2022 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)
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, theipsilateral 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 thebrain 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 and the
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 thatis 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 verificationof cause-effect relationship of neural activity and its generatedfunctions and behaviors. (b) Function-oriented neural
encoding, which constitutes a model that may incorporate any
knowledge of brain functions into gray-box modeling or
hypothesizes the structure of a model based on statisticalmethods.
III. ACTIVITIESFollowing events were held by management group.
A. Activities organized by the project- 4th plenary conference
Date: July 19, 2022 9:00-18:00
July 20, 2022 9:00-15:45
Place: Hagi-Hall, Tohoku University
Contents: The 4th Plenary Meeting of the Hyper-adaptability
Area was held as the first meeting after the addition ofresearchers in the second phase of the A05 group. For the
purpose of mutual understanding and for future collaboration,
all the principal investigators and funded co-investigators gaveoral presentation about their research. In addition, poster
session was held in conjunction with the meeting. There were
100 registrants and 47 posters were presented. This was thefirst on-site meeting after the kick-off symposium, and boththe oral presentations and the poster session were very heated.
- 9th management meeting
Date: July 20, 2022 12:10-13:10
Place: Hagi-Hall, Tohoku University
Contents: members of the management group discussed about
the operation method, symposiums, etc.
- The 2nd open symposium
Date: Oct 16, 2022 9:00-14:40
Place: Osaka International Convention Center
Contents: The "Hyper-adaptability" open symposium was held
as a co-sponsored symposium at the 20th Annual Meeting of
the Japan Society of Physical Therapy. Following a special

lecture by A01 Isa on " Hyper-adaptability in the process offunctional recovery after brain and spinal cord injury," A02
Seki, B02 Funato, A04 Takakusaki, and B01 Chiba presented
the latest research results in the morning symposium on"Muscle Synergy and Gait" and A03 Imamizu, B03 Izawa,
and A03 Tsutsui in the afternoon symposium on "Motor
Learning and Functional Recovery. The latest research results
were explained in an easy-to-understand manner to theaudience, which consisted mainly of therapists. Of the 2,360
on-site participants at the conference, more than half attended
the " Hyper-adaptability" symposium. In the symposium, there
were many questions from the audience. In the general
discussion, researchers and therapists had a heated debate on
how the findings of fundamental research should be applied torehabilitation.
- 10th management meeting
Date: Oct 16, 2022 15:00-16:00
Place: Osaka International Convention Center
Contents: members of the management group discussed about
the operation method, management of symposium, etc.
- 5th plenary conference
Date: March 3th - 4th, 2023
Place: Takeda Hall, The University of Tokyo.
Contents: summary of the researches in the year was presented
by project representative and researchers in the project.
- 11th management meeting
Date: March 8th, 2022. 12:20-13:20
Place: Hongo Campus, The University of Tokyo
Contents: members of the management group discussed about
the operation method, management of symposium in the nextyear, etc.
B. Journal Special Issue / Organized Session- Journal Special Issue: Journal of Robotics and Mechatronics
Special Issue on “Special Issue on Systems Science of HyperAdaptability” was organized in Journal of Robotics andMechatronics, Vol. 34, no. 4. 14 papers were published.
- Journal Special Issue: Journal of The Society of Instrument
and Control Engineers
Special Issue on “System Theoretical Approaches toUnderstanding Hyper-Adaptability in the Human Body andBrain” was organized in Journal of The Society of Instrument
and Control Engineers, Vol. 61, no. 4. 9 papers were published.
- OS: RSJ2022 (40th Annual Conference of the Robotics
Society of Japan)
Date: September 6th and 9th, 2022
Contents: Nine researches presented at a symposium on“Hyper-adaptability” on Sep. 6th. Open forum “Hyperadaptability in Rehabilitation Prompted by Robotics”
composed with four lectures and panel discussion was held on
Sep. 9th.

- OS: MHS 2022 (33rd International Symposium on MicroNanoMechatronics and Human Science)
Date: November 28th, 2022
Contents: eight researches including two keynote talks were
presented at a symposium on “Hyper-adaptability”. Moreover,B05-2 Ogihara presented his researches as a plenary lecture.
- OS: Next Generation Brain Project, Winter symposium
Date: December 14th, 2022
Contents: An organized symposium with Seven researches andpanel discussion were held.
- OS: 35th SICE Symposium on Decentralized Autonomous
Systems.
Date: January 22nd-23id, 2023
Contents: 14 researches were presented at a symposium on“Hyper-adaptability”.
- OS: The 100th Annual Meeting of the Physiological Societyof JapanDate: March 15th, 2023
Contents: Symposium of “Surprise-induced brain hyperadaptability” is organized.
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. Study Session
In this year, three study sessions were held from Sep. to Oct.
2022 to deepen the understanding of network analysis, amethod used by many researchers in this research area, andyoung researchers (Isao Nambu (Nagaoka University ofTechnology), Reona Yamaguchi (Kyoto University), Kenji
Yoshinaga (Kyoto University), Megumi Miyashita (Tokyo
University of Agriculture and Technology) and Jihoon Park
(NICT) provided explanations and discussions. The materials
and videos of the workshop are available on the website andSlack, and we are striving to expand the common knowledge.
B. Data Sharing withing research field
To promote joint research between Group A, which handles
biological data obtained from humans and experimental
animals, and Group B, which takes a systems science-based
approach, this year we established a framework for sharing
cortical EEG, electromyography, and movement data recorded
by a monkey model of spinal cord injury within the field.
V. FUTURE PERSPECTIVE
We plan to conduct the following activities of the next fiscal
year:- 2nd international symposium on October, 2023
- fiscal year-end plenary conference on March, 2024.

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(Neurosciences) 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 the researches 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. In thenonhuman primate studies, Isa and colleagues clarified thechanges in excitatory and inhibitory connectives associatedwith disinhibition after spinal cord injury. Naito and colleagues
investigated hyper-adaptation in the brain of a wheelchair racetop Paralympians. Aizawa and colleagues tested cellularmechanism underlying the interhemispheric inhibition in mice.
Group A02 (Seki) aimed to investigate the mode of adaptationof neural function to rapid musculoskeletal structuralmodification from the viewpoint of reconstructing thebiological structure of hyper-adaptation and to elucidate theprinciples behind this process. This year, based on the analysisof the tendon replacement model of the extensor digitorum andflexor digitorum superficialis, we confirmed that the adaptation
mechanism to the body modification consists of two stages.
Among them, it was shown that the improvement of motor
function after the initial adaptation is brought about by the
temporary replacement of muscle synergy, while thesubsequent recovery may be attributed to other factors.
Group A03 (Imamizu and Tsutusi) conducted manipulative
studies to uncover mechanisms by which a sense of agency
and emotion facilitate motor learning. In human experiments,
fMRI brain activity was measured to investigate how
parameters of brain stimulation cause changes in activity. We
conducted psychological experiments and showed thatsynchrony between action and consequence plays a key role inthe sense of agency in a novel learning task. In monkey
experiments, we studied the different roles of prefrontal

subregions in the generation and modulation of motivation, by
TMS neural interventions. We have established a system tostudy sense of agency in monkeys in collaboration with an
external research group.
Group A04 (Takakusaki, Hanakawa) investigated the roleof dopamine (DA) in the alteration of the brain network
dynamics associated with posture-gait control (animal
experimentation) and the higher-order executive function(human experiments). Takakusaki and colleagues revealed thatthe midbrain DA neurons contributed to the regulation of
postural muscle tone by acting on the pedunculopontine
tegmental nucleus (PPN) neurons, possibly via thereticulospinal pathways. Hanakawa and colleagues succeededin explaining a synthetic MRI contrast using multiple
histological contrasts, including tyrosine hydroxylase (TH)staining.
In addition, 16 members (below) joined as the A05 group, whowere selected through the applications.
Yuichi Takeuchi “A closed-loop brain stimulation forreinforcing
hyper-adaptability”
A
technology
withdiscriminative cross-spectral factor analysis (a machinelearning technology focusing on oscillatory brain activities) has
been developed to decode seizure susceptibility from largescale electrophysiological recordings of cerebral activities
during the interictal period of a rat model of epilepsy. It was
successful in five out of six cases.
Shinichi Izumi “Comprehensive understanding of themechanisms of adaptive changes in brain, body consciousness
and arm use underlying upper limb recovery in stroke patients”
conducted a longitudinal study using fMRI and DTI to
elucidate the relationship between arm use and brain function
and structure underlying upper limb recovery after stroke. As aresult, they found that different brain regions and neuralpathways were associated with arm use and functional recovery.
Rie Kimura “Elucidation of neural circuits for optimal
adaptation to the external environment.” We aim to understandthe neural basis of optimal adaptation to the external
environment by examining the visual responses in
multidimensional brain systems. This year, we observed
behavioral-level adaptations to a variety of visual stimuli.
Akihiro Funamizu ”Neural substrate of unified learningtheory for adaptive behavior” We hypothesize that multiplebehavioral strategies are driven by a unique learning algorithm
in the brain. To understand the algorithm, we used head-fixed
mice and performed a behavioral task which required multiple
strategies. We also developed a setup for recording the neuralactivity of mice during the task.
Riki Matsumoto “Mechanism of Hyper-Adaptability of thehuman premotor area: electrophysiological connectomes
analysis with electrocorticogram” In the Go/No-Go task, task

performance was more impaired upon high frequency
electrical cortical stimulation (HFECS), when more CCEP
responses were observed within the task-activated corticalregion. This suggests HFECS exerts its effect at the networklevel via electrophysiological connections.
Yoko Yazaki-Sugiyama “Enhanced neuronal circuits for
vocal learning with extensive experiences during
development” Zebra finches learn to sing by listening andmemorizing, then mimicking a tutor’s song (TS) during
development. We found the transient axonal projections fromthe neuronal TS ensembles in higher auditory forebrain, NCM
into song motor control site, HVC. Enriched learning
experiences retained NCM connectivity to HVC into
adulthood.
Hiroyuki Miyawaki, “Regulatory mechanisms of interregional network changes underlying hyper-adaptation frommal-adaptation state caused by fear memory.” investigatedlocal and inter-regional network dynamics that support
extinction learning of learned fear by using multi-regionallarge-scale electrophysiological recordings on freely moving
rodents. The study revealed that activities of extinction-related
neuronal ensembles were enhanced prior to extinction learning.
Takaki Maeda advanced clinical studies of cognitive
rehabilitation for mental illnesses including schizophrenia,
ASD, ADHD and eating disorders in order to tune up
precision of SoA. We tried to show whether this rehabilitation
method could improve spatial SoA in addition totemporal SoA.
Tomohiko Takei “Adaptive mechanisms for perturbations in
the neural manifold” We recorded and analyzed frontalparietal cortical activity in a monkey performing a contextdependent feedback motor task and showed that theorthogonalization of neural dimensions during the preparation
and response periods, suggesting a flexible preparation of
motor responses.
Hironobu
Osaki,"Spatio-temporalmodulation ofdisinhibition for super-recovery from motor dysfunction in thechronic phase of stroke" Group A05-11 (Osaki) aims todevelop a method of neural modulation to enhance functional
recovery from cortical infarction. To quantitatively monitor
the functional recovery of mice’s behavior, we developed abehavioral monitoring system that can also record andmodulate neural activity.
Rieko Osu “Hyper adaptive changes in spatial recognition
Using Augmented reality (AR)” We constructed a system topresent all spaces in the right space that are not ignored inpatients with hemispatial neglect. We also designed a task totest the characteristics of attention in patients with hemispatial
neglect.
Kosei Takeuchi “Creation of hyper-adaptability by syntheticorganizers and micro-environmental control of neuralreconstruction” aimed to induce hyper-adaptation andrecovery by combining the synthetic synapse connector in theprocess of recovery from brain and spinal cord injury. We
show that maintaining inhibitory-synapses to prevent
disinhibition delays recovery by the newly created inhibitorysynapse connector.

Tatsuya Mima “Brain reorganization in stroke patients withhyper-recovery by measuring EEG modulation induced bystatic and dynamic magnetic fields.” As an example ofhyperadaptation, we plan to elucidate brain reorganization in
hyper-recovered stroke survivors using EEG modulationinduced by brain stimulation. In FY2022, we established anexperimental environment that enables stable measurement of
TMS-EEG, and completed the study in healthy volunteers.”
Atsushi Nambu “Brain adaptation after limb loss” examinedplastic changes in the primary motor cortex (M1) using a
Japanese monkey who accidentally lost its forelimb. The M1
region corresponding to the lost forelimb shrunk while thewhole forelimb region remained intact.
Noriyuki Higo “Adaptive mechanism occurring in bothhemispheres after unilateral brain damage” VBM andhistochemical analyses were performed in macaques withfocal infarcts in the unilateral internal capsule. During theperiod of motor recovery, gray matter volume and dendritic
arborization of pyramidal cells were increased in the ventral
premotor cortex of the hemisphere contralateral to the infarcts.
Osamu Yokoyama “Functional reorganization of motor andsomatosensory cortices during recovery of motor functions
after the loss of peripheral sensory inputs” Transecting thedorsal rootlets of the monkey cervical cord caused
impairments of, in addition to somatosensory function, motor
function in the upper limb, which recovered in about 2 weeks.
High-gamma activity in the primary motor and somatosensory
cortex during movement execution increased immediately
after injury and decreased with functional recovery.
III. ACTIVITIESFrom October 31, 2022, the data obtained in the A01 Groupare shared by the whole “Hyper-adaptation” research groups
including Group B to facilitate the collaborative researches foe
development of mathematical models and examination ofconnectivity analysis.
IV. FUTURE PLAN
Group A should further facilitate discussion among the A0105 groups and summarize the “Hyper-adaptation” –related
phenomenon observed across the electrophysiological andmolecular levels and promote the collaboration with 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 changes in excitatory
and inhibitory connectives associated with disinhibition after
spinal cord injury, hyper-adaptation in the brain of a wheelchairrace-top Paralympians and cellular mechanism underlying theinterhemispheric 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, and that the diffuse projection systems
such as monoaminergic neurons would be involved in theprocess. However, details of the underlying neural mechanism
are still elusive. On the other hand, human studies suggested
that such disinhibition mechanism is declined as aging, which
may be related to the difficulty in recovery for the aged people.
This research group will study these issues and wish to propose
the strategies to prevent the decline in latent 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 disinhibition
across the large-scaled brain network in the macaque model ofrecovery from the spinal cord injury or lesion of the primaryvisual cortex. In rodents, Aizawa Group will perform theactivity measurement and optogenetical stimulation of
monoaminergic systems including dopaminergic andserotonergic neurons, to supply the information about the
global disinhibition in the cortex. In humans, Naito-AsadaGroup will capture the chronic disinhibitory state of the aged
people by fMRI and propose the effective training methods toimprove the brain functions using the disinhibitory state as ameasure of progression of aging-related frailty.

III. RESEARCH TOPICS
A. Global disinhibition after partial spinal cord injury inmonkeys (Isa)In our previous experiment, we investigated the recovery
process of motor function after sub-hemisection at the C4/C5
segment in macaque monkeys. We found that monkeys
experienced severe paralysis in the affected forelimb soonafter the sub-hemisection, however, through intensive training
and electrical stimulation to motor related areas, the reaching
and grasping movements considerably recovered. We also
tested the interhemispheric interaction from ipsilesional
premotor cortex (PM) to contralesional primary cortex (M1)by a conditioning paradigm. We found that the effect ofconditioning stimulus was inhibitory before the lesion as has
been reported in human studies, however, the effect was
switched to facilitation during the earlier stage of recovery.
According to these results, we hypothesized that the change of
excitation-inhibition balance is considered as neural basis ofplastic changes which can improve the recover from sever
impairment. Granger causality can assess the directionality of
the connectivity between each brain area, but the polarity of
the connectivity among brain regions associated withexcitation-inhibition balance is still unclear. In this year, weinvestigated the polarity of the connectivity associated withthe motor recovery after spinal cord injury by using a novel
causal inference model based on a statistical causal discovery
method, LiNGAM (linear non-Gaussian acyclic model). We
applied LiNGAM to brain activity at each frequency band
before and after spinal cord injury. In this analysis, we focusedon pathway from ipsilesional PM to contralesional M1 whichshowed remarkable disinhibition by a conditioning paradigm
after the lesion. As for the excitatory connectivity, there wereno significant difference in early and late phases of recovery
compared with preoperative stage. However, intensity of
inhibitory connectivity significantly decreased in in early andlate phases of recovery. These results indicated that reductionof inhibitory connectivity induce disinhibition associated withmotor recovery from spinal cord injury.
[1] Sawamura M, Onoe H, Tsukada H, Uemura N, Isa K, Isa T, Takahashi R.
“α-synuclein propagation via olfactory pathway induces olfactory bulb
atrophy and widespread glucose hypometabolism in a non-human primate
model. (P1-1.Virtual),” Neurology, 98 (18 Supplement) 2422. 2022.
[2] Tsujihana K, Tanegashima K, Santo Y, Yamada H, Akazawa S, Nakao R,
Tominaga K, Saito R, Nishito Y, Hata RI, Nakamura T, Murai I, Kono Y,

Sugawa M, Tanioka M, Egawa G, Doi M, Isa T, Kabashima K, Hara T,
Okamura H. “Circadian protection against bacterial skin infection by
epidermal CXCL14-mediated innate immunity,” Proc. Natl. Acad. Sci. U.
S. A., 119 (25):e2116027119. doi: 10.1073/pnas.2116027119. 2022.
[3] Yamauchi K, Okamoto S, Ishida Y, Konno K, Hoshino K, Furuta T,
Takahashi M, Koike M, Isa K, Watanabe M, Isa T, Hioki H.
“Fluorochromized tyramide-glucose oxidase as a multiplex fluorescenttyramide signal amplification system for histochemical analysis,” Sci. Rep.,
12 (1):14807. doi: 10.1038/s41598-022-19085-9. 2022.
[4] Isa T, Tohyama T, Kinoshita M. “Phylogenetic view of the compensatory
mechanisms in motor and sensory systems after neuronal injury,” Curr.
Res. Neurobiol., 3:100058. doi: 10.1016/j.crneur.2022.100058. 2022.
[5] Umeda T, Isa T, Nishimura Y. “Temporal dynamics of the sensorimotor
convergence underlying voluntary limb movement,” Proc. Natl. Acad. Sci.
U. S. A., 119 (48):e2208353119. doi: 10.1073/pnas.2208353119. 2022.
[6] Janssen P, Isa T, Lanciego J, Leech K, Logothetis N, Poo MM, MitchellAS. “Visualizing advances in the future of primate neuroscience research,”
Curr. Res. Neurobiol., 4:100064. doi: 10.1016/j.crneur.2022.100064. 2022
[7] Takakuwa N, Isa K, Yamaguchi R, Onoe H, Takahashi J, Yoshida M, Isa
T. “Protocol for making an animal model of "blindsight" in macaquemonkeys,” STAR Protoc. 4 (1):101960. doi: 10.1016/j.xpro.2022.101960.
2022.

B. Elucidation of hyper-adaptation in the brain of awheelchair race-top Paralympians and verification ofdifferences between younger and older adults in thedisinhibition patterns of ipsilateral sensorimotor regions
during complex hand movements (Naito, Asada, Nakano)
In collaboration with Asada group (Osaka Univ), Naito group(NICT, CiNet) has used functional MRI to elucidate hyperadaptation phenomena observed in the brain of a wheelchairracing top Paralympian. The foot section of the motor cortex(M1) is normally inhibited during hand movements, but in thisathlete, the foot section was activated during hand movements(Morita et al. Front Syst Neurosci 2022). In addition, hand
proprioceptive information processing was enhanced in thisathlete, and the action inhibition network functioned in acharacteristic manner to prevent occurrence of actual
movement during passive hand movements (Morita t al. Brain
Science 2022). Furthermore, the transcallosal fibers between
the left and right M1 were well developed, and the bilateralmotor cortices were active even during right hand movements,suggesting that the motor control of the right hand was in abilateral control mode in this athlete. With regard to complex
hand movements, in collaboration with Nakano group (Kyoto
Tachibana Univ), we found, unlike younger adults,
involuntary electromyograms from the resting left hand during
right hand finger complex movements in older adults. During
complex hand movements, we found activations in theipsilateral sensorimotor cortices in younger adults. But among
these cortices, the dorsal premotor cortex (PMd) consistently
increased functional coupling with multiple contralateral
sensorimotor areas, and the PMd was more strongly recruited
in individuals with less dexterous performance. The ipsilateralM1 remained inhibited during complex as well as simple hand
movements in younger adults, whereas this inhibition
disappeared during complex movements in older adults,suggesting that this may be closely related to the appearance
of involuntary muscle activity in their left relaxed hand.

[8] Morita T, Hirose S, Kimura N, Takemura H, Asada M, Naito E. HyperAdaptation in the Human Brain: Functional and Structural Changes in theFoot Section of the Primary Motor Cortex in a Top Wheelchair Racing
Paralympian.
Front
Syst
Neurosci.
16:780652.
doi:
10.3389/fnsys.2022.780652. 2022
[9] Morita T, Naito E. Facilitation of Hand Proprioceptive Processing in
Paraplegic Individuals with Long-Term Wheelchair Sports Training.
Brain Sci. 12(10):1295. doi: 10.3390/brainsci12101295. 2022

C. Analysis of the cellular mechanism underlying theinterhemispheric inhibition and adaptive behaviors using
mice (Aizawa)
Aizawa group analyzed murine neural activity with
optogenetics in the cerebral cortex to address a cellularmechanism underlying the interhemispheric inhibition which
was altered in the monkey and human with hyper-adaptation as
revealed by Isa and Naito-Asada-Nakano groups. Topicalcortical application of GABAA receptor agonist increasedtransiently firing rate in a significant proportion of thepresumptive fast spiking neurons in the contralateral primary
motor cortex. Those neurons were also activated by cholinergic
antagonist, suggesting a role of acetylcholine system inmodulation of interhemispheric inhibition. On the other hand,
the group examined mouse model of local cerebral infarction
and sepsis in which the interplay between hemispheres werereported to be altered. Mice gradually developed reduction of
inhibitory effect of the callosal inputs from contralesional sidein association with lateralization of forelimb use after local
cortical infarction. Mice after sepsis exhibited long-lasting
behavioral impairments (Kikutani et al., Shock, 2023).
[10] Kikutani K, Hosokawa K, Giga H, Ota K, Matsumata M, Zhu M,
Takemoto H, Ji B, Ohshimo S, Shime N, Aizawa H. “GENETIC
DELETION OF TRANSLOCATOR PROTEIN EXACERBATESPOST-SEPSIS SYNDROME WITH ACTIVATION OF THE C1Q
PATHWAY IN SEPTIC MOUSE MODEL,” Shock., 59 (1):82-90. doi:
10.1097/SHK.0000000000002030. 2023.

IV. FUTURE PERSPECTIVE
In the next fiscal year, Isa group will summarize themechanism od disinhibition associated with functional
recovery and publish those papers. In addition, we will clarify
the molecular system for the disinhibition collaborating with
Aizawa group.
Naito-Asada-Nakano group will publish several papers on the
results of our work to date. Based on the results, we will
construct computational model that can explain the principles
of facilitation and inhibition between bilateral motor cortices
and propose a new rehabilitation method using theseprinciples, and verify the effectiveness of the method. Aizawa
group will identify specific cell type responsible forinterhemispheric inhibition and propose a novel method tomodulate the interhemispheric inhibition via manipulation ofcholinergic system using mouse model.

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

Abstract—In the FY2022, the aim of our research group was to1) confirm the previous result about the EMG changes to tendontransfer surgery, 2) visualize the functionality of muscle andtendon after the surgery, and 3) to evaluate the sensory prediction
error by measuring the extent of sensory gating during movement.

I. INTRODUCTION
From the cradle to the grave, the musculoskeletal structureof 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 “hyperadaptation”
correspondingtotheever-changing
musculoskeletal structure by establishing novel animal models
and cutting-edge technology. By developing the muscle
relocation model, where the association of muscle activity andits physical action will be surgically or ontogeneticallymanipulated, we will investigate how the CNS acquire a control
strategy of their body de novo. We will implement novel
neurophysiological tools for assessing the hyper-adaptation
occurring in the multiple levels of the CNS, from the spinal cord,
brain stem, and cerebral cortex.
III. RESEARCH RESULTS
In this fiscal year, we are preparing the paper about the
research results on the muscle relocation surgery obtained up to
the last fiscal year. Right now, we are about to finish the final
draft. In addition, research results on muscle synergy analysis
during rehabilitation trials for stroke patients have been
published in a paper (Ref. 1). Other progress was made in 1) the
follow-up experiments to confirm changes in muscle synergy
after repositioning surgery, 2) research to visualize tendon
gliding and repositioned muscle function before and after
surgery using ultrasound measurements, and 3) the
development of a method to quantify sensory prediction errors
in the cortex after tendon transfer surgery. Below, we report theresults of our research item by item.

1) Adaptation of muscle activities after the tendon-transfer
surgery (confirmation in the third monkey)
As a treatment to regain lost muscle function, tendonsof an injured muscle may be replaced with tendons of another
healthy muscle, and this is called a tendon transfer. Sometime
after tendon transfer, the patient becomes able to move thereplaced muscle freely and adaptive changes are expected tooccur in the nervous system during this process, but theadaptive mechanisms remain unexplained.
In this study, in order to clarify the adaptation process
after tendon transfer, we replaced EDC and FDS tendons in themacaque monkey and measured the task before and after
replacing for about 8 months. By using this data and analyzing
the changes in EMG and muscle synergy after the replacement,
we investigated what kind of adaptation process is manifested
in the nervous system.
Macaque monkeys were trained to perform a precision
grasping task, and EMG was measured by inserting wire
electrodes directly into the muscles. A comparison of EDC and
FDS muscle activity before and after tendon transfer revealed
that while EDC showed a tendency for the activity to change
after tendon transfer and then return to the same as before, FDS
showed no change in activity pattern or peak timing at any
period after tendon transfer.
Moreover, to investigate the nature of the changes in
the coordinated activity of muscle groups due to tendon transfer,
We performed muscle synergy analysis using non-negative
matrix factorization on the EMG data sets of all 14 muscles
measured and extracted two synergies: extensor synergy
consisting of EDC and its cooperating muscles, and flexor
synergy consisting of FDS and its cooperating muscles.
Observing changes in the spatial and temporal patterns of thesesynergies, we find that regarding the spatial pattern, while there
was no major change in the composition of synergy, there wasa long-term change in the projection ratio to each muscle,
regarding the temporal pattern, changes similar to the activityof EDC and FDS were shown.
These results suggest that the changes in muscle
activity in EDC and FDS occurred at a synergistic level and thatthese synergies were integrated through long-term adaptation
after tendon transfer.
2) Development of technology for image visualization ofgliding and function of the transferred muscles and tendons.
Various tendon transfer techniques have been used totreat nerve palsy and traumatic myotendinous defects, but themechanisms of the nervous system in postoperative functional
conversion remain unclear. We have been studying the adaptivefunction of the nervous system after tendon transfer using anelectrophysiological macaque forearm tendon transfer model.
We developed a method to evaluate transition tendon sliding
using direct muscle stimulation.
Cross-tendon transfer of the flexor digitorum
superficialis (FDS) and extensor digitorum commonus (EDC)of the macaque forearm was performed; the proximal FDS
transection was transferred directly to the EDC and theproximal EDC transection was sutured to the distal FDS
transection via a plantar tendon harvested from the lower
extremity as a graft tendon, After 1-week cast immobilization
plus upper extremity trunk immobilization, postoperativeevaluation of migration of the transition tendon by muscle
stimulation was performed at 1, 3, 5, 9, 15 and 19 weeks. The
target muscle was stimulated with a stimulator (DS8R,
Digitimer), the intramuscular tendon was identified byultrasound image B-mode, the M-mode wave derived from thetendon was extracted, and the amplitude, duration, and area of
the waveform were measured. The movement of the index andmiddle finger during muscle stimulation was captured by twocameras, and the three-dimensional coordinates werereconstructed and quantified by the DLT method.
Results indicated that the M-mode waveform
increased with stimulation intensity, and the area of the Mmode waveform during 50 mA stimulation was almost constantover time, indicating that constant muscle contraction was
obtained with direct muscle stimulation. Finger movements
showed IP joint flexion with EDC stimulation and MP joint
extension with FDS stimulation, confirming the continuity ofthe transition tendon until the final evaluation.
When studying changes in the nervous system induced
by tendon transfer, it is necessary to demonstrate objectively
that the effect of tendon transfer is sustained. The method
developed in this study was able to objectively demonstrate thecontinuity and sliding state of tendons by quantifying finger
movements under constant muscle contraction induced byexternal stimuli.
3) Extraction of sensory prediction errors represented at
the central nervous system in monkeys performing
voluntary movements.
Our sensory systems respond not only to externalsensory information but also to self-generated information
caused by our movement. For example, we feel less when we
move. This phenomenon is known as sensory attenuation.
Although the sensory attenuation of cutaneous afferent input tothe CNS is well documented, that of a proprioceptive afferent
signal is not. Furthermore, it is unclear if the comparable
modulation of afferent sensory input could also be triggered by

the non-overt motor events, e.g., action observation. To addressthese questions, we recorded somatosensory-evoked potentials(SEPs) from Brodman's area 3a (BA3a) by applying electricalstimuli to a cuff electrode implanted in the deep radial nerve(DR: muscle afferent of wrist extensor) (DR-SEPs) and thatfrom BA3b from the superficial radial nerve(SR: cutaneousafferent from wrist dorsum)(SR-SEPs) while monkeys
performed reaching and grasping movements (self-movement:SM) (n=2), or observed a person sitting in front of the monkeys
and performing the same task (action observation: AO)(n=1).
During SM, we found that the size of DR-SEPs (p <0.01), not only the SR-SEP (p < 0.01), was significantly
suppressed in the movement phase compared to the premovement control period (inter-trial interval; t-test). Therefore,
we conclude that the cortical SEPs evoked by muscle andcutaneous afferents show sensory attenuation during movement
execution. Interestingly, we also found two contrasting results
between DR- and SR-SEP's modulation during the SM and AO
tasks. First, while the onset of DR-SEP attenuation started after
the movement initiation, for SR-SEP attenuation started before
movement onset in the SM task (p < 0.01). Second, while wefound no difference in the size of DR-SEPs during the task, thesize of SR-SEPs was consistently larger throughout the AO
task; this was also a more significant pre-movement control
period during the SM task, both compared with the inter-task
interval period. These differences between DR-SEPs and SRSEPs suggest the differential source of sensory gain modulation
to the primary sensory cortex's proprioceptive and cutaneous
sensory input. While the sensory attenuation of the
proprioceptive input could be driven robustly by the efference
copy of volitional motor command, the cutaneous signal could
be more flexibly modulated depending on its value for current
and future movement.
IV. FUTURE PERSPECTIVE
In addition to the above, we are continuing to develop
evaluation techniques for cortical activity plasticity using
ECOG electrodes this year. After the technology is established,
we will identify the neural factors of rearranged muscle control
using methods such as cortico-cortical coherence and corticomuscular coherence.
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.

A03 Annual report of research project
Hiroshi Imamizu1 and Ken-Iichiro Tsutsui2
Graduate school of Humanities and Sociology, The University of Tokyo
2
Graduate school of Life Sciences, Tohoku University

1

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) We measured fMRI brain activity while manipulating a
sense of agency (a sense that "I am the one causing an action",
which is an aspect of body cognition) and found that activity wasmodulated in regions related to the sense of agency when arelationship between sensorimotor information and the sense ofagency was modulated. We provided a basis for manipulating thesense of agency to facilitate motor learning. 2) We showed how asense of agency is established in a novel learning situation where
the outcome of the action cannot be predicted. 3) We performed aneurotracing study visualizing the connectivity between two
important regions for emotional regulation, ventral medial frontal
cortex and amygdala, and identified a previously unknown
projection linking between the shallow layers of the pregenual
anterior cingulate cortex (pgACC) and the basolateral amygdala(BLA). This unique circuit, a cortico-basal ganglia projection
starting in shallow layers, is expected to play a special role inregulating emotion and generating motivation. 4) We performed a
psychophysical experiment on monkeys to confirm that they
possess “sense of agency” similar to humans, using a virtual-reality
system inducing temporal delays to the visual feedback of one’s
hand action.

I. INTRODUCTION
Previous studies in neuroscience and psychology haveinvestigated how feedback from the external world (such as
motor error and reward prediction error) contribute 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 atheoretical 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.

understanding the "hyper-adaptability" and future developmentof the methods for inducing and facilitating it.
III. RESEARCH TOPICS
A. Manipulation of a sense of agecy wiih brain stimulation
A group of the principal investigator (Hiromitsu, Asai andImamizu) continued experiments on manipulating the sense ofagency using high-definition transcranial alternating current
stimulation (HD-tACS). Participants moved a cursor with ajoystick to follow a target trajectory. The cursor movement was
mixed with the other's pre-recorded movement at a specificratio (10, 50 or 90%). After the movement, participants rated
how much the cursor movement resembled their own
movement (agency rating). They hypothesized that the neuralconnection between the right inferior parietal lobe (rIPL) andthe right inferior frontal gyrus (rIFG) contributes to agency
judgments. Based on this hypothesis, they conducted
experiments in which the connection between the two regions
was modulated by electronic stimulation. They found that antiphase stimulation of the regions altered the correlation between
cursor-joystick distance ( ≒ prediction error) and agency
judgments compared to in-phase stimulation and no stimulation(Fig. 1A). This year, they measured fMRI brain activity while
participants performed the same task. This replicated last year's
behavioral results (Fig. 1B). They found that activity decreased
in rIPL and increased in rIFG compared to the in-phase and nostimulation conditions (Fig. 1C). In addition, they confirmedthat anti-phase stimulation specifically increased activity in the

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 to

Fig. 1 Manipulation of prediction error - agency rating correlation by brain
stimulation. (A) Stimulation details. (B) Changes in prediction error agency rating correlation. (C) Changes in BOLD signal by stimulation
method.

two regions during the task-free resting state. Furthermore,
separate stimulation of the rIPL or rIFG did not cause asignificant change in the correlation, suggesting thatsimultaneous stimulation is critical for the change.
B. Formation of sense of agency in novel learning stiutation
A comparator model, a representative model of agency,
assumes that a difference between action outcome prediction
and sensory feedback (prediction error) is critical for agency.
However, it is difficult to predict action outcome in a novel
learning situation, which is what the hyper-adaptation project
assumes. How is the sense of agency formed in the novel
situation? A group of the principal investigator (Takumi Tanaka
and Imamizu) had participants learn to control a cursor in anovel rule in which the cursor position was determined by the
1st and 2nd components derived from principal component
analysis of joint sensor values of a data glove. They examinedchanges in the sense of agency with the novel learning task andfound that 1) synchrony between the action and its outcome isimportant for the sense of agency at the early stage of learning,and 2) the sense of agency is determined by the prediction error
as learning progresses and the outcome becomes predictable.
C. Towards the identification of the neural cirtuit involved in
the regulation of emotion and motivation
The group lead by co-investigator Tsutsui have demonstrated
that suppression of neuronal activity in the ventral medial
frontal cortex (vMFC) in monkeys induced depression-like
symptoms. To investigate the details of the projections from thevMFC to amygdala, they injected a retrograde viral tracer(AAV-retro) into the amygdala. As a result, it was confirmedthat deep cells in the subgenual anterior cingulate cortex(sgACC) send strong projections to the basolateral nucleus(BLA) and basomedial nucleus (BMA) of the amygdala. And
furthermore, it was found for the first time, that the superficial
layer of the pregenual anterior cingulate cortex (pgACC) sends
projections to BLA. This unique circuit, a cortico-basal ganglia
projection starting in shallow layers, is expected to play a
special role in regulating emotion and generating motivation.
D. Sense of agency in monkeys
The group lead by co-investigator Tsutsui conducted thefollowing research in collaboration with Dr. Kasahara's groupat Sony CSL. They have developed a virtual reality (VR)system that makes it possible to add temporal or spatial
displacement to the movement of the hand (visual feedback)projected on the display while making the monkey perform
reaching movements. They conducted a psychophysicalexamination to test monkeys' sense of agency by having thempick up small pieces of potatoes regularly placed on a board
using a device. First, when a bee was used as a startle stimulus
and the bee was superimposed on the displayed hand, thesubject immediately withdrew the hand and followed the
movement of the bee by the eyes. The heart rate showed asignificant increase with the appearance of bees. When themonkey's hand was not visible on the screen, the bee's
appearance did not increase the heart rate. This suggests that the
subject recognizes the hand projected on the screen as its own
hand. Next, when a time delay was added to the hand
movements displayed on the screen, we found that there was no
significant effect on the hand movements with a short delaytime, but the movement started to slow down when the delay
exceeded a certain period of time. This result can be considered
to reflect the status of the sense of agency that it is maintained
when the delay applied to visual feedback related to movement
is shorter than a certain time, but the sense of agency is lostwhen the delay exceeds the threshold. was taken. The above
psychophysical experiments suggest that monkeys may have asense of agency similar to humans.
E. Publications of research activityies
A group of the principal investigator published their study on
the sense of agency over speech in Psychological Science andissued a press-release [1]. They published their study on theneural basis for flexible switching of driving skills [2]. The
group lead by co-investigator Tsutsui published their study
inducing depression in monkeys by inhibiting the neuralactivity of the ventral medial frontal cortex by low-frequency
rTMS [3].
IV. FUTURE PERSPECTIVE
We made progress in developing a method to facilitate motor
learning by manipulating the sense of agency by finding
changes in brain activity due to stimulation, regional specificity
in stimulation-evoked activity, and the importance of
simultaneous stimulation of the two regions. We obtained an
important clue to understanding the role of a sense of agency inhyper-adaptation by finding a process of agency formation
during a novel learning situation. Furthermore, we could
establish the basis of research for studying sense of agency inmonkeys. Concerning motivation, we identified a new
projection circuit which is expected to play an important role in
the regulation of emotion and motivation.
REFERENCES
[1]
[2]
[3]

Ohata, R., Asai, T., Imaizumi, S., Imamizu, H. (2022) My voice, therefore
I spoke: The sense of agency over speech is enhanced by hearing selfvoice, Psychological Science. 33(8), pp. 1226–1239.
Ohata, R., Ogawa, K., & Imamizu, H. (2022). Neuroimaging Examinationof Driving Mode Switching Corresponding to Changes in the Driving
Environment. Frontiers in Human Neuroscience, 16, e788729.
Nakamura S, Kishimoto Y, Sekino M, Nakamura M, Tsutsui KI. (2022)
Depression induced by low-frequency repetitive transcranial magnetic
stimulation to ventral medial frontal cortex in monkeys. Exp Neurol.
357:114168

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 fourth 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 are associatedwith 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. For this purpose, we performed
animal experiments and human clinical studies in this 4th (FY2002) year. Animal experimentation with cats and rats has been
made to explore the brainstem-spinal cord posture-gait control
mechanisms by the interaction between midbrain DA-AChsystems. In human clinical studies, we developed measurement
procedures of brain network dynamics related to DA decrease
and Aβ accumulation. Further, we started to provide humandata for constructing mathematical models.

and dynamics of brain activity-connectivity in association withsenescence. To this end, the Hanakawa lab will takeadvantages of the PADNI cohort [3] which is a longitudinal
study involving healthy elderly people as well as patients with
PD and AD. They also have developed an advanced method toanalyze relationship between cognitive functions and dynamic
profiles of functional connectivity among distinct neuralnetworks. The Hanakawa lab also started basic research onneuromelanin MRI to assess DA production in the substantia
nigra (SN) in addition to DA transporter (DAT) SPECT for
evaluating the activity of the DA system.
III. RESEARCH TOPICS
The following points are the major achievements.
A. Posture-gait control by the midbrain DA system acting on
the brainstem (Takakusaki; Asahikawa)
Figures 1A and 2A schematically illustrate the working
hypotheses for the contribution of DA system to posture andlocomotor, respectively. The SNc-DA neurons and the PPNACh neurons have a mutual interaction in the midbrain. The
acute decerebrate cat, where the forebrain was disconnectedfrom the midbrain, exhibited tonic contractions of the hindlimb
extensor (soleus) muscles due to decerebrate rigidity so that it
maintained an upright standing posture (Fig.1B, left). However,microinjection of DA into the unilateral PPN abolishedbilateral soleus muscle activity leading to muscular atonia(Fig.1B, right). On the other hand, to block the inhibitory effectfrom the substantia nigra par reticulata (SNr) to the PPN,
muscimol (GABA-A receptor agonist) was microinjected in theSNr, resulting in an increase in the activity of the PPN-AChneuron which was accompanied by muscle tone suppression
and rapid eye movements (REMs) (Fig. 1C).

II. AIM OF THE GROUP
Takakusaki’s group has been in charge of the animal
experiment to evaluate the functions of ACh and DA systems
in the cognitive-motor linkage. Using decerebrate cats as
experimental animals, DA-ACh interactions between thesubstantia nigra pars compacta (SNc), and the pedunculopontine nucleus (PPN) in the midbrain have been tested on how
it modulated posture-gait control mechanisms in the brainstemspinal cord.
Hanakawa’s group has been conducting clinical studies.
They aim at discovering relationship among brain functions

Figure 1; DA and GABA effects on PPN postural control system

Glutaminergic (Glu) neurons in the cuneiform nucleus anda part of the PPN constitute the mesencephalic locomotor
region (MLR). A few minutes after DA microinjection into thisarea, the decerebrate cat exhibited spontaneous locomotion.
The locomotor pattern was changed according to the treadmill's
speed from walking to galloping (Fig.2B). These results
suggest that the DA pathway projecting to the PPN area,
including the MLR, acts on ACh and Glu neurons to regulate
postural muscle tone and locomotion, respectively.

reported impaired BCI performance in patients with PD.
Patients with PD often show characteristic gait disturbancecalled freezing of gait (FOG). FOG is an episode during whicha patient cannot walk as if the feet are glued to the floor
especially on the initiation of walking or turning. It is known
that the phenomenon of FOG is strongly influenced by sensory
stimuli in the environment, emotion, and attention. Using
resting-state fMRI, Hanakawa’s group studied network
correlates of FOG [5]. They found that FOG scores werecorrelated with connectivity of subcortical motor network,emotional network involving the amygdala, and fronto-parietal
network underlying cognition and attention. The group wasable to estimate the degree of FOG or presence of FOG using
a set of those connectivity. Hanakawa’s group also published acollaborative study with Dr. Abe at National Center of
Neurology and Psychiatry [6].
IV. FUTURE PERSPECTIVE

Figure 2; DA effects on MLR locomotor control system

B. Non-invasice multimodal measurement of dynamicchanges of brain activity and connectivity and DA imaging
in humans (Hanakawa, Kyoto)
Hanakawa and colleagues use non-invasive measurements
brain dynamics using fMRI and EEG to elucidate dynamic
adaptation mechanisms of human brain to physiological andpathological DA reduction. This year, as development of basic
methodology, they developed simultaneous EEG-fMRI
technology, which allowed them to investigate dynamicchanges of the basal ganglia-cortical circuits in a mu-rhythm
based brain-computer interface (BCI) task [4]. Increased
activity in the putamen was observed during successful than
unsuccessful BCI performance. Moreover, during successful
than unsuccessful BCI performance, the striatum more
effectively communicated with the globus pallidum, thalamus,
and motor cortices. This study indicated the basal ganglia,
which is under the strong influence of DA, play a pivotal rolein modulating mu-rhythm via the basal ganglia-corticalcircuits, thereby influencing BCI performance. We previously

In the next fiscal year, the Takakusaki group will continue
animal experiments to ensure the results of this year's research,in addition to examination of the mechanism of posture andwalking by the ACh and DA systems projecting to theforebrain. Specifically, we will use molecular geneticallyencoding techniques involving the ACh and DA systems toverify whether the cortico-reticular pathway, which connects
the cerebral cortex and the brainstem, plays a crucial role inthe cognitive aspect of posture-gait control. Hanakawa groupwill continue to elucidate dynamic changes of brain circuits inassociation with dopamine reduced states in humans. They
will also advance technology to examine the state of dopamine
in human brains.
REFERENCES
[1]
[2]
[3]

[4]
[5]

[6]

Figure 3; Network correlates of freezing of gait in Parkinson’s disease

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 and Hanakawa T: It is time to study overlapping molecularand circuit pathophysiologies in Alzheimer's disease and Lewy bodydisease spectrums. Front Systems Neurosci 15:777706, 2021.
2021.11.18 doi.org/10.3389/fnsys.2021.777706
Kasahara K, DaSalla CS, Honda M, Hanakawa T: Basal ganglia-corticalconnectivity underlies self-regulation of brain oscillations in humans.
Commun Biol 5(1):712, 2022. doi.org/10.1038/s42003-022-03665-6
Togo H, Nakamura T, Wakasugi N, Takahashi Y, Hanakawa T:
Interactions across emotional, cognitive, and motor networks underlying
thefreezingofgait.
Neuroimage:
Clin.
2023.
doi.org/10.1016/j.nicl.2023.103342
Takasawa E, Abe M, Chikuda H, Hanakawa T: A computational model
based on corticospinal functional MRI revealed asymmetricallyorganized motor corticospinal networks in humans. Commun Biol5(1):664, 2022 doi.org/10.1038/s42003-022-03615-2

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 four 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,
Identification of a brain activity that maintains positive mood in
rats; C, Establishment of a decoding and quantification system of
seizure susceptibility from large-scale electrophysiological brain
activity recordings during interictal states of epileptic rats; D,
Preparation of a recording and real-time intervention system for
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]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, identify
brain activity related to depression-like symptoms, decipher
brain activity patterns that explain seizure susceptibility inepilepsy, and implement a recording and real-time
intervention system for a mouse model of Alzheimer's disease[5].
III. RESEARCH TOPICS
A. Control of pathological fear memory of rats by timetargeted stimulation of the reward system in the brain
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,

Fig. 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. Identification of a brain activity that maintains positivemood in rats
Olfactory bulbectomized rats have been used as a model ofdepression because they show depression-like symptoms suchas 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 andpisiform cortices was related to the maintenance of positivemood as measured by the performance on a sucrose preference
test. [7]from large-scale electrophysiological brain activity recordings
during the interictal period using Cross-Spectral Factor
Analysis (CSFA) [8], a machine learning algorithm focusing
on inter-reginal oscillatory brain activities (Fig. 4).

Fig. 4. Quantification of seizure susceptibility by large-scale brain activity
recordings and a machine learning technology. A coefficient of the first crossspectral factor (a multi-regional brain activity pattern) decreased during thedaily electrical stimulation of the hippocampal commissure, which inversely
correlates with seizure susceptibility.

IV. FUTURE PERSPECTIVE
We have achieved a control of pathological fear memory witha time targeted stimulation of the brain reward system during
this fiscal year. Similar strategy will be employed to control
epilepsy, Alzheimer’s disease, and depression in the next
fiscal year.
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.

C. Establishment of a quantification system of seizure
susceptibility from large-scale brain activity recordings
If seizure susceptibility of epilepsy can be decoded in realtime from ongoing brain activities, it will be possible toselectively reinforce the brain states with lower seizure
susceptibility. Toward this goal, we here developed atechnology of decoding and quantifying seizure susceptibility

[5]
[6]
[7]
[8]

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 to reduce pathologic fear,” bioRxiv, 2022.07.24.501314, July2022.
Q. Li†, Y. Takeuchi†, J. Wang, et al., “Reinstating olfactory bulb
derived limbic gamma oscillations alleviates depression,” bioRxiv,
2022.02.01.478683, February 2022.
Y. Takeuchi, “Development of on-demand interventions of epileptic
seizures and a quantitative method of seizure susceptibility,” Medical
Science Digest, vol. 46, pp. 118-121, February 2020.

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．In addition, to understand the relationshipbetween amount of limb use and body consciousness in thephysically disabled, we measured body-specific attention to
prosthetic foot in lower limb amputees and found a use-dependent
increase in body-specific attention during the process of gait
acquisition. Furthermore, we examined the effect of a sense ofbody ownership, one of body consciousness, on the motor imitation
training in stroke patients, and found that the increase in sense ofbody ownership of the paretic hand facilitated the effect ofimitation training. We believe that these results contribute to a
multifaceted understanding of the relationship between body
consciousness and motor function in physically disabled persons,using the findings of stroke and amputation patients as a clue. We
have also made steady progress 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]. However, it is not clear the
relationship between the real-world arm use and brain function
and structure. In addition, the relationship between limb use andbody-specific attention in the physically disabled and the effects
of enhance of a sense of body ownership, one of body
consciousness, on upper limb motor function have not been fully
investigated.
II. AIM OF THE GROUP
1) 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. We longitudinally investigate plastic changes
in function and structure of the brain in stroke patients using
fMRI and DTI. In addition, we measure real-world arm use andUL function by using accelerometers and Fugl-Meyer
Assessment (FMA), respectively, to elucidate the relationshipamong them. 2) In addition, the aim of this study is to clarify
the relationship between amount of limb use and body
consciousness in the physically disabled, we measured bodyspecific attention to prosthetic foot in lower limb amputees andinvestigate the changes in body-specific attention during theprocess of gait acquisition. Furthermore, we elucidate the effectof enhance of body ownership, on the effect of motor imitation
training in stroke patients, and investigate that the increase in
sense of body ownership of the paretic hand facilitated theeffect 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 , 2 weeks, 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. Use-dependent increase in attention to the prosthetic footin patients with lower limb amputation
Although body-specific attention to hands and feet has beenstudied in healthy subjects[3], it is unclear how body-specificattention to the prosthetic foot changes during the process of gait
acquisition in lower-limb amputees. It is possible that amputees
who successfully use their prostheses pay more attention to theirprostheses. We aimed to longitudinally measure body-specificattention to the prosthetic foot in amputees beginning to practice
walking rehabilitation with a prosthesis and to examine its
relationship to walking ability. The study subjects were 11
lower-limb amputees. Body-specific attention was measured
using a visual detection task. In the initial phase of walking
rehabilitation, the index of attention to the prosthetic foot was
lower than that to the healthy leg. However, at the final stage,
there was no significant difference between the two attention
indices. Correlation analysis revealed that the longer theprosthetic foot was used, the higher the body-specific attention
to the prosthetic foot. These findings suggest that once a person
gains the ability to walk with a prosthetic foot, attention is
directed to the prosthesis in the same way as to the healthy lower
limb. The participants also commented that once they were able
to walk independently, the prosthetic foot felt as if it were partof their body. These findings suggest that the use of a prosthetic
foot causes the integration of visual information about theprosthesis and movement, resulting in subjective embodiment.
[4]
C. 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. In addition, to better understand the relationshipbetween amount of limb use and body consciousness in thephysically disabled, we measured body-specific attention to
prosthetic foot in patients with lower limb amputation andfound an increase in use-dependent body-specific attention
during the gait acquisition process. Furthermore, we examinedthe effect of sense of body ownership on the motor imitation
training, and found that an increase in body ownership in theparetic hand facilitated the effectiveness of imitation
movements. We believe that these findings will contribute to
the construction of rehabilitation strategies to enhance bodyawareness and increase motor function and arm use after stroke.
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.-I. Izumi, “Time-dependent decline ofbody-specific attention to the paretic limb in chronic stroke
patients,” Neurology, vol. 91, pp. e751-758, 2018

[2]

R. Otaki et al., “Relationship Between Body-specific attention to a
Paretic Limb and Real-World Arm Use in Stroke Patients: A
Longitudinal Study,” Front. Syst. Neurosci., vol. 15, 2022.

[3]

N. Aizu et al., “Body-Specific Attention to the Hands and Feet in
Healthy Adults,” Front. Syst. Neurosci., vol. 15, Jan. 2022

[4]

N. Aizu, Y. Oouchida, K. Yamada, K. Nishii, and I. Shin-Ichi,“Use-dependent increase in attention to the prosthetic foot inpatients with lower limb amputation,” Sci. Rep., vol. 12, no. 1, p.
12624, Dec. 2022

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.
Furthermore, they perceive fluorescent lights as flickering andpainful. It is thought that the brain is unable to regulate andadapt its neural activity to allow for comfortable perception. In
both instances, they have problems adapting to the external
environment. Over-adaptation to the external environment is aproblem, but so is failing to adapt. Moderate adaptation to theexternal environment is thought to be important for improving
quality of life. Filtering in the thalamus [1] is thought to play
an important 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. Furthermore, the neurons fired morestrongly in correct-choice than in incorrect-choice trials at low
contrast. They efficiently represented low-contrast orientations.
Even the primary visual cortex, which is thought to represent
the external world faithfully, can represent low-contrast
information well enough as a population by creating a new
function that fires strongly with weak input. Adaptation tochanges in the contrast of external visual input is now evident[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 adaptive mechanism is modified by different levels ofadaptation, stress load, and developmental characteristics in
autism. We seek 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 presented rats with various visual stimuli andobserved their visual adaptation to the stimuli. First, using thetouchscreen operant platform, freely moving rats learned avisual discrimination task to distinguish between vertical andhorizontal gratings. Then, the visual stimuli were randomly
rotated while maintaining the 90-degree difference in tilt
between the two gratings. The rats selected the discrimination
behavior depending on whether the presented stimulation wascloser to the vertical or horizontal grating; at a rotation of 45
degrees, the orientation was exactly between the vertical andhorizontal gratings, so the behavior selection was at chancelevel. However, up to approximately a 30-degree rotation, they
could select the vertical behavior if it was closer to the verticalgrating. In contrast, when the two gratings were rotated
gradually and continuously, keeping the 90-degree differencein tilt between the two gratings, the correct answer could still
be obtained in many sessions, even after a 90-degree rotation
or a reversal, in as short a period as one session. When rotatedrandomly, rats, of course, chose the behavior before such alarge rotation and could not select the correct behavior.
However, when the visual stimulation was rotatedcontinuously, rats were able to select the behavior after such alarge rotation. Next, in the experiment in which headrestrained rats perform visual discrimination, the difficultylevel was much higher for the reasons described in the next
chapter, and the rats could not adapt to the large rotation angle
as in the touchscreen experiment. However, a similar trend
was observed in the head-fixed condition. Rats were able toadapt to larger rotation angles to some extent in a shorter
period (one session) when visual stimulation was rotated
gradually and continuously than when the stimulation was
rotated randomly. At this time, when neuronal activities wererecorded from the primary visual cortex, some neurons wereobserved that maintained relatively stable visual responses tovertical and horizontal gratings even after continuous rotationand large rotation angles. Whether this is the neural basis ofperception adapted to the continuous rotation of visual
stimulation needs further verification.
We are currently conducting experiments to determine
whether this perceptual adaptation to the rotation of visual
stimuli is dependent on the degree of stress load, i.e., thedifficulty of the task. In addition, we are preparing to performlarge-scale multi-unit recording using the Neuropixels probe
to simultaneously record the firing activity of multiple neurons
across multiple brain regions and to perform widefield
calcium imaging of multiple brain regions by expressing thefluorescent calcium probe GCaMP specifically at therecording time with the Tet-On system.

Fig. 1. Schematic diagram of optimal adaptation to the external environment.

IV. FUTURE PERSPECTIVE
This year, we started our research and have not yet
produced many results; however, we have introduced a newexperimental system in which freely moving rats perform avisual discrimination task using a touchscreen operant
experimental apparatus. However, to accurately determine theparameters of the visual stimuli to be presented, it is effective
to conduct experiments in a head-fixed condition where thedistance between the eyes and the monitor can be kept
constant. Head fixation also facilitates recording of neuralactivity. However, if we want to present visual stimuli to thereceptive field at the recording location, only one visual
stimulation can be presented, and two gratings cannot becompared simultaneously. In addition, different symmetricalmovements must be made in response to each stimulation. For
these reasons, the difficulty of conducting experiments in thehead-fixed condition is greatly increased. To observe theadaptation to rotation of visual stimuli in a convenient manner,experiments using the touchscreen operant platform would be
effective. In the next year, we will use this experimental
system to examine how the adaptation alters to the changes invisual stimulation in rats subjected to stress or in autism
models created by the administration of valproic acid topregnant animals. Furthermore, by large-scale multi-unit
recording and widefield calcium imaging, which will be set up
this year, we aim to elucidate the neural basis of moderate
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 visual evoked 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, “Neocortical excitation/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': aBayesian 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 LongRange 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.
Wheeler-Kingshott, P. A. Boulby, G. J. Barker, E. L. Sillery, K. Sheehan,
O. Ciccarelli, A. J. Thompson, J. M. Brady, and P. M. Matthews, “Noninvasive 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 contrastpreferring neurons to information representation in the primary visualcortex after learning,” Sci Adv, vol. 7, no. 48, pp. eabj9976, Nov 26,
2021.

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. The neural mechanismof implementing multiple strategies is still unclear at least in asingle neuron level because a precise neural recording andmanipulations often require experiments with animals. Here we
perform a neural recording in head-fixed mice during a strategyswitching task to investigate the neural substrate of model-free
and inference-based strategy. We propose an AI (artificial
intelligence) -based approach to investigate the relationshipbetween the algorithms of brain and AI. Our study potentially
proposes an idea that the brain learns multiple strategies withlearning rules and switches the strategy with non-linear neuronal
circuits.

I. INTRODUCTION
Recent studies propose that the brain has multiple behavior
strategies in parallel and selects the strategy in a contextdependent manner [Ref.1]. In a field of engineering, behavioral
strategy is divided into model-free strategy, which decides
choices based on direct experiences of choice and reward(habitual behavior), and inference-based strategies, which
estimates a hidden context based on observed sensory inputs tomake flexible choices.
Because of the simplicity of behavioral tasks and therelated models, previous studies often investigate the neuralmechanism of model-free strategy [Ref.2]. In contrast, theinference-based strategy is mainly tested with humanexperiments [Ref.3]. The human experiments use sophisticatedbehavioral tasks to separate the model-free and inference-based
strategies [Ref.1,4], although human studies are sometimes
difficult to investigate the detail neuronal circuits because of
the methodological constrains.
Recent studies used rodents and started to investigate theneural mechanism of inference-based or model-based strategies[Ref.5-7]. These studies used optogenetics or pharmacologicaltools to inactivate a specific brain region and found that the
hippocampus or medial frontal cortices are necessity for theinference-based strategy. However, the neural representationsof inference-based strategy and of strategy switching are still
unclear.
This study performs a behavioral task in head-fixed mice todissociate the model-free and inference-based strategies.
During a strategy switching task, we record the neural activityof mice to investigate the neural correlates of the two strategies.
Because this study is still ongoing, we need to inform that themanuscript does not include the specific aims and the target
brain regions.

II. AIM OF THE GROUP
We propose a unified learning theory for model-free andinference-based strategies, and investigate the neural
implementation of the theory by recording the neural activity ofmice. We construct an artificial neural network with the
behavioral and neural data to model the strategy switching in
the brain.
III. RESEARCH TOPICS
We summarize the three achievements in this year as
follows:
A. Strategy-switching task in head-fixed mouse
We developed a strategy switching behavioral task todissociate the model-free and inference-based strategies. Mice
were head-fixed and placed on a spherical treadmill. We
presented a sound stimulus with various frequencies. When thesound frequency was low (5-10 kHz, state L), mice required tolick a left spout to get a reward of sucrose water. A highfrequency sound (20-40 kHz, state H) provided the reward bylicking the right spout. The state (L or H) of each trial was
determined with a transition probability (P) and the tone
frequency of previous trial [Ref.8]: the state was flipped in every
trial with the probability of P. Reinforcement leaning theoryshows that the probability (P) can dissociate the model-free andinference-based strategies:- Repeat condition (P = 0.2): Maximize the reward by repeating
the rewarded choice in a previous trial: model-free strategy.
- Zigzag condition (P = 0.9): Estimate the hidden state (L or H)
from the observed sensory inputs and outcomes. Maximize thereward by mainly making an opposite choice from a previous
trial. For example, when the previous state is L, the probabilityof receiving a reward at right choice is 80 % in the next trial(state H): inference-based strategy.
We continue to perform the strategy-switching task since
our different research project. In this year, we performed therepeat and switch conditions in separate groups of mice. To
maximize the reward amounts in both the conditions, mice not
only required to making choices based on the tone frequencies
but biasing the choices depending on the transition probability.
We analyzed the mice choices with a psychometric function
and found that mice biased the choices based on the transition
probability. The acquisition of behavioral biases was faster inthe repeat than in zigzag conditions (Fig. 1). This result

IV. CONCLUSION
We summarize the achievements in this year and briefly
explain the plan for the next year.

Fig. 1. Faster learning of behavioral biases in repeat
than in zigzag conditions
Left: Learning curve of behavioral biases. Means andstandard errors (* p < 0.01 in Wilcoxon signed rank test).
Right: Number of sessions to achieve the behavior biases.
Parentheses show the number of mice. The acquisition of
condition-dependent behavioral biases was faster in therepeat than in zigzag conditions (Mann-Whitney U-test).
suggests that the behavioral strategy in each condition was
different.
B. Behavioral modeling
We analyzed the choice behavior of mice with a model-freereinforcement learning (RL) model and a state-transition model.
This analysis used the data between the first and seven sessions(days) from introducing the transition probability in bothconditions (P = 0.2 or 0.9). The model-free RL updates the
expected reward for each choice (action value) based on the
direct experiences of choices and outcomes. In contrast, thestate-transition model estimates the transition probability (P)from the experiences. Based on the estimated P, the model
decided choices.
In the repeat condition, the mice choices matched to themodel-free RL than the transition model on average. In contrast,
the choices in zigzag condition were fit to the transition model.
These results suggest that different behavioral strategies wereused in the repeat and zigzag conditions.
In addition, we modeled the mice choices with an artificial
neural network, i.e., recurrent neural network (RNN) withLong Short Term Memory (LSTM). We trained the RNN withsimulated behavioral data and updated the connections with an
Advantage Actor-Critic (A2C). The RNN succeeded inmodeling the behavioral biases in both the repeat and zigzag
conditions. In this year, we are investigating whether the RNN
can simulate the faster learning in the repeat than in zigzag
conditions.
C. Setup for neural recording
We are introducing a fiber bundle imaging for neuralrecording. This method uses GCaMP [Ref.9] or G-proteincoupled receptor-activation-based (GRAB) sensors [Ref.9] toimage the temporal changes of neural activity orneuromodulators. We plan to simultaneously images theactivity of 4 to 6 brain regions. So far, we finished setting up
the rig and prepared the AAV virus for neural imaging. We are
currently setting up the surgery for recording.

In this year, we performed the strategy-switching task toseparate the model-free and inference-based strategy in headfixed mice. We analyzed the choice behavior in the repeat andzigzag conditions and found that (i) the learning of behavioral
biases was faster in repeat than in zigzag conditions and (ii) the
choice behavior in repeat and zigzag conditions were fit to amodel-free RL model and state-transition model, respectively.
These results suggest that the behavioral strategy was different
in each task condition. In addition, an artificial neural network
succeeded in modeling the behavioral biases of mice in bothtask conditions. Next, we are going to investigate whether an
artificial neural network models the different learning speeds ofmice in the repeat and zigzag conditions.
Currently, we are setting up a fiber bundle imaging torecord the neural activity during the task. With this method,
we are going to simultaneously image the activity of multiple
brain regions for understanding a unified learning theory ofmodel-free and inference-based strategies.
REFERENCES[1]

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.
[2] W. Schultz, P. Dayan, and P.R. Montague, “A neural substrate ofprediction and reward,” Science, vol. 275(5306), pp. 1593-9, Mar 1997.
[3] 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.
[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] 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.
[10] 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. Moreover, sequential song learning from two tutors ofdifferent species yielded distinct neuronal ensembles for twosong memories in the NCM, both of which retained
connectivity to HVC into adulthood. Here in this study, we try
to identify the timeline of axonal retraction which aresuggested from the transient NCM-HVC projections. We also
will try to investigate the underlying neuronal mechanism for
persistence of neuronal connection with enriched experiencesin juveniles. 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.

II. RESEARCH TOPICS
A. Identifying the timeline of axonal retraction
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

more exactly when axon retraction takes place especiallyregarding song learning. Zebra finches injected with the AAVcFOS-TetOn-EYFP-PEST at different ages were exposed to TS
playback concurrent with DOX in drinking water to induce
EYFP expression only in TS-responsive neurons. We foundTS-responsive NCM neurons projected their axons densely into
the HVC still in juveniles at 70 DPH, in contrast, in slightly
older juveniles (80 DPH) axonal projection from TSresponsive NCM neurons were sparce (Fig. 1). We found thesong of juveniles became stereotyped (less variable in thesequence in repetition) between 70-80 DPH, suggestingcorrelation of NCM-HVC neuronal connection with songmaturation. We will apply this timeline in the experiments

Fig. 1: Axonal projections in the song motor region, HVC from TSresponsive NCM neurons. Abundant axonal projections wereobserved in the HVC in the juveniles which were injected with AAVcFOS-TetOn-EYFP-PEST and provided TS playback with DO waterat 70 DPH, while fewer axons observed in juveniles which were
exposed to TS playback at slightly older age, 80 DPH.

described in the following sections.
B. Establishing in vivo imaging system for axonal retraction
We have been suggesting axonal retraction from the TSresponsive NCM neurons from the HVC by comparing
confocal images from the juveniles at 60 DPH and 90 DPH. To
confirm the phenomena of “axonal retraction” we have tried toestablish long-term time lapse in vivo imaging system in zebra
finch brain. Adult zebra finches were injected with AAV for
expressing GFP in HVC neurons. A cranial window waschronically established by placing a cover glass and sealed tothe scull on top of HVC. We then periodically examined HVC
neurons which expressed GFP under dissection microscope(Fig. 2). We successfully imaged HVC neurons, including theiraxons, stably for more than two weeks in vivo. Our experiment,
shown in the previous section A, suggest axonal retraction fromNCM TS-responsive neurons happening between 70 and 80
DPH. We will express GFP in the TS-responsive NCM
neurons by using AAV as described in the previous section,and track their axonal projection in the HVC those period
under confocal microscope in coming years. We will

simultaneously monitor song maturation to see the correlation
between song stabilization and axonal retraction.

tutoring with two tutors without isolation in between, 3) single
tutored with heterospecific (Bengalese finch) foster parents.
Persistent neuronal connection with enriched experiences
during juvenile period suggests increased song neuronal circuit
capacity. We further will investigate the possibility ofrecovering song re-learning in adults, implicating possibilities
of enhanced recovering ability during rehabilitation after injury.

III. FUTURE PERSPECTIVE

Fig. 2: In vivo imaging of HVC neurons expressing GFP with AAV
injections using dissection microscope. HVC neurons and theiraxons were periodically examined through cranial window.

C. Establishing in vivo imaging system for axonal retraction
Our recent study revealed retained axonal projection in
HVC from TS-responsive NCM neurons in adults which hadenriched song learning experiences. Those birds were fostered
to a Bengalese finch pair starting just after hatching (0-2 DPH)
for 30 days. Then, they were isolated for another 30 days after
which they were exposed to a conspecific zebra finch tutor for
30 days. Under this paradigm, juveniles memorized and copied
two TS sequentially, transiently learning from a Bengalesefoster father and quickly re-learning from the zebra finch
second tutor. So, it has yet to be clarified which experience,
exposing to tutors and learned songs of other bird species, orlearned songs in the extended developmental learning periodwith social isolation, caused persistence of axonal projection
into adulthood. To examine which experience is critical for
retention of neuronal projections, we have started raising zebra
finch juveniles with varies rearing conditions; 1) sequential
tutoring with two conspecific (zebra finch) tutors, 2) sequential

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.
Enriched experiences might enhance neuronal circuit capacity.
Our future experiments in coming years would tell us theexact timeline of axonal projection and retraction subserving
for vocal learning, as well as the underlying neuronalmechanism for enhanced neuronal circuits with experiences.
Novel approach for supporting rehabilitation for recoveringfrom injury are expect to be suggested.
REFERENCES
[1]

[2]
[3]
[4]

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. We record
electrocorticogram (ECoG) in epilepsy patients who
undergo intracranial electrode implantation in the frontal
and parietal lobes for preoperative evaluation of epilepsy
surgery. We probe neural signatures of higher-ordermotor control by recording wide-band ECoG activities
during higher-order motor tasks. To understand the brain
network associated with motor control and hyperadaptive reorganization, we made an electrophysiologicalconnectome using cortico-cortical evoked potentials(CCEPs) as an index of effective connectivity obtained bysystemic evaluation of the whole implanted electrodes. We
investigated the associations of performance impairment
by electrical cortical stimulation and CCEP spatial
distribution during the Go-NoGo task. We also elucidated
that the CCEP late-latency potentials (N2 responses) werecorrelated with the functional connectivity in the Human
Connectome Project database. We proceed with EEG or
ECoG network analysis using a time-varying graphicallasso.
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 partial 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. In Year 2022, because of the ongoing
COVID-19 pandemic that prevented us from recruiting
many patients, we focused on making theelectrophysiological connectome by means of corticocortical evoked potentials (CCEPs) since the understandingof the macroscopic human brain connectome is essential totangle the hyperadaptability of the premotor area andhuman brain in general. This fiscal year, we studied therelationship between the CCEP distribution and theperformance of Go/No-Go task that emerges during ECS,
and the relationship between the effective connectivity of
CCEP and the resting-state functional connectivity of the
Human Connectome Project (HCP) database. For intergroup collaboration, we collaborated with Group B andutilized the Time -Varying Graphical Lasso (TVGL) to
analyze the scalp and intracranial EEG recordings.
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 and 1062.
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
published papers on the CCEP connectivity differences in
the medial parietal lobe, and the dynamics of corticalinteractions in visual recognition of object category (living
vs. non-living) using ECoG high gamma activities. We
also studied the relationship between CCEP distribution
and performance on the Go/No-Go task during ECS, thatbetween effective connectivity (CCEP) and resting-statefunctional connectivity (fMRI) in the Human Connectome
Project (HCP) database. We also promote inter-groupcollaborative researches on the Time-Varying Graphical
Lasso (TVGL) for analysis of intracranial and scalp EEG
recordings with Group B.

III. RESEARCH TOPICS
We have carried out the following three research projects.
A. Relationship between CCEP Distribution and Task
Performance during High-Frequency Cortical
Electrical Stimulation (ECS) in Go-NoGo Task
ECS is the gold standard mapping technique for clinically
identifying functional brain localization, but the extent towhich brain networks are involved with this technique has
not been clarified.
In five patients with drug-resistant refractory epilepsy
who underwent invasive presurgical evaluation withintracranial electrodes, we performed a Go/No-Go task anddelineated areas with No-Go specific ERPs. ECS wasapplied to cortical areas with No-Go specific ERPs, and
No-Go performance was evaluated. We compared the
CCEP network from the stimulus site and the No-Go ERP
network. As the more CCEP responses were observed
within the No-Go ERP network, the poorer No-Go
performance was induced by ECS. This suggests that,
although ECS is a method for functional localization, ECS
is likely to impact on the electrically connected network.
Future studies are needed to elucidate whether the ECS
effect is weighted at the stimulus site or equally across the
network nodes [1].
B. Comparison of effective connectivity by CCEP andfunctional connectivity in the HCP database, andalteration of connectivity in the diseased brain

The CCEP distribution showed that the anterior part of the
precuneus has connections with the lateral premotor cortex,
the posterior part with the occipital lobe, and the posterior
cingulate gyrus firmly with the medial frontal lobe,
suggesting that these regions are involved in different ways
in motor and visual functions [2]. In addition, we clarified
the dynamics of cortical interactions in visual recognitionof object category (living vs. non-living) by means ofevent-related causality analysis on ECoG high gamma
activities. Specifically, we revealed that significantly
stronger neural propagation occurs among sites within theventral temporal lobe at early latencies, around 250 ms, forliving objects compared to non-living objects [3]. We also
compared effective connectivity by CCEP and functional
connectivity in the HCP database using resting state fMRI,
and found a strong correlation between N2 potentials in
CCEP and functional connectivity [4]. In terms of brain
plasticity, the relationship between epileptogenicity andbrain network alterations in the epileptic brain showed that
network alterations were observed in areas with severe
epileptogenicity but less so in areas with mild
epileptogenicity (Togo et al., to be presented at the 64th
Annual Meeting of the Japanese Society of Neurology).

C. Intracranial EEG network during supraventricular
EEG during sleep and reaching and grasping
movements using TVGL

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. We
will continue to brush up on the analysis method andincrease the number of cases to see if it is possible toidentify sleep stages and reaching and grasping movementsfrom the viewpoint of the network.
IV. FUTURE PERSPECTIVE

In the present investigations, because of the COVID-19
pandemic, we had to change our original plan to recruit
patients for the prospective study. Instead, by using thelarge retrospective CCEP data and some prospective data,
we focused on the human premotor area and constructed
electrophysiological or CCEP connectomes related to thepremotor area to elucidate the network characteristics atboth individual and group levels. We proceeded to analyze
the relationship between high-frequency electrical corticalstimulation and the distribution of CCEP, its correlation
with functional connectivity by fMRI, and its alteration by
epileptic pathology. In the next fiscal year, we plan to carryout our original proposal, namely, simulate thepostoperative CCEP connectome by using the CCEP
connectomic data to clarify the network-level HyperAdaptivity. We plan to compare the network changes to the
movement disability after surgery and its recovery, and try
to extract the “hyper-adaptive” indicators. We also plan tokeep collaborating with theory group B to seek forconstructing mathematical model of hyper-adaptation.
References
[1]
[2]

[3]

[4]

林 梢，武山 博文，十河 正弥ら. Go/No-Go 課題を用いた脳機能局在と CCEP の関連性について. 超適応夏の全体会議, 7/19-20,
2022
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)

A05-8 Annual report of the research project
Hiroyuki Miyawaki

The Osaka Metropolitan University

III. RESEARCH TOPICS
A. Identification of the neuronal ensembles emerged duringextinction learning.
By using multi-regional large-scale electro-physiologicalrecordings on fear-conditioned rats, we simultaneously
recorded the activity of neurons in the basolateral lateral
amygdala (BLA), ventral hippocampal CA1 region (vCA1),
and prefrontal cortex layer 5 (PL5). During these recordings,
we conducted fear-conditioning, extinction learning, and testfor retention of extinction were conducted [4]. To identify theneuronal ensembles, we applied independent component
analysis (ICA) on the z-scored spike count matrixes obtained
from these behavioral tasks. Ensemble detection was done ineach brain region separately. The similarities of the resulting
neuronal ensembles between extinction learning and the othertwo behavioral tasks were evaluated with the cosine similarityof the ICA basis vectors (Fig. 1). Considered ensembles were
Ensembles in
conditioning

Ensembles inretention of extinction

10

0.5

20

0.0

30
40

-0.5

**

**

**
**

**
**

10 20 30 40

**
** **
****

**

**

**

0.6

**

**
**

**

0.4
0.2

**

**

Simirarity**

WeightCell ID

I. INTRODUCTION
For animals, fear of situations associated with harmful
stimuli is an important ability for survival. However, an
excessive manifestation of fear can impede adaptive behavior,a condition referred to as post-traumatic stress disorder (PTSD)
in humans, which has a profound impact on daily life. Fearconditioning and its extinction learning have been intensively
studied as animal models of the development and treatment of
PTSD. Previous studies have demonstrated that fear extinction
is not a process of forgetting, but rather a hyper-adaptation
process in which adaptive behaviors are regained while fear
memories persist [1]. The amygdala, ventral hippocampus, andprefrontal cortex have been identified as crucial structures in
the regulation of fear conditioning and extinction learning [2].
However, the dynamic changes that occur in the networks
among these brain regions during the transition from maladaptation to hyper-adaptation states and the underlying
mechanisms of regulation remain to be elucidated.
Understanding these elements will provide insight into thepathophysiology of memory- and emotion-related disorders,
including PTSD and anxiety disorders.

adaptation from mal-adaptation states associated with fear andthe neuronal mechanisms that govern these changes. In thebrain, information is represented as the activity of "neuronalensembles," which are relatively small groups of neurons thatexhibit synchronous activation [3]. Previous research within
this project has revealed that the local neuronal ensembles
become activated synchronously across multiple brain regions
following fear memory acquisition [4]. On the other hand, it
has also been suggested that the neuronal ensembles detectedduring fear-conditioning exist even before fear-conditioned
learning [4]. In light of these findings, this year we aim todetermine the nature of the neuronal ensembles that are active
during extinction learning and when the activities of theextinction-related neuronal ensembles commence.

Ensembles inextinction

Abstract— Animals that have undergone traumatic
experiences develop maladaptive states in which they are unable
to take adaptive behavior due to fearful memories, and thefollowing fear-extinction learning induces hyper-adaptation thatenables the animals to regain their capacity for adaptive
behaviors. Multiple brain regions, including the amygdala,ventral hippocampus, and prefrontal cortex, are known to be
involved in fear-related mal-adaptation and hyper-adaptation,
yet the changes in local and inter-regional networks remain to be
fully understood. To shed light on this issue, we conducted multiregional large-scale electrophysiological recordings in fearconditioned rats and analyzed the dynamics of neuronalensemble activity across the sessions for fear-conditioning,
extinction learning, and testing retention of extinction. Ourfindings revealed that while a significant fraction of neuronalensembles remained active throughout these sessions, newneuronal ensembles emerged during the session for extinction
learning and persisted to ones for testing retention of extinction.
Notably, within the prelimbic cortex, ensembles that were active
during extinction learning and retention of extinction enhanced
activity during sleep prior to the extinction learning. Further
research will elucidate the dynamics of the local and global
networks that underlie fear-extinction.

0.0

Cell ID

II. AIM OF THE GROUP
This research project endeavors to shed light on changes inlocal and inter-regional brain networks that underlie the hyper-

Fig. 1. Examples of cosine similarity among PL5 neuronal ensembles
detected during fear conditioning, extinction learning, and test for retention of
extinction.

considered to be similar if their cosine similarity wassignificantly higher than the shuffle surrogates.

BLA

Based on the presence of similar ensembles during fear
conditioning and the retention of extinction test, we classified
the neuronal ensembles detected during extinction learning as"Maintained" (ensembles that had similar ensembles during
both fear conditioning and the retention of extinction test),"Terminated" (ensembles that had similar ensembles only
during fear conditioning), “Initiated” (ensembles that had
similar ensembles only during retention of extinction test), or“Transient” (ensembles that had no similar ensembles). Our
analysis revealed a significant difference in the fraction of eachclass in the PL5 compared to the BLA and vCA1 (p<0.01, chisquare test with Bonferroni correction; Fig. 2).
BLA
20

3

PL5
14

84

27

63

vCA1
20

7

3
9

14

TerminatedTransient
Maintained
Initiated

Ensenble activation rate (1/min)

10
0
PL5
20

** ** **

**

*

10
0
vCA1
20
10

TerminatedTransient
Maintained
Initiated

0

Befo

A
A
A
re co fter con fter exttin fter rete
ntion
nditio ditioning ction
test
ning

**

Fig. 3. Activation event rate of extinction-related neuronal ensembles during
non-REM sleep preceding and following behavioral task.

B. Dynamics of extinction-related neuronal ensembles during
sleep.
During sleep, especially during non-REM sleep, theneuronal ensemble activity observed during wakefulness isreactivated, which is believed to play a role in modifying brain
network dynamics [5]. Therefore, we examined whether theneuronal ensembles of each class were activated during nonREM sleep before and after the behavioral task (Fig. 3). We
found that the Maintained and Terminated ensembles in PL5,which were detected during both fear conditioning andextinction learning, increased activation rate following fear
conditioning (Fig. 3). The change in Maintained class was
statistically significant (p < 0.01, Steel-Dwass test).
Interestingly, a similarly significant change was also observedin the Initiated class, despite its absence during fear
conditioning (p < 0.01, Steel-Dwass test). These findings
suggest that the PL5 neuronal ensemble activity involved in
extinction learning and its retention is elevated during sleep
prior to the actual learning process.

This year, our research has focused on the dynamics ofneuronal ensembles related to extinction. Of particular interestis the finding from PL5 ensembles, which indicates that thepre-extinction activity of neuronal ensembles influences theiractivity during extinction learning. In the future, we plan to
examine the cross-regional interactions of these ensembles togain a deeper understanding of the neural activities thatfacilitate extinction learning. This work is expected tocontribute to the advancement of our knowledge on the neuralbasis of hyper-adaptation.

**

Fig. 2. Fraction of extinction-related neuronal ensemble classes identified in
the BLA, PL5, and vCA1.

IV. FUTURE PERSPECTIVE

REFERENCES
[1]
[2]
[3]
[4]
[5]

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.
Diekelmann S, and Born J. The memory function of sleep. Nat. Rev.
Neurosci. 2010;11(2): p114-26.

A05-9 Annual report of research project
Takaki Maeda, Oi Hiroki
Department of Neuropsychiatry, Keio University School of Medicine
Abstract

III. RESEARCH TOPICS
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.
Actually, we have developed digital applications for cognitive
rehabilitation of SoA in order to tune up precision of SoA. Our
main achievements in this fiscal year are as follows. 1) We havedeveloped a new SoA experiment task which was introduced
spatial biases compared to the former task using temporal biases.
2) We have released the new digital application for agency tuning
for artists and athletes: the Agency Tuner for artists in US and
Poland as well as Japan [1]. The application is created in order to
use rehabilitation for patients with occupational dystonia, socalled the yips. 3) In order to evaluate effects of cognitive
rehabilitations of SoA, we developed a new application formonitoring trends of mood swing [2].

I. INTRODUCTION
We have studied pathophysiology of neurological andpsychiatric illnesses from the stand of the SoA. Moreover, wehave tried to establish the method to recover from those illness
through reorganization of neural systems on the SoA. Actually,
we have developed several digital applications for cognitive
rehabilitation on the SoA and other mental state including
mood swing.

II. AIM OF THE GROUP
Digital applications have high potential to improve neuraldysfunction through cognitive rehabilitations from thestandpoint of accessibility & availability. In the future, we
intend to develop these applications as digital drugs in clinicalpractice of neurology and psychiatry. Moreover, we havedeveloped the new sense of agency task using spatial biases.
And, we have started a research whether cognitive
rehabilitations using these applications could be useful togeneralize and tune up precision of SoA even in the SoA taskusing spatial biases.

A. The new sense of agency task using spatial biases
SoA experiments are mainly divided into two types:
experiments introducing a temporal bias and experiments
introducing a spatial bias. This year, we have developed SoA
experiments that introduce a spatial bias. In the spatial bias task,
subjects reach the cursor toward the target, but then receive
visual feedback of their trajectory with a random spatial bias in
the left-right direction. We conduct two types of tasks: a selfattribution task and an adaptation task. In the self-attributiontask, SoA at the cognitive level is evaluated based on selfattribution judgments, and in the adaptation task, SoA at thesensorimotor level is evaluated based on adaptation rates. In
the self-attribution task, we expect to understand therelationship between time and space in SoA generation bycomparing with the results of our previous experiments
introducing temporal bias. In the adaptation task, healthy
subjects show a higher adaptation rate when the spatial bias
increases, but when the spatial bias increases beyond a certain
level, they put more weight on their own proprioceptive sense
and, conversely, show a lower adaptation rate. However,patients with schizophrenia are likely to relatively put moreweight on external information due to the lower accuracy of
their own proprioceptive sense (internal model). Therefore, theadaptation rate of patients is expected to increase even when
the spatial bias increases above a certain level.

C. Develpopment of the Mood Swing Monitor
The mood is one of the most important metal state in humandaily living. In order to evaluate effects of cognitive
rehabilitations of SoA, we developed a new application formonitoring trends of mood swing.
Mood must be monitored form the longitudinal perspective,
however, there have been no tool for daily monitoring andevaluating monitor in psychology and psychiatry. Monitoring
trends of mood swing is methodologically useful to analyze inthe computational neuroscience and psychiatry. we intend touse this new application for monitoring trends of mood swing
for evaluating generalized effects of cognitive rehabilitation of
SoA.

B. Develpopment of the Agency Tuner for Artists
We have studied pathophysiology of neurological andpsychiatric illnesses from the stand of the SoA. We havedeveloped methods for cognitive rehabilitation of SoA inorder to tune up precision of SoA. We have released theapplication for agency tuning: the Agency Tuner, for generaluse in Japan. We hypothesize that this tuning of SoA could
reorganize neural systems of SoA and achieve hyperadaptation of patients with those illnesses in their living
environment.
Furthermore, we have arranged the Agency Tuner in order to
use this methodology for patients with occupational dystonia
who are suffering from slump in their professional occupation
including musician and athletes. This newly original
application is designed to be hard to perform where users must
to be highly attentive into the task, and result in the state ofno-mindedness.

IV. FUTURE PERSPECTIVE
We have made advances in developing an original method
for cognitive rehabilitation of the SoA: Agency Tuning for
Artists, and the Mood Swing Monitor. We will advanceclinical experiments of patients, and analyze their leaning
processes of predictive models on the SoA for establishingfeasible method of cognitive rehabilitation of the SoA, using
these applications.
REFERENCES[1]

Takaki Marda：「AGENCY TUNER for Artists」（APP on Google
Play in JPN, US, Poland）, Feb 15, 2023.
https://play.google.com/store/apps/details?id=jp.art.re.agencytuner.blp&
pli=1[2] Takaki Maeda：「Mood Swing Monitor」（APP on Google Play andApple Store in JPN, Poland, Turkey）, Nov 26, 2022～Mar 1, 2023.
https://play.google.com/store/apps/details?id=com.moodswingmonitor.blp&hl=jp&gl=jp
https://apps.apple.com/jp/app/mood-swing-monitor/id1658996068（Japanese Patent-Pending 2022-158647: Shared Monitoring System）

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

Brain Science Institute, Tamagawa University
Abstract— To elucidate the neural mechanisms of "HyperAdaptability" that occur during recovery from a large-scale
restructure of the central nervous system (e.g., spinal cord injury)
or musculoskeletal systems (e.g., tendon transfer), it is necessary
to identify the flexibility and constraints of the adaptability of thecentral nervous system. This research project aims to identify lowdimensional spaces of a frontal-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 asked to perform acontext-dependent feedback motor control task, and frontalparietal cortical neural activity was recorded with
Electrocorticogram (ECoG). The results showed that the neuralsubspaces for motor preparation and motor response were
orthogonally arranged. This result was confirmed with theartificial neural network modelling which was trained to solved
the similar behavioural task. These results suggests that
segregation of neural subspaces for motor preparation and motor
response is essential to achieve flexible feedback motor control.

I. INTRODUCTION
In the mammalian central nervous system, acute and chronic
injuries are known to cause a large-scale neural network
restructure, or "Hyper-Adaptability," that is beyond the extent of
normal adaptation. For example, in monkeys hemiparalysed due
to spinal cord injury, the ipsilateral motor cortex, which isnormally inactive in healthy condition, is activated, and bilateralmotor cortex takes place in a motor control of paralyzed handthat is different from the normal contralateral motor control[1,2]. It is also known that the visual cortex, which is normallyinactivated by 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 common motor learning tasks such as
visuomotor rotation and force field adaptation tasks [4]. The
coactivation pattern of this neuronal population is called the“neural manifold” and has been shown to be stable over a longterm for years [5]. This neural manifold constrains theadaptability of the central nervous system. Using a braincomputer interface (BCI), monkeys were trained to move acursor by either activating motor cortex neurons with a different
coactivation pattern than original manifold (out of manifold) or
coactivating them within the original manifold (within
manifold). The results showed that the within manifold
condition can be learned in a few hours of training, whereas it
requires a longer period of learning over several days for the outof manifold condition [6]. These findings suggest that (1) astable neural manifold exists in the motor system, which enables
stable but flexible motor control and motor learning, and (2)adaptation to deviate from this neural manifold requires a longer
period of training to induce significant neural circuit restructure.
Based on these findings, we hypothesized that "normal
adaptation" occurs in existing neural manifolds, whereas"Hyper-Adaptability" requires restructuring of new neuralmanifolds".
II. AIM OF THE GROUP
The purpose of this research program is to establish a method
for recording frontal-parietal cortical neural activity in monkeys
to identify neural manifolds, as well as to understand neuraldynamics during adaptive behavior based on neural manifolds.
In particular, we will clarify what neural dynamics underlie theflexible feedback motor control by comparing the results ofneural recordings with artificial neural circuit models.
III. RESEARCH TOPICS

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

A. Establishment of a technique for recording frontal-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
the task trial, a target was first presented as a contextual cue at
one of 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 starttarget 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). One monkey that had completed
training was implanted with electrodes of a 32-channel corticalelectroencephalogram (ECoG). As a result, we succeeded insimultaneously recording the cortical activity of dorsal
premotor, primary motor, primary somatosensory, and parietal 5
areas during the task.

B. Identification of frontal-parietal cortical neural dynamics
during a flexible feedback motor task
We performed frequency decomposition with Wavelet
transformation and dimensionality reduction with Principal
Component Analysis on the ECoG signals during this task to
examine the neural subspace during motor preparation andmotor response. The results showed that the neural subspaces
during motor preparation and motor response were orthogonal
to each other in the neural space. This result was confirmed bybuilding a recurrent neural network model that was trained togenerate a similar input-output relation and finding that a similar
orthogonality emerged in the trained network (Figure 2). Thissuggests that the orthogonality of the neural subspaces
represents an important neural activity structure for flexible
feedback motor control.
IV. FUTURE PERSPECTIVE
This year, we trained two monkeys in a flexible feedback
response task and recorded fronto-parietal cortical activity(ECoG) in one monkey. The results revealed that the neuralsubspaces during motor preparation and motor response were
orthogonal. This orthogonality was reproduced in the artificial
neural network, suggesting that the orthogonality is an
important feature for flexibly switching a feedback gain. In thenext year, we plan to record frontal-parietal cortical potentials
in one more individual to examine the homology anddifferences between individuals.
REFERENCES
[1]
[2]
[3]
[4]

Figure 2 Artificial neural network model for a flexible
feedback motor task.

[5]
[6]

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.
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.

A05-11 Annual report of research project
Hironobu Osaki

Doshisha University Graduate School of Brain Sciences
Abstract— During the acute phase of stroke, alterations in thecentral nervous system, such as disinhibition, would facilitate
functional rapid recovery. During the chronic period, however,functional recovery becomes slower. Consequently, enhancing
functional recovery during the chronic phase of stroke is crucial
for improving rehabilitation outcomes. To establish a new
strategy for accelerating the rate of recovery, we first created amacro Ca2+ imaging system in combination with a rodent motor
task. Monitoring the behavior of mice before and after a motor
cortex infarction allowed us to successfully examine the activityof the motor cortex. We have designed an X-Y galvo-controlledlaser stimulation system of the motor cortex during behavior to
regulate neural network activity. By utilizing these systems, wewill attempt to identify the circuits that promote functional
recovery during the chronic phase of stroke.

I. INTRODUCTION
Cortical infarction induces loss of function, which will be
partially recovered in the acute phase with the help of restored
neural circuit compensations. These compensations are thought
to be involved in the disinhibition of the whole brain network.
In the primary motor cortex (M1) infarction, we have shownthat such disinhibition disrupted sensory processing in the
primary somatosensory cortex through the cortico-cortical
network in the acute phase and recovered in the sub-acutephase of stroke [1]. Due to this disinhibition may recruit
residual circuits may be activated to compensate for thefunction of the damaged brain area. In the chronic phase of
stroke, these alterations and functional recovery, however, slow
down and persist.
In this study, we use the rodent M1 infarction model.
However, the network from M1 to spinal motoneuron is quite
different between rodent and primate, including human [2]. For
example, the rodent has no direct excitatory input from M1 tothe spinal motoneuron, and this excitatory input increases
according to the increase of the ability of fine hand movement[2]. Therefore, in rodents, most motor functions could becontrolled indirectly by M1 but directly by the subcortical
network that includes the brain stem [3].
II. AIM OF THE GROUP
Taking into account the differences between the neuralnetworks of rodents and humans, our group focuses on thechange of the neural network from the acute phase to thechronic phase of stroke and will attempt to identify the circuitsthat promote functional recovery. More specifically, we use abehavioral task that requires skilled motor behavior combinedwith Ca2+ imaging and electrophysiological recording. Using
these systems, we will monitor the neural activity before andafter M1 infarction. Our goal is to find the ways that neuralcircuit mechanisms to enhance functional recovery after an
infarction.
III. RESEARCH TOPICS
A. Ladder rung walking task for monitoring M1 recovery
after infarction
First, we developed a ladder-rung walking task, which is
widely used to measure motor dysfunction after M1 infarction
in rodents [4]. The movement of each limb is monitored bymultiple cameras. Our custom program combined with
Deeplabcut[5] automatically detects the limb falling. The M1
infarction was made by photothrombotic infarction previously
reported [1]. After Rose Bengal was injected intraperitoneally,a green LED was illuminated on the M1. The size and positionof infarction were controlled by the power of illumination andthe position of optical fiber on the brain surface. We
successfully observed the increase in the fall rate of thecontralateral limbs to the M1 infarction. Using this index, we
will examine recovery after M1 infarction.

Fig. 1. Falling limb detection in ladder rung walking task. The falling limb
was detected by combining with pose tracking of limb position by
DeepLabCut and each limb’s speed, and distance from the ladder.

B. In vivo macro Ca2+ imaging system
Second, we developed in vivo macro Ca2+ imaging systemand optogenetic laser stimulation system (Fig. 2, 3). Thisimaging system can observe the area 3 x 3 mm of Ca2+ signal(GCaMP6f) at 25 Hz for each 490 (blue) and 405 nm (violet).
The 405 nm wavelength illumination was an isosbestic pointof the GCaMP signal, which is thought of as Ca2+ independent
fluorescence ( ⊿R). These signals are also included in thesignal observed at 490 nm wavelength (⊿F), especially inmacro-imaging. Therefore, we removed this noise from
activity-dependent Ca2+ signal by the following equation. ⊿R
was multiplied with a to fit with ⊿F[6].

⊿Fc = ⊿F – a⊿R(1)This imaging and optogenetics stimulation system weremodified from the system used in our system previously
reported[7], showing the nociceptive region in the primarysomatosensory cortex. This work was also supported by thearea of Hyper-adaptability.

IV. FUTURE PERSPECTIVE
Using these systems, we will investigate the change inneural activity following the M1 infarction. We will also
examine the specific circuit that induces functional recovery by
optogenetic neural activity modulation. The Ca2+ imaging
system can only detect the signal of neural activity from thebrain surface. Therefore, we will also use anelectrophysiological recording system and acquire the activity
from the deeper brain, such as the deeper cortex, striatum, andthalamus in the next year. We aim to control neural activity andimprove functional recovery during the chronic phase of stroke.

Fig. 2. System for macroscopy for GCaMP Ca2+ signal and optogenetics
laser stimulation. Left, the unit for macro-imaging Ca2+ signal. Excitation
LEDs (409 and 490 nm) were alternately controlled by TTL signals, which isalso synchronized with CMOS camera shutter. Right, the light path for
optogenetic activation and inhibition. The two wavelength lasers are
combined with a dichroic filter and stimulate the specific region controlled by
two-axis galvano mirrors. The optical fiber and the scan lens were not shownin this schema. The dichroic filter (ZT470/640rpc-UF2) is used during opticalstimulation but removed during the imaging session.

REFERENCES

[1]

[2]
[3]
[4]

[5]

[6]

[7]
Fig. 3. Macroimaging of GCaMP6f signal in M1. Scale bar: 500µm。

Fukui, A., H. Osaki, Y. Ueta, K. Kobayashi, Y. Muragaki, T. Kawamata,and M. Miyata, Layer-specific sensory processing impairment in the
primary somatosensory cortex after motor cortex infarction. Scientificreports, 2020. 10(1): p. 3771.
Lemon, R.N., Descending pathways in motor control. Annu Rev
Neurosci, 2008. 31: p. 195-218.
Ruder, L., R. Schina, H. Kanodia, S. Valencia-Garcia, C. Pivetta, and S.
Arber, A functional map for diverse forelimb actions within brainstem
circuitry. Nature, 2021. 590(7846): p. 445-450.
Bechay, K.R., N. Abduljawad, S. Latifi, K. Suzuki, H. Iwashita, and S.T.
Carmichael, PDE2A Inhibition Enhances Axonal Sprouting, Functional
Connectivity, and Recovery after Stroke. J Neurosci, 2022. 42(44): p.
8225-8236.
Mathis, A., P. Mamidanna, K.M. Cury, T. Abe, V.N. Murthy, M.W.
Mathis, and M. Bethge, DeepLabCut: markerless pose estimation ofuser-defined body parts with deep learning. Nat Neurosci, 2018. 21(9): p.
1281-1289.
Allen, W.E., I.V. Kauvar, M.Z. Chen, E.B. Richman, S.J. Yang, K. Chan,
V. Gradinaru, B.E. Deverman, L. Luo, and K. Deisseroth, Global
Representations of Goal-Directed Behavior in Distinct Cell Types of
Mouse Neocortex. Neuron, 2017. 94(4): p. 891-907 e6.
Osaki, H., M. Kanaya, Y. Ueta, and M. Miyata, Distinct nociception
processing in the dysgranular and barrel regions of the mouse
somatosensory cortex. Nat Commun, 2022. 13(1): p. 3622.

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. This
year, we designed a task to discriminate the attentional network
damaged in hemispatial neglect and developed an Augmented
reality (AR) intervention system.

I. AIM OF THE GROUP
A. Development of the Attention Network Assessment Method
for hemispatial neglect
Psychological and neuroscientific findings indicate that
there are two types of spatial attentional networks: those thatwork actively, such as selecting and focusing attention on an
object (top-down attention network), and those that respondpassively to suddenly appearing salient stimuli (bottom-up
attention network). The former consists mainly of theprefrontal cortex of the bilateral hemispheres, including thefrontal eye fields, and the posterior parietal lobes and is called
the dorsal attentional network. The latter consists mainly of theinsular cortex, temporal-parietal junction and inferior frontal
gyrus in the right hemisphere and is called the ventral
attentional network [1]. In hemispatial neglect, symptoms andthe recovery process may also differ depending on which ofthese networks is damaged; Karnath et al [2] have shown that
hemispatial neglect that persists into the chronic phase isconfined to the damage in right temporal lobe. Deficit inbottom-up attention in hemispatial neglect due to damage toventral attention network may have a poor prognosis, but may
be unnoticed by the therapist or the patient, causing difficulties
in life after discharge.
In clinical situations, the Behavioural Inattention Test
(BIT) is commonly used to detect semi-lateral spatial neglect.

The BIT consists of several desk tests, such as line bisection
test, line cancellation test, and design copying test, but despitea negative BIT result for hemispatial neglect, neglect
symptoms are sometimes observed in daily life situations, andnot all neglect symptoms can be revealed by the BIT test. In
recent years, the Posner cue paradigm, which applies
psychological experiments, has been shown to be superior in
detecting hemispatial neglect [3]. However, both of theseassess disorders in a top-down attention and do not differentiate
it from disorders in bottom-up attention. In the present study,
therefore, the LANT (lateralised attention network test)
paradigm [4], which can detect both bottom-up and top-down
attention, was modified to adapt it to cases of patients withhemispatial neglect in order to detect which of the two
networks is more impaired. By measuring gaze at the sametime, the relationship between attention and gaze will also be
investigated.
B. Development of AR-based intervention systems forhemispatial neglect
One of the clinically applied treatments for hemispatial
neglect is prism adaptation, where prism glasses shift the visual
space to the right by 10-15 degrees and patients adapt their
movements under the shifted environment, which is known tosometimes improve the symptoms of hemispatial neglect after
the glasses are removed [5]. Systems have also been proposedthat use virtual reality (VR) to perform tasks under the shifted
virtual environments to encourage attention to the left [6].
However, although prism glasses can manipulate real space,
they have optical limitations, and VR is limited in its
generalization to real space. In this regard, the use of AR,
which is technologically advanced and offers improved
resolution and real-time performance, could be a possible
option. With AR, significant spatial transformations are
possible, such as shifting the entire real space to the right space,
where attention is allocated, and displaying it in real time. If
the brain can hyper-adapt to an environment in which the entire
real space is displayed in right space, it could be used byhemispatial neglect patients in their daily lives as glasses tocorrect visual space. They could also be used for recovery

training, such as gradually returning the space presented in theright to the left and directing attention to the left.
II. PROGRESS MADE NI THIS FISCAL YEAR
A. Development of the Attention Network Assessment Method
for hemispatial neglect
To assess top-down and bottom-up attention
simultaneously, we designed the task shown in Fig. 1. The task
consists of a preceding cue stimulus (Fig. 1b) and a target
stimulus (Fig. 1c). The cue stimulus directs passive attention to
the left space or the right space or both and can detects bottomup attention by its presence or absence. Target stimuli detect
top-down attention by actively directing attention to thedirection of an arrow that appears in the center in a Frankertype task consisting of congruent (adjacent arrows are the same
direction with target arrow) and incongruent (adjacent arrows
are the opposite direction with target arrow) stimuli. Reaction
times and gaze to stimuli presented in left and right space aremeasured and quantitatively assessed to determine whether
bottom-up or top-down attention results in shorter reaction
times and guided gaze. In addition, stimulus locations were
subdivided and presented in order to accommodate the visual
space of hemispatial neglect (Fig. 1d).

B. Development of AR-based intervention systems forhemispatial neglect
A system that transforms and presents real space using AR
technology was developed: a special camera (ZED mini) was
attached to the front of VR goggles (HTC VIVE pro eye) tocapture images of the real world as seen by the subject, which
was processed in real time and presented on the screen of the
VR goggles (Fig. 2). In cases of hemispatial neglect, thesubject did not perceive the left space, so information on theentire space, including the left space, was presented in the rightvisual field of each VR screen of the left and right eyes.
The development of the system was completed this year,
and the wearing feeling of the system in patients withhemispatial neglect will be tested in the next step.

In this fiscal year, we completed the development of thetask has been completed and we plan to run a feasibility study
on healthy participants and on patients with hemispatial neglect.

Fig. 2. Experimental paradigm

III. FUTURE PERSPECTIVE
We designed a task to discriminate the attention network
damaged in hemispatial neglect and developed an AR-based
intervention system this year. We will implemented andevaluated these system with patients with hemispatial neglect.
REFERENCES
[1]
[2]
[3]

[4]
[5]
[6]
Fig. 1. Experimental paradigm

M. Corbetta and G.L. Shulman, "Spatial neglect and attention
networks," Annu Rev Neurosci, 34, pp. 569-99, 2011.
H.O. Karnath, J. Rennig, L. Johannsen, and C. Rorden, "The anatomy
underlying acute versus chronic spatial neglect: a longitudinal study,"
Brain, 134(Pt 3), pp. 903-12, 2011.
J. Rengachary, G. d'Avossa, A. Sapir, G.L. Shulman, and M. Corbetta,"Is the posner reaction time test more accurate than clinical tests in
detecting left neglect in acute and chronic stroke?," Arch Phys Med
Rehabil, 90(12), pp. 2081-8, 2009.
D.J. Greene, A. Barnea, K. Herzberg, A. Rassis, M. Neta, A. Raz, and E.
Zaidel, "Measuring attention in the hemispheres: the lateralized attention
network test (LANT)," Brain Cogn, 66(1), pp. 21-31, 2008.
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.

A05-13 Annual report of reseach project
Kosei Takeuchi

Dept. of Medical Cell Biol., Sch. of Med., Aichi Medical University
Abstract— In the treatment of spinal cord injury andtraumatic brain injury, it is believed that adult nerve axons do
not regenerate, and there is no fundamental treatment. Therefore,
many attempts such as iPS cell transplantation are being tried forspinal cord injury treatment. The various trials to apply
molecules that promote nerve axonal regeneration to drug
discovery have also been made in model animals around theworld. We develop applications to this problem by applying newconcepts and methods of preserving lost circuits and exploiting
latent circuits. Furthermore, we are trying to build a system thatcan estimate the functional recovery of model animals to humans
using an AI learning system.
One of the new methodologies is the application of synaptic
connectors. Synapse connector “CPTX” is an synthetic chimeric
protein inspired by a synaptic organizer ”Cbln1”, and has rapid
and strong synaptic formation ability by cross-linking pre-and
post-synaptic molecules. We have shown that this synaptic
connector enables recovery from chronic-phase of spinal cord
injury (chronic-SCI) models. This chronic phase was difficult
despite various trials for recovery of SCI. In addition, a synaptic
connector and a system for improving the regenerativeenvironment that facilitates the hyper-adaptation have been
prepared. In order to analyze the physiological recovery processby these interventions after SCI, we also proceeded with theconstructions of AI systems that analyzes motor function in detail.
Taking advantage of this result, we will challenge the control ofinhibitory neuronal circuits using next generation- synaptic
connectors.

I. INTRODUCTION
We attempt to establish the super-recovery mouse from SCI
by inducing artificial synapse connect and providing extra
cellular matrix field suitable for regeneration. This mousewill allow us to dissect the neural basis of adaptive circuits
during recovery. To evaluate how the adaptive neuralcircuits generate the locomotory outputs, we areconstructing the novel motion capture analysis system
operated with AI algorithm. This system could detect andextract the behavioral elements specific to the superrecovery mouse. We aim that our AI motion capture system
will become powerful and rigorous system for highthroughput analysis of rodent behaviors. This research
consists of the principal investigator (Takeuchi), theresearch collaborators in our lab (Dr. Sasakura, Dr. Ikeno(Aichi medical Univ.). and the collaborators (Dr. Yuzaki(Keio Univ.), Dr. Yanagihara (University of Tokyo)).

II. AIM OF THE GROUP
The purpose of this research is to (1) further explore thefunction of synapse- connectors, and (2) simultaneously
improve the regeneration environment by suppressing theexpression of the regeneration-inhibitor chondroitin sulfate(CS). We propose a regeneration model from the subacutestage to the chronic stage, which has been difficult to treat
until now. Furthermore, we will try to develop new
synaptic-connectors and apply them to the spinal cord. The
correlation with the physiological recovery process at thattime will be examined with the aim of promoting theextraction of important factors and a versatile physiological
function recovery evaluation system while promotinginterventions such as rehabilitation.
III. RESEARCH TOPICS
A. The recovery of the chronic and sub-acute phase of SCI
by CPTX
Our group (Takeuchi), Dr. Sasakura as a leading role of the
project, has conducted studies of recovery from spinal cord
injury using an artificial synaptic connector (named CPTX).
As an international collaborative study with Oxford
University & MRC Radu Aricescu in the United Kingdom
and DZNE Alexander Dityatev in Germany, we reported
that CPTX have outstanding property of restoring
neurological function in Alzheimer's model mouse andcerebellar ataxia, and in SCI (Science. 2020) [1]. CPTX is a
novel synthetic molecule that crosslinks a presynaptic
molecule (Nrx: neurexin) and a postsynaptic molecule(AMPAR: AMPA receptor)(Fig1). It was created as an
artificial chimeric protein (CPTX) in which Nrx binding
domain of Cbln1 and AMPAR binding domain of NP1 arejoined.

Last year, we tried if CPTX is effective to chronic phase of
SCI. It is well known that recovery of chronic phase of SCI
is extremely difficult. Despite the difficulty, majority ofpatients suffering from SCI are clinically chronic phase.
CPTX restored the chronic phase of SCI. CPTX injection
activated the hind leg movement within a few days, which
otherwise showed the permanently poor movement.
Furthermore, from the design and synthesis of novel
synaptic connectors (Fig. 2), we started to analyze spinal
cord injury and motor function. In particular, we attempted
to analyze new connectors that are more specific than
CPTX for excitatory synaptic receptors and connectors forinhibitory nerves. At present, no drug that surpasses CPTX
in terms of recovery function has been obtained, but one
that enhances sensory receptor function has been obtained.
In addition, we found that the recovery after spinal cord
injury was delayed in connectors for inhibitory synapses.
We believe that this is a delay in recovery due to the
suppression of the disinhibition function, and we areproceeding with the investigation. From these, we showedthat the new concept of neural circuit reorganization
connection can be a powerful analytical tool [3]C. Construction of AI trace system and capture of
behavioral parameter in hyper-adaptability mouse
Using a super-recovery model after spinal cord injury, weare proceeding with thorough walking function analysis,
such as the introduction of AI, in order to obtain thecorrelation of neural reorganization through quantitative
motor function analysis and extraction of its factors.
From the analysis and comparative examination in various
time series, we succeeded in extracting a characteristic
recovery pattern in super recovery by inducing artificial
synaptic connections by CPTX administration. In addition,
we will analyze the application of analysis to chronic
treatment, the effect of rehabilitation, and the motor
function in disinhibition.

III. FUTURE PERSPECTIVE

B. Regenerative environment maintenance and superrecovery model by suppressing nerve regeneration
inhibitors and analysis of sensory reception
The dramatic post-injury recovery of chondroitin sulfate(CS) KO mice, which is the greatest inhibitor of nerve
regeneration after spinal cord injury, has demonstrated theeffectiveness of its application [4] [5]. Aiming at clinicalapplication, furthermore, for circuit formation andfunctional analysis, we have been developing antisense
oligos (ASO) that can knock down the expression in atissue site-specific manner, and have obtained effective
ASOs by last year. We analyzed changes in sensory
reception at synaptic connectors along with recovery by
this ASO. It was clarified that ASO to chondroitin sulfate
decreases pain reception, and that the above-mentioned
synaptic connector CPTX does not increase sensory
reception (lower pain threshold). Considering theapplication, it led to the fact that it is extremely effective
without the possibility of causing allodynia.

In the application of synaptic connector CPTX to spinal
cord injury, we obtained results such as adaptation to thechronic phase. It is now possible to extract importantelements of physiological function recovery by AI tracingfrom this recovery process and analyze the correlation withthe connection circuit. From these analyses, it is also
becoming clear that "restoration of sensory reception" isimportant for improving physiological function [6]. The
final goal of this research, the hyperadaptive function by"disinhibition", was analyzed from this spinal cord injury
model using a novel synaptic connector, and an analysis
was started to directly prove the function of the inhibitory
circuit in physiological recovery. From the analysis of thecorrelation with physiological functions, we plan toproceed with the artificial intervention for hyperadaptationand its analysis.

REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]

Christopher S. et.al., Traumatic spinal cord injury. Nature Reviews
Disease Primers volume 3, Article number: 17018 (2017)
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)
Takeuchi K. et al., Chondroitin sulphate N-acetylgalactosaminyltransferase-1 inhibits recovery from neural injury. Nat. Comm. 4, 2740
(2014)
Mercedes Balcells, “Two Proteins, One Stone” Science Translational
Medicine 11:5, Issue 215, pp. 215ec203 (2014)
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 (2021)

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—In FY2022, we established a methodology for
simultaneous recording of transcranial magnetic stimulation(TMS) and electroencephalogram (EEG). To evaluate human
functional neural networks noninvasively and quantitatively,
multichannel EEG was continuously recorded while TMS wasapplied to the primary motor cortex (M1). TMS evoked
potentials (TEP) obtained in contralateral M1 are thought toreflect interhemispheric inhibition (IHI). Therefore, IHI
asymmetry has been considered a biomarker of poor recovery inprevious studies of stroke patients. In FY2023, we plan to apply
this methodology to hyper-recovered stroke survivors.

neurofeedback methods for rehabilitation of sensory disorders,with B05-09 (Prof. Sakurada).
II. AIM OF THE GROUP
The purpose of this study is to explore neural connections
that are latent in healthy subjects but become active during theprocess of hyper-adaptation by measuring functional neuralnetworks using TMS/tSMS-EEG and elucidating thespecificity of neural network reconstruction in hyperrecovering individuals.

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 toscientifically elucidate the so-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 FY2022 are as follows.
(1) We published several research papers on human motor
control [1, 2].
(2) We published several case reports on the application of
stroke rehabilitation methods to neuro-degenerative disorders[3, 4].
(3) We published review articles on "meta-plasticity" and "Nof-1 studies" which are related to hyper-adaptation [5, 6].
(4) The TMS-EEG method was studied on healthy subjects,
and the recording and analysis method was established.
(5) We initiated a cross-disciplinary collaboration between
medicine and engineering to develop a new method to use

III. RESEARCH TOPICS
Two major specific results for FY2022 are listed below andoutlined respectively.
A. Hyper-adaptation from the Perspective of N-of-1 Trials
The concept of evidence-based medicine (EBM) that
dominates rehabilitation science today emphasizes statisticaltests of average efficacy in a patient population. As a result, it
has been difficult to conduct research on hyper-recovering
individuals, who are highly singular. Therefore, this research
project aims to conduct exploratory research to create "newquestions and innovative working hypotheses" for brain
reconstruction from single individualized data from theperspective of precision medicine and N-of-1 trials [7].
This can be paraphrased as a theoretical paradigm thatemphasizes the perspective of "exception" and "minority" withrespect to “hyper” in the concept of hyper-adaptation. In otherwords, we approach exceptional events such as hyperadaptation and hyper-recovery from the viewpoint of
individual case singularity (N-of-1), which does not fit into
group averaging and standardization. For this reason, it isnecessary to create experimental protococls that are strictly
randomized and blinded, with controls set up within individual
subjects. We formulated the methodology for applying suchmethods to clinical neuroscience research [5].
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).

Figure 2: TMS-EEG recording (left) and raw EEG waveforms
without pre-processing (right).
Ten right-handed healthy adults (22.2 ± 0.6 years old, 6
males) were recorded with a 64-channel EEG (sampling
frequency: 5 kHz). The TMS was given by Magstim-200 and afigure-8 coil at the M1 of the right first dorsal interosseousmuscle (n = 120). The intensity was set to the resting motor
threshold.
EEG data were down-sampled to 1 kHz, formatted withrespect to the global mean, and epoched for 1 second before
and after TMS. Then, to remove artifacts due to TMS, artifact
estimation and removal by signal source, removal of non-brain
signal components by ICA method, and removal of AC noise
components by band-stop filtering were performed, and thenaveraged.
As a result, we succeeded in detecting the following TEP
components reported in previous studies: the largest positive
potential, P30, at the left M1, the site where TMS was given,
the negative potential, N45, found at contralateral M1, andN100, a negative potential widely found in the frontal andcentral regions.
The TEP obtained at contralateral M1 is thought to reflect
interhemispheric inhibition (IHI). Therefore, asymmetry of IHI
is a biomarker of poor recovery in previous studies on stroke
victims [8]. There are also reports that alpha wave power
induced in M1 by TMS, but not TEP, is a good biomarker that
correlates with recovery [9]. As a next step, we plan to record
these parameters in hyper-recovering stroke survivors.

recordings in healthy subjects. In FY2023, we will analyze
neural networks by TMS-EEG and tSMS-EEG in subjects
who are hyper-recovering from stroke.

Figure 3: Superimposition of TEP waveforms in one subject(upper) and the scalp topography of TEP at 30, 40, 50 and 100
ms (lower).
REFERENCES
[1]

[2]

[3]

[4]

[5]
[6]
[7]
[8]

IV. FUTURE PERSPECTIVE
In the first year of the study, we clarified the theoretical basis
for the importance of studying "hyper-recovering" stroke
survivors and established a method for analyzing TMS-EEG[9]Shibata, S., Watanabe, T., Matsumoto, T., Yunoki, K., Horinouchi, T.,
Kirimoto, H., Zhang, J., Wang, H., Wu, J., Onishi, H., & Mima, T.
(2022). Triple tSMS system (“SHIN jiba”) for non-invasive deep brain
stimulation: a validation study in healthy subjects. Journal of
NeuroEngineering and Rehabilitation, 19(1), 129.
Shibata, S., Watanabe, T., Otsuru, N., Onishi, H., Mima, T. (2023) Very
low-frequency transcranial electrical stimulation over the primary motor
area can influence the voluntary movement initiation in humans. Brain
Stimulation 16(2): 442-444.
Shima, A., Tanaka, K., Ogawa, A., Omae, W., Miyake, T., Nagamori, Y.,
Miyata, Y., Ohata, K., Ono, Y., Mima, T., Takahashi, R. *Koganemaru,
S. (2023) Case Report: Backward gait training combined with gaitsynchronized cerebellar transcranial alternating current stimulation inprogressive supranuclear palsy. Frontiers in Human Neuroscience-Brain
Imaging and Stimulation 17
Shima, A., Miyake, T., Tanaka, K., Ogawa, A., Omae, E., Nagamori, Y.,
Miyata, Y., Ohata, K., Maki, T., Ono, Y., Mima, T., Takahashi, R.
*Koganemaru, S. (2023) Case report: A novel approach of closed-loop
brain stimulation combined with robot gait training in post-stroke gait
disturbance. Frontiers in Human Neuroscience-Brain Imaging andStimulation 17
Mima, T., et al. (2022) How to perform N-of-1 trials? The Japanese
Journal of Rehabilitation Medicine 59(11): 1111-1117.
Mima, T. (2022) Meta-plasticity. Journal of Clinical Rehabilitation
31(6): 567-570.
Mirza RD, Punja S, Vohra S, Guyatt G.(2017) The history anddevelopment of N-of-1 trials. J R Soc Med: 110(8): 330–340.
Casula, E. P. et al. (2021) Evidence for interhemispheric imbalance instroke patients as revealed by combining transcranial magnetic
stimulation and electroencephalography. Hum Brain Mapp. 42:1343–
1358.
Pellicciari, M. C., et al. (2018) Dynamic reorganization of TMS-evoked
activity in subcortical stroke patients. NeuroImage, 175: 365–378.

A05-15 Annual report of research projectAtsushi Nambu, Professor, National Institute for Physiological Sciences
Nobuhiko Hatanaka, Assistant Professor, National Institute for Physiological Sciences
Satomi Chiken, Assistant Professor, National Institute for Physiological Sciences
Pimpimon Nondhalee, NIPS Research Fellow, 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) indicate thatthe face region may have expanded to the lost hand region in
the M1. However, such expansion of the face region could not
be observed in the ICMS mapping on nonhuman primates.
Studies on somatotopic differences between the healthy andaffected sides after loss of the forelimb and hindlimb in themonkey M1 found that neuronal activity in the lost body partcodes the movements of the stump, a remaining part of thelimb. The ICMS threshold to induce muscle movements was
similar between the amputated and healthy sides or between
the amputated and normal monkeys.

II. AIM OF THE GROUP
The aim of this group is how such somatotopy is affected
when the subjects lose 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.
On the other hand, the supplementary motor area (SMA) is
located on the medial wall anterior to the M1 leg region, andits somatotopy from face to leg is arranged antero-posteriorly.
The distal-proximal segregation in the SMA was not clear.
However, there are no reports about effects of the loss of bodyparts on the SMA somatotopy using electrophysiologicalapproach. To address this question, we also investigatedsomatotopic changes after loss of the forelimb in the SMA as
well as in the M1.
The somatosensory cortex (S1) located in the postcentral
gyrus and the posterior bank of the central sulcus. It is wellknown that this cortical area plays a role in the somatosensory
processing and sensorimotor integration. Further, recent
physiological studies revealed that the S1 received efference
copy from the M1 just before sensory feedbacks from thespino-thalamic system. The S1 also showed a reorganizationof the somatotopy; the face representation expanded into theformer 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 (Figure 1).

Left hand

Right hand

Amputated

Right cortex

Left cortex

SMA
CS
Anterior

Healthy side

M1
S1

Affected side

Figure 1. Somatotopic arrangements in the M1, SMA, and S1of Japanese monkeys were examined by ICMS-evoked
movement and somatosensory inputs under awake states, andtheir differences between healthy and affected sides werecompared.

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: Neurons
in this region responded to the palpation of the stump, andICMS in this region induced movements of the stump at
threshold < 10 µA (core region). Therefore, the distal forelimb
region in the M1 is considered to be substituted by the stump
region. Other general somatotopic map of the M1, such as
orofacial, proximal forelimb, trunk, and hindlimb regions,seems to be the same as that in the healthy sides. The coreregion in the affected side was smaller than that in the healthyside.
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. Therefore, the area that had
been dedicated to the distal forelimb region was considered tobe partly dedicated to the stump. Other body parts, 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 andhealthy 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
forelimb region in the healthy side, while the total forelimb
regions were similar between the affected and healthy sides.

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. This
discrepancy between the motor and somatosensory cortices
might be the basis of phantom limb, the sensation that an
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— Compensatory plastic changes in the remaining
intact brain regions are supposedly involved in functional
recovery following stroke. Previously, a compensatory increase incortical activation occurred in the ventral premotor cortex (PMv),
which contributed to the recovery of dexterous hand movement
in a macaque model of unilateral internal capsular infarcts. In
this project, we investigated the structural plastic changes
underlying functional changes together with voxel-based
morphometry (VBM) analysis of magnetic resonance imaging
data and immunohistochemical analysis using SMI-32 antibody
in a macaque model. We observed significant increases in thegray matter volume (GMV) and the dendritic arborization oflayer V pyramidal neurons in the contralesional rostral PMv.
Therefore, compensatory structural changes occur in the area
during motor recovery following internal capsular infarcts, andthe dendritic growth of pyramidal neurons is partially correlatedwith GMV increase. In addition, using a macaque model ofcentral post-stroke pain (CPSP), we have confirmed the
involvement of increased activity of the posterior insular cortex(PIC) and secondary somatosensory cortex (SII) to
somatosensory stimuli in mechanical allodynia by a combinationof imaging techniques with local pharmacological inactivation.
However, it is unclear whether the same intervention would be
effective for thermal hyperalgesia. Therefore, using the macaquemodel, we examined behavioral responses to thermal stimuli
following pharmacological inactivation of the PIC/SII. Our dataemphasize that increased activity in the PIC/SII after appearanceof thalamic lesions can contribute to abnormal pain of multiple
modalities and modulation of PIC/SII activity may be atherapeutic approach for thermal hyperalgesia.

I. INTRODUCTION

recovery of precision grip after the primary motor cortex
lesions [2]. However, clinically, cerebral hemorrhage orinfarction in the subcortical white matter is often a problem.
The purpose of this study is to gain knowledge that can beseamlessly applied to clinical practice by studying macaques
that have created infarcts and hemorrhages in the subcortex.
III. RESEARCH TOPICS
A. Structural chnages after focal infarction of the macaqueinternal capsule
Structural changes in neurons are thought to underlie
functional recovery and accompanying compensatory changes
in brain activity after cerebral infarction. We therefore
performed voxel-based morphometry (VBM) analysis using
T1-weighted MRI and immunohistochemical staining in apreviously established macaque model of internal capsular
infarction [3]. Previous studies using functional near-infrared
spectroscopy (fNIRS) to measure macaque motor cortex
activity during grasping tasks have shown that before infarction,
there was increased activity in the hand area of the primary
motor cortex with the performance of grasping movements
whereas increased activity in the ventral part of the premotor
cortex was observed during functional recovery [4].
The results of VBM analysis suggested that graymatter increased in the ventral part of the contralateral
premotor cortex during the period of motor function recovery
after infarction (Fig. 1) [5].

When the brain is damaged, the function of the lost brain
region is impaired, however, brain function can be restored
through rehabilitative training. This is a typical example of"hyper-adaptivity" of the brain. Elucidating these changes willlead to innovative neurorehabilitation technologies that induce
appropriate changes in the brain.
II. AIM OF THE GROUP
We have been studying the recovery process after damage tothe primary motor cortex, which is the central region of motor
output from the brain, using macaque monkeys, which have asimilar brain and musculoskeletal structure to humans, as amodel animal. As a result, it was found that flaccid paralysis
occurs after primary motor cortex lesion, but rehabilitative
training after lesion accelerates the recovery of precision grip[1]. In addition, our brain imaging analysis suggested thatchanges of brain activity occur in uninjured motor areas during

Fig. 1. Brain regions displaying a significant GMV increase post infarction.
A) Vertical white lines indicate the location of the coronal slices displayed inB. B) Significant GMV increase is observed in the contra-F5 as one
consecutive cluster with a maximum at the third slice (x, contraipsilateral = 24.0, y, rostrocaudal = 5.0, and z, dorsoventral = 4.0). The clusterdefining threshold is set at Puncorrected < 0.001, with an extent threshold
Pcorrected < 0.05 (FWE correction). The scale bar indicates the T score. F5,
ventral–rostral part of the premotor cortex; CS, central sulcus; ASu, upper
limb of the arcuate sulcus; ASl, lower limb of the arcuate sulcus.

Immunohistochemical staining using SMI-32 antibody,which stains pyramidal cells, revealed degeneracy of pyramidalcells in layer V of the primary motor cortex in the ipsilateralhemisphere of the infarct. This may be due to retrogradeneurodegeneration after the internal capsular infarction (Fig. 2).
On the other hand, pyramidal cells in layer V of the ventral
premotor cortex showed increased dendritic branching. These
results suggest that compensatory structural changes in motor
output cells in the ventral part of the contralateral premotor
cortex are important for the recovery of motor function after
infarction and that structural changes in dendrite projections
underlie the GMV changes observed in the VBM analysis.

Our previous studies in a macaque model of thalamic stroke
have reported abnormal increase of brain activity and structuralchanges in areas called the posterior insular cortex andsecondary somatosensory cortex [5, 6]. Furthermore, we havereported that pharmacological inactivation of these brain
regions attenuates allodynia to mechanical stimuli. This year,
we tested whether allodynia and hyperalgesia to thermal
stimuli have a shared neural basis as a pathological mechanism
by means of pharmacological inactivation experiments [7] andshowed that administration of the GABA agonist muscimol tothe posterior insular cortex and secondary somatosensory
cortex in the macaque model reduced hyperalgesia to thermal
stimuli (Fig. 3). These results suggest that there is a common
neural basis for the abnormal pain evoked by tactile andthermal stimuli.

Fig. 3. Effect of PIC/SII inactivation on withdrawal latency. Withdrawal
latencies (in sec) of the contra-lesion hand for each thermal stimuli after
muscimol or vehicle injection. The withdrawal latencies for thermal
stimulation (50-52°C) were increased compared to that after vehicle injection.

IV. FUTURE PERSPECTIVE
Studies in this project have elucidated some of the adaptive
and nonadaptive changes that occur after subcortical stroke.
Further cellular analysis will reveal the detailed mechanisms
underlying the adaptive and nonadaptive changes.
REFERENCES[1]

Fig. 2. IHC using SMI-32 antibody in M1 (A–F) and F5 (G–L). A, C, G, andI) Nissl-stained sections adjacent to the SMI-32 sections displayed in B, D, H,
and J. E, F, K, and L) High magnification images of the pyramidal neurons in
the layer V within the squares in B, D, H, and J, respectively. SMI-32-positive
pyramidal neurons with smaller cell body are frequently observed in the layer
V in the hand area of ipsi-M1, relative to that in the M1 of intact macaques.
Contrarily, the neurons with extensive arborization are more frequently
observed in the contra-F5 than in the intact F5. Scale bars: 100 μm.

B. Causality assessment of brain activity and pain in themacaque model of centrtal post stroke pain
Stroke lesions of somatosensory pathways are known tocause chronic pain called central post stroke pain (CPSP).
Specifically, about 10% of all stroke patients experience
allodynia, in which pain is produced by touch, andhyperalgesia, in which pain is more intense than normal pain
stimuli. Brain imaging studies of CPSP patients and animalmodels have revealed maladaptive plasticity in pain-related
brain regions as an underlying pathophysiological mechanism.

[2]

[3]
[4]

[5]
[6]

[7]

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
Y. Murata, N. Higo, "Development and Characterization of a MacaqueModel of Focal Internal Capsular Infarcts", PLoS ONE 11(5): e0154752,,
2016
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. Nagasaka, I. Takashima, K. Matsuda, N. Higo, “Brain activity
changes in a monkey model of central post-stroke pain”. Exp Neurol 323,
113096, 2020
K Nagasaka, K Nemoto, I Takashima, D Bando, K Matsuda, N Higo,“Structural Plastic Changes of Cortical Gray Matter Revealed by VoxelBased Morphometry and Histological Analyses in a Monkey Model ofCentral Post-Stroke Pain”, Cerebral Cortex, 31 (10), 4439-4449, 2021
K. Nagasaka, I. Takashima, K. Matsuda, N. Higo, “Pharmacologicalinactivation of the primate posterior insular/secondary somatosensory
cortices attenuates thermal hyperalgesia”. Eur J Pain 26 (8), 1723-1731,
2022

A05-17 Annual report of research project
Osamu Yokoyama

Tokyo Metropolitan Institute of Medical Science
Abstract— Somatosensory functions play an important role inmotor control. In the present study, we generated a monkey
model of somatosensory impairment in which the input ofsomatosensory information to the central nervous system wasselectively impaired by transection of the dorsal root entering the
spinal cord. By longitudinally recording and analyzing neuronal
activity in the motor cortex and the somatosensory cortex, we
aimed to reveal the adaptive mechanism in the cerebral cortex
that supports functional recovery from motor impairment caused
by a lack of somatosensory input to the central nervous system.
Immediately after transection of the dorsal root entering thecervical spinal cord, the motor performance of the upper limb
was markedly impaired, and an increase in the high-gamma (80–120 Hz) activity was observed in the primary motor cortex andprimary somatosensory cortex in relation to movement. Motor
performance gradually recovered to the pre-lesion level in about
2 weeks, and high-gamma activity in the primary motor cortex
and the primary somatosensory cortex also decreased to the prelesion level through a similar time course. These activity changes
in the motor and somatosensory cortices may contribute to therecovery of motor function. These findings revealed an exampleof the way by which the cerebral cortex adapts to an abrupt lossof information by reorganizing its activity.

II. AIM OF THE GROUP
The aim of this study is to investigate reorganization of
cerebral activity in the adaptive process during recovery of
motor function after the loss of peripheral, somatosensory
information.
III. RESEARCH TOPICS
After two Japanese macaques were trained on a reach-grasp
task using the right upper limb, the dorsal roots that enter theright lower cervical spinal cord (C6–8), which is thetransmission pathway for somatosensory signals from the rightupper limb, were surgically transected. (Fig. 1 left). We
confirmed that the response of the primary somatosensory
cortex (somatosensory evoked potential) to peripheral (upperlimb) electrical stimulation under anesthesia was attenuatedafter transection compared to before transection (Fig. 1, right).

I. INTRODUCTION
For at least the following two reasons, somatosensory
information plays an important role not only in the sensory
functions of perceiving the current state of one's own body andthe external world, but also in motor control to accurately
generate body movements. First, when the primary motor
cortex generates motor commands, it is necessary to utilize
somatosensory information such as the position of body parts,
joint angles, and output forces. Second, during motor execution,
spinal motor neurons receive sensory signals from peripheral
sensory receptors in addition to descending motor commandsignals from the motor cortex, and integrate them to generatemuscle activity [1]. Indeed, patients who cannot receive
somatosensory information due to peripheral sensory
neuropathy exhibit motor dysfunction in addition tosomatosensory impairment [2–4]. Similarly, a monkey modelof somatosensory impairment in which upper extremity
somatosensory input to the central nervous system was
impaired by transection of the dorsal root entering the cervicalspinal cord exhibited motor deficits in the upper limb [5]. Suchmovement impairment in the lesioned monkeys recovers withinweeks to months [5]. In the recovery process of motor function,
the central nervous system must adapt to the abrupt loss ofsomatosensory information input that could have been received
before the injury. However, the details of this adaptive process
have not been well understood.

Fig. 1. (Left) A schema of the somatosensory and motor nerve pathways thatconvey somatosensory information and motor commands between the
periphery and the central nervous system, and the position of the dorsal roottransected in the present study. (Right) Somatosensory evoked potentials in
the primary somatosensory cortex to stimulation of the adductor pollicis
brevis muscle under anesthesia were attenuated after transection of the dorsal
root (C6-C8) into the lower cervical spinal cord.

On the day after dorsal root transection, the animals wereable to perform the reach-grasp task using the upper limb on
the injured side, but the time taken to complete reaching
movement was prolonged (Fig. 2). During the following days,
the time taken to complete reaching movement gradually
decreased, and returned to the pre-lesion level about 14 days
after the transection (Fig. 2). In contrast, somatosensory evoked
potentials to peripheral somatosensory stimuli had notrecovered at that time. These results suggest that the animals
regained motor performance through adaptive changes in thecentral motor control system in response to the abrupt loss of
peripheral information that must have been received during
voluntary movements.

IV. FUTURE PERSPECTIVE

Fig. 2. (Left) Immediately after dorsal root transection, the time taken fromthe onset of movement to completion of reaching movement prolonged.
(Right) Immediately after dorsal root transection, compared to before dorsal
root transection, an activity increase in high-gamma band was observed at
multiple sites in primary motor and primary somatosensory cortex (right; redindicates time and frequency of increased activity intensity).

Then, cortical electroencephalograms of the primary motor
cortex and the primary somatosensory cortex during the reachgrasp task in the process of recovering motor performance after
dorsal root transection were recorded from a sheet, multielectrode array and analyzed. We found that, the day after
transection, high gamma (80–120 Hz) activity in the primary
motor cortex and the primary somatosensory cortex at initiatingand executing reaching movement increased despite theattenuated inputs from the periphery. Then, the activity level
gradually decreased with the recovery of motor performanceand returned to the pre-lesion level (Fig. 2) in the time course
similar to the reduction of time taken to complete reaching
movement. These results suggest that the changes in highgamma activity may contribute to motor recovery from motor
impairment caused by dorsal root transection.

We prepared a monkey model of somatosensory
impairment by transecting cervical dorsal roots in two
Japanese macaques. Motor function was impaired immediately
after the lesion, but recovered in about 2 weeks. By
longitudinally examining the activity of the primary motor
cortex and the primary somatosensory cortex during therecovery of motor function, we succeeded in identifying acomponent of movement-related activity that covaried withthe recovery of motor function. In the next year, in addition to
the magnitude of brain activity in individual areas involved in
motor and somatosensory functions, we will examine
information flow between those areas by using mathematicalmethods such as Granger causal analysis. We will examine
what changes in information flow account for the observedchanges in the magnitude of brain activity after lesion andduring recovery. We have already found a characteristic
pattern of information flow between the primary motor cortex
and the primary somatosensory cortex during voluntary
movements in healthy, pre-injury monkeys [6]. By
accumulating such findings, we will be able to gain a deeper
understanding of the adaptation mechanism of cerebral
activity to the abrupt loss of information input that is supposed
to be received in the normal condition. It is also expected tolead to a more comprehensive understanding of the neuralmechanisms that control voluntary movements from theperspective of the dynamics of the neural network consistingof both the motor control system and the somatosensory
system.
REFERENCES
[1]
[2]
[3]

[4]
[5]

Fig. 3. Changes in motor performance and activity in the primary motor
cortex and primary somatosensory cortex before and immediately after dorsal
root transection, and during recovery of motor function.

[6]

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.
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, M. Suzuki, and Y. Nishimura, “Motor stage- andfrequency-dependent bidirectional information flows between the
primary motor cortex and the primary somatosensory cortex during
voluntary movements,” The 16th annual meeting of the Japanese Society
for Motor Control, 2022/08/26.

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, trying 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 is expected to offer novel
research hypotheses to be investigated.
II. M EMBERS
To achieve the above mentioned research objective, weorganized the following four research projects. From this year,
10 new proposed research projects have joined the group.
Please refer to the report of each research project for theirresearch outcomes in the fiscal year.
Planned Research Groups
B01 Systems modelling of hyper-adaptation mechanism forreconstruction of neural structure
Principal investigator: Toshiyuki Kondo (TUAT), Funded
co-investigator: Ryosuke Chiba (Asahikawa Med Univ)
Research Outline: This research group aims to realize
systems modeling of hyper-adaptability mechanism with functional dis-inhibition observed in the impaired brain, especiallyfrom the viewpoint of reconstruction of neural structure. To
clarify the underlying adaptability mechanism of a large-scaleand complex network system such as the brain, the constructive approach is indispensable, in which a phenomenon can bemodeled with the minimum degrees of freedom, and behaviorof the model is verified by computer simulations.
In this year, (1) they worked on the estimation of latent
structure in brain activity (EEG), and applied a weaklycoupled dynamic neuron model they proposed last year fordata augmentation. (2) They proposed a method to search forparameters under conditions that facilitate gait by increasing
the stiffness of each joint and search the parameters using an
optimization method. (3) They analyzed NIRS signals underan attention task (continuous performance test) for quantitativeevaluation of human attention levels.
B02 Modeling of ultra-adaptive to body change
Principal investigator: Yasuharu Koike (Tokyo Tech),
Funded co-investigator: Tetsuro Funato (UEC)
Research Outline: This research group aims at the modelingof the mechanism underlying the recovery of the motor
function after the alteration of the body, especially from theviewpoint of the reconstruction of a neural structure in hyperadaptability.
In this year, (1) they constructed a musculoskeletal modelof monkey to elucidate the adaptation mechanism after tendon
transfer using dynamical simulation. (2) They experimentallyconfirmed that muscle synergy can be learned through motor
learning while presenting similarities in muscle synergy.
B03 Systematic understanding and realization of hyperadaptive phenomena focusing on cognition and emotion
Principal investigator: Hajime Asama (U Tokyo), Funded
co-investigator: Jun Izawa (U Tsukuba), Wen Wen (U Tokyo),
An Qi (Kyushu U)
Research Outline: This research group focuses on rehabilitation, the mechanism of hyper-adaptability is investigated froma systematical approach, and new rehabilitation methods aredeveloped based on the investigation of effect of cognition andemotion on behavior adaptation and motor learning.
In this year, (1) they proposed a computational model ofhyper-adaptation mechanism for de-novo motor learning. (2)They investigated the effect of body cognition on muscle
synergy. (3) They investigated the effect of a neuromodulation intervention based on the characteristics of the EEG
power spectrum during motor learning. (4) They examined thechanges in cerebral and cerebrum volumes in spinocerebellar
degeneration patients. (5) They examined the subprocesses underlying the abnormality of sense of agency in Schizophrenia.

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)
Research Outline: The research group aims to verify thefollowing 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 patients with neurodegenerative disorders, adjust
the activity levels in various brain areas and coupling strength
between neuronal circuits as well as control the multitasking
function.
In this year, (1) they used a standing posture control
model they previously proposed to analyze abnormal posturein patients with Parkinson’s disease. (2) They constructeda regression model of 3D DAT-SPECT images. (3) They
investigated cortical mechanisms during voluntary sway, andanalyzed the effect of foot pain on posture during gait.
Proposed Research Groups
B05-1: Motor learning of modularity in musculoskeletal models toward the emergernce of muscle synergy
Principal investigator: Mitsuhiro Hayashibe (Tohoku U)
Principal investigator: Daichi Nozaki (U Tokyo)
B05-2: A neural network model for hyper-adaptability of
bipedal locomotion
Principal investigator: Naomichi Ogihara (U Tokyo)
B05-3: Hyper-adaptation of bodily and neural sensorimotor
information structures in early developmental stage
Principal investigator: Hoshinori Kanazawa (U Tokyo)
B05-4: Low-dimensional functional connectivity across bilateral motor-related areas for hyper-adaptability
Principal investigator: Isao Nambu (Nagaoka U of Tech)
B05-5: Application of motor learning model for partial relationship reuse to reconstruction of muscle synergy
Principal investigator: Yuichi Kobayashi (Shizuoka U)
B05-6: Hierarchical understanging of adaptation to a new
relationship between the eye and the bodyPrincipal investigator: Michiteru Kitazaki (Toyohashi U
Tech)
B05-7: Supraspinal mechanisms of the human upright posturalcontrol based on the EEG dynamics associated with microfalls
Principal investigator: Taishin Nomura (Osaka U)
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)

B05-9: Individual differences in suitable neural circuits forattention control and its effect on motor control
Principal investigator: Takeshi Sakurada (Seikei U)
B05-10: Brain mechanisms for generating exploratory adaptation: Modeling the brain function based on meta-reinforcement
learning
Principal investigator: Yuki Ueyama (NDA Japan)
III. ACTIVITIES
Activities mainly organized by the members in Group B are
described as follows:• November, 28th, 2022, HMS2022 Organized session(Nagoya University)
After a COVID-19 infection, the international symposium
was held as face-to-face style. Prof. Ogihara gave plenary
talk. In the organized session, Prof. Izawa and Prof.
Kutsuzawa gave keynote talks and 7 researchers in the
research project presented their recent research results.
• January 22-23rd, 2022, Organized session at the symposium on Distributed Autonomous Systems (Online), 14
oral presentations.
IV. F UTURE PLAN
Next fiscal year is the last year of the Hyper-adaptability
research area. We proceed fruitful collaborations with GroupA (Neuroscience group), continue deeper discussion on the
research topics, and summarize the research outcomes in theplanned and proposed research projects.

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, weinvestigate (1) Probabilistic latent variable model for analyzing
long-term multi-modal data, (2) Integration of computational
brain network and musculoskeletal models, and (3) Motorlearning experiments with VR/Robot technologies 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 technique toquantify 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,
and applied a weakly-coupled dynamic neuron model they
proposed last year for data augmentation.
Specifically, the channel, frequency, and time components
were obtained as a tensor by performing time-frequency
analysis (the short-time Fourier transform) on the multichannel time-series data of sleep EEG provided by Prof.
Matsumoto (A05-7). After performing tensor decomposition(non-negative CP decomposition), the tensor were expressedas the sum of latent variables. The time component of
the latent variables was divided by a pre-detedmined time

Fig. 1. Structural change of functional connectivity underlying in sleep
EEG identified by using CP decomposition and TVGL.

window. For each time window, the correlation between thelatent variables was calculated, and the dynamic structureof the latent variables was estimated by the Time-varying
Graphical Lasso (TVGL) method. As shown in Fig. 1, we canextract the relationship between latent variables as a graphstructure specific to sleep stages, and visualize the characteristics of the dynamics as a combination of channels andfrequency bands (synergy). Moreover, they applied the EEG
dataset of BCI Competition IV 2a (4-class motor imagery)to the weakly coupled neuron model they proposed last year,
and developed a data augmentation technique based on themodel. They confirmed that the classification accuracy using
a convolutional neural networks (CNN) as a classifier was
significantly improved by performing the data augmentation
method (unpublished). Furthermore, they proposed a novel
reinforcement learning algorithm, and confirmed the superior
performance in black-box optimization tasks with constraints[1].
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

[6] [7]. In this fiscal year, they analyzed NIRS signals
under an attention task (continuous performance test) for
quantitative evaluation of human attention levels [4]. They
found that 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 tomeasure the amount of finger usage in daily life of 20 stroke
hemiplegic patients [8]. They investigated the correlation
between the rate of finger usage on the paralyzed side andgeneral clinical indeces (FMA-UE, ARAT, STEF, MAL). As
a result, they confirmed that the measure correlated with thequantitative indices FMA-UE, ARAT, and STEF, but not with
the qualitative index MAL.
III. F UTURE PERSPECTIVE
Fig. 2.

Constructive modeling approach.

young and elderly subjects, based on the differences in theparameters of a simulator using a musculoskeletal model(Fig. 2).
Toward this goal, they constructed a three-dimensional
computer model of gait initiation because modulation of the
CoP’s movement not only in the forward/backward direction
but also in the left/right direction may cause falls. In order
to construct the computer model, it is necessary to constructcomputer models of standing and gait.
They proposed a neural controller that enables threedimensional gait. A three-dimensional model of standing
posture are utilized that of previous studies. However, it isvery difficult to design the activations of 70 muscles andobtain a reasonable gait because of the large number ofparameters. Therefore, we proposed a method to search forparameters under conditions that facilitate gait by increasing
the stiffness of each joint and search the parameters using
an optimization method, and then reducing the stiffness inturn. As a result, it was confirmed that the gait was within areasonable range, although 500[Nm/rad] of stiffness was left
at the hip joint and the waist joint. [2], [3]They also proposed a neural controller for the gait initiation that links the above posture maintenance and gait.
They confirmed that just switching from posture-maintaining
control to gait control causes the model to fall. Therefore,
the posture and timing of the switch were obtained by
optimization. The evaluation index was the possibility oftransitioning to gait. As a result, they obtained a solutionthat enabled a smooth transition. As a result of the analysis,
it was observed that the CoP was moved to the supportingleg after being moved to the swing leg side. This may be an
anticipatory postural adjustment to the gait motion, and it isinteresting that this was observed even though the design was
not intended for CoP movement but only for gait transition.
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 [5]In consideration of future collaborations with neuroscience
research groups, we proposed several methodologies formodeling the hyper-adaptability from both statistical andconstructive modeling standpoints.
Next fiscal year, we will continue to deepen the modelingmethodology, and we further apply the models to actual neurophysiological data. Moreover, we will investigate the motor
tasks that can induce ”reconstruction of neural structure”
with dis-inhibition in the brain of elderly people under thefrailty state.
R EFERENCES[1] Megumi Miyashita, Shiro Yano, and Toshiyuki Kondo, Evaluation of
Safe Reinforcement Learning with CoMirror Algorithm in a NonMarkovian Reward Problem. In: Petrovic et al. (eds) Intelligent
Autonomous Systems 17. Lecture Notes in Networks and Systems,
vol 577. Springer, Cham. (2023)[2] Hitohiro Etoh, Yuichiro Omura, Kohei Kaminishi, Ryosuke Chiba,
Kaoru Takakusaki, and Jun Ota. (2022). Motion generation of anticipatory postural adjustments in gait initiation. the 2022 IEEE International
Conference on Bioinformatics and Bioengineering (BIBE), Taichung,Taiwan (11/7, 2022).
[3] Hitohiro Etoh, Yuichiro Omura, Kohei Kaminishi, Ryosuke Chiba,
Kaoru Takakusaki, and Jun Ota. Investigation of a method to extend a2-dimensional gait to 3-dimensions in a human musculoskeletal modelwith 70 Muscles. Proc. The 33rd 2022 International Symposium on
Micro-NanoMechatronics and Human Science (MHS2022), Nagoya,Japan (11/28, 2022).
[4] Saki Niiyama, Shiro Yano, and Toshiyuki Kondo, Cerebral ActivityBased Quantitative Evaluation for Attention Levels, Journal of
Robotics and Mechatronics, Vol.34, No.4, pp.739–745, 2022
[5] Samirah Altukhaim, Toshiyuki Kondo, Yoshikatsu Hayashi, Enhancement of Sense of Ownership and Sense of Agency using Virtual Reality and Haptic Feedback, Proceedings of the 44th Annual International
Conference of the IEEE Engineering in Medicine and Biology Society(EMBS), Glasgow, UK, (7/11-15, 2022).
[6] Ozge Ozlem Saracbasi, William Seymour Harwin, Toshiyuki Kondo,
Yoshikatsu Hayashi, Dual Instability against to Sequential Learning
via Haptic Interaction, Proceedings of the 44th Annual International
Conference of the IEEE Engineering in Medicine and Biology Society(EMBS), Glasgow, UK, (7/11-15, 2022).
[7] Eiko Matsuda, Daichi Misawa, Shiro Yano, and Toshiyuki Kondo,
Olfactory Cues to Reduce Retrograde Interference During the Simultaneous Learning of Conflicting Motor Tasks, Journal of Robotics andMechatronics, Vol.34, No.4, pp. 746–755, 2022
[8] Naoya Yamamoto, Takato Matsumoto, Tamami Sudo, Megumi
Miyashita, and Toshiyuki Kondo, Ring-shaped wearable device for
logging finger usage in daily life, Proceedings of the IEEE 2022
International Symposium on Micro-NanoMechatronics and Human
Science (MHS2022), Nagoya, Japan (11/28, 2022).

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

Abstract—We obtained the following two results.
1. We constructed a musculoskeletal model of monkey for
dynamical simulation of tendon transfer in monkeys.
2. We experimentally conﬁrmed that muscle synergy can belearned through motor learning while presenting similarities inmuscle synergy.

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. Monkey musculoskeletal model for tendon transfer
To investigate the neural adaptation mechanism after tendon
transfer, our group constructs a simulation model that can reproduce the adaptation process after tendon transfer. Until lastﬁscal year, the effects of tendon transfer on muscle activity had
been investigated using a musculoskeletal model on the musculoskeletal software OpenSIM[1]. Here, the musculoskeletal

Fig. 1. A: Musculoskeletal model. B: Estimated muscle activities

model used was created by scaling a human musculoskeletal
model to monkey size, and there were problems with themuscle attachment positions and other differences from thoseof monkeys. This year, in collaboration with Prof. Ogiharaof B05-2, we constructed a monkey musculoskeletal modelon MuJoCo software, based on the monkey musculoskeletal
model created by Prof. Ogihara, and created an environmentthat enables anatomically valid analysis of muscle activity.
Fig. 1A shows the monkey model constructed on MuJoCo.
The skeletal system consists of the trunk, head, scapula,
humerus, ulna, radius, and carpal bones. The skeleton has six
degrees of freedom: three degrees of freedom in the shoulder
joint (shoulder ﬂexion, abduction, and external rotation), andone each in elbow ﬂexion, forearm rotation, and wrist ﬂexion.
The number of muscles is 29, and they are arranged as redlines in Fig. 1A.
To validate the model, muscle activities were estimatedfrom the measured monkey’s grasping motion and comparedto the measured muscle activities of the monkey. Fig. 1Bshows the estimated muscle activities. In the grasping motion,
the monkey extended their arms forward mainly around 1 s,reached the food around 3 s, and then carried the food to theirmouth. The estimated results in Fig. 1B (red lines) show apattern somewhat similar to the monkeys’ measured muscle
activities (blue lines), especially in the critical timing of thereaching and mouth-carrying motions. Thus, the constructed
model was able to reproduce the characteristics of the monkey
musculoskeletal model to some extent.

B. Motor Learning using muscle synergy similarity
When attempting to learn a new environment in a virtualsurgery that involves muscle replacement, the correct solution
may not be found through a trial-and-error search. Therefore,
we investigated whether feedback of muscle synergy similarity
could facilitate motor learning.

Fig. 2. Experimental Setup

Virtual task environment using KINARM is shown in
Fig. 2(A). Participants held a pair of manipulanda (blue), andmoved them to control the position of the polished object (redline). The main goal of the task was to smoothen the polishedobject by moving it against the grinder (white rectangle). Contact between both objects caused the manipulanda to generate
forces simulating the contact interaction, which also changed
the shape of the polished object. The EMG and smoothness
scores were displayed above the task workspace. Red dotson the arms indicate the location of the EMG electrodes.
In Fig. 2(B), deformation of the polished object by contact
forces with the grinder. The polished object is shown withand without scaling in the vertical direction. The smoothnessof the object was quantiﬁed as the area between the outline ofthe object and a horizontal line at the mean vertical positionof all contact points. Smoothness and EMG similarity scores
throughout a trial.
Figure 3 shows how the smoothness score and muscle
synergy similarity score changed after ﬁve training sessions
on different days for the group that received no feedback andthe group that was presented with muscle synergy similarity
to the expert’s muscle synergy. The results show that smoothness scores were higher with or without presenting muscle
synergy similarity, but muscle synergy similarity scores weresigniﬁcantly higher for the group that was given feedback. For
the angle between the grinder and the object, the group thatwas presented with the muscle synergy similarity score wassigniﬁcantly smaller. This indicates that the force exerted isstable, or that the direction of exertion is such that the object
is maintained horizontally. The smoothness of the object’s
surface can be conﬁrmed by visual feedback, so the value
is high regardless of the similarity of the muscle synergy, but
the group displaying the similarity of the muscle synergy has
a similar style to the skilled workers in terms of the way of
applying force. This indicates that the group of participants

Fig. 3. Score change during training

who displayed the similarity of muscle synergy had a moresimilar style to the skilled workers, who were polishing a large
quantity of products to provide a certain quality, and that they
were able to learn how to apply force for that purpose as
training for motor learning. In the future, we would like toinvestigate whether this kind of feedback is also effective forlearning virtual surgery.
IV. F UTURE PERSPECTIVE
This year, to elucidate the adaptation mechanism after
tendon transfer using dynamical simulation, we constructed a
musculoskeletal model of monkey. We plan to use this model
to approach the learning and control mechanisms that can
reproduce adaptation after tendon transfer. In addition, we
experimentally conﬁrmed that muscle synergy can be learned
through motor learning while presenting similarities in muscle
synergy. From next ﬁscal year onwards, we plan to promotejoint research with other groups.
R EFERENCES[1] Uchida N., et al., Synergy Analysis of Musculoskeletal Model for
Adaptation Mechanism of Monkey with Tendon Transfer, MHS 2021,
MP2 1 3, 2021
[2] Victor R. Barradas and Woorim Cho and Yasuharu Koike, EMG space
similarity feedback promotes learning of expert-like muscle activation
patterns in a complex motor skill, Frontiers in Human Neuroscience,
16,2023

B03 Annual Report of Hyper-Adapt Project
ASAMA Hajime

The University of Tokyo
Abstract—B03 group aims to clarify the influence of body
consciousness and emotion on the hyper-adaptation, and toestablish mathematical model for hyper-adaptation which canquantitatively predict the state of body motor control ability.
Furthermore, B03 group also aims to use the proposed
mathematical model to develop and evaluate new methods formotor rehabilitation in future research. In the past year, we
accomplished the following works: 1) We proposed a
computational model of hyper-adaptation mechanism for denovo motor learning. 2) We investigated the effect of bodycognition on muscle synergy. 3) We investigated the effect of aneuromodulation intervention based on the characteristics of the
EEG power spectrum during motor learning. 4) We examinedthe changes in cerebral and cerebrum volumes in spinocerebellar
degeneration patients. 5) We examined the subprocesses
underlying the abnormality of sense of agency in Schizophrenia.

I. INTRODUCTION
B03 group focuses on the cognitive aspects such as body
consciousness and emotion in the processes of hyperadaptation. We aim to understand the processes of hyperadaptation through a systematical approach, and to propose
novel intervention methods that trigger hyper-adaptations via
body consciousness and emotion. To do this, we aim toquantitatively measure the process of hyper-adaptation andestablish a mathematical model, thereafter, develop a brain
decoding method that can predict the semantic function.
Moreover, we aim to develop a robotics platform for neuralintervention, to further estimate our model-based intervention
rehabilitation methods. In specific, we will examine the hyperadaptation for post-stroke patients, and to model their motor
recovery, and to examine the effect of model-based methods.
II. AIM OF THE GROUPIn B03 group, we aim to quantitatively measure body
consciousness, emotion, and the hyper-adaptation of bodymotor control, establishing a mathematical model that is able topredict optimized intervention method for rehabilitation.
III. RESEARCH TOPICS
B03 accomplished the following studies in the past year.
A. A computational model of hyper-adaptation mechanism forde-novo motor learning
Last year, Izawa’s group (University of Tsukuba) foundthat the participants can be categorized by one who exhibits
hyper-adaptation and the other who does not exhibit hyperadaptation when human participants adapt their motor control
ability for novel body and task structures. They also found thatthe hyper-adaptation group exhibited a large motor commandvariance during the adaptation training. Thus, this year, Izawa’s

group developed the computational model to explain this
hyper-adaptation phenomenon, given the hypothesis that large
exploration is the clue to the hyper-adaptation ability[1]. The
simple model was developed first, where the linear matrix gave
the novel task structure. We proved that the covariance matrixof the exploration noise has to be a regular matrix and showedthat the norm of the covariance matrix determined the learning
speeds, which was confirmed via simple simulation
experiments. Then, we developed the neural network model toexplain previous reports of reach adaptation tasks, de novo
learning tasks, and virtual surgery tasks and replicated theseresults with the model. Further, we found that the magnetic
stimulation of the premotor cortex facilitates learning [2]. We
also developed the hyper-adaptation model for rehabilitation
where the recovery valley was overcome [3].
B. Investigation of the effect of body cognition on muscle
synergyAn’s (The University of Tokyo) research group has shownthat there are four muscle synergies involved in human sit-tostand motion: forward bending of the upper body, rising hipfrom a chair, extension of the whole body, and stabilization of
posture. In addition to sensory information such as vision andvestibular sense, they newly investigated the effect of bodycognition on human sit-to-stand motion, particularly on muscle
synergy. To alter body cognition virtually, the participants
were asked to perform the sit-to-stand motion while wearing anapparatus that restricted the range of motion of the knee joint,
and the changes in standing motion caused by the apparatus
were examined. As a result, it was found that restricting therange of motion of the joints increased the activity of muscle
synergy 1 contributing to the forward bending of the upper
body, as shown in Figure 1. This led to a strategy of more
forward bending to generate momentum. At the same time, theactivity of the muscle synergies 2 and 4 which contribute torising hip and stabilization of posture decreased, compensating
for the changes in the lower limbs by upper body movement.
After removing the apparatus, the activity of muscle synergy
returned to the same level as before wearing the apparatus,suggesting that the participants were able to adjust the activityof muscle synergy immediately in response to the changes intheir own body [4].
C. The effect of neuromodulation intervention on motor
learning
Asama, Wen, and Hamada’s group (The University ofTokyo) investigated the effect of a neuromodulationintervention based on the characteristics of the EEG power
spectrum on motor learning [5]. Transcranial alternating
current stimulation of the gamma band was applied to thefrontal and lateral parietal regions during hand motor learning

in healthy participants to investigate its modulatory effects on
the learning process. Contrary to the hypothesis, thestimulation group showed less proficiency in hand motor
learning than the sham group. Intervention based on the EEG
power spectrum related to motor learning was shown to beineffective during motor learning, and further investigation of
the timing and methods of intervention was found to benecessary.

have a significant negative correlation with the volume of thefrontal and temporal regions of the cerebrum, and many ofthese regions were significantly increased compared to thehealthy group [6] [7] (Fig. 2). These increased regions wereconsidered to be responsible for compensating for the control
system of the cerebellum.
E. The subcomponents underlying the abnormality in sense ofagency in Schizophrenia
At last, Asama’s group examined three subcomponents
underlying the sense of agency in Schizophrenia bycollaborating with Maeda’s group in Keio University. It is
known that patients with schizophrenia often show abnormal
agency judgment. However, it is unclear whether suchabnormal judgment is due to biased prior belief of impaired
sensorimotor processes. The current study used three different
tasks to target the agency judgment, control detection, andcontrol planning, respectively. By analyzing the action plans inthese tasks, we found that patients with schizophrenia are lackof action diversity, and their action plans were barely
influenced by the sense of agency [8].

Figure 1. Changes of muscle synergy due to restriction of range
of joint motion.

IV. FUTURE PERSPECTIVE
In the past year, we proposed the computational model ofhyper-adaptation mechanism for de-novo motor learning. We
examined the effect of body cognition on muscle synergy andthe effect of brain simulation on motor learning. We also
examined the neural and behavioral changes in different typesof patients. Those results are useful for understanding thehyper-adaptation processes in both healthy people and patients.
REFERENCES
[1]
[2]
[3]

Figure 2. Comparison of cerebral volume in the patient groupand the healthy group

[4]

[5]

D. The volumes of cerebral and cerebrum in spinocerebellar
degeneration patients
Furthermore, Asama, Wen, and An’s group identified
regions that undergo plastic changes by calculating the cerebral
volumes using brain structural image analysis to reveal how the
motor control system impairment in spinocerebellar
degeneration patients is compensated for in the cerebrum. The
volume of the cerebellum in the patient group was found to

[6]
[7]
[8]

Dal’Bello, L.R., and Izawa, J. "Computational role of exploration noisein error-based de novo motor learning." Neural Networks 153 (2022):349-372
Sugiyama, T., Nakae, K., and Izawa, J. "Transcranial magnetic
stimulation on the dorsal premotor cortex facilitates human visuomotor
adaptation." NeuroReport 33.16 (2022): 723-727.
Izawa, J. Higo, N., and Murata, Y. "Accounting for the valley ofrecovery during post-stroke rehabilitation training via a model-based
analysis of macaque manual dexterity." Frontiers in rehabilitation
sciences 3 (2022): 1042912.
Yoshida, K., An, Q., Hamada, H., Yamakawa, H., Tamura, Y.,
Yamashita, A., and Asama, H. “Analysis of muscle activity of Sit-tostand motion when knee movability is disturbed,” Journal of Robotics
and Mechatronics 34.4 (2022): 767-776.
Hamada, H., Wen, W., Kawasaki, T., Yamashita, A. & Asama, H. “EEG
Power Spectra Changes with the Proficiency of Motor Learning
NEURO2022, Japan, 2022
濵田 裕幸, 菊地 豊, 温 文, 山下 淳, 淺間 一，脳構造画像解析に基づ
く脊髄小脳変性症患者の大脳による制御メカニズムの理解，第 40回日本ロボット学会学術講演会，2022
濵田 裕幸, 菊地 豊, 温 文, 安 琪，山下 淳, 淺間 一，脳構造画像解析に基づく脊髄小脳変性症患者の大脳の制御システムの理解，第35 回自律分散システム・シンポジウム，2022
Chang, A. Y-C., Oi, H., Maeda, T. and Wen, W. “The sense of agency
as active causal inference at an abstract action plan level”. 第 35 回自律分散システム・シンポジウム，2022

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 24 coinvestigators (Shirafuji, Kaminishi, Takamido, Hasegawa,
Kohno, Kishimoto, Yuine, Ishibashi, Hamada, Miyata, Osaki,
Kanaya, Kawano, Kanai, Omura, Etoh, Sonoda, Fujiwara,
Huang, Makino, Orihara, Nishizawa, Ishikawa).
II. RESEARCH RESULTS AND FUTURE PLANS
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 previously proposed a standing posture control
model (with muscle tone and feedback control parameters as
control parameters) that introduces control mimicking thedescending tracts [1]. This year, we used this to analyze

abnormal posture in patients with Parkinson's disease. To test
the hypothesis that the abnormal posture is one of the posturesin which the patient can stand with small sway, we went
through the following process: 1) Based on the postural data ofpatients, calculate the muscle tone parameters that allow themusculoskeletal model to stand; 2) After setting the muscle
tone parameters, adjust the feedback control parameters andposture to reduce sway. The results showed that the standing
posture closer to the experimental results was achieved whenthe muscle tone was larger than the value equivalent to that of anormal subject (Fig. 1). In addition, the adjusted posture closest
to the experimental results did not differ significantly from theexperimental postural data (the difference in each joint angle
was less than 5.2°), which supports the hypothesis.

Fig. 1. Differences between simulation and experimental results for indicesrelated to posture, sway, and co-contraction. In all participants' data, thedifferences were small when the muscle tone index,||uff|||2, was as high as 6 or 8.

Dopamine transporter single-photon emission computed
tomography (DAT-SPECT), which is used to diagnose
Parkinson's disease, was also analyzed. The 3D informationfrom DAT-SPECT is often reduced to a 1D scalar value. The
relationship between DAT-SPECT and various motor
symptoms has not been fully analyzed. Therefore, wedeveloped a system that captures 3D DAT-SPECT informationand examines the relationship between it and each motor
symptom using deep learning technology. Using DAT-SPECT
images as input, we constructed a model for regression analysis
of scores related to each motor symptom, and visualized whichparts of the image the model focused on (Fig. 2). In addition to
improving the accuracy, we aim to describe the relationshipbetween the neurotransmitter status and the behavior in
combination with the model in the future.

Fig. 2. Example of how the regression analysis model visualizes the regions
around the striatum during the estimation of the muscle rigidity-related score.
The closer the color is to red, the higher the degree of attention. The putamen(lower) was more important than the caudate nucleus (upper) in the analysis.

In addition, we attempted to characterize the postural sway ofstroke patients by adjusting the parameters of themusculoskeletal model and the standing posture control model
to reproduce the standing data of stroke patients and applying
dimensionality reduction to these parameters [2]. As a result,differences in gain parameters related to whole-body extension
were identified between stroke patients and young healthy
subjects. These are necessary to maintain knee extension andwhole-body balance while suppressing rapid extension of thelumbar region and ankles.
B. Evaluation of the role of neurotransmitters in posturalcontrol while multitasking
Yozu et al. evaluated the role of neurotransmitters inpostural control while multitasking. To further understand thissubject, we conducted a review of prior research on the subjectof multitasking [3]. 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) [4]. However, ourfindings indicated the lack of a discernible effect ofneurotransmitters on multitasking during static standing. 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 as a motor task,
given its versatility in terms of task difficulty. We conducted a
pre-patient study on healthy subjects before the patient subjects.
Furthermore, as pain is often comorbid in Parkinson’s disease,it still remains unclear how pain affects the postural and motor
control. Therefore, we investigated the effects of pain on
postural and motor performance in healthy subjects.
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[5] 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 [4]. 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.
Although prefrontal cortex activity is still under analysis,
voluntary sway, in which movements are made in response to a
specified frequency, can be interpreted as a multitasking task
consisting of motor and cognitive tasks. In the future, we plan
to investigate the effects of neurotransmitters on posturalcontrol by measuring voluntary sway in Parkinson’s disease
patients with diurnal fluctuations.
2) 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 [7].
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.
REFERENCES
[1]
[2]

[3]
[4]

[5]
[6]
[7]

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.
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. Mori, T. Hasegawa, K. Kaminishi, R. Chiba, J. Ota, A. Yozu.
Evaluation of voluntary sway during standing in human. Proceedings ofthe 34th SICE DAS Symposium. Online, 2022.
N. Orihara, T. Hasegawa, K. Kaminishi, R. Chiba, J. Ota, A. Yozu.
Proceedings of the 35th SICE DAS Symposium. Osaka, 2023.
K. Sonoda, T. Hasegawa, K. Kaminish, M. Osumi, M. Sumitani, R.
Chiba, J. Ota, A. Yozu, Effects of plantar pain on gait. The 33rd 2022
International Symposium on Micro-NanoMechatronics and Human
Science (MHS2022). Nagoya, 2022.

B05-1 Motor learning of modularity inmusculoskeletal models toward
the emergence of muscle synergy
Mitsuhiro Hayashibe, Kyo Kutsuzawa
Tohoku University
Abstract—Neural integrators offer a promising model-free
approach for handling high-dimensional control problems. It
still remains an open problem on how we can create motor
learning of modular structure and its generalization. In thisstudy, we investigated neural integrators for inducing dynamic
equilibrium for multi-limb multi-joint system and motor synergy
generalization framework for unlearned motions.

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 themanipulator and environment is explicitly given. For the manipulation task, the dynamics conditions are always unknown.
It still remains an open problem on how we can create humanlike synergetic motion. This study provides neural integrator
framework to induce dynamic equilibrium and motor synergy
generalization framework.
II. N EURAL INTEGRATOR TO INDUCE DYNAMIC
EQUILIBRIUM

The synchronization phenomenon is common to many
natural mechanical systems. Joint friction and damping inhumans and animals are associated with energy dissipation.
A coupled oscillator model is conventionally used to manage
multiple joint torque generations to form a limit cycle in anenergy dissipation system. The coupling term design and thefrequency and phase settings become issues when selecting theoscillator model. The relative coupling relationship between
oscillators needs to be predefined for unknown dynamics
systems, which is quite challenging problem. We present a
simple distributed neural integrators method to induce the limit
cycle in unknown energy dissipation systems without using
a coupled oscillator. The results demonstrate that synergetic
synchronized oscillation could be produced that adapts todifferent physical environments. Finding the balanced energy
injection by neural inputs to form dynamic equilibrium is not atrivial problem, when the dynamics information is not priorly
known. The proposed method realized self-organized patterngeneration to induce the dynamic equilibrium for different mechanical systems. The oscillation was managed without using
the explicit phase or frequency knowledge. However, phase,frequency, and amplitude modulation emerged to form anefficient synchronized limit cycle [1]. This type of distributedneural integrator can be used as a source for regulating multijoint coordination to induce synergetic oscillations in natural
mechanical systems.
III. M OTOR S YNERGY G ENERALIZATION
Humans can rapidly adapt to new situations, even though
they have redundant degrees of freedom (d.f.). Previous studies
in neuroscience revealed that human movements could be
accounted for by low-dimensional control signals, known as
motor synergies. Many studies have suggested that humans
use the same repertories of motor synergies among similartasks. However, it has not yet been confirmed whether the
combinations of motor synergy repertories can be re-used
for new targets in a systematic way. Here we show that the
combination of motor synergies can be generalized to newtargets that each repertory cannot handle. We use the multidirectional reaching task as an example. We first trained multiple policies with limited ranges of targets by reinforcement
learning and extracted sets of motor synergies. Finally, we
optimized the activation patterns of sets of motor synergies
and demonstrated that combined motor synergy repertories
were able to reach new targets that were not achieved witheither original policies or single repertories of motor synergies[2]. We believe this is the first study that has succeeded in
motor synergy generalization for new targets in new planes,
using a full 7-d.f. arm model, which is a realistic mechanicalenvironment for general reaching tasks.
IV. C ONCLUSION
The first study gives inspiration on how neural integrators
manage the amplitude, frequency, and phase modulation to
the unknown dynamics [1]. The second study reveals that a
linear combination of motor synergies extracted from multiple
policies can be generalized to new targets. Notably, only withlearning in horizontal and sagittal reaching, and totally new
frontal reaching could be achieved through synergy generalization thanks to modularity advantage [2].
R EFERENCES[1] M. Hayashibe and S. Shimoda, Synergetic synchronized oscillation by
distributed neural integrators to induce dynamic equilibrium in energy
dissipation systems, Scientific Reports, 12, 17163, 2022.
[2] K. Kutsuzawa and M. Hayashibe, Motor synergy generalization framework for new targets in multi-planar and multi-directional reaching task,
Royal Society Open Science, vol. 9, 211721, 2022.

Fig. 1. (A) Phase portrait of two double pendulums connected to a common floating base. (B) The shoulder and the elbow move over time in anti-phase.
When the common base was fixed, there was no interaction between the two pendulums. When the common base was floating, the pendulums achieved
anti-phase oscillation instantly. This implies that ”phase modulation” could be achieved with this method. (C) When neural integration was not used, theoscillation could not be sustained.

Fig. 2. Results in sagittal policies. (E) An example of snapshots in the sagittal targets. (F) Trajectories in the sagittal targets. (G) Learning progress. (H)Trajectories in the various targets with different θ, including unlearned ones.

B05-2: A neural network model for hyper-adaptability of
bipedal locomotion
Naomichi Ogihara

Department of Biological Science, Graduate School of Sciences, The University of Tokyo
Abstract— It is generally accepted that the humanmusculoskeletal system is evolved in such a way that it facilitates
generation of robust, energetically efficient bipedal locomotion.
The fact that the human musculoskeletal system ismorphologically adapted to generation of habitual bipedal
locomotion possibly indicates that the human nervous system
inherently possesses an ability to adaptively make use of suchalteration of body structure and morphology to facilitate control
and to reduce energetic cost of bipedal locomotion by hyperadaptive reorganization of the locomotory nervous system. In thisstudy, we attempt to elucidate such hyper-adaptive mechanism of
human bipedal locomotion based on a neuro-musculoskeletal
forward dynamic simulation.

I. INTRODUCTION
For animals, locomotion is one of the most importantbodily functions for their survival and reproductive success, i.e.,
escaping from predators, searching for food, and even meetinga mate and producing offspring. Locomotion is accomplishedby the mechanical interaction of the musculoskeletal systemwith its inhabiting environment. Therefore, the morphology ofthe musculoskeletal system of an animal is shaped to adapt toits principal mode of locomotion. Humans are no exception.
The human body has evolved over millions of years to adapt tothe 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 profile seen in humans. Such differences inkinematics and kinetics of bipedal walking between humans
and nonhuman primates exist because of structural differencesin the musculoskeletal system. Specifically, the evolutionary
alterations of the hip and foot musculoskeletal structures (i.e.,the bowl-like structure of the pelvis and longitudinal arch of
the foot) are suggested to be the key for the acquisition of
stable and efficient bipedal walking in humans.

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 ground reaction force are appropriately
controlled. However, any changes of the musculoskeletal
structure directly affect the way the body mechanicallyinteracts with the ground, and should drastically alter
coordinated dynamics of bipedal walking, more likely to
disturb successful generation of stable bipedal locomotion. The
fact that the selective pressure was applied to the humanmusculoskeletal structure during the evolution of humanbipedal locomotion strongly indicates that the nervous system
possesses an ability to spontaneously reorganize itself in such a
way to adaptively make use of the morphological change in the
body structure to accomplish more stable, robust and efficient
bipedal locomotion. If the neuronal mechanism underlying
such “hyper-adaptability” of human locomotion can beelucidated, the findings will not only contribute to clarifying
the neural basis of the evolution of human bipedal locomotion,
but also provides implications for effective therapeutic or
rehabilitative interventions to restore walking ability in oldadults 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 reticulospinal and vestibulospinal tracts to realize stable,robust, and efficient bipedal locomotion while adaptively
coping with the morphological change in the human bodystructure.
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.

The above nervous model incorporated the mathematicalmodel of the CPG, but this is not sufficient to explain themechanism of “hyper-adaptability”. The reticulospinal tractthat involves in the control of muscle tone during postural andlocomotor control, and the vestibulospinal tract responsible forvestibulospinal reflex necessary to maintain postural balanceagainst external perturbations should be incorporated. Since
reticulospinal tract neurons relay inputs from themesencephalic locomotor region (MLR) to the CPG, we aremodeling it such as to adjust muscle tones in response tochanges in sensory information during locomotion. On theother hand, the vestibulospinal reflex is a reflex in which thevestibular organ senses the acceleration of the body due toexternal perturbations and adjusts the muscle tone of the limbs
to maintain its body equilibrium. In this study, we hypothesizethat the vestibular nucleus estimates the ground reaction force
vector necessary to control trunk orientation in response to a
perturbation based on information from the vestibular input andconverts it to muscle torques and activations based on thetranspose of the leg Jacobian matrix.
IV. RESULTS AND DISCUSSIONSThe kinematics of the simulated bipedal locomotion in theJapanese macaque based on the CPG model generally agreed

A

B

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.
Pattern Formation
MLR
RhythmGeneration
Reticulo‐
spinal tract

CPG

・
・・

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].

with the measured data. The simulated gait also captured themain features of the ground reaction force profiles in theJapanese macaque [6]. However, bipedal gait simulation basedon a nervous model incorporating the reticulospinal andvestibulospinal tracts has not yet been realized. In the future,
we intend to clarify the mechanism of "super-adaptation"
associated with the alterations of the body structure byrealizing the gait simulation incorporating the models of thesedescending tracts.

Vestibulo‐
spinal tract

Muscleactivations

Sensory information

Fig. 2. Framework for the modeling of locomotor nervous system.

REFERENCES
[1]
[2]

[3]
[4]
[5]

[6]

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.

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 often assumed that sensorimotor experiences
evoked by infantile spontaneous movements have an essential rolefor development of sensorimotor coordination. We focus on
analyzing sensorimotor data during such spontaneous movements
in neonates and infants, to understand the specific functional
recovery and compensatory processes that occur in early
development. We collected and analyzed motion data from neonates
and infants to identify a dynamic transition in sensorimotor
interactions, such as “sensorimotor wandering,” which emerged
spontaneously in neonates and infants. In this year, we showed thatneonates and infants learn patterns of sensorimotor interaction not
only in the spatial direction but also in the temporal direction. We
also developed a model to emerge muscle synergies in task-freemovements, which enabled the verification of acquired muscle
synergies contribution to developmental behavioral acquisition.

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.

III. RESEARCH TOPICS
A. “Senseorimotor wandering” in neonates and infants
We has collected and analyzed motion data from neonates
and infants before the emergence of intentional motor control,and has examined the spatial and temporal structuring of therelationship between muscle activity and proprioceptive inputs
during their spontaneous movements. In this year, we adopted
ensemble spectral clustering based on orthogonal non-negative
matrix factorization (ortho-NMF), as the time-varying
clustering methodology to analyze time-varying aspects of theinformation density of sensorimotor interactions. We
determined the rank number based on the information criteria
defined by Bai et al. To obtain the clusters at each time step,
Ortho-NMF with k = 12 was iterated 10,000 times forcalculating the information density of sensorimotor interactions
considering Gaussian noise. We eventually, we conducted
spectral clustering considering coincidence probability
calculations and obtained 10,000 clustering results at each time
step. Finally, we obtained 12 states and delineated a dynamictransition in sensorimotor interactions, expressing the
spontaneous emergence of sensorimotor states, such as“sensorimotor wandering” in neonates and infants (Fig. 1).

In FY2022, we analyzed sensorimotor data during
spontaneous movements in neonates and infants, anddeveloped a model which generate self-organized muscle
groups.
II. AIM OF THE GROUP
The specific aim of this research item is to examine thespecific functional recovery and functional compensatory
processes that occur in early development. We therefore
investigated the developmental changes in sensorimotor
interactions in neonates and infants, constructing models to
explain these changes, and examining the changes andadaptations caused by dysfunction.

Fig.1 Example of transition of sensorimotor interactions.
To describe the developmental changes based on theproperties of dynamic transition, we estimated group-leveltransition probabilities as a Markov chain (Fig. 2). The red dots
in each transition probability matrix represent the points thatexceeded the threshold generated by random permutation,
indicating that when a participant is in one state, he or she thentends to transition to a certain state. To examine whether thetransition probability matrix of neonate and infant group is far
from random, we conducted the permutation test, in which thenull hypothesis was that each matrix would have the same
number of state transitions exceeding the threshold. Theseresults clarified that the state transition of the infant group
significantly differed from random (p=0.0210), whereas
neonate group do not differ (p=0.6776).

Fig.2 Transition matrix in sensorimotor interactions

Subsequently, in order to confirm whether the acquired
muscle synergies contribute to developmental behavioral
acquisition, the learning of rolling over in reinforcement
learning using Soft Actor Critic was verified. A comparison oflearning performance with and without muscle synergies
showed that while there was no learning at all when muscle
synergies were not acquired (Figure 4, blue), rolling over could
be learned after muscle synergies were acquired.
From these experiments, it has been demonstrated that
sensorimotor learning through spontaneous movements in theearly developmental stage would be acquired through relatively
simple learning rules using task-free random movements, andthe results can be utilized in later movements or behaviors.

Furthermore, we aimed to assess the presence of a temporal
pattern in spontaneous sensorimotor interactions and associatedage-related changes. With regard to temporal patterns, wedefined the dyadic and triadic state sequences based on
previous and subsequent sensorimotor states when statetransitions occurred. The repetition ratio of each dyadic andtriadic sequence pattern was calculated as the co-occurrence
probability over the entire recording time and acrossparticipants. We consequently identified a higher cooccurrence probability of the dyadic and triadic state sequences
in infants than in neonates.
These results indicates that infantile spontaneous
movements, despite being task-free, structure and organize
sensorimotor interactions in the entire body during early
development.
B. NMF-based muscle synergy in spontaneous movement.
Major purpose of this project is to understand the specificfunctional recovery and functional compensatory processes that
occur in early development. It requires a learning model thatinduces developmental change, as described in A, as a platform
to enable this to be verified. In this year, we proposed a modelfor the acquisition of muscle synergy in task-free conditions
such as infantile spontaneous movements. First, we input
random muscle activity into an infant musculoskeletal modelwith 186 muscles throughout the body to generate spontaneous
whole-body movements. We applied k-shape clustering or
NMF to the resulting proprioceptive inputs and updated theweight matrices of network of muscle activities. Finally, theinfant musculoskeletal model achieved muscle synergies (Fig3).

Fig.4 Comparison of rewards for learning to rolling over.

Fig.5 Examples of rolling over with muscle synergy..
IV. FUTURE PERSPECTIVE
This year, through the analysis of motor measurements in
actual neonates and infants, we have shown that they learn
patterns of sensorimotor interaction not only in the spatial
direction but also in the temporal direction through task-freespontaneous movements in the early stages of development. In
addition, using an infant musculoskeletal model, we haveshown that they can acquire muscle synergies by learning theco-occurrence of random muscle activities during task-freespontaneous movements, and that the acquired muscle
synergies can be used for specific learning. Next year, we will
investigate how these learning processes are disrupted andhow they can be improved in cases where abnormalities occur
in the body or nervous system.
REFERENCES
[1]

[2]

Fig.3 weight matrices of network of muscle activities

Kanazawa, H., Yamada, Y., Tanaka, K., Kawai, M., Niwa, F., Iwanaga,
K., & Kuniyoshi, Y. (2023). Open-ended movements structure
sensorimotor information in early human development. Proceedings of
the National Academy of Sciences, 120(1), e2209953120.
Shinomiya Y, Kanazawa H, KimD, Kuniyoshi Y, Action acquisition of
musculoskeltal infant agent using motor primitives and deep
reinforcement learning, International Symposium on Adaptive Motion ofAnimals and Machines 2023 (submitted).

B05-4Annual report of research project
Isao Nambu

Nagaoka University of Technology
Abstract—Functional connectivity is an important measure
to evaluate relationship among different brain regions.
While several recent studies suggest that neural population
dynamics is represented as low-dimensional subspace, suchthe dynamic spaces are not investigated for functional
connectivity yet. This study aims to develop a method todetect low-dimensional subspace for the functional
connectivity. For this purpose, we examined the results
using a method using auto-regressive models forelectroencephalogram during reaching and a method using
time-varying graphical lasso for electroencephalogram
during sleep and electrocorticogram for reaching task. We
found the possibility to detect dynamics related to the
behavioral states in the low-dimensional subspace while
many issues are to be solved.
I. INTRODUCTION
There are various brain regions that communicates
information among them to realize perception, behaviors, andso on. Functional connectivity is a measure to quantify thestatistical dependence of time-series data among such multiple
brain regions [1]. Therefore, functional connectivity is used to
examine the relationship between multiple brain regions.
Several functional connectivity methods have been proposed.
However, it is difficult to interpret all relationships between
multiple regions in a unified manner.
On the other hand, it has recently become clear that theneural representation of movements depends on the state in the
low-dimensional space (Neural Manifold) consisting of neural
populations [2]. For example, it has been suggested that in the
motor cortex, the neural populations during motor planning andexecution are represented in a low-dimensional space [2,3].
However, it is unclear how multiple brain regions are related tothe movements and the relationships and interactions among
these brain regions. If low-dimensional dynamics at the wholebrain level involving multiple brain regions can be clarified, we
may be able to gain a deeper understanding of human
movements, adaptation, and learning. Recently, studies haveattempted to find the low dimensionality of functional
connectivity for functional magnetic resonance imaging data
[4,5]. Thus, it would be useful to elucidate the mechanism offunctional connectivity if the dynamics can be examined inelectroencephalogram or electrocorticogram with superior
temporal resolution. In addition, changes in bilateral motorrelated regions [6] are considered to be important in "hyperadaptability," and it would be important to detect changes indynamics of the bilateral motor-related regions. Therefore, inthis study, we attempt to develop a method to identify the brain
activity involved in movement as low-dimensional state-space

dynamics and aim to elucidate the mechanism of "hyperadaptability".
II. AIM OF THE GROUP
The purpose of this research is to develop a method to identify
state-space representations for elucidating hyper-adaptability.
We also aim to detect changes during hyper-adaptation byusing the developed method to examine the dynamics in lowdimensional space, especially in bilateral motor-related
regions. For this purpose, we continued to develop a method
to find the low-dimensional subspace using directed graphs(AR-based method) for electroencephalogram (EEG) data
during human reaching movements. Also, the lowdimensional spatial identification method using undirected
graphs (TVGL-based method) was also examined on anotherdata sets.
III. RESEARCH TOPICS
We examined two types of the methods as follows.
A. Development of the directed-graph method to identifying
low dimensional subspace for EEG data
Continuing from the previous fiscal year, we studied amethod that combines auto-regressive models (AR models)
and the Kalman filter. This method incorporates a model of
directed graphs that can clarify the directionality of functional
connectivity. For directed graphs, we use the commonly usedauto-regressive model. The coefficients of the autoregressivemodel are estimated using the Kalman filter. Distance wascalculated from the coefficients obtained with these methods
and then visualized using manifold learning.
An example of the results obtained from this method for EEG
data during reaching movements [6] is shown in Fig. 1.
Projection of the trial-averaged data onto a 2-dimensional or
3-dimensional space confirmed a smooth transition from theresting state to the execution state of the movements. Thistendency became clearer when smoothing was applied, and thetemporal changes in the low-dimensional space could be
captured under limited conditions. This tendency was also
observed in data from different subjects, but the trajectories in
low-dimensional space showed different tendency. The
temporal changes at different frequencies were also confirmed
by using a band-pass filter with each specific frequency bands,suggesting transitions in multiple frequency bands. Therefore,
it is considered necessary to further study the low
dimensionality in different frequency bands.

identification method using TVGL may be able to capture thelow-dimensional dynamics of functional connectivity.
However, this is a preliminary study on a single subject.
Further investigation for other data, parameter search, andexamination of brain regions corresponding to low-dimensional
space are needed.

IV. FUTURE PERSPECTIVE

Fig. 1. An example of three-dimensional (left) and two-dimensional
representation of the states using AR-based models for the reaching data.
Upper figures show data without smoothing and lower figures show resultswith smoothing. Each color indicates different behavioral states (rest, cue, andexection).

B. Development of the non-directed method to identifying low
dimensional subspace for EEG data
Next, a low-dimensional visualization method using an
non-directed graphical model was investigated. EEG andelectrocorticogram (ECoG) are dynamically changing between
regions from moment to moment. Therefore, it is desirable toconstruct a model that takes such dynamic nature into account.
In a previous study, a stochastic graphical model, TimeVarying Graphical Lasso (TVGL) has been proposed [8]. In
this study, we continued to investigate this method from theprevious fiscal year. We applied it to different data sets (sleep
EEG data and ECoG data.
For the sleep EEG data, we examined whether there were
any changes related to sleep stage. It was confirmed that data at
the same sleep stage but at different times were not confirmed
to be in close states even in the low-dimensional space,suggesting rather reflecting the proximity of time. Therefore, it
is considered necessary to consider the variation associatedwith time in the analysis.
Next, we also applied this method to the ECoG data during
reaching and grasping movements. We found that a trend of
gradual change from before to during the movements wasobserved in low-dimensional space. This tendency wasparticularly found in low-frequency bands such as theta andalpha bands. This suggests that a low-dimensional

In this study, we tried to develop a method to identify the low
dimensional subspace for EEGs or ECoGs in humans. We
tested two types of methods in this fascial year. We found a
possibility that these methods can detect dynamics related tothe task/behavioral states in the low-dimensional space.
However, it is also observed temporal variations unrelated tothe task states. In the next fiscal year, we will further examinesuch the methods specifically and apply it to data with bilateralmotor-related areas for understanding of hyper-adaptability．
REFERENCES
[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.
T. Semoto., I. Nambu., and Y. Wada. The Relationship Between the
Movement Difficulty and Brain Activity Before Arm Movements.
In International Conference on Neural Information Processing, pp. 522529, 201
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.

B05-5 Application of motor learning model forpartial relationship reuse to reconstruction ofmuscle synergy
Yuichi Kobayashi
Faculty of Engineering, Shizuoka University
Abstract—When partial dysfunction occurs in the humanbody or brain, it is possible to adaptively recover function byreusing previously acquired neural circuits. We propose a motor
learning model that describes such an adaptive process. While ourprevious motor learning model that estimates the transformationof partial dependencies of motor systems is based on the feedback
control mechanisms, this study also focuses on the process ofacquiring the feedforward control. In addition to estimating thetransformation of coefﬁcient information in feedback control, byconsidering the transformation of feedforward terms, we aim to
explain the process of reusing already acquired motor control
information such as muscle synergy.

reference

Feedback
controller

observation

body

Feedback matrix(partial relationship),,reference

Feedback
controller
bodyobservation

control input= ( ( ))
control input

synergy acquisition

I. I NTRODUCTION
The human adaptive capacity encompasses the ability to dynamically recover functionality following partial impairmentsin the body or brain by reutilizing acquired neural pathways.
For instance, it has been investigated that, as an alternative
to neural circuitry, when one hand is paralyzed, it can becontrolled through a distinct neural pathway [1].
We have constructed a motor learning model capable ofexplaining the aspect of ‘neural pathway reutilization,’ which
has not been sufﬁciently represented in conventional humanmotor learning models. This model explains the process ofreusing a portion of the acquired motor control model in
response to situational demands. In upper limb motor control,
there exist multiple causal (dependent) relationships, suchas the kinematics that relate joint angles and hand position
and the motion equations that relate joint torque and joint
acceleration, among several sensor variables (proprioceptive
information) with different modalities.
In the ﬁrst stage of our research in the project, we proposeda model based on previous research results that estimates
the dependency relationships between various sensor signals
in motor control and automatically generates controllers [2].
We have developed a motor learning model that explains
the process of reusing partial causal relationships in acquired
controllers by introducing a mechanism for estimating transformations between mappings in the model [3].
On the other hand, in the process of motor learning andmotor function recovery in living organisms, it is known
that multiple muscles cooperate and activate together witha certain temporal pattern, a function called muscle synergy.
Measuring the expression, generation, and recovery processesof muscle synergy is a powerful means of understanding theprocess of super-adaptation. Muscle synergy is thought to be

After
structural change

Beforestructural change

reference

observation

Feedforward
generator

Feedback
controller
bodyreference+

control input

observation

Feedforward sequence(muscle synergy weight)

Feedforward
generator

Feedback
controller
body+

control input=( ( ))(estimated)

Fig. 1. Motor learning model of reuse of controller information including
muscle synergy

acquired through repeated movements, and it is important
to consider reusing knowledge about motor skills based on
the acquisition of learning. In addition, both feedback (FB)
control and feedforward (FF) control are used in human bodycontrol, and there are cases that suggest that by repeating
movements, control can be switched from FB control to FF
control, enabling unconscious control [4].
II. A IM OF THE G ROUP
We propose an explainable motor learning model that extends our previously proposed feedback control-based knowledge reuse model to include the acquisition of feedforward
time-series signals similar to muscle synergies. The generationof feedforward time-series control input based on the feedback
control system is shown in the transition from the top to thebottom of Fig. 1. The reuse of motor information throughvariable transformation that we have previously proposed isrepresented by the left-to-right arrows in the top row. The leftto-right arrows in the bottom row represent the reuse processof acquired feedforward time-series signals through transformation. By considering the transformation that includes theacquisition of feedforward control, we can reproduce motor
learning and adaptation processes, including the reuse of
muscle synergies.

Feedforward control (72 times learning)
Feedback control

Feedback control input
Feedforward control input

Reference
trajectory

Reference trajectory
Feedforward control (42 times learning)
Feedback control (0 times learning)

0.33

Sampling period

0.32

y [m]

y [m]

Control Input

1.0

0.6

State
observation

Fig. 2. Long period control

Time

Fig. 3. 2-DoF manipulator motor
learning

III. R ESEARCH T OPICS
As an approach to constructing the motor learning model inFig. 1, we conducted a more precise veriﬁcation of the variabletransformation proposed in [3] (the process from left to rightin the upper row of the ﬁgure) and proposed a learning modelthat generates FF time series based on the FB control system(the process from the upper to the lower row in the ﬁgure).

0.2
0.2

0.31

0.6

x [m]

1.0

0.3

0.8

0.85

x [m]

0.9

0.95

Fig. 4. Control result after learning Fig. 5. Comparison of trajectories inthe ﬁrst section

By conducting 100 trial-and-error attempts on a 2-link
manipulator control system for arc path tracking shown in
Fig. 3, a long-period control was obtained, as shown in Fig. 4.
The green area in the ﬁgure represents the interval controlled
without state observation based on the FF time series. As
shown in Fig. 5, which extracts the trajectory in some intervals,
it was conﬁrmed that the reference path was followed without
degradation compared to when FB control was used.
IV. F UTURE P ERSPECTIVES

A. Motor information transformation and reuse in feecback
control system
In the motor learning method based on the algorithm [2]locally weighted regression is used to estimate the relationships between sensor variables. Based on this regression
information, we proposed a variable transformation that canestimate the mirror relationship between the left and right arms
using an adaptive grid distribution algorithm [3]. However, thisalgorithm has the problem that the representation freedom ofvariable transformations is high, and the search space becomeslarge for large-scale problems. To estimate variable transformations more efﬁciently with lower degrees of freedom, we
proposed a method of estimating the relationship between local
linear transformations in the form of transformation matrices,and veriﬁed it on a simple problem assuming the estimation
problem of the mirror relationship of the left and right arm
controllers [5].
B. Acquisition of feedforward sequence by extension of feedback control cycle
As a motor learning method for acquiring the time-series
signal in FF control, we propose a learning process in which aperson gradually becomes able to move smoothly by repeating
the same movement. This is achieved by controlling withless FB information through repetition within a certain rangeof movement. In contrast to the conventional combinationof FF control and FB control, which mainly focuses oncontrol performance and robustness against disturbances, themain objective of this proposal is to reduce the frequency of
FB control by adjusting FF control. To acquire such longperiod control, we use a reinforcement learning, Blind Action
Sequence Learning with EM (BASLEM) [6], which is stateindependent and time-dependent.

We proposed an explainable motor learning model that
includes the reuse of knowledge, including the acquisitionof time-series control signals such as muscle synergies inmotor learning. We found that the motor learning model
based on feedback control could represent the estimation ofvariable transformation with simpler calculations than before.
In addition, we proposed a motor learning method called‘long-term potentiation of feedback control,’ which made it
possible to explain the process of ‘becoming able to performsmooth and natural movements without conscious effort byrepeating the movement.’ In the future, we aim to verify these
two achievements in more practical motor learning settingsand develop a motor learning model that can be comparedwith biological experiments and knowledge.
R EFERENCES[1] T. Isa, M. Mitsuhashi, R. Yamaguchi, Alternative routes for recovery ofhand functions after corticospinal tract injury in primates and rodents,
Current opinion in neurology, Vol.6, No.5, pp.836-843, 2019.
[2] 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.
[3] S. Nakamura, Y. Kobayashi and T. Matsuura, Grid-Based Estimationof Transformation Between Partial Relationships Using a Genetic Algorithm, Journal of Robotics and Mechatronics, Vol. 34, No. 4, pp.
786-794, 2022.
[4] X. Zhang, S. Wang, J. B. Hoagg, The Roles of Feedback and Feedforward as Humans Learn to Control Unknown Dynamic Systems, IEEE
Transactions on Cybernetics，Vol. 48, No. 2, pp. 543–555, 2018.
[5] Yuichi Kobayasi and Sota Nakamura, Transfer of Partial Informationof Motor Controller Based on Estimation of Coordinate Transformation
Parameters, Proc. of The 33rd 2022 International Symposium on MicroNanoMechatronics and Human Science, MA2-1-5, 2022.
[6] D. Nakano, S. Maeda and S. Ishii, Proposal of New Reinforcement
Learning with a State-independent Policy, Trans. of the Institute of
Systems, Control and Information Engineers, Vol. 27, No. 8, pp. 327–
332, 2014．

B05-6 Annual report of research project
Michiteru Kitazaki

Toyohashi University of Technology
Abstract—We have built a system for virtual eyes on the handexperiment, presented a demonstration exhibit and designed theexperiment. For the system for virtual eyes on the back of the head,the experimental system was constructed, and data acquisition
started; We found that the motor learning occurred, and thewalking became smoother and faster during 10 days. In addition,
experiments were conducted to verify the adaptability of humans
for long-term learning of an impossible body with joints that bendin opposite directions, a virtual walking system with different
directions of gaze, and a system where two people operate a single
avatar.

I. INTRODUCTION
Research on embodiment, such as the Rubber hand illusion,
has developed in the aspect of body transformation using virtual
reality (VR). By changing the appearance of avatars
synchronized with body movements, it is possible to change theskin color, become a child's body, become transparent, makebody parts scrambled, use one body for two people, and so on,
and a sense of body ownership is induced in these avatars,
changing attitudes and behavior to match the avatar's attributes(Fig. 1) [1-3]. However, in most studies, the body shape remains
human and is typically manipulated by a single subject with asingle avatar. In order to elucidate the limits andplasticity/adaptability of human body perception, furthertransformations of avatar shape and function need to besystematically investigated.

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
human's perception and behavior. Normally, the human eyes
are located in front of the head. This correspondence between
body and sensory organs determines the hierarchicalrelationship between perception and environment. We
manipulate the 'eye-body relationship' to clarify how human's
perception and behavior change with long-term adaptation.
Specifically, bodies in which the relationship between thevisual organ and the body is manipulated, such as a body witheyes behind the head and a body with eyes at the tips of thehands, are constructed using VR, and three different levels('level of conscious action strategy', 'level of unconscious
action' and 'level of perception') are focused on and clarified
through psychophysical experiments and behavior analysis (Fig.
2).

Fig. 2 A body with eyes on the back of the head (left), and a body with eyeson the hand

III. RESEARCH TOPICS

Fig. 1 Invisible body (top), and Shared body (bottom)

A. A body with eyes on the hand
In the VR space, a system was implemented and exhibited inwhich the left and right eyes (virtual cameras) were placed on
the left- and right-hand controllers and could be moved freely by
the left and right hands. When the binocular images were notfused, binocular rivalry occurred. However, when something
was found and the eyes (i.e. hands) were moved to look that way,that field of vision became dominant and a consistent visualexperience was obtained. When eye movements were measured,eye movements similar to the vestibular oculomotor reflex wereobserved when the hand, which is the eye, was moved [4].
B. 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 three 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. 3). 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

C. Adaptive learning for bodies with joints bent in opposite
directions
An experiment was conducted by creating a body in which
the elbow joints of the left and right arms were bent in opposite
directions, and manipulating the direction of joint bending andthe synchronization 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 [5].
D. Virtual walking system with different avatar gaze direction
from user’s gaze direction
It is known that virtual walking experiences can be induced
by presenting only foot vibrations and visual images without
moving the legs in a sitting position. Our experiments showedthat virtual walking experiences can be induced to some extent
in a lying position on the back, where the direction of gaze in the
VR space and the actual direction of gaze of the experimental
participants are different [6].
IV. FUTURE PERSPECTIVE

Walking

Fig. 3 Walking experiment with body with eyes behind the head.
Day1

Day2

Day3

Day4

Day5

Day6

Day7 2 Day8

Day9

Day10

Day1

Day2

Day3

Day6

Day7

2 Day8

1.5

1.5

1

1

0.5

0.5

0
-2

-1
-0.5

REFERENCES

0
0

1

2

-2

-1

-0.5

-1

-1

-1.5

-1.5

-2

-2

For the two main experiments, a system was constructed for theeye on the hand experiment, a demonstration exhibition washeld and a plan for the experiment was drawn up. For the eyeon the back of the head experiment, the experimental system
was constructed, and data acquisition was started. In addition,
experiments were conducted to verify the adaptability ofhumans for long-term learning of an impossible body withjoints that bend in opposite directions, a virtual walking system
Day4
with
aDay5different direction of gaze, and a system where two
Day9peopleDay10
operate a single avatar [7]. In the next year, dataacquisition and analysis will be carried out, and more detailed
models will be built based on the data.

0

Fig. 4 Data on the walking trajectories of a participant in the experimentover a period of 10 days

For the participants' 10 days of circumferential walking, thewalking trajectories became smoother as the days progressed(Fig. 4) and the walking time became shorter (faster walking
speed); a similar trend was observed in the complex course
walking task performed only on days 0 and 11. When changes
in the direction of the head relative to the trunk were calculated,the difference 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 acquired
or influenced by learning.

[1]1

[2]
[3]
[4]
[5]

[6]
[7]

2 R., Sugimoto, M., Minamizawa, K., Hoshi, T., Inami, M., andKondo,

Kitazaki, 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
Onodera, R., Mimura, T., Hirose, S., Inoue Y., and Kitazaki, M. (2022).
Espcape of eyes: Destruction of relationop between body and eyes,
Annual Conference of Japan Virtual Reality Society, 1G-01, Sapporo.
Hapuarachchi, H., Ishimoto, H., Sugimoto, M., Inami, M., and Kitazaki,
M. (2022). Embodiment of an Avatar with Unnatural Arm Movements,
2022 IEEE International Symposium on Mixed and Augmented Reality(ISMAR), 17 – 21 October 2022, Singapore
Nakamura, J., Ikei, Y., and Kitazaki, M. (2023). The Effect of Posture on
Virtual Walking Experience Using Foot Vibrations. Augmented Humans
2023, 12-14 March 2023, Grasgow, UK.
Hapuarachchi, H., Hagiwara, T., Ganesh, G., and Kitazaki, M. (2023).
Effect of connection induced upper body movements on embodimenttowards a limb controlled by another during virtual co-embodiment. PLoS
One, 18(1): e0278022

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

Abstract— Beta rebound, synchronous brain activities in thebeta frequency band that appear after execution (Go) and/or
suppression (No-Go) of movements, has been studied in thecontext of motor decision-making processes during upper limb
voluntary movements. In the first stage of the HyperAdaptability project, we showed, for the first time, that there
exists a beta rebound of electroencephalogram (EEG) activity
during perturbed upright stance in response to a brief supportsurface perturbation, using the perturbation-onset as a triggering
event. Particularly, the long-lasting beta rebound appeared
during a period of time when the postural state recovers slowly
toward upright position along stable manifold of the unstablesaddle-type upright equilibrium of the postural control system
without active feedback control (OFF-period of the active
control). Here, in the second stage of the project, we are examining
whether a similar beta rebound can be observed during quiet stance.
In this case, the “micro-recovery” is the target timing of our
examination, during which the postural state recovers slowly
toward upright position following the occurrence of the preceding
micro-fall. As predicted by the the intermittent control hypothesis,
we showed that EMG activity level of mediolateral gastrocnemius
was low during the micro-recovery, implying that the posturalstate recovers during the OFF-period of the active control. Then,
we are currently analyzing the Event-Related Spectral
Perturbation map during the micro-recovery, using the onset of
micro-fall as a triggering event. The interim result suggests theexistence of beta rebound in the micro-fall during quiet stance.

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 hypothesis [1,2]. Theintermittent control model switches off the ankle joint active
torque intermittently 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 shownthat the intermittent control model exhibits a much higher
accuracy in model fitting of experimental postural sway data
during quiet stance, compared to the conventional continuous
stiffness control model [3]. In this study, electromyography ofankle muscles (EMG) and brain activity (EEG) related toneural control of posture were measured in addition to theposture sway during quiet stance. We aim to elucidate thebrain 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 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 medial gastrocnemius muscle, i.e., thereticular formation in the brainstem receiving projections fromthe peduncle tegmental nucleus, is activated or inactivateddepending on the postural state of the bodily mechanicalsystem. 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 on Innovative Areas, wemeasured 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 high-frequency β band appears in the EEG in thevicinity of parietal association area with a long latency (about1.5 seconds) and for a long time (about 3 seconds) [4]. The βERS that appears immediately after completion or cessation ofvoluntary movement of the upper extremities or fingers iscalled β-rebound or status quo [5]. It is also known that βrebound appears at a timing similar to that of the Go responseeven for the case of the No-Go response in Go/No-Go tasks.
Such β-rebound is thought to reflect motor control and brain
activity based on sensory re-afferent information [6]. The βrebound during 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 the active ankle joint torque. Since the muscle
activation of the mediolateral gastrocnemius was small during
this process, the corresponding postural recovery isdetermined passively solely by the bodily mechanics. Such βrebound may reflect active postural monitoring associatedwith the ON/OFF selection process of motor commands forcontrolling activity of the medial gastrocnemius [4].
In the second stage of this Research on Innovative Areas,
we are investigating whether the β-rebound that appeared
during the postural recovery process in response to theperturbation also appears during postural sway during quiet
stance with no perturbation. Postural sway during quiet stancecan be regarded as a stochastic process in which a forward
micro-fall followed by a micro-recovery repeats randomly.
Micro-falls occur about once every second. Our brain
responds appropriately to each micro-fall and maintains ourstance. Thus, healthy adults are the experts in postural control
who have been avoiding falls tens of thousands times
throughout their developmental stage from infancy.
III. RESEARCH TOPICS

Then, EMG and wavelet-transformed EEG time-frequency
signals were averaged to obtain Event-Related Spectral
Perturbation (ERSP) as shown in Fig. 2. In Fig. 2, activity ofthe medial gastrocnemius muscle increased with theoccurrence of the micro-fall, but when the CoM started toshow the micro-recovery, the activity of the medial
gastrocnemius muscle decreased and formed a plateau
waveform, which might correspond to the OFF-period of theintermittent control. Furthermore, ERS was confirmed in the
β-band of ERSP during this OFF-period of the control.
IV. FUTURE PERSPECTIVE
The β-band ERS in Fig. 2 is considered to be β- reboundreflecting active postural monitoring during the microrecovery process, although further careful examinations
including statistical tests are necessary.
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.
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.
Engel, A. K., and Fries, P. (2010). Beta-band oscillations—signallingthe status quo? Curr. Opin. Neurobiol. 20, 156–165.
Takakusaki K. (2017). Functional Neuroanatomy for Posture and Gait
Control. J. Mov. Disord. 10, 1-17.

Fig. 1 shows the anteroposterior variation of Center of Mass
(CoM), which represents postural sway during quiet stance,
and the corresponding EMG of the mediolateral gastrocnemius
muscle. A large number of vertical dotted lines in the figure
represent the sequence of occurrences when the CoM toppling
velocity attains a positive peak for every micro-fall. Using theonset of the forward micro-fall of the CoM, the micro-fall andsubsequent micro-recovery processes were extracted as epochs.

Fig. 1. Postural sway in anterior-posterior (AP) direction and thecorresponding EMG activity of medial gastrocnemius during human quiet
stance. Top, middle and bottom traces exemplify time series data of CoMAP (center of pressure in AP direction) measured by a motion capturesystem, CoM velocity, and corresponding full-rectified and low-passfiltered EMG from mediolateral gastrocnemius (MG), respectively. Verticaldotted lines indicate the sequence of micro-fall events.

Fig. 2. Time-locked average of epochs for time series of CoM-AP, its
velocity, rectified EMG of mediolateral gastrocnemius (MG), andwavelet-transformed EEG at Cz electrode (ERSP).

B05-8 Annual report of research project
Kazuhiro Sakamoto
Faculty of Medicine Tohoku Medial and Pharmaceutical University, Tohoku University Graduate School of Medicine
Abstract—Here, we developed a model for learning a twotarget search task. In the task, the agent is required to gaze oneof the four presented light spots. Two neighboring spots areserved as the correct target alternately, and the correct target
pair is switched after a certain number of consecutive successes.
In order for the agent to obtain rewards with a high probability,
it is necessary to make decisions based on the actions and resultsof the previous two trials. Our previous work achieved this byusing a dynamic state space. However, to learn a task that
includes events such as fixation to the initial central spot, themodel framework should be extended. For this purpose, here we
propose a “history-in-episode architecture.” Specifically, we
divide states into episodes and histories, and actions are selectedbased on the histories within each episode. The proposed model
performed the task close to the theoretical optimum. The
learning model including the proposed history-in-episodearchitecture and dynamic state scape provides a basis for hyperadaptable learning systems to complex environments.

I. INTRODUCTION
In the real world, the unexpected often occurs. An
environment in which we cannot even define the probability
space of what can happen is called an indefinite environment.
To truly adapt to such new situations, it is necessary to
proactively engage the world and generate the framework foradaptation itself. To elucidate the principle of proactive
outreach to such an indefinite environment, Sakamoto et al.
has constructed a reinforcement learning model thatdynamically and autonomously defines the probability spaceor state space based on the criteria of experience saturation
and action decision uniqueness[1][2]. However, the model isfar from adequately reproducing the advanced adaptive
abilities of primates.
II. AIM OF THE GROUP
In this research project, we aim to construct a learning modelof higher-order brain functions, especially in the higher motor
cortex, by extending the dynamic state-space reinforcement
learning model described above. So far, we have developed alearning model based on a two-target search task (Fig. 1A). In
this task, four light spots are presented in front of the animal'seyes, and the animal is rewarded for fixating on the correct
target hidden in the spots. Two adjacent light spots arealternately used as the correct target (correct pair), but after acertain number of consecutive successes (exploitation phase),
the correct pair is switched without informing the animal, andthe animal has to search for a new correct pair (exploration
phase). In the actual task, there were several events within asingle trial (Fig. 1B). In the two-target search task, a fixation
point first appeared, followed by four light spots around it.

The disappearance of the fixation point in the center served ago signal, and the animal had to look at the correct light spot
within a certain period of time to receive a reward. The
question is how to handle the double time flow? That is, how
to reconcile the learning of behavior based on the history ofactions and their results across trials with the learning of
behavior at each event within a trial? Reinforcement learningmodels basically determine the next action based on theprevious state. To obtain a high correct response rate in the
two-target search task, it is necessary to make decisions basedon the actions and their results of the last two trials, but the
subject has to fixate the center spot at the time physically justbefore shifting the gaze to the correct light spot.
A Two-Target Search Task
N-2th trial

N-1th

Nth

N+1th

N+2th

N+3th

N+4th

N+5th

…
…

correct correct correct
error
error
correct correct correct
Pair Switch<------Exploitation Phase-------> <-------Exploration Phase--------> <--Exploitation Phase--

B Episode: 1

st

2nd

3rd

4th

Correct
RewardTrial Start(500 ms)

Fixation
Spot On(500 ms)

Peripheral
Spots On
(500 ms)

Go &Gaze Shift(500 ms)

Error No Reward

Fig. 1. Two target search task. (A) An illustration of a valid-pair switch.
Green circle: correct target; arrow: choice (B) The event sequence of a trial.

III. METHODS
To solve this problem, we devised a history-in-episodestructure (Fig. 2)[3]. An episode here was specifically an event
within a trial that was characterized by the presented stimulus.
For example, an episode in which the subject was required toactually look at one of the four light spots was defined by thepresentation of the four light spots (Go & Gaze-Shift episode).
A memory set, i.e., a dynamically expanding and contracting
Q-table, was assigned in response to each new presented
stimulus (Fig. 2A). In the case of the two-target search task,
the history of prior trials was traced back in the Go & GazeShift episode, while in other episodes, the subject simply
needed to fixate on the center and did not need to go back to
the previous trial (Fig. 2B). Rewards were given only in Go &Gaze-Shift episodes, but behaviors in the preceding episodes
were learned by handing over the reward prediction value.
IV. RESEARCH TOPICS
The results are shown in Fig. 3. As a comparison, we used a
fixed 10-state model, i.e., a model in which the behavior wasdetermined based on the action and the result (upper right,upper left, lower right, lower left, lower left, center) x (correct,
error) of the previous trial in each episode, and a traditional
SARSA model, i.e., a model with only one state in each
episode. Although all models performed perfectly in thesimplest task, in which one light point remained correct all thetime (Fig. 3A), the SARSA model showed very low
performance in the task in which two fixed light points
alternated (Fig. 3B) and in the one-target search task, in which
one light point was correct for a while and then switched tothe other (Fig. 3C). In the two-target search task, the fixed 10state model failed to show a high rate of correct responses (Fig.
3D).

History-in-Episode(Dynamic State)A
Correct Response Rate

4th

Correct Response Rate0

Sequence of EpisodesobWorking Memory

w.m.

w.m.

w.m.

w.m.

Dynamic Q-table

QQ11Q …

QQ11Q …

Q1

Q1

a

a

1

1

Generation of new episode-dependent
memory sets along the time step, t

B During Learning of Two-Target Search Task (Nth trial)

1

Trial No

104

1

Trial No

104

Fig. 3. Comparison of the performance on each task between models. (A)Evolution of the correct response rate in the fixed one-target task. (B) The
fixed two-target task. (C) One-target search task. Dashed line: ideal
performance. (D) Two-target search task. Dashed line: ideal performance.

N-2thTime step in the episode= Sequence of trials, N

REFERENCES

N-1th

t-3

Fixed Two-Target Task

D Two-Target Search Task

One-Target Search Task

100%

New episodes

ob

Conventional SARSA

0

C

3rd

Episode-Dependent
Memory Set

B

Fixed One-Target Task

100%

A 1st Trial of One-Target Task

Time step of episodes, tHistory-in-Episode(Fixed 10-State)

[1]

t-2

t-1

t

ob

ob

ob

ob

w.m.

w.m.

w.m.

w.m.

QQ11Q …
1

QQ11Q …
1

QQ11Q …
1

QQQ11Q
1 1 ..

a

a

a

a

Nth

Generationof a newQ-tablereferring
previous
two trials

Fig. 2. Schematic diagram of model operations as training progresses. (A)The first trial of the one-target task after completion of the fixation task.
Episode-dependent memory sets corresponding to task events 3 and 4 are
newly generated. (B) While it is learning the two-target search task. The
number of states in task event 4 is still increasing. w.m.: working memory;arrow: choice; ob: observation; a: action.

V. FUTURE PERSPECTIVE
This year, we added a history-in-episode structure to ourprevious dynamic state-space reinforcement learning model[1][2] and constructed a learning model that can makedecisions based on the inter-trial history while learning actionsat intra-trial events [3]. In the future, we plan to construct amodel that can learn more advanced tasks including delay
periods, but we have already obtained preliminary results[4][5]. Through these studies, we would like to construct alearning model that is hyper-adaptable to unexpected
situations[6]. In addition, we would like to draw a novel
picture of the brain that actively acts on an indefinite
environment and achieves creative hyper-adaptation throughelucidating and modeling of dynamic encoding of informationin the prefrontal cortex [7][8][9][10].

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. (references)[2] K. Sakamoto, T. Katakura, H. Mushiake, “Decision uniqueness andexperience saturation define the appropriateness of state expansion:comparison between dynamic state space reinforcement learning modeland Dirichlet models,” NEURO2022, 3P-261, July 2022.
[3] K. Sakamoto, H. Yamada, N. Kawaguchi, Y. Furusawa, N. Saito, H.
Mushiake, “Reinforcement Learning Model With Dynamic State Space
Tested on Target Search Tasks for Monkeys: Extension to Learning
Task Events,” Front. Comput. Neurosci., vol. 16, 784604, June 2022.
[4] N. Tamura, H. Mushiake, K. Sakamoto, “A dynamic state-space
reinforcement learning model explaining functional differentiation ofhigher motor areas in the cerebral cortex,” IEICE Tech. Rep., vol. 122,
NC2022-42, pp. 44-48, Sept. 2022.
[5] N. Tamura, H. Mushiake, K. Sakamoto, “A reinforcement learningmodel with dynamic state space reproduces functional differentiation
between higher motor cortices,” NEURO2022, 1P-310, July 2022.
[6] K. Sakamoto, “The brain beyond machine learning: Perspectives fromneurophysiology and computational neuroscience of higher brain
functions,” APSIPA BioSiPS Workshop, Sept. 2022.
[7] K. Sakamoto, “Multidimensional analysis of extracellular potentials in
the cerebral cortex,” Nara Institute of Science and Technology,
Information Science, Colloquium A, June 2022.
[8] K. Sakamoto, N. Saito, S. Yoshida, H. Mushiake, “A dynamic,
economical, and robust coding scheme in the lateral prefrontal neuronsof monkeys,” The 29th International Conference on Neural Information
Processing, p113, Nov. 2022.
[9] K. Sakamoto, N. Saito, S. Yoshida, H. Mushiake, “Increased firing
variability may be an early warning signal of bifurcation in neuronal
networks: Validation in action planning-related cells of monkey
prefrontal cortex,” Neuroscience 2022, 736.17, Nov. 2022.
[10] 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.

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 SSSEP,
B) we improved the proposed neurofeedback training system,
and C) we preliminarily examine the training effect based on
SSVEP.

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. Furthermore, we
propose a model for the diversity of neural circuits acquired
through neurofeedback training. In conventional neurofeedback
training, a common goal (neural circuit to be obtained) is often
set for the trainees [1]. However, in such training protocols,
individual differences in brain function [2][3] are notconsidered, which increases the possibility that the training
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 thebrain region responsible for the targeted brain function. We
expect that this approach promotes the activation of the neuralcircuits that each trainee is most likely to acquire, andultimately aims to improve attention function and motor
control ability efficiently.
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.
We adopt a training approach based on the outcome of"whether attention is focused or not" by indirectly estimating
the attention state from the activity state of sensory areas,
without explicitly setting a desired activity state for higher
brain regions such as the prefrontal cortex and posteriorparietal cortex, which are neural bases of attention function.
Through this approach, we aim to demonstrate theeffectiveness of an efficient neurofeedback training systemthat is independent of individual neural characteristics.

III. RESEARCH TOPICS
A. Body-specific attention training based on SSSEPThe attention to a specific body part is called body-specificattention. To train body-specific attention, we used a
neurofeedback training system with SSSEP. Participants wererandomly assigned to either a Real group (where feedback was
based on their own brain activity) or a Sham group (where
feedback was based on another person's pre-recorded brain
activity, serving as a control group) and underwent 8 days of
training. EEG electrodes were placed around the bilateralsomatosensory cortexes, and mechanical vibration stimuli were
presented to the fingertips of both hands (22Hz to the left hand,
25Hz to the right hand). During the presentation of thesemechanical vibration stimuli, a fast Fourier transform was
performed on a 5-second buffer of EEG data. The signal-tonoise ratio of the SSSEP was calculated as an indicator of theattention state (i.e., the strength of attention to the body part
where the sensory stimulus was presented). During training,audio speakers were placed in front of the participants on bothsides, and their volumes were determined based on thecalculated signal-to-noise ratio. Participants were instructed todirect their attention to their left hand. When attention wasappropriately directed to the left hand, an increase in SSSEP
response (i.e., an increase in signal-to-noise ratio) was expectedin the right somatosensory cortex, resulting in an increase in
the volume of the left speaker. In addition, to evaluate theeffects of neurofeedback training on behavior, the GoNogo
task and Hand Choice task were performed before and after the
training.
As a result of 8 days of neurofeedback training, a marked
increase in the response from the right somatosensory cortex
was observed in the Real group, as compared to the left
somatosensory cortex. In contrast, no such neural modulation
was observed in the Sham group. Regarding the evaluation
tasks conducted before and after the neurofeedback training, atendency for a reduction in reaction time to the Go signal
presented to the left hand was observed only in the Real groupin the GoNogo task after the training. Additionally, in the Hand
choice task, an increase in the area where reaching movements
were performed using the left hand (i.e., an increase in thefrequency of using the left hand) was observed only in the Realgroup after the training (Fig. 1). These behavioral changes
support that the acquired brain activity by the neurofeedback
training contributes to improving attention function,
demonstrating the effectiveness of the neurofeedback training
system proposed in this study.

(participant No.1) had an amplified response in the right visual
field, while the other group (participants No.2 and 3) had adecreased response. In terms of the training goal, the former
was considered a success in neural modulation, while the latter
was considered a failure. Next, in the GoNogo task, only
participants who succeeded in neural modulation showed adecrease in reaction time. Furthermore, in the line bisectiontask, only participants who succeeded in neural modulation
showed a shift in the midpoint position towards the left visual
field, which was the target space for training (Fig. 2).
Fig. 1. Behavioral changes in SSSEP neurofeedback training.
A: GoNogo task. B: Hand choice task.

B. Improvement of the proposed neurofeedback system
We had been using a self-made vibration device with abrushless DC motor as an actuator to induce SSSEP. This
device had the advantage of being noiseless but had also
disadvantages such as being loud due to the sound of the motor
and requiring a separate system for motor control. To addressthese issues, we adopted a more compact and easy-to-control
haptic reactor as a new vibration stimulation device and used a
microcontroller from the M5 Stack series for its control. As aresult, the stimulus device became much more compact, and it
became possible to present any vibration stimulus at a carrier
frequency of around 200 Hz. Regarding the sensory stimulus
setting during neurofeedback training, we had been using thesame frequency throughout the entire training period. However,with this stimulus setting, it was not possible to exclude thepossibility of adaptation or habituation to a specific frequency
stimulus. Thus, we set different stimulus frequencies for eachtraining day in new training protocols. Furthermore, based on
the previous results, we attempted to reduce the burden on the
trainees by shortening the training period from 8 days to 5 days.
C. Spatial attention training based on SSVEP
We conducted preliminary experiments on neurofeedback
training using SSVEP for training spatial attention. Participants
were assigned to the Real group and underwent 5 days ofneurofeedback training. EEG electrodes were placed aroundthe left and right visual areas, and flicker stimulation waspresented to both visual fields from a high-frequency display(beta-band stimulation to the left visual field and alpha-band
stimulation to the right visual field). Similar to the SSSEP
training mentioned above, we estimated the attentional statebased on the response of the sensory areas and provided
feedback as the speaker volume. Participants were instructed todirect their attention to the left visual field. GoNogo and line
bisection tasks were performed before and after neurofeedback
training.
The results of the 5-day neurofeedback training revealed
that the participants were divided into two groups: one group

Fig. 2. Behavioral changes in SSVEP neurofeedback training.
A: GoNogo task. B: Line bisection task.

IV. FUTURE PERSPECTIVE
In B05-9, we confirmed the effectiveness of the proposed
neurofeedback training system and made improvements to thesystem to achieve more efficient and effective training. While
results supporting the training effect have been obtained, we
have only evaluated the lower sensory areas that reflectattentional states. Therefore, in the next academic year, we
conduct whole-brain measurements including the prefrontal
cortex and the posterior parietal cortex to reveal the acquired
neural circuits and their individual differences. Finally, we try
to propose a model for the diversity of neural circuits acquiredby the training system proposed in this study.
REFERENCES
[1]
[2]
[3]

[4]

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.

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 using a visuomotor task. Thisfiscal year, we built the computational model and reproduced
human behavior data in an exploratory task.

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 built a reinforcement learning modelthat realizes exploratory adaptation, and reproduced behavioral
data obtained in experiments.

Target+5˚Cursor rotation(invisible)×-5˚

Feedback（visible）

+5˚

Shoot

Hit:
Fail:

Fig. 1 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 the presentation of
the fixation cross. Participants were asked to move a stylus pen tip (dashedcircle, invisible) from the start position to the center of the visual target within
1 s after target presentation. When the pen hit the target, participants received
reward feedback (a filled white box explosion). Unbeknownst to theparticipants, the reward zone was shifted by ±5° in some of the trials.

III. RESEARCH TOPICS
We built a computational model to perform thereinforcement learning task and compared the simulation
results with the results of previous experiments with humanparticipants.
A. Experimental task
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. 1). In this task, participantscannot know the visual position of the cursor and perform thetask 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.
B. Computational model
We modeled the performance of the experimental task to areinforcement learning using an actor-critical framework. The
direction of cursor on the k-th trial is called by yk, and isexpressed as follows:
yk = xk + ek + nk + pk 

()

0

80

160

2

0.1

0
-2
-4

p(Δu|R+)

PTB- PTB+0p(Δu|R-)

-15

Trial
RewardE+10Change in reach angle [deg]Memory [deg]Exploration SD [deg]

0
+1
0
-1
+1.5

10
5
0

R+

R-

Reward prediction error
Value

0

7

Slope

Exploration SD [deg]

-10
+10

15

15

F

0

+0.5

0

Std. of Change [deg]

-10

0.2

4

Probability

0

D

C

B

Reward zone

+10

Reach Response [deg]Reach Angle [deg]

A

6
5

-6

-12

4
3
2
1

0

80

160

Trial

0.5

1.3

Value

Fig. 2 Simulation result. Model simulation for behavioral data from participants (N = 25). The model adopted estimated hyperparameters that fit eachparticipant's actual data. (A) The course of the reward zone angle over trials is indicated by the shaded area (cyan). The gray line is the reach angle of thesimulated agent responding to the reward zone shift. (B) The average reach response across the simulated participants for + 5 degrees of rotation (PTB+)
and –5 degrees of rotation (PTB–). (C) The histogram of the change in reach angle between n and n+1 trials after a successful trial (green, R+) and after
an unsuccessful trial (R–). (D) The average of the change in reach angle for R+ and R– across the simulated participants. (E) The transition of thevariables of the learning agent. From the top, Rewards, Memory, Exploration SD, Rewad precition error and Value over trials. (F) Control policy of
Exploration SD. The scatter of Exploration SD over the values is plotted with the linear regression line. The bar graph in the upper right corner is theestimated slope value across the simulated participants.

where xk is the motor memory, ek is the exploration noise, nk is
the motor noise, and pk is the perturbation of the cursor as anangle of rotation. Both the exploration noise and motor noise
are assumed to be Gaussian noise, and exploratory adaptationis achieved by controlling the intensity of the exploration noise
by its standard deviation (SD). The SD of the exploration noiseis learned as well as the motor memory to maximize the reward
by the reward prediction error δk, represented as follows: k = rk +1 +  Vk +1 − Vk ()where rk+1, and Vk+1 are the reward and expected value function
at trial k+1, respectively. In addition, γ is the discount rate, a
hyperparameter unique to each participant, along with thelearning rate required to update the motor memory and SD of
the exploration noise.
We simulated the model using the hyperparameters
obtained from the human experimental data, and the
reproduced data fit the actual behavioral data with highaccuracy.

IV. FUTURE PERSPECTIVE
During this fiscal year, we built a computational model andconducted simulations of human behavior in an experimental
task for exploratory adaptation. Some of the results of thisyear's work, together with experimental data from previous
years, have been published in a paper that has already been
submitted for publication.
In the next fiscal year, we will acquire imaging data
through fMRI experiments and compare them with the model
to elucidate the functional network structure of the brain torealize exploratory adaptation.
REFERENCES
[1]

[2]

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.

List of Publications (2022)

Journal Papers
1.

Hayashi R, Yamashita O, Yamada T, Kawaguchi H, Higo N, Diffuse optical tomography using fNIRS
signals measured from the skull surface of the macaque monkey, Cerebral Cortex Communications,3(1), tgab064, 20222.

Miyawaki H, Mizuseki K, De novo inter-regional coactivations of preconfigured local ensembles
support memory, Nature Communications, 1, 1272, 2022

3.

Asai T, Hamamoto T, Kashihara S, and Imamizu H, Real-Time Detection and Feedback of CanonicalElectroencephalogram Microstates: Validating a Neurofeedback System as a Function of Delay,
Frontiers in Systems Neuroscience, e786200, 2022

4.

Ohata R, Ogawa K, and Imamizu H, Neuroimaging Examination of Driving Mode Switching
Corresponding to Changes in the Driving Environment, Frontiers in Human Neuroscience, e788729,
2022

5.

Tokio Katakura, Mikihiro Yoshida, Haruki Hisano, Hajime Mushiake, Kazuhiro Sakamoto,Reinforcement Learning Model with Dynamic State Space Tested on Target Search Tasks for
Monkeys: Self-Determination of Previous States Based on Experience Saturation and Decision
Uniqueness, Frontiers In Computational Neuroscience, 784592, 2022

6.

Ogata K, Kadono F, Hirai Y, Inoue K, Takada M, Karube F, Fujiyama F, Conservation of the direct andindirect pathways dichotomy in mouse caudal striatum with uneven distribution of dopamine receptor
D1- and D2-expressing neurons, Frontiers in Neuroanato, 16, 809446, 2022

7.

R Otaki, Y Oouchida, N Aizu, T Sudo, H Sasahara, Y Saito, S Takemura, S Izumi, Relationship Between
Body-specific attention to a Paretic Limb and Real-World Arm Use in Stroke Patients: A Longitudinal
Study, Frontiers in Systems Neuroscience, 15, 806257, 2022

8.

N. Aizu, R. Otaki, K. Nishii, T. Kito, R. Yao, K. Uemura, S. Izumi, K. Yamada, Body-Specific Attention
to the Hands and Feet in Healthy Adults, Frontiers in Systems Neuroscience, 15, 805746, 2022

9.

T. Sakurada, M. Yoshida, K. Nagai, Individual optimal attentional strategy in motor learning tasks
characterized by steady-state somatosensory and visual evoked potentials, Frontiers in Human
Neuroscience, 15, 784292, 2022

10. T. Sakurada, M. Matsumoto, S. Yamamoto, Individual sensory modality dominance as an influential

factor in the prefrontal neurofeedback training for spatial processing: a functional near-infrared
spectroscopy study, Frontiers in Systems Neuroscience, 16, 774475, 2022
11. Yusuke Sekiguchi, Keita Honda, Shin-Ichi Izumi, Effect of Walking Adaptability on an Uneven Surface
by a Stepping Pattern on Walking Activity After Stroke, Frontiers in Human Neuroscience, 15, 762223,
2022
12. Wen W, Okon Y, Yamashita A, & Asama H, The over-estimation of distance for self-voice versus othervoice, Scientific Reports, 12, 420, 2022
13. Wen W. & Imamizu H, The sense of agency in perception, behaviour and human‒machine interactions,
Nature Reviews Psycholo, 1, 211-222, 2022
14. Omura Y, Kaminishi K, Chiba R, Takakusaki K, and Ota, J, A Neural Controller Model Considering
the Vestibulospinal Tract in Human Postural Control, Frontiers in Computational Neuroscience, 25,
16, 785099, 2022
15. K Ataka, T Sudo, R Otaki, E Suzuki, S Izumi, Decreased Tactile Sensitivity Induced by Disownership:
An Observational Study Utilizing the Rubber Hand Illusion, Frontiers in Systems Neuroscience, 15,802148, 2022
16. Isa K, Tokuoka K, Ikeda S, Karimi S, Kobayashi K, Sooksawate T, Isa T, Amygdala underlies theenvironment-dependency of defense responses induced via superior colliculus, Frontiers in Neural
Circuits, 15, 768647, 2022
17. Isa T, Double viral vector intersectional approaches for pathway-selective manipulation of motor
functions and compensatory mechanisms, Experimental Neurology, 3349, 1113959, 2022
18. Suzuki M, Inoue K, Nakagawa H, Ishida H, Kobayashi K, Isa T, Takada M, Nishimura Y, A
multisynaptic pathway from the ventral midbrain toward spinal motoneurons in monkeys, Journal of
Physiology, 600(7), 1731-1752, 2022
19. Hirayama K, Otaka Y, Kurayama T, Takahashi T, Tomita Y, Inoue S, Honaga K, Kondo K, & Osu R,
Efficiency and Stability of Step-To Gait in Slow Walking, Frontiers in Human Neuroscience, 15,779920, 2022
20. Togawa J, Matsumoto R, Usami K, Matsuhashi M, Inouchi M, Kobayashi K, Hitomi T, Nakae T,
Shimotake A, Yamao Y, Kikuchi T, Yoshida K, Kunieda T, Miyamoto S, Takahashi R, Ikeda A,
Enhanced phase-amplitude coupling of human electrocorticography in the posterior cortical region
during rapid eye movement sleep, Cereb Cortex, 33(2), 486-496, 2022
21. Ito K, Nakamura T, Suzuki R, Negishi T, Oishi M, Nagura T, Jinzaki M, Ogihara N, Comparative
functional morphology of human and chimpanzee feet based on three-dimensional finite element
analysis, Frontiers in Bioengineering and Biotechnology, 9, 760486, 2022

22. Bohnen NI, Costa RM, Dauer WT, Factor SA, Giladi N, Hallett M, Lewis SJG, Nieuwboer A, Nutt JG,
Takakusaki K, Kang UJ, Przedborski S, Papa SM; MDS-Scientific Issues Committee, Discussion of
Research Priorities for Gait Disorders in Parkinson's Disease, Mov Disord, 37(2), 253-263, 2022
23. Takahashi M, Nakajima T, Takakusaki K, Preceding Postural Control in Forelimb Reaching
Movements in Cats, Front Syst Neurosci, 15, 792665, 2022
24. Obara K, Chiba R, Takahashi K, Matsuno T, Takakusaki K, Knee dynamics of the take-off and landingof the spike jump in volleyball players with patellar tendinopathy, J Phys Ther Sci, 34(2), 103-109,
2022
25. Takakusaki K, Mirai T, Noguchi T, Chiba R, Neurophysiological mechanisms of gait disturbance inadvanced Parkinsonʼs disease patients, Neurology and Clinical Neuroscience,https://doi.org/10.1111/ncn3.12683, 2022
26. Sato Y, Kondo Y, Uchida A, Sato K, Yoshino-Saito K, Nakamura M, Okano H, Ushiba J, Preserved
intersegmental coordination during locomotion after crvical spinal cord injury in common marmosets,
Behav Brain Res, 425, 113816, 2022
27. Ohata, R., Asai, T., Imaizumi, S., and Imamizu, H., I Hear My Voice; Therefore I Spoke: The Sense of
Agency Over Speech Is Enhanced by Hearing One's Own Voice, Psychological Science, 33(8), 12261239, 2022
28. Yihao Wu, Masahiko Morita, Jun Izawa, Reward prediction errors, not sensory prediction errors, playa major role in model selection in human reinforcement learning, Neural Networks, 154, 109-121, 2022
29. LR DalʼBello, J Izawa, Computational role of exploration noise in error-based de novo motor learning,
Neural Networks, 153, 349-372, 2022
30. Kasahara K, DaSalla CS, Honda M, Hanakawa T, Basal ganglia-cortical connectivity underlies selfregulation of brain oscillations in humans, Commun Biol, 5(1), 712, 2022
31. Takasawa E, Abe M, Chikuda H, Hanakawa T, A computational model based on corticospinal
functional MRI revealed asymmetrically organized motor corticospinal networks in humans, 5(1), 664,
2022
32. Hosokawa T, Xu M, Katori Y, Yamada M, Aihara K, Tsutsui KI, Monkey Prefrontal Single-Unit
Activity Reflecting Category-Based Logical Thinking Process and Its Neural Network Model, Journalof Neuroscience, 42(33), 6380-6391, 2022
33. Nakamura S, Kishimoto Y, Sekino M, Nakamura M, Tsutsui KI, Depression induced by low-frequency
repetitive transcranial magnetic stimulation to ventral medial frontal cortex in monkeys, Experimental
Neurology, Experimental Neurology, 357, 114168, 2022

34. Soccini Agata, Clocchiatti Alessandro, Inamura Tetsunari, Effects of frequent changes in extended selfavatar movements on adaptation performance, Journal of Robotics and Mechatronics, 34(4), 756-766,
2022
35. Tetsuro Funato, Noriaki Hattori, Arito Yozu, Qi An, Tomomichi Oya, Shouhei Shirafuji, Akihiro Jino,
Kyoichi Miura, Giovanni Martino, Denise Berger, Ichiro Miyai, Jun Ota, Yury Ivanenko, AndreadʼAvella, Kazuhiko Seki, Muscle synergy analysis yields an efficient and physiologically relevant methodof assessing stroke, Brain Communications, 4(4), fcac200, 2022
36. Sentong Wang, Kazunori Hase, Shunsuke Kita, Shinya Ogaya, Biomechanical effects of medial
meniscus radial tears on the knee joint during gait: A concurrent finite element musculoskeletalframework investigation, Frontiers in Bioengineering and Biotechnology, 10, 957435, 2022
37. Naoki Aizu, Yutaka Oouchida, Kouji Yamada, Kazuhiro Nishii, Shin-Ichi Izumi, Use-dependent
increase in attention to the prosthetic foot in patients with lower limb amputation, Scientific Reports,
12(10), 1295, 2022
38. Tomoyo Morita, Satoshi Hirose, Nodoka Kimura, Hiromasa Takemura, Minoru Asada, Eiichi Naito,
Hyper-Adaptation in the Human Brain: Functional and Structural Changes in the Foot Section of thePrimary Motor Cortex in a Top Wheelchair Racing Paralympian, Frontiers in Systems Neuroscience,
16, 780652, 2022
39. Tomoyo Morita, Eiichi Naito, Facilitation of Hand Proprioceptive Processing in Paraplegic Individuals
with Long-Term Wheelchair Sports Training, Brain Sciences, 12(10), 1295, 2022
40. Kikutani K, Hosokawa K, Giga H, Ota K, Matsumata M, Zhu M, Takemoto H, Ji B, Oshimo S, Shime
N, Aizawa H, Genetic deletion of translocator protein exacerbates post-sepsis syndrome with activationof the C1q pathway in septic mouse model, SHOCK, 59(1), 82-90, 2022
41. Sakamoto K, Yamada H, Kawaguchi N, Furusawa Y, Saito N, Mushiake H, Reinforcement Learning
Model With Dynamic State Space Tested on Target Search Tasks for Monkeys: Extension to Learning
Task Events, Frontiers in Computational Neuroscience, 16, 784604, 2022
42. Sakamoto K, Kawaguchi N, Mushiake H, Shape and Rule Information Is Reflected in Different Local
Field Potential Frequencies and Different Areas of the Primate Lateral Prefrontal Cortex, Frontiers in
Behavioral Neuroscience, 16, 750832, 2022
43. Nagasaka K, Takashima I, Matsuda K, Higo N, Pharmacological inactivation of the primate posterior
insular/secondary somatosensory cortices attenuates thermal hyperalgesia, European Journal of Pain,
26(8), 1723-1731, 2022

44. Higo N, Motor cortex plasticity during functional recovery following brain damage, Journal of Robotics
and Mechatronics, 34(4), 700-709, 2022
45. Matsuda K, Nagasaka K, Kato J, Takashima I, Higo N, Structural plasticity of motor cortices assessedby voxel-based morphometry and immunohistochemical analysis following internal capsular infarcts in
macaque monkeys, Cerebral Cortex Communications, 3(4), tgac046, 2022
46. C. Zheng, G. Li, and M. Hayashibe, Joint Elasticity Produces Energy Efficiency in Underwater
Locomotion: Verification with Deep Reinforcement Learning, Frontiers in Robotics and AI, 9, 957931,
2022
47. K. Kutsuzawa and M. Hayashibe, Motor synergy generalization framework for new targets in multiplanar and multi-directional reaching task, Royal Society Open Science, 9, 211721, 2022
48. M. Hayashibe and S. Shimoda, Synergetic synchronized oscillation by distributed neural integrators to
induce dynamic equilibrium in energy dissipation systems, Scientific Reports, 12, 17163, 2022
49. C. Phunruangsakao, D. Achanccaray, S. Izumi, M. Hayashibe, Multibranch Convolutional Neural
Network with Contrastive Representation Learning for Decoding Same Limb Motor Imagery Tasks,
Frontiers in Human Neuroscience, 16, 1032724, 2022
50. 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,
34(6), 1451-1462
51. He Z, Qin Z, Koike Y., Continuous Estimation of Finger and Wrist Joint Angles Using a Muscle
Synergy Based Musculoskeletal Model., Applied Sciences., 12(8), 3772, 2022
52.

Cho W, Barradas VR, Schweighofer N, Koike Y., Design of an Isometric End-Point Force Control
Task for Electromyography Normalization and Muscle Synergy Extraction From the Upper Limb
Without Maximum Voluntary Contraction, Frontiers in Human Neuroscience, 16, 805452, 2022

53. Qin Z, He Z, Li Y, Saetia S, Koike Y., A CW-CNN regression model-based real-time system for virtual
hand control, Front Neurorobot, 16, 1072365, 2022
54. Takeshi Sakurada, Ayaka Horiuchi, Takashi Komeda, Sensorimotor activities and their functional
connectivity elicited by robot-assisted passive movements of lower limbs, Journal of Robotics andMechatronics, 34(4), 777-785, 2022
55. T Sugiyama, K Nakae, J Izawa, Transcranial magnetic stimulation on the dorsal premotor cortex
facilitates human visuomotor adaptation, NeuroReport, 33(16), 723-727, 2022
56. J Izawa, N Higo, Y Murata, Accounting for the valley of recovery during post-stroke rehabilitation
training via a model-based analysis of macaque manual dexterity, Accounting for the valley of recovery

during post-stroke rehabilitation training via a model-based analysis of macaque manual dexterity, 3,1042912, 2
57. Togo M, Matsumoto R(CA), Usami K, Kobayashi K, Takeyama H, Nakae T, Shimotake A, Kikuchi T,
Yoshida K, Matsuhashi M, Kunieda T,, Distinct connectivity patterns in human medial parietal
cortices: Evidence from standardized connectivity map using cortico-cortical evoked potential.,
Neuroimage, 263, 119639, 2022
58. Kimura N, Sato M, Kobayashi Y, Naito E, Augmented activity of the forearm extensor muscles inducedby vibratory stimulation of the palm of the hand in individuals with subacute post-stroke hemiplegia,
Brain Injury, 36(6), 782-791, 2022
59. 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, Cerebral Cortex, bhac456, 2022
60. Kimura N, Furuta T, Miura G, Naito E, Combining motor imagery and action observation withvibratory stimulation increases corticomotor excitability in healthy young adults, Journal of Behavioral
and Brain Science, 12(5), 177-195, 2022
61. Morita T, Hirose S, Kimura N, Takemura H, Asada M, Naito E, Hyper-adaptation in the human brain:
Functional and structural changes in the foot section of the primary motor cortex in a top wheelchairracing Paralympian, Frontier in Systems Neuroscience, 16, 780652, 2022
62. Morita T, Naito E, Facilitation of hand proprioceptive processing in paraplegic individuals with longterm wheelchair sports training, Brain Sciences, 12(10), 1295, 2022
63. Matsuda E, Misawa D, Yano S, and Kondo T, Olfactory Cues to Reduce Retrograde Interference
During the Simultaneous Learning of Conflicting Motor Tasks, Journal of Robotics and Mechatronics,
34(4), 746-755, 2022
64. Saki Niiyama, Shiro Yano, and Toshiyuki Kondo, Cerebral Activity-Based Quantitative Evaluation for
Attention Levels, Journal of Robotics and Mechatronics, 34(4), 739-745, 2022
65. S. Nakamura, Y. Kobayashi and T. Matsuura, Grid-Based Estimation of Transformation Between
Partial Relationships Using a Genetic Algorithm, Journal of Robotics and Mechatronics, 34(4), 786794, 2022
66. Takakuwa N, Isa K, Yamaguchi R, Onoe H, Takahashi J, Yoshida M, Isa T., Protocol for making an
animal model of “blindsight” in macaque monkeys, STAR Protocols, 4(1), 101960, 2022
67. Umeda T, Isa T, Nishimura Y., Temporal dynamics of the sensorimotor convergence underlying
voluntary limb movement., Proceedings of the National Academy of Sciences, 119(48), e2208353119,
2022

68. Isa T, Tohyama T, Kinoshita M., Phylogenetic view of the compensatory mechanisms in motor andsensory systems after neuronal injury., Current Research in Neurobiology, 3, 100058, 2022
69. Takeuchi Y, Li Q, Kawano T, Nagai J, Mima T, Editorial: Oscillotherapeutics ‒ Toward real-time
control of pathological oscillations in the brain, Frontiers in Behavioral Neurosci, 16, 1021616, 2022
70. Yunxiang Xia, Tatsuma Okazaki, Kenya Uemura, Shinichi Izumi, Implicit Body Representation of theHand Enlarged by Repetitive Peripheral Magnetic Stimulation within the Boundary of a Real Hand,
Applied Sciences, 12(10), 5250, 2022
71. Zhi-Ming Tang, Yutaka Oouchida, Meng-Xin Wang, Zu-Lin Dou, Shin-Ichi Izumi, Observing errors
in a combination of error and correct models favors observational motor learning, BMC Neuroscience,23(1), 4, 2022
72. Dongting Tian, Shin-Ichi Izumi, Transcranial Magnetic Stimulation and Neocortical Neurons: The
Micro-Macro Connection., Frontiers in neuroscience, 16, 866245, 2022
73. Yunxiang Xia, Kento Tanaka1, Man Yang, Shinichi Izumi, Body representation underlies response of
proprioceptive acuity to repetitive peripheral magnetic stimulation, Frontiers in Human Neuroscience,
16, 924123, 2022
74. Shibata, S., Watanabe, T., Matsumoto, T., Yunoki, K., Horinouchi, T., Kirimoto, H., Zhang, J., Wang,
H., Wu, J., Onishi, H., Mima, T., Triple tSMS system (“SHIN jiba”) for non-invasive deep brain
stimulation: a validation study in healthy subjects, Journal of NeuroEngineering and Rehabilitation,19(1), 129, 2022
75. Tetsunari Inamura, Digital Twin of Experience for Human-Robot Collaboration through Virtual
Reality, International Journal of Automation Technology, 17(3), 2023
76. Yamao Y, Sawamoto N, Kuieda T, Inano R, Shibata S, Kikuchi T, Arawaka Y, Yoshida K, Matsumoto
R, Ikeda A, Takahashi R, Fukuyama H, Miyamoto S, Changes in distributed motor network
connectivity correlates with functional outcome after surgical resection of brain tumors., Neurosurgery
Practice, 4(1), e00028, 2023
77. Makoto Ozawa, Yasuyuki Suzuki, Taishin Nomura, Stochastic Physiological Gaze-Evoked Nystagmus
With Slow Centripetal Drift During Fixational Eye,Movements at Small Gaze Eccentricities.,
Stochastic Physiological Gaze-Evoked Nystagmus With Slow Centripetal Drift During Fixational Eye
Movements at Small Gaze Eccentricities., 16, 842883, 2023
78. Harin Hapuarachchi, Takayoshi Hagiwara, Gowrishankar Ganesh, Michiteru Kitazaki, Effect ofconnection induced upper body movements on embodiment towards a limb controlled by anotherduring virtual co-embodiment, PLOS ONE, 18(1), 2023
79. Kanazawa H, Yamada Y, Tanaka K, Kawai M, Niwa F, Iwanaga K, Kuniyoshi Y, Open-ended

movements structure sensorimotor information in early human development, Proceedings of the
National Academy of Sciences, 120(1), e2209953120, 2023,
80. Barradas VR, Cho W, Koike Y, EMG space similarity feedback promotes learning of expert-like muscle
activation patterns in a complex motor skill, Fronties in Human Neuroscience, 16, 805867, 2023
81. Shibata, S., Watanabe, T., Otsuru, N., Onishi, H., Mima, T., Very low-frequency transcranial electricalstimulation over the primary motor area can influence the voluntary movement initiation in humans,
Brain Stimulation, 16(2), 442-444, 2023
82. Shima, A., Tanaka, K., Ogawa, A., Omae, W., Miyake, T., Nagamori, Y., Miyata, Y., Ohata, K., Ono,
Y., Mima, T., Takahashi, R. *Koganemaru, S., Case Report: Backward gait training combined with gaitsynchronized cerebellar transcranial alternating current stimulation in progressive supranuclear palsy,
Frontiers in Human Neuroscience-Brain Imaging and Stimulation, 17, 1082555, 2023
83. Shima, A., Miyake, T., Tanaka, K., Ogawa, A., Omae, E., Nagamori, Y., Miyata, Y., Ohata, K., Maki,
T., Ono, Y., Mima, T., Takahashi, R. *Koganemaru, S., Case report: A novel approach of closed-loop
brain stimulation combined with robot gait training in post-stroke gait disturbance., Frontiers in
Human Neuroscience-Brain Imaging and Stimulation, 17, 1082556, 2023

International Conference1.

Ohata, R., Uehigashi, Y., Asai, T., Imaizumi, S., and Imamizu, H., Prospective and retrospective
inferences of hearing oneʼs voice in the sense of agency over speech, The Federation of European
Neuroscience Societies (FENS) Forum 2022, Paris, France, 2022

2.

Tanaka, T. and Imamizu, H., The influence of changes in direction and velocity of feedback over thesense of control., The 22nd meeting of the European Society for Cognitive Psychology (ESCoP), Lille,
France, 2022

3.

Hapuarachchi, H., Ishimoto, H., Sugimoto, M., Inami, M., and Kitazaki, M., Embodiment of an Avatarwith Unnatural Arm Movements, 2022 IEEE International Symposium on Mixed and Augmented
Reality (ISMAR), Singapore, 2022

4.

Hitohiro Etoh, Yuichiro Omura, Kohei Kaminishi, Ryosuke Chiba, Kaoru Takakusaki, and Jun Ota,
Motion Planning of Anticipatory Postural Adjustments in Gait Initiation, 2022 The 22nd IEEE
International Conference on BioInformatics and BioEngineering, Taiwan, 2022

5.

Hitohiro Etoh, Yuichiro Omura, Kohei Kaminishi, Ryosuke Chiba, Kaoru Takakusaki, and Jun Ota,
Investigation of a Method to Extend a 2-Dimensional Gait to 3-Dimensions in a Human
Musculoskeletal Model with 70 Muscles, 33rd 2022 International Symposium on Micro-

NanoMechatronics and Human Science (From Micro & Nano Scale Systems to Robotics &Mechatronics Systems), Nagoya, 2022
6.

Yuichiro Omura, Hiroki Togo, Kohei Kaminishi, Tetsuya Hasegawa, Ryosuke Chiba, Arito Yozu,
Kaoru Takakusaki, Mitsunari Abe, Yuji Takahashi, Takashi Hanakawa, and Jun Ota, Muscle Tone in a
Musculoskeletal Model to Represent the Abnormal Posture in Parkinson's Disease, 33rd 2022
International Symposium on Micro-NanoMechatronics and Human Science (From Micro & Nano
Scale Systems to Robotics & Mechatronics Systems), Nagoya, 2022

7.

Sakamoto K, Multidimensional analysis of extracellular potentials in the cerebral cortex, Nara Instituteof Science and Technology, Information Science, Colloquium A, Nara, 2022

8.

Sakamoto K, The Brain Beyond Machine Learning:Perspectives from Neurophysiology andComputational Neuroscience of Higher Brain Functions, APSIPA BioSiPS Workshop 2022, Kitami,
2022

9.

Sakamoto K, Saito N, Yoshida S, Mushiake H, Increased firing variability may be an early warning
signal of bifurcation in neuronal networks: Validation in action planning-related cells of monkey
prefrontal cortex, Neuroscience 2022, San Diego, USA, 2022

10. Sakamoto K, Saito N, Yoshida S, Mushiake H, A dynamic, economical, and robust coding scheme inthe lateral prefrontal neurons of monkeys, The 29th International Conference on Neural Information
Processing, IIT Indore, India, 2022
11. Kim D, Kanazawa H, Kuniyoshi Y, Simulating a Human Fetus in Soft Uterus, IEEE International
Conference on Development and Learning, ICDL, London, United Kingdom, 2022
12. Kim D, Kanazawa H, Kuniyoshi Y, Human fetus simulation in soft uterus environment, International
Congress of Infant Studies (ICIS), Online, 2022
13. M.H. Ahmed, S. Shimoda, M. Hayashibe, Deep Reinforcement Learning Based Motion Synthesis for
Prosthetic Elbow Motion Generation, The SICE Annual Conference 2022, Kumamoto, Japan, 2022
14. K. Tada, K. Kutsuzawa, D. Owaki, M. Hayashibe, Quantifying Motor and Cognitive Function of theUpper Limb Using Mixed Reality Smartglasses, 44th Annual Int. Conf. of the IEEE Engineering in
Medicine and Biology Society, Glasgow, United Kingdom, 2022
15. K. Shen, A. Chemori, M. Hayashibe, Classification of Human Balance Recovery Strategies throughKinematic Motor Synergy Analysis, 44th Annual Int. Conf. of the IEEE Engineering in Medicine andBiology Society, Glasgow, United Kingdom, 2022
16. Sonoda,Kohta,

Hasegawa,Tetsuya,

Kaminisho,Kohei,

Osumi,Michihiro,

Sumitani,Masahiko,

Chiba,Ryosuke, Ota,Jun, & Yozu,Arito, Effects of plantar pain on gait, The 33rd 2022 International
Symposium on Micro-NanoMechatronics and Human Science (MHS2022), Nagoya, Japan, 2022

17. K. Kaminishi, Y. Omura, R. Chiba, K. Takakusaki, and J. Ota, Effects of Increased Arm Muscle Tone
on Postural Recovery from External Forces: A simulation study, 2022 IEEE 22nd International
Conference on Bioinformatics and Bioengineering (BIBE), Online, Taiwan, 2022
18. Matsumoto R, Physiology of the Cortico-Cortical Evoked Potential (Educational Course: Managementof the Awake Patient Surgeries), 8th Congress of the International Society of Intraoperative
Neurophysiology and Educational Course (ISIN2022), Online, USA, 2022
19. Matsumoto R, Brain Evoked Potentials in the context of Epilepsy and Beyond., Workshop on Brain
Evoked Potentials in the context of Neurosurgery, Montpellier, France, 2022
20. Matsumoto R, Cortico-cortical evoked potential: its past, present and future, BCI&Neurotechnology
Spring School, Online, Austria, 2022
21. Naoya Yamamoto, Takato Matsumoto, Tamami Sudo, Megumi Miyashita, and Toshiyuki Kondo, Ringshaped wearable device for logging finger usage in daily life, IEEE 2022 International Symposium on
Micro-NanoMechatronics and Human Science (MHS2022), Nagoya, Japan, 2022
22. Megumi Miyashita, Shiro Yano and Toshiyuki Kondo, Evaluation of Safe Reinforcement Learning with
CoMirror Algorithm in a Non-Markovian Reward Problem, 17th International Conference onIntelligent Autonomous Systems (IAS-17), Zagreb, Croatia, 2022
23. Samirah Altukhaim, Toshiyuki Kondo, Yoshikatsu Hayashi, Enhancement of Sense of Ownership andSense of Agency using Virtual Reality and Haptic Feedback, The 44th Annual International
Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), Glasgow, UK, 2022
24. Ozge Ozlem Saracbasi, William Seymour Harwin, Toshiyuki Kondo, Yoshikatsu Hayashi, Dual
Instability against to Sequential Learning via Haptic Interaction, The 44th Annual International
Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), Glasgow, UK, 2022
25. Y. Kobayashi and S. Nakamura, Transfer of Partial Information of Motor Controller Based on
Estimation of Coordinate Transformation Parameters, The 33rd 2022 International Symposium on
Micro-NanoMechatronics and Human Science (MHS2022), Nagoya, Japan, 2022
26. Tadashi Isa, Roles of midbrain dopamine-related systems in motivation, decision and recovery after
neuronal injuries, The 9th Annual Meeting of the Mongoloan Neuroscience Society, Ulaanbaatar,
Mongolia, 2022
27. Tadashi Isa, Sensorimotor and cognitive functions of blindsight macaques, International school ofneuroscience, Erice, Italy, 2022
28. Tadashi Isa, Visuomotor pathway and cognitive capacity of blindsight macaques, JANUBET
Symposium, Kyoto, Japan, 2022

29. Sierra RO, Pedraza LK, Barcsai L, Peijin A, Kozák G, Takeuchi Y, Lőrincz M, Berényi A, Controlling
pathologic fear expression through closed-loop brain stimulation, The 52nd SfN Annual Meeting,
San Diego, U. S. A., 2022
30. Yoshida K, Chan M, Li Q, Pedraza L, Sierra R, Minami M, Berényi A, Takeuchi Y, Devinsky O, Buzsáki
G, Berényi A, Reinstating olfactory bulb derived limbic gamma oscillations alleviates depression-likebehavioral deficits in rodents, The 52nd SfN Annual Meeting, San Diego, U. S. A. 2022
31. Masaya Togo, Riki Matsumoto, Kiyohide Usami, Katsuya Kobayashi, Hirofumi Takeyama, Takuro
Nakae, Akihiro Shimotake, Takayuki Kikuchi, Kazumichi Yoshida, Masao Matsuhashi, Takeharu
Kunieda, Susumu Miyamoto, Ryosuke Takahashi, Akio Ikeda, Distinct connectivity patterns in human
medial parietal cortices: evidence from standardized connectivity map using cortico-cortical evoked
potential., American Epilepsy Society 2022, Nashville, USA, 2022
32. Matsumoto R, The value of Cortico-Cortical Evoked Potentials in a neurosurgical perspective (Keynote
Lecture 2), International Symposium: Bridging Neurosciences and Neurosurgery - New Frontiers in
Intraoperative Neurophysiology, Verona, Italy, 2023
33. Hapuarachchi, H., Ishimoto, H., Kato, Y., and Kitazaki, M., Embodiment of an Unnatural Avatar
studied in VRChat Laboratory, 2023 the 7th International Conference on Virtual and Augmented
Reality Simulations (ICVARS 2023), Sydney, Australia, 2023
34. Nakamura, J., Ikei, Y., and Kitazaki, M., The Effect of Posture on Virtual Walking Experience Using
Foot Vibrations, Augmented Humans 2023, Glasgow, UK, 2023
35. Rie Kimura, Kenichi Ohki, Yumiko Yoshimura, Neuronal activity that allows us to perceive familiar
images even at low contrast, 3rd International Symposium on Brain Information Dynamics 2023,
Tokyo, Japan, 2023
36. Rie Kimura, Kenichi Ohki, Yumiko Yoshimura, A neural mechanism underlying the perception of
low-contrast familiar objects, 5th Stockholm-Tokyo Workshop 2023, Tokyo, Japan, 2023

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

Wen Wen (Project Associate Professor, The University of Tokyo)

co-investigator

Qi An (Associate Professor, The University of Tokyo)

co-investigator

Arito Yozu (Associate Professor, The University of Tokyo)

Research Project A01: Elucidation of the hyper-adaptation mechanism by reconstruction of bio-structure
and challenges 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)
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)

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

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）

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, 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

Kenji Yoshinaga (Postdoctoral fellow, NCNP)

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)

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, Aichi Medical 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

Soichiro Fujiki (Assistant Professor, Dokkyo Medical University)

Co-investigator

Wang Sentong (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 Wen Wen (Project Associate Professor, The University of Tokyo)
Funded co-investigator Qi An (Associate Professor, The University of Tokyo)
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 (Project Assistant Professor, The University of Tokyo)

Co-investigator

Hoang Hiep Ly (Project Researcher, The University of Tokyo)

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)

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 Womenʼs Medical University)

Co-investigator

Moeko Kanaya (Assistant Professor, Tokyo Womenʼs 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

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 reconstructionof 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)

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

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)