motor plan. Therefore,
it is expected that the AR intervention can modulate planningnoise and promote the acquisition of new skills.
IV. FUTURE PERSPECTIVE

If the components of the motion noise, planning noise rt andexecution noise nt, are normally distributed and their standard
deviations are r and n, respectively, then the fraction ofplanning noise in the motion noise is as follows:



xt

*

150

0



)

rt

UpdateError [mm]B. Facilitating exploration in skill acquisition
The purpose of this study was to develop a method to assist
subjects in the acquisition of motor skills. We then tested theeffectiveness of an intervention using augmented reality (AR)technology on dart throwing performance.

Error

)

Noise fraction (w)task using only the success or failure of the task given after
each trial as a cue. The cursor is rotated +5° or –5° around thestarting point every few trials, and this information is notknown to the participants in advance. Therefore, theparticipants must learn the correct direction of movement by
groping and changing the direction of movement of the stylus
pen accordingly. In this experimental task, we define thisprocess as exploratory adaptation.



We conducted an experiment using the AR technology
developed. According to the results, we confirmed that darting
skills improved before and after the AR technology
intervention (Fig. 2b). However, dart skills did not change
during the AR intervention compared to the pre-intervention
period. On the other hand, when the proportion of planningnoise included in the motion noise was examined using
equation (3), it was clear that the proportion of planning noise
increased when the AR intervention was used, compared tobefore and after the intervention (Fig. 2c). It indicates that AR

This fiscal year, in addition to analyzing functional brain
imaging data, we developed an assistive technology to facilitate
the exploration for the next step in motor skill acquisition using
AR technology.
Some of the results of this year's work, together withexperimental data from previous years, have been published ina paper that has already been submitted for publication.
REFERENCES
[1]

[2]

[3]

A. Nagabandi, I. Clavera, S. Liu, RS. Fearing, P. Abbel, S. Levine, and
C. Finn., Learning to Adapt in Dynamic, Real-World Environments
through Meta-Reinforcement Learning, In 7th International Conference
on Learning Representations (ICLR 2019), 2019.
DJ. Palidis, JGA. Cashaback, and PL. Gribble., Neural signatures ofreward and sensory error feedback processing in motor learning, Journalof Neurophysiology, vol. 121(4), pp. 1561-1574, 2019.
R. J. van Beers, "Motor Learning Is Optimally Tuned to the Propertiesof Motor Noise," Neuron, vol. 63, no. 3, pp. 406 417, 2009.

List of Publications (2023)

Journal Papers
1.

Yamamoto, N., Matsumoto, T., Sudo, T., Miyashita, M., and Kondo, T., Quantitative measurement offinger usage in stroke hemiplegia using ring-shaped wearable devices, Journal of NeuroEngineeringand Rehabilitation, Volume 20(73), 2023

2.

Negishi, T., Ogihara, N., Regulation of whole-body angular momentum during human walking,
Scientific Reports, Volume 13, 8000, 2023

3.

Negishi, T., Ogihara, N., Functional signifcance of vertical free moment for generation of humanbipedal walking, Scientific Reports, Volume 13, 6894, 2023

4.

S. Koseki, K. Kutsuzawa, D. Owaki, M. Hayashibe , Multimodal Bipedal Locomotion Generation with
Passive Dynamics via Deep Reinforcement Learning, Frontiers in Neurorobotics, Volume 16, 1054239,
2023

5.

G. Li, J. Shintake and M. Hayashibe, Soft-Body Dynamics Induces Energy Efficiency in Undulatory
Swimming: A Deep Learning Study, Frontiers in Robotics and AI, Volume 10, 1102854, 2023

6.

M.H. Ahmed, J. Chai, S. Shimoda and M. Hayashibe, Synergy-Space Recurrent Neural Network forTransferable Forearm Motion Prediction from Residual Limb Motion, Sensors, Volume 23(9), 4188,
2023

7.

M.H. Ahmed, K. Kutsuzawa and M. Hayashibe, Transhumeral Arm Reaching Motion Prediction
through Deep Reinforcement Learning-based Synthetic Motion Cloning, Biomimetics, Volume 8(4),367, 2023

8.

A. Purnomo and M. Hayashibe, Sparse Identification of Lagrangian for Nonlinear Dynamical Systems
via Proximal Gradient Method, Scientific Reports, Volume 13, 7919, 2023

9.

K. Shen, G. Li, A. Chemori and M. Hayashibe, Self-Organizing Neural Network for Reproducing
Human Postural Mode Alternation through Deep Reinforcement Learning, Scientific Reports,
Volume 13, 8966, 2023

10. T. Sugiyama, K. Kutsuzawa, D. Owaki, M. Hayashibe, Latent Representation-based Learning
Controller for Pneumatic and Hydraulic Dual Actuation of Pressure-driven Soft Actuators, Soft
Robotics, 2023

11. Morita T, Takemura H, Naito E, Functional and structural properties of interhemispheric interaction
between bilateral precentral hand motor regions in a top wheelchair racing Paralympian, Brain
Sciences, Volume 13(5), 715, 2023
12. Yuanhao Li, Badong Chen, Yuxi Shi, Natsue Yoshimura, and Yasuharu Koike, Robust Sparse Brain
Activity Decoding", IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, Volume 70( 8),2416-2429, 2023
13. Satoshi Zuguchi, Kazuhiro Sakamoto, Norihiro Katayama, Hajime Mushiake, A High-speed
Measurement System for Treadmill Spherical Motion in Virtual Reality for Mice and a Robust Rotation
Axis Estimation Algorithm Based on Spherical Geometry, IPSJ Transactions on Bioinformatics,
Volume 16, 1-12, 2023
14. Kazuhiro Sakamoto, Naohiro Saito, Shun Yoshida, Hajime Mushiake , A Dynamic, Economical, andRobust Coding Scheme in the Lateral Prefrontal Neurons of Monkeys, Lecture Notes in Computer
Science, Volume 13624, 13-24, 2023
15. Hamada H, Wen W, Kawasaki T, Yamashita A, Asama H, Characteristics of EEG power spectra
involved in the proficiency of motor learning, Frontiers in Neuroscience, Volume 17, 2023
16. Suzuki, Y., Togame, K., Nakamura, A., Nomura, T., A Markov chain approximation of switched
Fokker–Planck equations for a model of on–off intermittency in the postural control during quiet
standing, Communications in Nonlinear Science and Numerical Simulation, Volume 126, 107488,
2023
17. Akihiro Nakamura, Ryota Miura, Yasuyuki Suzuki, Pietro Morasso, Taishin Nomura, Discrete corticalcontrol during quiet stance revealed by desynchronization and rebound of beta oscillations,
Neuroscience Letters, Volume 814, 137443, 2023
18. Li Q, Takeuchi Y, Wang J, Gellért L, Barcsai L, Pedraza LK, Nagy AJ, Kozák G, Nakai S, Kato S,
Kobayashi K, Ohsawa M, Horváth G, Kékesi G, Lőrincz ML, Devinsky O, Buzsáki G, Berényi A,
Reinstating olfactory bulb derived limbic gamma oscillations alleviates depression-like behavioral
deficits in rodents, Neuron, Volume 111, 2065-2075, 2023
19. Sierra RO, Pedraza LK, Barcsai L, Peijin A, Li Q, Kozák G, Takeuchi Y, Nagy AJ, Lőrincz ML,
Devinsky O, Buzsáki G, Berényi A, Closed-loop brain stimulation augments fear extinction in malerats, Nature Communications, Volume 14, 3972, 2023
20. Mishima, M.*, Hayashida, K.*, Fukasaku, Y., Ogata, R., Ohsawa, K., Iwai, K., Wen, W., Morioka, S.,
Adaptability of the sense of agency in healthy young adults in sensorimotor tasks for a short term,
Behavioral Sciences, Volume 13(2), 132, 2023
21. Kosugi A, Saga Y, Kudo M, Koizumi M, Umeda T, Seki K, Time course of recovery of different motor

functions following a reproducible cortical infarction in non-human primates, Front Neurol. 2023;
Frontiers in Neurology, Volume 14, 1094774, 2023
22. Tetsuya Hasegawa, Tomoki Mori, Kohei Kaminishi, Ryosuke Chiba, Jun Ota, Arito Yozu, Effect of
Sway Frequency on the Joint Angle and Center of Pressure in Voluntary Sway, Journal of motor
behavior, Volume 55(4), 373-383, 2023
23. Arito Yozu, Tetsuya Hasegawa , Naomichi Ogihara , Jun Ota, Peak vertical ground force of hand-knee
crawling in human adults, Journal of physical therapy science, Volume 35(4), 306-310, 2023
24. Masayuki TETSUKA, Takeshi SAKURADA, Mayuko MATSUMOTO, Takeshi NAKAJIMA, Mitsuya
MORITA, Shigeru FUJIMOTO and Kensuke KAWAI, Higher prefrontal activity based on short-term
neurofeedback training can prevent working memory decline in acute stroke, Frontiers in Systems
Neuroscience, Volume 17, 1130272, 2023
25. Kasaragod DK, Aizawa H., Deep ultraviolet fluorescence microscopy of three-dimensional structuresin the mouse brain, Scientific Reports, Volume 13(1), 8553, 2023
26. Li WR, Nakano T, Mizutani K, Matsubara T, Kawatani M, Mukai Y, Danjo T, Ito H, Aizawa H,
Yamanaka A, Petersen CCH, Yoshimoto J, Yamashita T., Neural mechanisms underlying uninstructedorofacial movements during reward-based learning behaviors, Current Biology, Volume 33(16), 34363451, 2023
27. Takakuwa N, Isa T, Visuomotor coordination and cognitive capacity in blindsight, Current Opinion in
Neurobiology, Volume 82, 102764, 2023
28. Sawada M, Yoshino-Saito K, Ninomiya T, Oishi T, Yamashita T, Onoe H, Takada M, Nishimura Y, Isa
T., Reorganization of Corticospinal Projections after Prominent Recovery of Finger Dexterity from
Partial Spinal Cord Injury in Macaque Monkeys, eNeuro, Volume 10(8), 0209-23, 2023
29. Kimura R, Flexible information representation to stabilize sensory perception despite minor external
input variations, Neuroscience Research, Volume 195, 1-8, 2023
30. Ueyama Y, Harada M, Augmented Reality-Based Trajectory Feedback Does Not Improve Aiming in
Dart-Throwing, IEEE Access, Volume 11, 64738-64744, 2023
31. Katsumata, Y., Inoue, Y., Toriumi, S., Ishimoto, H., Hapuarachchi, H., and Kitazaki, M., Shared Avatar
for Hand Movement Imitation: Subjective and Behavioral Analyses, IEEE Access, Volume 11, 96710 –
96717, 2023
32. Sano H, Nambu A, Behavioral effects of zonisamide on L-DOPA-induced dyskinesia in Parkinson's
disease model mice, Front Aging Neurosci, Volume 15, 1221341, 2023
33. Rios A, Nonomura S, Kato S, Yoshida I, Matsushita N, Nambu A, Takada M, Hira R, Kobayashi K,

Sakai Y, Kimura M, Isomura Y, Reward experience uniformly modifies action-related activity anddiversely modifies outcome-related activity in topographic nigral and striatal systems, Commun Biol,
Volume 914, 2023
34. Abe Y, Yagishita S, Sano H, Sugiura Y, Dantsuji M, Suzuki T, Mochizuki A, Yoshimaru D, Hata J,
Matsumoto M, Taira S, Takeuchi T, Okano H, Ohno N, Suematsu M Inoue T, Nambu A, Watanabe
M, Tanaka KF, Shared GABA transmission pathology in dopamine agonist- and antagonist-induced
dyskinesia, Cell Rep Med, Volume 4(10), 101208, 2023
35. Yoshida T, Otaka Y, Kitamura S, Ushizawa K, Kumagai M, Yaeda J, Osu R, Influence of motivation on
rehabilitation outcomes after subacute stroke in convalescent rehabilitation wards,

Frontiers

in

Neurology, Volume 14 , 2023
36. Nozu T, Miyagishi S, Ishioh M, Takakusaki K, Okumura T, Imeglimin prevents visceral
hypersensitivity and colonic hyperpermeability in irritable bowel syndrome rat model, J

Pharmacol

Sci, Volume 153(1), 26-30, 2023
37. Takakusaki K, Gait control by the frontal lobe (Review), Handb Clin Neurol, Volume 195, 103-126,2023
38. Nozu T, Miyagishi S, Ishioh M, Takakusaki K, Okumura T Phlorizin attenuates postoperative gastricileus in rats, Neurogastroenterol Motil, e14659, 2023
39. Harin Hapuarachchi, Hiroki Ishimoto, Michiteru Kitazaki, Maki Sugimoto, Masahiko Inami, Temporalvisuomotor synchrony induces embodiment towards an avatar with biomechanically impossible arm
movements, i-Perception, Volume 14, 1-17, 2023
40. Arito Yozu, Kohta Sonoda, Tetsuya Hasegawa, Kohei Kaminishi, Michihiro Osumi, Masahiko Sumitani,
Ryosuke Chiba, Jun Ota, Effect of experimentally induced plantar pain on trunk posture during gait,
Journal of physical therapy science, Volume 35(9), 613-618, 2023
41. C. Phunruangsakao, D. Achanccaray, S. Bhattacharyya, S. Izumi, M. Hayashibe, Effects of VisualElectrotactile Stimulation Feedback on Brain Functional Connectivity during Motor Imagery Practice,
Scientific Reports, Volume 13, 17752, 2023
42. Barradas, V. R., Koike, Y., Schweighofer, N., Theoretical limits on the speed of learning inverse models
explain the rate of adaptation in arm reaching tasks, Neural Networks, Volume 170, 376-389, 2023
43. Li, Y., Chen, B., Wang, G., Yoshimura, N., Koike, Y., Partial maximum correntropy regression forrobust electrocorticography decoding, Frontiers in Neuroscience, Volume 17, 2023
44. Sentong Wang, Kazunori Hase, Tetsuro Funato, Computational prediction of muscle synergy using afinite element framework for a musculoskeletal model on lower limb, Frontiers in Bioengineering andBiotechnology, Volume 11, 1130219, 2023

45. Matsugi A, Nishishita S, Bando K, Kikuchi Y, Tsujimoto K, Tanabe Y, Yoshida N, Tanaka H, Douchi
S, Honda T, Odagaki M, Nakano H, Okada Y, Mori N, Hosomi K., Excessive excitability of inhibitory
cortical circuit and disturbance of ballistic targeting movement in degenerative cerebellar ataxia,
Scientific Reports, Volume 13(1), 13917, 2023
46. Nakano H, Murata S, Kodama T, Nakae H, Soma M., Effect of Rhythmic Finger Movement Training
on Freezing of Gait and Electroencephalography Activity in People With Parkinson Disease :A Case
Study, Topics in Geriatric Rehabilitation, Volume 39(3), 185-190, 2023
47. Sawai S, Fujikawa S, Ohsumi C, Ushio R, Tamura K, Yamamoto R, Kai Y, Murata S, Shima K, Nakano
H., Effects of neurofeedback on standing postural control task with combined imagined and executed
movements, Frontiers in Neurosciene, Volume 17, 1199398, 2023
48. Sawai S, Murata S, Fujikawa S, Yamamoto R, Nakano H., Effects of θ High Definition-Transcranial
Alternating Current Stimulation in the Anterior Cingulate Cortex on the Dominance of Attention
Focus in Standing Postural Control, Behavioral Sciences, Volume 13(6), 477, 2023
49. Sawai S, Murata S, Fujikawa S, Yamamoto R, Shima K, Nakano H., Effects of neurofeedback training
combined with transcranial direct current stimulation on motor imagery: A randomized controlled trial,
Frontiers in Neurosciene, Volume 17, 1148336, 2023
50. Nakano H, Matsugi A, Ito T, Oku K, Sakita M., Editorial: Pushing the limits of motor function recovery
in rehabilitation: Basic to applied research based on neuroscience, Frontiers in Human Neuroscience,
Volume 17, 1160632, 2023
51. Schlosser-Perrin F, Rossel O, Duffau H, Matsumoto R, Mandonnet E, Bonnetblanc F., The orientationof the stimulating bipolar probe modulates axono-cortical evoked potentials, Brain Stimul, Volume16(4), 1009-1011, 2023
52. Rossel O, Schlosser-Perrin F, Duffau H, Matsumoto R, Mandonnet E, Bonnetblanc F., Short-range
axono-cortical evoked-potentials in brain tumor surgery: Waveform characteristics as markers of directconnectivity, Clin Neurophysiol, Volume 153, 189-201, 2023
53. Eiro T, Miyazaki T, Hatano M, Nakajima W, Arisawa T, Takada Y, Kimura K, Sano A, Nakano K,
Mihara T, Takayama Y, Ikegaya N, Iwasaki M, Hishimoto A, Noda Y, Miyazaki T, Uchida H, Tani H,
Nagai N, Koizumi T, Nakajima S, Mimura M, Matsuda N, Kanai K, Takahashi K, Ito H, Hirano Y,
Kimura Y, Matsumoto R, Ikeda A, Takahashi T., Dynamics of AMPA receptors regulate
epileptogenesis in patients with epilepsy, Cell Rep Med, Volume 4(5), 101020, 2023
54. Usami K, Matsumoto R, Korzeniewska A, Shimotake A, Matsuhashi M, Nakae T, Kikuchi T, Yoshida
K, Kunieda T, Takahashi R, Crone NE, Ikeda A, The dynamics of cortical interactions in visual
recognition of object category: living vs non-living, Cereb Cortex, Volume 33(9), 5740-5750, 2023

55. Mizuseki, K., Miyawaki, H., Fast network oscillations during non-REM sleep support memory
consolidation, Neuroscience Research, Volume 186, 3-12, 2023
56. Naoki Aizu, Tamami Sudo, Yutaka Oouchida, Shin-Ichi Izumi, Facilitation of imitative movement inpatients with chronic hemiplegia triggered by illusory ownership, Scientific reports, Volume 13(1),16143-16143, 2023
57. Saeka Tomatsu, GeeHee Kim, Shinji Kubota, Kazuhiko Seki, Presynaptic gating of monkey
proprioceptive signals for proper motor action, Nature Communications, Volume 14, 6537, 2023
58. Y. Omura, H. Togo, K. Kaminishi, T. Hasegawa, R. Chiba, A. Yozu, K. Takakusaki, M. Abe, Y.
Takahashi, T. Hanakawa, and J. Ota, Analysis of abnormal posture in patients with Parkinson's diseaseusing a computational model considering muscle tones, Frontiers in Computational Neuroscience,
Volume 17, 1218707, 2023
59. Ogawa A, Koganemaru S, Takahashi T, Takemura Y, Irisawa H, Goto K, Matsuhashi M, Mima T,
Mizushima T, Kansaku K., Swallow-related Brain Activity in Post-total Laryngectomy Patients: A Case
Series Study, Progress in Rehabilitation Medicine, Volume 8, 20230026, 2023
60. Yamaoka T, Takagi Y, Shimomura R, Murata Y, Shimotake K, Itoh A, Mima T, Koganemaru S., N-of1 Trial of Electrical Sensory Stimulation Therapy on the Tibial Innervated Area during Gait in a Caseof Post-stroke Sensory Disturbance., Progress in Rehabilitation Medicine, Volume 8, 20230018,
2023
61. Nojima I, Horiba M, Sahashi K, Koganemaru S, Murakami S, Aoyama K, Matsukawa N, Ono Y, Mima
T, Ueki Y., Gait-combined closed-loop brain stimulation can improve walking dynamics in
Parkinsonian gait disturbances: a randomised-control trial, J Neurol Neurosurg Psychiatry, Volume
94(11), 938-944, 2023
62. Shimomura R, Shibata S, Koganemaru S, Minakuchi M, Ichimura S, Itoh A, Shimotake K, Mima T.,
Transcranial static magnetic field stimulation (tSMS) can induce functional recovery in patients withsubacute stroke, Brain Stimulation, Volume 16(3), 933-935, 2023
63. Sumiya Shibata, Satoko Koganemaru, Tatsuya Mima, Non-invasive Brain Stimulation in Post-stroke
Dysphagia Rehabilitation: A Narrative Review of Meta-analyses in 2022, Progress in Rehabilitation
Medicine, Volume 8, 1-10, 2023
64. Yukawa Y, Shibata S, Koganemaru S, Minakuchi M, Shimomura R, Nakamura K, Mima T., Lowfrequency repetitive transcranial magnetic stimulation can alleviate spasticity and induce functional
recovery in patients with severe chronic stroke: A prospective, non-controlled, pilot study, Heliyon,
Volume 9(4), e15564, 2023
65. Ryota Shimomura*, Sumiya Shibata*, Satoko Koganemaru, Masatoshi Minakuchi, Sachimori Ichimura,Akihiro Itoh, Katsumi Shimotake, Tatsuya Mima, Transcranial static magnetic field stimulation(tSMS) can induce functional recovery in patients with subacute stroke, Brain Stimulation, Volume
16(3), 933-935, 2023
66. Sumiya Shibata, Hirotaka Takahashi, Yu Miida, Tatsuya Mima, Hideaki Onishi, Priming effects oftranscutaneous vagus nerve stimulation on the neuromodulation induced by transcranial static
magnetic field stimulation in human motor cortex, Clinical Neurophysiology, Volume 154, 194-197,
2023
67. Takuya Matsumoto, Tatsunori Watanabe, Kanami Ito, Takayuki Horinouchi, Sumiya Shibata, Hiroshi
Kurumadani, Toru Sunagawa, Tatsuya Mima, Hikari Kirimoto, Effect of transcranial static magnetic
stimulation over unilateral or bilateral motor association cortex on performance of simple and choice
reaction time tasks, Frontiers in Human Neuroscience, Volume 17, 1-10, 2023
68. Chiyohara S, Furukawa JI, Noda T, Morimoto J, Imamizu H., Proprioceptive short-term memory in
passive motor learning, Scientific Reports, Volume 13(1), e20826, 2023
69. K. Tada, Y. Sorimachi, K. Kutsuzawa, D. Owaki, and M. Hayashibe, Integrated Quantitative Evaluationof Spatial Cognition and Motor Function with HoloLens Mixed Reality, Sensors, Volume 24(2),Volume 528, 2024
70. S. Koseki, M. Hayashibe, D. Owaki, Identifying essential factors for energy-efficient walking control
across a wide range of velocities in reflex-based musculoskeletal systems, PLOS Computational Biology,
Volume 20(1), e1011771, 2024
71. Konosu Akira, Matsuki Yuma, Fukuhara Kaito, Funato Tetsuro, Yanagihara Dai, Roles of the cerebellar
vermis in predictive postural controls against external disturbances, Scientific Reports, Volume 14,
3162, 2024
72. Ueyama Y, Harada M, Basketball free-throw training with augmented reality-based optimal shot
trajectory for novice shooters, Scientific Reports, Volume 14, 891, 2024
73. Matsugi A, Yoshida N, Nakano H, Okada Y., The Neurorehabilitation of Neurological Movement
Disorders Requires Rigorous and Sustained Research, Journal of Clinical Medicine. Volume 13( 3),852, 2024
74. Cox CR, Rogers T, Shimotake A, Kikuchi T, Kunieda T, Miyamoto S, Takahashi R, Matsumoto R,
Ikeda A, Lambon-Ralph MA., Representational similarity learning reveals a graded multidimensionalsemantic space in the human anterior temporal cortex, Imaging Neuroscience, early access, 2024
75. Adya saran Sinha*, Sumiya Shibata*, Yasuyuki Takamatsu*, Tenpei Akita, Atsuo Fukuda and TatsuyaMima, Static magnetic field stimulation enhances shunting inhibition via a SLC26 family Cl− channel,
inducing intrinsic plasticity, Journal of Neuroscience, 2024

International Conference
1.

Ryoya Kihara, Qi An, Kensuke Takita, Shu Ishiguro, Kazuto Nakashima, Ryo Kurazume, Analysis ofForce Applied to Horizontal and Vertical Handrails with Impaired Motor Function, 2023 IEEE/SICE
International Symposium on System Integration (SII), Atlanta USA, 2023

2.

Koike, Y., Development of a musculoskeletal model that estimates muscle tension fromelectromyography, joint torque, impedance, and equilibrium position, ISB 2023 Congress XXIX
Fukuoka, Japan, 2023

3.

Kazuhiro Sakamoto, Nao Matsumoto, Naoki M.Tamura, Hajime Mushiake, A dynamic state-space
reinforcement learning model explaining neuronal activities of the supplementary motor area in the
cerebral cortex, The 46th Annual Meeting of the Japan Neuroscience Society Sendai, Japan, 2023

4.

Takahashi, K., Inoue, Y. and Kitazaki, M., "Effects of Source Location, Loudness, and Understandingof Speech on Interpersonal Distance in a Virtual Environment", The 29th ACM Symposium on Virtual
Reality Software and Technology (VRST), Christchurch, New Zealand, 2023

5.

Haraguchi, G. and Kitazaki, M., Deforming Skin Illusion by Visual-tactile Stimulation, The 29th ACM
Symposium on Virtual Reality Software and Technology (VRST), Christchurch, New Zealand, 2023

6.

Xu Y, Yoshida K, Takeuchi Y, Toward non-invasive, precise control of internal organs via ultrasoundneuromodulation of the autonomic nervous system, 9th Biomedical Imaging and Sensing Conference,
Yokohama, Japan, 2023

7.

Takeuchi Y, Closed-loop control of neurological and psychiatric disorders, Summer Program, Taipei
Meicial University, Online, Japan, 2023

8.

Taishin Nomura, control during human quiet stance: A greedy strategy for postural stabilization, The
International Society for Postire and Gait World Congress 2023, Brisbane, Australia, 2023

9.

Tomohiko Takei, Neural dynamics of motor cortex for flexible feedback motor control, The 8th CiNet
Conference: Beyond Motor Control: Bridging the gap between action and perception, Suita, Japan,
2023

10. Ota, Jun, Science of Hyper-adaptability: An Introduction, 2023 45th Annual International Conferenceof the IEEE Engineering in Medicine and Biology Society, Sydney, Australia, 2023
11. Tadashi Isa, Large-scaled adult brain plasticity for recovery from spinal cord injury, NIPS international
symposium, Multi-disciplinary approach to understant neuronal network architecture to control motor
actions, Okazaki, Japan, 2023
12. Tadashi Isa, Large-scaled adult brain plasticity for reovery after spinal cord injury, Neuro-BRB (Breachthe Research Barrier) 2023, Online, 2023
13. Tadashi Isa, Neural circuits and cognitive capacity underlying blindsight in monkeys, Systems Vision
Science Summer School & Symposium, Online, 2023
14. Hiroshi Imamizu, Sense of agency facilitates motor learning, Sydney, Australia, 2023
15. D Kim, H Kanazawa, Y Kuniyoshi, Emergence of Reaching using Predictive Learning as Sensorimotor
Development in Complex Dynamics, The 11th International Symposium on Adaptive Motion ofAnimals and Machines, Kobe, Japan, 2023
16. Y Shinomiya, H Kanazawa, D Kim, Y Kuniyoshi, Self-organized acquisition of muscle synergy andbehavior with whole body musculoskeletal infant model, The 11th International Symposium on
Adaptive Motion of Animals and Machines, Kobe, Japan, 2023
17. T Jülg, F Walter, D Kim, H Kanazawa, A Knoll, Y Kuniyoshi, Multi-Modal Representation Learningfor Mapping Between Body Motion and Visual Imagery, The 11th International Symposium on
Adaptive Motion of Animals and Machines, Kobe, Japan, 2023
18. Hoshinori

Kanazawa,

The

Embodied

Brain

Model

for

Early

Human

Development,

http://cyber.felk.cvut.cz/seminars/?event=1578, Prague, Czech, 2023
19. N Yoshida, H Kanazawa, Y Kuniyoshi, Homeostatic Reinforcement Learning through Soft Behavior
Switching with Internal Body State, 2023 International Joint Conference on Neural Networks (IJCNN),
Queensland, Australia, 2023
20. Nambu A, Somatotopic reorganization of the macaque sensorimotor cortex after accidental arm
amputation, CIN-NIPS-Asia Pacific Systems Neuroscience Symposium, Tuebingen, Germany. 2023
21. Nambu A, In search of a unified view for the pathophysiology of movement disorders, 2023 IBAGS
Conference, Stockholm, Sweden, 2023
22. Tomu Makino, Tetsuya Hasegawa, Shouhei Shirafuji, Jun Ota, Arito Yozu, Evaluation of the GivingWay-Prevention Function of a Soft Exosuit Incorporating the Multi-articular Muscle Mechanism, The
34rd 2023 International Symposium on Micro-NanoMechatronics and Human Science (MHS2023),
Nagoya, Japan, 2023
23. C. Herneth, M. Hayashibe, D. Owaki, Learnable Tegotae-based Feedback in CPGs with Sparse
Observation Produces Efficient and Adaptive Locomotion, IEEE Int. Conf. on Robotics andAutomation, London, United Kingdom, 2023
24. S. Koseki, M. Hayashibe, D. Owaki, Energy-Efficient Speed Control in a Reflex-based Bipedal Walking
Model, Proc. of 11th Int. Symposium on Adaptive Motion of Animals and Mechanics, Kobe, Japan,2023

25. L. Sulpice, D. Owaki, M. Hayashibe, Deep Reinforcement Learning for Tailorable Natural Quadruped
Gait Generation, Proc. of 11th Int. Symposium on Adaptive Motion of Animals and Mechanics, Kobe,
Japan, 2023
26. Barradas, V., Theoretical limits on the speed of learning inverse models explain the rate of adaptationin arm reaching tasks, IEEE EMBC2023, Sydney, Australia, 2023
27. Kazuhiro Sakamoto, Higher brain function research needs good questions and theories, NIPS
workshop 2022 "Multi-disciplinary approach to understand neuronal network architecture forcontrolling motor actions", Okazaki, Japan, 2023
28. Kazuhiro Sakamoto, A Reinforcement Learning Model for Higher Motor Areas, The 2nd International
Symposium “Hyper -Adaptability”, Kyoto, Japan, 2023
29. Kazuhiro Sakamoto, Tomoaki Kinugasa, Futo Ono, Solving the Traveling Salesman Problem using
Kuramoto Oscillators, 2023 International Symposium on Nonlinear Theory and Its Applications,
Catania, Italy, 2023
30. Murakami, T., and Kitazaki, M., Design of body transformation experience, ICAT-EGVE 2023, Dublin,
Ireland, 2023
31. Matsumoto R., Mapping language network for presurgical evaluations of epilepsy and tumor surgery,
BCI&Neurotechnology Spring School, Online, 2023
32. Matsumoto R., Cortico-cortical evoked potential for arcuate fasciculus monitoring, 5th Congress ofAsia Oceanian Society of Intraoperative Neurophysiology Society (AOSIN2023), Taipei, Taiwan, 2023
33. Matsumoto R., Artifacts in EEG, Japan Epilepsy Society-Nihon Kohden Epilepsy Treatment Training
Course in Indonesia, Online, 2023
34. Matsumoto R., Brain Potentials Evoked by Direct Electrical Stimulation for Better Guiding of
Neurosurgery, Paris-Kobe workshop on Brain Evoked Potentials for Neurosurgery 2023, Paris, France,
2023
35. Matsumoto R., Evoked stimulation responses to guide surgical decisions, International Epilepsy
Congress 2023, Dublin, Ireland, 2023
36. Matsumoto R., Neural basis of apraxia of speech: insights from the electrical cortical stimulation
mapping and CCEP connectome, International Kobe-Cambridge Workshop for Neurobiology ofLanguage and Related disorders, Kobe, Japan, 2023
37. Nakano H, Murata S, Sawai S, Fujikawa S, Yamamoto R, Shima K, Motor Imagery-based
Neurofeedback Using Visual, Auditory, Vibrotactile, and Proprioceptive Senses: A Randomized
Controlled Trial, 16th Asian Confederation for Physical Therapy Congress, Bangkok, Thailand, 2023

38. Nakano H, Nakagawa K, Nagashima M, Naito E, Improving paralyzed hand motor function in stroke
patients with vibratory stimulation and mirror therapy, The 2nd International Symposium HyperAdaptability, Kyoto, Japan, 2023
39. Fujikawa S, Murata S, Sawai S, Yamamoto R, Shizuka Y, Goda A, Shiraiwa K, Horie J, Nakano H,
Quantitative assessment of finger dexterity in older adults and relationship to physical function, The
2nd International Symposium Hyper-Adaptability, Kyoto, Japan, 2023
40. Sawai S, Murata S, Sakano Y, Fujikawa S, Yamamoto R, Shizuka Y, Nakano H, The dominance ofattentional focus in standing postural control in the elderly, The 2nd International Symposium
Hyper-Adaptability, Kyoto, Japan, 2023
41. Nakano H, Sawai S, Fujikawa S, Ohsumi C, Ushio R, Tamura K, Yamamoto R, Kai Y, Murata S,
Shima K, Effects of neurofeedback on standing postural control task with combined imagined andexecuted movements: A randomized controlled trial, The Hong Kong Physiotherapy Association 60th
Anniversary Conference, Hong Kong, China, 2023
42. Ryoji Otaki, Yutaka Oouchida, Naoki Aizu, Tamami Sudo, Hiroshi Sasahara, Yuki Saito, Sunao
Takemura and Shin-Ichi Izumi, Functional and structural brain reorganization associated with realworld arm use underlying upper limb recovery after stroke: A longitudinal fMRI and DTI study The
2nd International Symposium Hyper-Adaptability, Kyoto, Japan, 2023
43. Seki K, Neural adaptation to a physically-modified body in non-human primates, The 2nd
International Symposium Hyper-Adaptability, Kyoto, Japan, 2023
44. Philipp R, Uchida N, Ohta N, Oya T, Hara Y, Funato T, Seki K, Neural Adaptation in Response to
Tendon Transfer in the Primate Forearm Part I: Amplitude and Temporal Features of EMG Patterns
During a Grasping Movement, The 2nd International Symposium Hyper-Adaptability, Kyoto, Japan,
2023
45. Seki K, Centrally generated presynaptic inhibition selectively modulates proprioceptive and tactile
reafferent signals at the spinal cord of behaving monkeys, Motor Control: Spinal Circuits andBeyond2023, St Andrews, Scotland, 2023
46. Seki K, "Muscle synergy analysis yields an efficient and physiologically relevant way of assessing
stroke, 26th Thai Neuroscience Society International Conference, Bangkok, Thailand, 2023
47. R. Huang, K. Kaminishi, T. Hasegawa, A. Yozu, R. Chiba, and J. Ota, Estimation of center ofpressure information by smartphone sensors for postural control training, 2023 45th Annual
International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Sydney,
Australia, 2023

48. Y. Omura, H. Togo, K. Kaminishi, T. Hasegawa, R. Chiba, A. Yozu, K. Takakusaki, M. Abe, Y.
Takahashi, T. Hanakawa, and J. Ota, Analysis of the Relationship Between Muscle Tones andAbnormal Postures in a Computational Model, 2023 45th Annual International Conference of theIEEE Engineering in Medicine & Biology Society (EMBC), Sydney, Australia, 2023
49. K. Kaminishi, Y. Omura, R. Chiba, K. Takakusaki, and J. Ota, Musculoskeletal simulation toinvestigate strategies for arm use during postural recovery, 2023 45th Annual International
Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Sydney, Australia,
2023
50. K. Kaminishi, M. Ishikawa, T. Hasegawa, R. Chiba, K. Takakusaki, and J. Ota, CNN-based analysis of
the relationship between DAT SPECT and motor function in patients with Parkinson’s disease, The
34th 2023 International Symposium on Micro-NanoMechatronics and Human Science (MHS2023),
Nagoya, Japan, 2023
51. Y. Kobayashi, Proposal of Motor Learning Model toward Explainability of Adaptability in Multiple
Levels, The 34th 2023 International Symposium on Micro-NanoMechatronics and Human Science(MHS2023), Nagoya, Japan, 2023
52. Rie Kimura, Yumiko Yoshimura, Kenichi Ohki, Visual information representation in V1 adapts tochanges in the external environment after learning, The 2nd International Symposium HyperAdaptability, Kyoto, Japan, 2023
53. Matsumoto R., Accurate diagnosis of Drug-Resistant Epilepsy for new emerging therapies, 25th
Cairo International Neurology Conference, Cairo, Egypt, 2024

Member List
Steering Committee (X00): Administrative research on hyper-adaptability for overcoming body-brain
dysfunction
Principal investigator

Jun Ota (Professor, The University of Tokyo)

Funded co-investigator Tadashi Isa (Professor, Kyoto University)
Funded co-investigator Toshiyuki Kondo (Professor, Tokyo University of Agriculture and Technology)
Funded co-investigator Tetsuro Funato (Associate Professor,
The University of Electro-Communications)
co-investigator

Eiichi Naito (Director, CiNet)

co-investigator

Hidenori Aizawa (Professor, Hiroshima University)

co-investigator

Kazuhiko Seki (Director, NCNP)

co-investigator

Hiroshi Imamizu (Professor, The University of Tokyo)

co-investigator

Ken-Ichiro Tsutsui (Professor, Tohoku University)

co-investigator

Kaoru Takakusaki (Professor, Asahikawa Medical University)

co-investigator

Takashi Hanakawa (Professor, Kyoto University)

co-investigator

Ryosuke Chiba (Associate Professor, Asahikawa Medical University)

co-investigator

Yasuharu Koike (Professor, Tokyo Institute of Technology)

co-investigator

Hajime Asama (Professor, The University of Tokyo)

co-investigator

Jun Izawa (Associate Professor, University of Tsukuba)

co-investigator

Qi An (Associate Professor, The University of Tokyo)

co-investigator

Arito Yozu (Associate Professor, The University of Tokyo)

co-investigator

Wen Wen (Project Associate Professor, Rikkyo University)

Research Project A01: Elucidation of the hyper-adaptation mechanism by reconstruction of bio-structure andchallenges for prevention of decline in latent adaptive capacity
Principal investigator

Tadashi Isa (Professor, Kyoto University)

Funded co-investigator Eiichi Naito (Director, CiNet)
Funded co-investigator Hidenori Aizawa (Professor, Hiroshima University)
Funded co-investigator Minoru Asada (Specially Appointed Professor, Osaka University/ Vice President,International Professional University of Technology in Osaka)
Funded co-investigator Hideki Nakano (Associate Professor, Kyoto Tachibana University)
Co-investigator

Hirotaka Onoe (Project-specific Professor, Kyoto University)

Co-investigator

Satoko Koganemaru (Program-specific Associate Professor, Kyoto University)

Co-investigator

Reona Yamaguchi (Project Assistant Professor, Kyoto University)

Co-investigator

Ryo Sasaki (Assistant Professor, Kyoto University)

Co-investigator

Atsushi Shima (Assistant Professor, Kyoto University)

Co-investigator

Kazuki Tanaka (Research Assistant, Kyoto University)

Co-investigator

Kaoru Isa (Technical Stuff, Kyoto University)

Co-investigator

Satoko Ueno (PhD Student, Kyoto University)

Co-investigator

Masahiro Mitsuhashi (PhD Student, Kyoto University)

Co-investigator

Saya Kitazume (Graduate Student, Kyoto University)

Co-investigator

Erika Omae (Graduate Student, Kyoto University)

Co-investigator

Yiping Sun (Research Student, Kyoto University)

Co-investigator

Tomoyo Morita (Senior Researcher, CiNet)

Co-investigator

Jihoon Park (Specially Appointed Assistant Professor, Osaka University/ Researcher, CiNet )Co-investigator

Tomohiko Takei (Associate Professor, Tamagawa University)

Co-investigator

Satoshi Hirose (Associate Professor, Otemon Gakuin University)

Co-investigator

Miho Matsumata (Assistant Professor, Hiroshima University)

Co-investigator

Deepa Kamath Kasaragod (Assistant Professor, Hiroshima University)

Co-investigator

Takashi Handa (Assistant Professor, Hiroshima University)

Co-investigator

Gen Miura (Graduate Student, Osaka University)

Co-investigator

Tomoya Furuta (Graduate Student, Osaka University)

Co-investigator

Tang Yandi (Graduate Student, Osaka University)

Co-investigator

Fumino Fujiyama (Professor, Hokkaido University)

Co-investigator

Huyuki Karube (Associate Professor, Hokkaido University)

Co-investigator

Yasuharu Hirai (Assistant Professor, Doshisya University)

Co-investigator

Fuko Kadono (Graduate Student, Hokkaido University)

Research Project A02: Elucidation of neural mechanisms of super-adaptation to body change
Principal investigator

Kazuhiko Seki (Director, NCNP)

Co-investigator

Shinji Kubota (Section Chief, NCNP)

Co-investigator

Yuki Hara (Orthopedic director, NCNP Hospital)

Co-investigator

Roland Phillipp (Postdoctoral Fellow, NCNP)

Co-investigator

Amit Yaron (Postdoctoral Fellow, NCNP)

Co-investigator

Akito Kosugi (Postdoctoral Fellow, NCNP)

Co-investigator

Satomi Kikuta (Postdoctoral Fellow, NCNP)

Co-investigator

Junichiro Yoshida (Postdoctoral Fellow, NCNP)

Co-investigator

Shun Nakamura (Undergraduate Student, NCNP)

Co-investigator

Shiro Egawa (Special Postdoctoral Researcher, RIKEN
Center for Biosystems Dynamics Research）

Research Project A03: Mechanisms of body cognition and emotion inducing hyper-adaptability
Principal investigator

Hiroshi Imamizu (Professor, The University of Tokyo)

Funded co-investigator Ken-Ichiro Tsutsui (Professor, Tohoku University)
Co-investigator

Ryu Ohata (Researcher, Karolinska Institutet)

Co-investigator

Kentaro Hiromitsu (Researcher, The University of Tokyo)

Co-investigator

Tomohisa Asai (Researcher, ATR)

Co-investigator

Hiroshi Kadota (Associate Professor, Kochi University of Technology)

Co-investigator

Shu Imaizumi (Assistant Professor, Ochanomizu University)

Co-investigator

Shinya Nakamura (Assistant Professor, Tohoku University)

Co-investigator

Shinya Ohara (Assistant Professor, Tohoku University)

Co-investigator

Takayuki Hosokawa (Associate Professor,
Kawasaki University of Medical Welfare)Co-investigator

Yu Takagi (Post-doctoral fellows, The University of Tokyo)

Co-investigator

Takumi Tanaka (Researcher, The University of Tokyo)

Research Project A04: Alteration of brain dynamics as underlying mechanisms of hyper-adaptability inneurotransmitter disorders
Principal investigator

Kaoru Takakusaki (Professor, Asahikawa Medical University)

Funded co-investigator Takashi Hanakawa (Professor, Graduate School of Medicine,
Kyoto University)
Co-investigator

Tomohiro Noguchi (Lecturer, Asahikawa Medical University)

Co-investigator

Mirai Takahashi (Visiting Assistant Professor, Asahikawa Medical University)

Co-investigator

Syusei Hukuyama (Assistant Professor, Asahikawa Medical University)

Co-investigator

Toshikatsu Okumura (Professor, Asahikawa Medical University)

Co-investigator

Tsukasa Nozu (Professor, Asahikawa Medical University)

Co-investigator

Seiji Matsumoto (Professor, Asahikawa Medical University)

Co-investigator

Hitoshi Sasajima (Lecturer, Asahikawa Medical University)

Co-investigator

Sadaharu Miyazono (Lecturer, Asahikawa Medical University)

Co-investigator

Tatsuya Umeda (Associate Professor, Kyoto University)

Co-investigator

Yoshifumi Mori (Associate Professor, Kyoto University)

Co-investigator

Kenji Yoshinaga (Associate Professor, Kyoto University)

Co-investigator

Tatsuhiro Nakamura (Project Researcher, Kyoto University)

Co-investigator

Yuki Oi (Graduate Student, Kyoto University)

Co-investigator

Masakazu Hirose (Graduate Student, Kyoto University)

Co-investigator

Toshi Nakajima (Associate Professor, University of Toyama)

Co-investigator

Hiroki Togo (Postdoctoral fellow, NCNP)

Co-investigator

Toma Matsushima (Undergraduate Student, Tokyo University of Agriculture andTechnology / Research Student, NCNP)

Research Project A05-1: A closed-loop brain stimulation for reinforcing hyper-adaptability
Principal investigator

Yuichi Takeuchi (Associate Professor, Hokkaido University)

Co-investigator

Michele Chan (Specially Appointed Assistant Professor, Hokkaido University)

Research Project A05-2: Comprehensive understanding of the mechanism of adaptive changes in brain, body
consciousness and arm use underlying upper limb recovery in stroke patients
Principal investigator

Shinichi Izumi (Professor, Tohoku University)

Co-investigator

Shintaro Seki (Part-time Lecturer, Tohoku University)

Co-investigator

Ryoji Otaki (Ph.D Student, Tohoku University)

Co-investigator

Tamami Sudo (Researcher, Oouchi Hospital / Part-time Lecturer,
TohokuUniversity)

Co-investigator

Ryuko Ishimoda (Assistant Technical Staff, Tohoku University)

Co-investigator

Naoki Aizu (Assistant Professor, Fujita Health University)

Co-investigator

Juan Wu (Graduate Student, Tohoku University)

Co-investigator

Kouta Ataka (Graduate Student, Tohoku University)

Research Project A05-3: Elucidation of neural circuits for optimal adaptation to the external environment
Principal investigator

Rie Kimura (Specially Appointed Assistant Professor, The University of Tokyo)

Research Project A05-4: Neural substrate of unified learning theory for adaptive behavior
Principal investigator

Akihiro Funamizu (Lecturer, The University of Tokyo)

Co-investigator

Maria Ines de Sa Ribeiro (Research Technician, The University of Tokyo)

Research Project A05-5: Enhanced neuronal circuits for vocal learning with extensive experiences during
development
Principal investigator Yoko Yazaki-Sugiyama (Associate Professor, The Okinawa Institute of Science andTechnology Graduate University / Project Associate Professor, The University ofTokyo)
Research Project A05-7: Mechanism of Hyper-Adaptivity of the human premotor area: electrophysiologicalconnectomes analysis with electrocorticogram
Principal investigator

Riki Matsumoto (Professor, Kobe University)

Co-investigator

Takashi Sasayama (Professor, Kobe University)

Co-investigator

Yosuke Fujimoto (Assistant Professor, Kobe University)

Co-investigator

Masaya Togo (Assistant Professor, Kobe University)

Co-investigator

Kento Matoba (Assistant Professor, Kobe University)

Co-investigator

Takayuki Kikuchi (Lecturer, Kyoto University)

Co-investigator

Akihiro Shimotake (Assistant Professor, Kyoto University)

Co-investigator

Kiyohide Usami (Assistant Professor, Kyoto University)

Co-investigator

Hirofumi Takeyama (Visiting researcher, Kyoto University)

Co-investigator

Masamune kimura (Graduate Student, Kobe University)

Co-investigator

Kozue Hayashi (Graduate Student, Kyoto University)

Research Project A05-8: Interregional brain network dynamics enabling hyper-adaptation from a fearmemory induced maladaptive state.
Principal investigator

Hiroyuki Miyawaki (Lecturer, Osaka Metropolitan University)

Research Project A05-9: Research on developing a cognitive rehabilitation method for neurological andpsychiatric diseases thorough improving precision on the sense of agency
Principal investigator

Takaki Maeda (Lecturer, Keio University)

Co-investigator

Yuichi Yamashita (Section Chief, National Center of Neurology and Psychiatry)Co-investigator

Tsukasa Okimura (School of Medicine, Keio University)

Co-investigator

Hiroki Oi (School of Medicine, Keio University)

Research Project A05-10: Adaptive mechanisms for perturbations in the neural manifold.
Principal investigator

Tomohiko Takei (Associate Professor, Brain Science Institute, Tamagawa
University)

Co-investigator

Akihiro Masaoka (Adjunct Research Fellow, Tamagawa University)

Research Project A05-11: Spatio-temporal modulation of disinhibition for super-recovery from motor
dysfunction in the chronic phase of stroke
Principal investigator

Hironobu Osaki (Program-Specific Associate Professor, Doshisha University)

Co-investigator

Yoshito Masamizu (Professor, Doshisha University)

Co-investigator

Kaneyasu Nishimura (Associate professor, Doshisha University)

Co-investigator

Kotaro Tezuka (Graduate student, Doshisha University)

Research Project A05-12: Hyper adaptive changes in spatial recognition
Principal investigator

Rieko Osu (Professor, Waseda University)

Co-investigator

Kento Hirayama (Ph.D Student(Assistant), Waseda University)

Co-investigator

Yuki Ito (Master Student, Waseda University)

Co-investigator

Taiki Yoshida (Assistant Professor, Fujita Health University)

Co-investigator

David Franklin (Prof. Dr., Technical University of Munich)Research Project A05-13: Creation of hyper-adaptability by synthetic synaptic organizers and microenvironmental control of neural reconstruction
Principal investigator

Kosei Takeuchi (Professor, Aichi Medical University)

Co-investigator

Hiroyuki Sasakura (Assistant Professor, Aichi Medical University)

Co-investigator

Masashi Ikeno (Associate Professor, Aichi Medical University)

Co-investigator

Satoko hattori (Associate Professor, Aichi Medical University)

Co-investigator

Yuki Morioka (Research Technician, Aichi Medical University)

Research Project A05-14: Brain reorganization in stroke patients with hyper-recovery by measuring EEG
modulation induced by static and dynamic magnetic fields.
Principal investigator

Tatsuya Mima (Professor, Ritsumeikan University)

Co-investigator

Sumiya Shibata (Professor, Niigata University of Health and Welfare)Co-investigator

Satoko Koganemaru (Program-specific Associate Professor, Kyoto University)

Research Project A05-15: Brain adaptation after limb loss
Principal investigator

Atsushi Nambu (Professor, National Institute for Physiological Sciences)

Co-investigator

Nobuhiko Hatanaka (Assistant professor, National Institute for Physiological
Sciences)

Co-investigator

Satomi Chiken (Assistant professor, National Institute for Physiological Sciences)

Co-investigator

Pimpimon Nondhalee (Research fellow, National Institute for Physiological
Sciences)

Research Project A05-16: Adaptive mechanism occurring in both hemispheres after unilateral brain damage.
Principal investigator

Noriyuki Higo (Group Leader, National Institute of Advanced Industrial Scienceand Technology)

Co-investigator

Toru Yamada (Senior Researcher, National Institute of Advanced Industrial
Science and Technology)

Co-investigator

Hiroshi Kawaguchi (Senior Researcher, National Institute of Advanced Industrial
Science and Technology)

Research Project A05-17: Functional reorganization of motor and somatosensory cortices during recovery of
motor functions after the loss of peripheral sensory inputs
Principal investigator

Osamu Yokoyama (Senior Researcher, Tokyo Metropolitan Institute of Medical
Science)Research Project B01: Systems modelling of hyper-adaptation mechanism for reconstruction of neuralstructure
Principal investigator

Toshiyuki Kondo (Professor, Tokyo University of Agriculture and Technology)

Funded co-investigator Ryosuke Chiba (Associate Professor, Asahikawa Medical University)
Funded co-investigator Megumi Miyashita (Assistant Professor, Tokyo University of Agriculture andTechnology)
Co-investigator

Koji Ito (Emeritus Professor, Tokyo Institute of Technology)

Co-investigator

Shiro Yano (Visiting Associate Professor, Tokyo University of Agriculture andTechnology)

Co-investigator

Tamami Sudo (Assistant Professor,
Tokyo University of Agriculture and Technology)

Co-investigator

Yoshikatsu Hayashi (Associate Professor, University of Reading)

Co-investigator

Tetsunari Inamura (Associate Professor, Principles of Informatics Research
Division, National Institute of Informatics)

Co-investigator

Fuminari Kaneko (Project Associate Professor, School of Medicine,
Keio University)

Research Project B02: Modeling of ultra-adaptive to body change
Principal investigator

Yasuharu Koike (Professor, Tokyo Institute of Technology)

Funded co-investigator TetsuroFunato (Associate Professor, The University of Electro
Communications)
Co-investigator

Natsue Yoshimura (Associate Professor, Tokyo Institute of Technology)

Co-investigator

Victor Baradas (Project Assistant Professor, Tokyo Institute of Technology)

Co-investigator

Dai Yanagihara (Professor, The University of Tokyo)

Co-investigator

Shinya Aoi (Professor, Osaka University)

Co-investigator

Kazuo Tsuchiya (Emeritus Professor, Kyoto University)

Co-investigator

Hiroyuki Kanbara(Associate Professor, Tokyo Polytechnic University)

Co-investigator

Soichiro Fujiki (Assistant Professor, Dokkyo Medical University)

Co-investigator

Wang Sentong (Assistant Professor, Faculty of Science and Technology,
Seikei University)

Co-investigator

Akira Konosu (Project Researcher, The University of Electro-Communications)

Research Project B03: Systematic understanding and realization of hyper-adaptive phenomena focusing on
cognition and emotion
Principal investigator

Hajime Asama (Professor, The University of Tokyo)

Funded co-investigator Jun Izawa (Associate Professor, University of Tsukuba)
Funded co-investigator Qi An (Associate Professor, The University of Tokyo)
Funded co-investigator Wen Wen (Associate Professor, Rikkyo University)
Co-investigator

Masafumi Yano (Professor Emeritus, Tohoku University)

Co-investigator

Atsushi Yamashita (Professor, Graduate School of Frontier Sciences,
The University of Tokyo)

Co-investigator

Hiroyuki Hamada (Project Assistant Professor, The University of Tokyo)

Co-investigator

Acer Chang (Assistant Professor, Rikkyo University)

Co-investigator

Yukio Honda (Project Researcher, The University of Tokyo)

Co-investigator

Ningjia Yang (Researcher, RIKEN)

Co-investigator

Yutaka Kikuchi (Department Chief, Institute of Brain and Blood Vessels
Mihara Memorial Hospital)

Co-investigator

Yuta Okuda (Institute of Brain and Blood Vessels Mihara Memorial Hospital)

Co-investigator

Dal’Bello Lucas (Researcher, Laboratory of Neuromotor Physiology,
Fondazione Santa Lucia)

Research Project B04: Modelling of hyper adaptability in human postural control considering the role ofneurotransmitters
Principal investigator

Jun Ota (Professor, The University of Tokyo)

Funded co-investigator Arito Yozu (Associate Professor, The University of Tokyo)
Co-investigator

Shohei Shirafuji (Assistant Professor, The University of Tokyo)

Co-investigator

Kohei Kaminishi (Postdoctoral Fellow, The University of Tokyo)

Co-investigator

Enrico Piovanelli (Postdoctoral Fellow, The University of Tokyo)

Co-investigator

Ryota Takamido (Postdoctoral Fellow, The University of Tokyo)

Co-investigator

Tetsuya Hasegawa (Postdoctoral Fellow, The University of Tokyo)

Co-investigator

Yutaka Kohno (Professor, Ibaraki Prefectural University of Health Sciences)

Co-investigator

Daisuke Ishii (Assistant Professor,
Ibaraki Prefectural University of Health Sciences)

Co-investigator

Hiroshi Kishimoto(Lecturer, Ibaraki Prefectural University of Health Sciences)

Co-investigator

Hiroshi Yuine (Assistant Professor, Ibaraki Prefectural University of Health
Sciences)

Co-investigator

Kiyoshige Ishibashi (Ibaraki Prefectural University of Health Sciences)

Co-investigator

Hiroyuki Hamada (Assistant, Bunkyo Gakuin University)

Co-investigator

Mariko Miyata (Professor, Tokyo Women’s Medical University)

Co-investigator

Moeko Kanaya (Assistant Professor, Tokyo Women’s Medical University)

Co-investigator

Hironobu Osaki (Program-Specific Associate Professor, Doshisha University)

Co-investigator

Michihiro Kawano (Professor, Saku University)

Co-investigator

Yoshihide Kanai, (Lecturer, Saitama Medical University)

Co-investigator

Yuichiro Omura (Ph.D Student, The University of Tokyo)

Co-investigator

Hitohiro Etoh (Master Student, The University of Tokyo)

Co-investigator

Xiyu Hou (Master Student, The University of Tokyo)

Co-investigator

Kota Sonoda (Master Student, The University of Tokyo)

Co-investigator

Yuhei Fujiwara (Graduate Student, The University of Tokyo)

Co-investigator

Huang Rui (Graduate Student, The University of Tokyo)

Co-investigator

Huyutake Makino (Master Student, The University of Tokyo)

Co-investigator

Naoki Orihara (Undergraduate Student, The University of Tokyo)

Co-investigator

Ritsuki Nishizawa (Undergraduate Student, The University of Tokyo)

Co-investigator

Moichi Ishikawa (Undergraduate Student, The University of Tokyo)

Research Project B05-1: Motor learning of modularity in musculoskeletal models toward the emergence ofmuscle synergy
Principal investigator

Mitsuhiro Hayashibe (Professor, Tohoku University)

Co-investigator

Kyo Kutsuzawa (Assistant Professor, Tohoku University)

Co-investigator

Ahmed Hannan (Ph.D Student, Tohoku University)

Co-investigator

Li Guanda (Ph.D Student, Tohoku University)

Co-investigator

Akito Fukunishi (Master Student, Tohoku University)

Co-investigator

Taku Sugiyama (Master Student, Tohoku University)

Research Project B05-2: A neural network model for hyper-adaptability of bipedal locomotion
Principal investigator

Naomichi Ogihara (Professor, The University of Tokyo)

Research Project B05-3: Hyper-adaptation of bodily and neural sensorimotor information structures in early
developmental stage
Principal investigator

Hoshinori Kanazawa (Research Assistant Professor, The University of Tokyo)

Co-investigator

Yasuo Kuniyoshi (Professor, The University of Tokyo)

Co-investigator

Masahiko Kawai (Project Associate Professor, Kyoto University)

Co-investigator

Dongmin Kim (Graduate Student, The University of Tokyo)

Co-investigator

Yohei Nomoto (Graduate Student, The University of Tokyo)

Co-investigator

Akito Yoshida (Graduate Student, The University of Tokyo)

Co-investigator

Yamato Shinomiya (Graduate Student, The University of Tokyo)

Research Project B05-4: Low-dimensional functional connectivity across bilateral motor-related areas for
hyper-adaptability
Principal investigator

Isao Nambu (Associate Professor, Nagaoka University of Technology)

Co-investigator

Yasuhiro Wada (Professor, Nagaoka University of Technology)

Co-investigator

Hiroshi Yokoyama (Project Assistant Professor, NIPS)

Research Project B05-5: Application of motor learning model for partial relationship reuse to reconstruction

of muscle synergy
Principal investigator

Yuichi Kobayashi (Associate Professor, Shizuoka University)

Co-investigator

Sota Nakamura (Graduate student, Grauate school of Science and Technology,
Shizuoka University)

Co-investigator

Taisei Matsuura (Graduate student, Grauate school of Science and Technology,
Shizuoka University)

Co-investigator

Haruki Mamiya (Undergraduate Student, Grauate school of Scienceand Technology, Shizuoka University)

Co-investigator

Izumi Wada (Undergraduate Student, Department of Mechanical Engineering,
Faculty of Engineering, Shizuoka University)

Research Project B05-6: Hierarchical understanding of adaptation to a new relationship between the eye andthe body.
Principal investigator

Michiteru Kitazaki (Professor, Department of Computer Science and Engineering,
Toyohashi University of Technology)

Research Project B05-7: Supraspinal mechanisms of the human upright postural control based on the EEG
dynamics associated with micro-falls
Principal investigator

Taishin Nomura (Professor, Osaka University)

Co-investigator

Akihiro Nakamura (Graduate School of Engineering Science, Osaka University /Research Fellow, JSPS)

Co-investigator

Yasuyuki Suzuki (Lecturer, Graduate School of Engineering Science,
Osaka University)

Co-investigator

Matija Milosevic (Assistant Professor, Graduate School of Engineering Science,
Osaka University)

Co-investigator

Kimitaka Nakazawa (Professor, The University of Tokyo)

Co-investigator

Saburo Sakoda (Honorary Director,
National Hospital Organization Osaka Toneyama Medical Center)Co-investigator

Takuyuki Endo (Doctor,
National Hospital Organization Osaka Toneyama Medical Center)Research Project B05-8: Higher brain functions as hyper-adaptability: an exploration of the principle ofproactive outreach to an indefinite environment
Principal investigator

Kazuhiro Sakamoto (Associate Professor, Faculty of Medicine, Tohoku Medical
and Pharmaceutical University)

Co-investigator

Yoshiya Matsuzaka (Professor, Faculty of Medicine, Tohoku Medical
and Pharmaceutical University)

Co-investigator

Hajime Mushiake (Professor, School of Medicine, Tohoku University)

Co-investigator

Tadaho Nakamura (Associate Professor, Faculty of Medicine, Tohoku Medical
and Pharmaceutical University)

Co-investigator

Makoto Osanai (Professor, Graduate School of Medicine, Osaka University)

Co-investigator

Satoshi Zuguchi (PH.D Student, School of Medicine, Tohoku University)

Research Project B05-9: Individual differences in suitable neural circuits for attention control and its effecton motor control.
Principal investigator

Takeshi Sakurada (Associate Professor, Faculty of Science and Technology,
Seikei University)

Co-investigatorShunsuke Taguchi (Undergraduate student, Faculty of Science and Technology,
Seikei University)

Co-investigator

Daisuke Nakajima (Undergraduate student, Faculty of Science and Technology,
Seikei University)

Co-investigator

Yuri Hayashi (Undergraduate student, Faculty of Science and Technology,
Seikei University)

Research Project B05-10: Brain mechanisms for generating exploratory adaptation: Modeling the brain
function based on meta-reinforcement learning
Principal investigator

Yuki Ueyama (Associate Professor, National Defense Academy of Japan)

Co-investigator

Hiroshi Imamizu (Professor, University of Tsukuba)

Co-investigator

Jun Izawa (Associate Professor, University of Tsukuba)