2021 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 A05-01
Principal Investigator, Shinichi IZUMI (Professor, Tohoku University)
Annual report of research project A05-03
Principal Investigator, Riki MATSUMOTO (Professor, Kobe University)
Annual report of research project A05-05
Principal Investigator, Mitsunari ABE (Director, IBIC)
Annual report of research project A05-06
Principal Investigator, Hideki HIDA (Professor, Nagoya City University)
Annual report of research project A05-07
Principal Investigator, Hiroyuki MIYAWAKI (Assistant Professor, Osaka City University)
Annual report of research project A05-08
Principal Investigator, Takaki MAEDA(Assistant Professor/Senior Assistant Professor, Keio University)
Annual report of research project A05-09
Principal Investigator, Takahiro KONDO (Assistant Professor, Keio University)
Annual report of research project A05-11
Principal Investigator, Rieko OSU (Professor, Waseda University)
Annual report of research project A05-12
Principal Investigator, Kosei TAKEUCHI (Professor, Aichi Medical University)

Annual report of research project A05-16
Principal Investigator, Noriyuki HIGO (Group Leader, AIST)
Annual report of research project A05-17
Principal Investigator, Masatoshi YOSHIDA(Specially Appointed Associate Professor, Hokkaido University)
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 B05-01
Principal Investigator, Mitsuhiro HAYASHIBE (Professor, Tohoku University)
Annual report of research project B05-02
Principal Investigator, Daichi NOZAKI (Professor, The University of Tokyo)
Annual report of research project B05-03
Principal Investigator, Naomichi OGIHARA (Professor, The University of Tokyo)
Annual report of research project B05-04
Principal Investigator, Isao NAMBU(Associate Professor, Nagaoka University of Technology)
Annual report of research project B05-05
Principal Investigator, Yuichi KOBAYASHI (Associate Professor, Shizuoka University)
Annual report of research project B05-06
Principal Investigator, Yasuhisa HASEGAWA (Professor, Nagoya University)
Annual report of research project B05-07
Principal Investigator, Taishin NOMURA (Professor, Osaka University)

Annual report of research project B05-08
Principal Investigator, Kazuhiro SAKAMOTO(Associate Professor, Tohoku Medical and Pharmaceutical University)
Annual report of research project B05-09
Principal Investigator, Takeshi SAKURADA (Assistant Professor, Ritsumeikan University)
Annual report of research project B05-10
Principal Investigator, Tetsunari INAMURA(Associate Professor, National Institute of Informatics)
Annual report of research project B05-11
Principal Investigator, Hoshinori KANAZAWA(Research Assistant Professor, The University of Tokyo)
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- International Symposium on Hyper-Adaptability HypAd
2021
Date: May 26th, 2021. 16:00-20:00
May 27th, 2021. 16:00-19:00
Place: Online
Contents: The 1st International Symposium on HyperAdaptation (HypAd2021) was held. Professor Andrea d'Avellaof the University of Messina, Italy, gave an invited lecture
entitled “Virtual surgeries to investigate motor learning and toenhance motor rehabilitation”. The content of the lecture wasrelated to the learning process after muscle rearrangement,
which is also investigated as a model for hyper-adaptation inthis field, and provided useful suggestions for the developmentof research in this field. In the talk session, the representativesof the planning groups reported on the progress, and in theposter session, the representatives of the subscribed researchgroups made presentations. Although the conference waslimited to whom involved in the area, 161 people registered toattend and 58 poster presentations were made, making theconference a great success.
- 7th management meeting
Date: May. 27th, 2021. 10:30-12:00
Place: Online
Contents: members of the management group discussed about
the operation method, symposiums, and counterplans against
Covid-19, etc.

- B group meeting
Date: Sep. 7th, 2021. 13:00-15:10
Place: Online
Contents: Three talks by area researchers were given, focusing
on research topics related to the promotion of Group B's
overall research. The talks were: “Understanding hyperadaptation using RNN model” by Q. An (Kyushu University),“Quantitative evaluation of posture control system based on
motion measurement and system model of bipedal standingrats” by T. Funato (University of Electro-Communications),
and "Study on identification of time-varying brain functional
connectivity for clarification of hyper-adaptation" by I.
Nambu (Nagaoka University of Technology).
- A group meeting
Date: Sep. 16th, 2021. 13:30-16:00
Place: Online
Contents: A group meeting was held for the purpose of
exchange within and outside Group A. 22 group members
gave presentation, and after the lectures, a social event using
the virtual conference system oVice was held.
- 3rd plenary conference
Date: March 7th - 8th, 2022
Place: Online
Contents: summary of the researches in the year was presented
by project representative and researchers in the project.
- 8th management meeting
Date: March 8th, 2022. 12:20-13:20
Place: Online
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: Advanced Robotics
Special Issue on “Hyper-adaptability for overcoming bodybrain dysfunction” was organized in Advanced Robotics, Vol.
35, no. 13-14. Nine papers were published.
- OS: 44th Annual Meeting of the Japan Neuroscience Society
Date: July 28th, 2021
Contents: Five researches were presented at a symposium on“Hyper-adaptation in the brain”.
- OS: MHS 2021 (32nd International Symposium on MicroNanoMechatronics and Human Science)
Date: December 6th, 2021
Contents: Seven researches including one keynote talk were
presented at a symposium on “Hyper-adaptability”.
- OS: 51th Annual Meeting on the Japanese Society ofClinical Neurophysiology
Date: December 16th, 2021
Contents: Four researches were presented at a symposium on“Neurophysiology of Hyper-adaptability”.
- OS: 34th SICE Symposium on Decentralized Autonomous
Systems.
Date: January 22th, 2022

Contents: Nine researches were presented at a symposium on“Hyper-adaptability”.
- OS: The 99th Annual Meeting of the Physiological Societyof JapanDate: March 18th, 2022
Contents: Symposium of “Physiology of hyper-adaptability” isorganized.
C. Activities for publications- Explanatory meeting for subscribed research groups
Explanatory meeting for subscribed research groups were held
on Sep 7th, 2021, 15:30-15-50 and on Sep 16th, 2021, 13:0013:20.
IV. ACTIVITIES BY YOUNG RESEARCHERS
The Young Scientists' Group, chaired by Dr. Qi An (Kyushu
University), has been organized to promote the activities ofyoung scientists in this field. This year's activities are listed
below.
A. Study Session
In order to deepen the understanding of the analytical andexperimental methods used in this field, the following threestudy sessions were held this year. The materials of thesessions are available on the website and Slack, and we aretrying to expand the common knowledge base.
- Jun.-Jul. 2021 Study session of Winter's book
“Biomechanics and Motor Control of Human Movement”.
- Dec. 2021: Study session on experimental methods inneuroscience (photogenetics, calcium imaging, chemical
genetics)-January-February 2022: Study group on electrophysiology
B. Lecture Meetings
We held three online lectures on emotion and motor control,
which are important research topics in this field, inviting
researchers from inside and outside the field, as follows- Sep. 06, 2021 Prof. Ken-ichiro Tsutsui (Tohoku University)- Sep 07, 2021 Prof. Taishin Nomura (Osaka University)- October 07, 2021 Team Leader Wataru Sato (RIKEN)
C. Organizing Symposium
A joint symposium for young researchers was held at the Next
Generation Brain Project Winter Symposium jointly with
Grant-in-Aid for Scientific Research on Innovative Areas of“Brain Information Dynamics” and “Chronogenesis”. Tworesearchers from this area, Associate Professor Wen Wen andAssistant Professor Hoshinori Kanazawa, gave presentations,and a panel discussion was held with the participants.
V. FUTURE PERSPECTIVE
We plan to conduct the following activities of the next fiscal
year:- plenary conference including new publicly group members,in spring or summer, 2022,- 2nd public symposium on October, 2022, and- fiscal year-end plenary conference on March, 2023.

Group A: Neurosciences
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) aimed at revealing
the global “disinhibition” associated with the recovery frombrain and spinal cord injury (experiments on nonhumanprimates), or aging (experiments on humans) and at elucidating
its neuronal mechanisms in rodents. In the nonhuman primate
studies, Isa and colleagues clarified that the interhemisphericinteraction between the bilateral motor and premotor cortices
switches from inhibitory to facilitatory during the recovery
process from the subhemisection injury of the spinal cord.
Naito and colleagues showed the decline of interhemisphericinhibition in aged subjects by measuring the negative BOLD
signal of the fMRI.
The aim of Group A02 (Seki) was to evaluate the adaptationof the central nervous system to rapid changes in bodystructure by muscle rearrangement surgery in monkeys, whose
musculoskeletal structure is similar to that of humans. In thisyear, we established the basic technology to intervene in thesensory prediction error, which is necessary to study the
background mechanism of adaptation. Specifically, we aimed
to establish a technique to introduce foreign genes into
primates (marmosets) with high efficiency for specific types of
sensory nerves.
Group A03 (Imamizu and Tsutusi) conducted studies onmechanisms which the sense of agency (the feeling that I am
the source of the action) and motivation facilitate motor
learning. A group of Imamizu conducted several
psychological experiments showing that the sense of agency
facilitates motor learning. They confirmed that a highdefinition electric stimulation to the brain affects the sense ofagency. A group of Tsutsui developed a device forinvestigating sense of agency and motivation commonly inhumans and monkeys. They also established a long-lasting

electrocorticogram recording method covering wide area ofthe monkey cerebral cortex.
Group A04 (Takakusaki, Hanakawa) researched changes inmotor function (postural control) and brain network dynamics
with aging. Based on animal experimentation, Takakusaki andcolleagues suggested the possibility of the dysregulation of thereticulospinal and vestibulospinal tracts due to thedegeneration of dopaminergic and cholinergic systems in
Parkinson's disease (PD) induce antero-flexion posture.
Hanakawa and colleagues compared the functional
connectivity (FC) in major four (default mode, dorsal attention,
salience, and central executive) network states in f-MRI ofelders. They found the sparse FC condition in cognitively
impaired PD and Alzheimer's disease but not in the healthyelderly and cognitively normal PD.
In addition, 15 members (below) joined as the A05 group, whowere selected through the applications.
Shin-ichi Izumi “Title: Elucidation of the hyper-adaptation
mechanism of upper limb recovery in stroke patients” Group
A05-1 (Izumi) observed subacute stroke patients for 6 months
and found that the frequency of use of the affected upper limb
was related to its recovery and body-specific attention of thehemiplegic hand. The results suggest a relationship between
use-dependent plastic changes and body representation.
Riki Matsumoto, “Mechanism of Hyper-Adaptivity of thehuman premotor area: electrophysiological connectome
analysis with electrocorticogram” Electrophysiologicalconnectome, namely, cortico-cortical evoked potential (CCEP)
connectome was generated by means of systemic lowfrequency electrical stimulation to all the implanted electrodes
to record CCEP. The network analysis revealed that thehigher-order motor area had significantly larger outboundconnections compared to the primary motor cortex and thelanguage areas.
Mitsunari Abe “Development of non-invasive brain
stimulation techniques that can increase recruitment of thecorticospinal motor indirect pathway during acquisition ofhand motor skills” We discovered the corticospinal network inhumans, operationally called bilateral M1-network, that
integrated signals from bilateral primary motor cortices onto
the spinal cord. We are developing the stimulation protocols
that enable to increase recruitment of the bilateral M1-network.
Our design is to induce neuronal plasticity in the relay neurons
implemented in this network.
Hideki Hida “ Analysis of the regulatory system change infunctional recovery with the rehabilitation after stroke.” He
performed selective blockage in the cerebellum-rubral pathway
using double-virus infection method and revealed that CNO
treatment caused the inhibition of the functional recovery thatis induced by forced-limb use after intracerebral hemorrhage,
confirming the cerebellum-rubral pathway is involved in thefunctional recovery by the rehabilitation.

Hiroyuki Miyawaki, “Regulatory mechanisms of interregional network changes underlying hyper-adaptation frommal-adaptation state caused by fear memory.” Group A05-7
investigated neuronal mechanisms of fear-memory relatedmal- and hyper-adaptation by performing multi-regional largescale electrophysiology in freely moving rodents. The
analyses revealed that inter-regional coactivation developed
through the transition to the mal-adaptation state, and thecoactivation accompanied fast network oscillations in the
involved regions.
Takaki Maeda “Facilitating hyper-adaptation in neurologicaland psychiatric diseases through improving precision on thesense of agency” GroupA05-8 developed a method forcognitive rehabilitation of Sense of Agency(SoA) in order totune up precision of SoA, and showed abnormal learning
patterns in patients with psychiatric disorders including
schizophrenia, ADHD and ASD.
Takahiro Kondo, “The role of inhibitory neurons related toskilled hand movements after spinal cord injury.” To
understand motor deficits and reconstruction after spinal cord
injury, we analyzed joint movement coordination generated by
the central nervous system. We found that the degree of
impaired coordination in joint movements correlated with thedegree of damage in the spinal cord.
Rieko Osu “Activating neural circuits that prefer affected sideof the body using neural modulation by brain stimulation andbehavioral technique” We showed that electrical stimulation on
the wrist could modify the probability of hand choice during
the reaching task. We demonstrated that the stroke patients'
motivation for rehabilitation is extrinsic mainly rather thanintrinsic. We developed a questionnaire to evaluate motivation
for rehabilitation in subacute stroke patients.

Kosei
Takeuchi“Hyper-adaptivefunctionbymicroenvironment and synapse connection.“ We found that thesynthetic synaptic organizer has a recovery effect even in thechronic-phase of spinal cord injury, and succeeded in
extracting the physiological recovery process by AI-deeplearning (with/B02:Yanagihara&Funato).
Noriyuki Higo “Adaptive mechanism occurring in bothhemispheres after unilateral brain damage” We performed
structural MRI VBM analysis and immunohistochemistry
using SMI-32 antibody in a macaque model of internal
capsular infarcts, and found increases in both gray mattervolume and dendritic arborization of pyramidal neurons in theventral premotor cortex of the contralesional hemisphere
during the period of motor function recovery.
Masatoshi Yoshida, “Establishment and circuit manipulationof an animal model of unilateral spatial neglect in marmosets”
To establish an animal model of unilateral spatial neglect bymaking lesions in the ventral attention network of common
marmosets, 1) we identified area TPO using ECoG. 2) weevaluated saliency-guided eye movements during free-viewing,
and 3) we measured Ca signals simultaneously from bilateralposterior parietal cortices.
III. ACTIVITIESThe whole Group A meeting was held on September 16 (Thu)in 2021, by online with Zoom software and the networking
session by oVice software.
IV. FUTURE PLAN
We will soon organize the 1st whole group Meeting andfacilitate the collaboration with the Group B members.

A01. Elucidation of the hyper-adaptation mechanism
by reconstruction of biostructures and challenges forprevention of decline in latent adaptive capacity
Tadashi Isa, Professor, Kyoto University

Eiichi Naito, Research Manager, CiNet
Hidenori Aizawa, Professor, Hiroshima University
Minoru Asada, Professor, Osaka University
Abstract—We have demonstrated the critical role ofinterhemispheric inhibition in the monkeys with spinal cord
injury using the pathway-selective manipulation, and in aged
humans using fMRI. We are also studying the involvement of
dopamine system in the global disinhibition I the rodent model.

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
is still elusive. On the other hand, human studies suggested thatsuch 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)Previously we found that after the lesion of the corticospinal
tract at the C4/C5 segments in macaques, the precision grip
movements are once impaired but could recover in several
weeks through rehabilitative trainings. Furthermore, we foundthat during the early stage of the recovery, the ipsilesional
motor cortex increased its activity and contribute to recovery
by reversible inactivation using microinjection of GABA
receptor agonist muscimol (Nishimura et al. Science, 2007).
Moreover, by multi-channel ECoG recordings from bilateral
premotor (PM), primary motor and somatosensory cortex, we
found that signal flow from the contralesional to ipsilesional
PM is enhanced during the early recovery using Granger
causality (GC) analysis (Chao et al. Cerebral Cortex 2019). To
demonstrate that the direct callosal pathway from thecontralesional PM to the ipsilesional PM is critical, we tried thepathway-selective blockade of the pathway. We injected
AAV2retro-Cre into the ipsilesional PM and AAV2.1-DIOhM4Di into the contralesional PM and administered DCZ tothe monkey and confirmed that GC from the contralesional PM
to ipsilesional PM was unidirectionally reduced and thepathway-selective blockade was successful. The selective
blockade of the pathway was not effective during the intact
state, but during the early stage of the recovery after thecorticospinal tract lesion, the precision grip behavior was
impaired by administration of DCZ. Thus, we could
demonstrate the crucial role of the contralesional PM toipsilesional caoolsal pathway is critical for the early stage
recovery after the spinal cord injury.
[1] Suzuki M, Inoue K-I, Nakagawa H, Ishida H, Kobayashi K, Isa T, Takada
M, Nishimura Y (2022) Multisynaptic pathway from the ventral midbrain tospinal motoneurons via the primary motor cortex in monkeys” Journal of
Physiology (London), doi: 10.1113/JP282429..
[2] Isa K, Tokuoka K, Ikeda S, Karimi S, Kobayashi K, Sooksawate T, Isa T(2021) Amygdala underlies the environment-dependency of defense responses
induced via superior colliculus. Frontiers in Neural Circuits, 15:768647. doi:
10.3389/fncir.2021.768647. eCollection 2021.
[3] Kasai M, Isa T (2021) Effects of light isoflurane anesthesia onorganization of direction and orientation selectivity in the superficial layer ofthe mouse superior colliculus. Journal of Neuroscience, JN-RM-1196-21.
doi: 10.1523/JNEUROSCI.1196-21.2021.
[4] Kato R, Zeghbib A, Redgrave P. Isa T (2021) Visual instrumental learninginblindsightmonkeys.
Scientific
Reports, 11, 14819.

doi.org/10.1038/s41598-021-94192-7[5] Zubair M, Murris S, Isa K, Onoe H, Koshimizu Y, Kobayashi K,
Vanduffel W, Isa T (2021) Divergent whole brain projections from the ventral
midbrain in macaque monkeys. Cerebral Cortex, bhaa399. doi:
10.1093/cercor/bhaa399.
[6] Takakuwa N, Isa K, Onoe H, Takahashi J, Isa T (2021) Contribution ofpulvinar and lateral geniculate nucleus to the control of visually guided
saccades in blindsight monkeys. Journal of Neuroscience, 41:1755-1768.
[7] Kato R, Hayashi T, Onoe K, Yoshida M, Tsukada H, Onoe H, Isa T, Ikeda
T (2021) The posterior parietal cortex contributes to visuomotor processing
for saccades in blindsight macaques. Communications Biology, 4(1):278. doi:
10.1038/s42003-021-01804-z.
[8] Isa T (2021) Double viral vector intersectional approaches for pathwayselective manipulation of motor functions and compensatory mechanisms.
Experimental Neurology, 349:113959. doi: 10.1016/j.expneurol.2021.113959.
[9] Isa T, Yoshida M (2021) Neural mechanism of blindsight in a macaquemodel.
Neuroscience,(Forefront
review),
469:
138-161.
https://doi.org/10.1016/j.neuroscience.2021.06.022[10] Isa T, Marquez-Legorreta E, Grillner S, Scott EK (2021) The
tectum/superior colliculus as the vertebrate solution for spatial sensory
integration and action. Current Biology (review), 31(11):R741-R762.

B. Improved understanding of interhemispheric inhibitory
mechanisms in human motor cortex and improvement ofaging-degraded inhibitory system by training (Naito andAsada)
In collaboration with Asada group (Osaka University), Naito
group (NICT, CiNet) has used functional MRI to facilitate
understanding of the mechanism of interhemisphericinhibition in the human motor cortex. It has been proposedthat the interhemispheric inhibition between the foot sectionsof the left and right motor cortices is weak or absent because
humans walk bipedally. Although robust interhemisphericinhibition was not observed during motor execution of only
one leg in young adults, significant interhemispheric inhibition
was observed during kinesthesia information processing of
one leg (Naito et al. Brain Science 2021). These results
suggested that an interhemispheric inhibition mechanism also
exists between the foot sections of the two motor cortices, andthis inhibition is weakened during motor execution. As forage-degraded inhibitory system, we found that older adults
with degraded inhibition in the hand section of the ipsilateral(right) motor cortex during right hand movements had poorer
dexterity of the right hand/finger as assessed by a peg test.
Then, we divided healthy older adults (65-78 years old) into
two groups (bimanual and right-hand groups). The bimanualgroup underwent coordination training in which the left andright fingers performed different movements for two months,
while the right-hand group underwent only right-handed
training. Neither group was trained in the peg test itself, but
after training, hand/finger dexterity improved in the bimanualgroup only. Furthermore, we found that those who improved
their hand/finger dexterity well had suppressed activity in theipsilateral motor cortex (Naito et al. Scientific Reports 2021).
This study showed that age-degraded interhemisphericinhibitory system can be improved by coordination training,which also leads to improvement of hand/finger dexterity.
[1] Naito E, Morita T, Kimura N, and Asada M Existence of interhemisphericinhibition between foot sections of human primary motor cortices: Evidence
from negative blood oxygenation-level dependent signal. Brain Sciences, 11,1099. https://doi.org/10.3390/brainsci11081099, 2021.

[2] Naito E, Morita T, Hirose S, Kimura N, Okamoto H, Kamimukai C andAsada M Bimanual digit training improves right hand dexterity in older adults
by reactivating declined ipsilateral motor-cortical inhibition. Scientific
Reports, 11, Article number: 22696, 2021.

C. Modulatory mechanism underlying interhemiphesphericinhibition and adaptive behaviors of the animals usingrodent model (Aizawa)To address mechanism underlying modulation of theinterhemispheric inhibition observed upon hyper-adaptation by
Isa and Naito-Asada groups, Aizawa group (Hiroshima
University) established an experimental paradigm combining
electrophysiology and optogenetics in mice. Pharmacologicalexamination in the mouse secondary motor cortex revealed thatGABAB agonist and antagonist enhanced and suppressed theinterhemispheric inhibition, respectively. Since cholinergic
antagonist also enhanced the interhemispheric inhibition in thesame region, suggesting that those neurotransmitter system
could modulate the cerebral interaction between hemispheres.
The group also examined the adaptive response of the murine
cerebral cortex upon hyperactivation or ischemia as spreading
depolarization (Terai et al., J Neurophysiol, 2021). For the
behavioral assessment of the animals adapting to the stress, anovel IoT system reporting wheel-running activity of mice athome cage was developed (Zhu et al., eNeuro, 2021).
[1] Terai H, Gwedela MNV, Kawakami K, Aizawa H. Electrophysiologicaland pharmacological characterization of spreading depolarization in the adultzebrafishtectum.
J
Neurophysiol.
126(6):1934-1942.
doi:
10.1152/jn.00343.2021, 2021.
[2] Zhu M, Kasaragod DK, Kikutani K, Taguchi K, Aizawa H. A Novel
Microcontroller-Based System for the Wheel-Running Activity in Mice.
eNeuro. 8(6) doi: 10.1523/ENEURO.0260-21.2021, 2021.

IV. FUTURE PERSPECTIVE
Isa group demonstrated the crucial role of theinterhemispheric pathway from the contralesional premotor
cortex to the ipsilesional premotor cortex in the recovery of
precision grip behavior during the recovery from spinal cord
injury in the macaque monkeys by the pathway-selective
blocking technique.
Naito-Asada group has advanced the understanding of theinterhemispheric inhibitory system in the human motor cortex,
and shown that the aging-degraded inhibitory system can be
improved by coordination training. In the next year, we will
clarify the differences in the recruitment mode of ipsilateralmotor cortex during complex hand movements between young
and older adults, and summarize the hyperadaptive
phenomena observed in the brain of a top wheelchair racing
Paralympian with congenital paraplegia.
Aizawa group has begun to identify the neurotransmitter
system modulating the interhemispheric inhibition for hyperadaptation. Since the global neuromodulation could be basedon diffuse modulatory system using dopamine and serotoninand hormones released by organs outside the brain, the groupwill apply the mouse model to address roles of those systems
in modulation of the interhemispheric inhibition upon hyperadaptation.

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

Abstract—In the FY2021, our research group aimed toestablish an physiological way to manipulate sensory prediction
error by artificially changing sensory feedback at specificperipheral afferent. As the first step, we developed a novel method
to introduce marker gene into the specific type of peripheral
afferent.

I. INTRODUCTION
From the cradle to the grave, the musculoskeletal structureof the human body is changing continuously. It can change witha prolonged time constant with 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 “hyperadaptiation”
correspondingtotheever-changing
musculoskeletal structure by establishing the novel animalmodels and the cutting-edge technology. By developing themuscle relocation model, where the association of muscle
activity and its physical action will be surgically or
optogenetically manipulated, we will investigate how the CNS
acquire control strategy of their body de novo. We will
implement novel neurophysiological tools for assessing thehyper-adaptation occurring the multiple levels of the CNS, fromthe spinal cord, brain stem, and cerebral cortex.

The aim of this subproject was to examine if the target
specificity of AAV6 and AAV9 vectors to DRG neurons thatrelay nociceptive or other somatosensory signals, respectively(as established in rodent models), can be reproduced in
nonhuman primates. We injected each of the vectors into thesciatic nerve in both the rat and common marmoset (a New
World monkey) and compared their target specificity within
DRG neurons.
Eight young male Jcl:Wistar rats (4 weeks old) and 12
adult common marmosets of either sex (four males and eightfemales) were used. In both the rat and marmoset, we foundvigorous fluorescence in the ipsilateral sciatic nerve, DRGs,
and spinal cord 4 weeks after vector injection (Fig. 1). Thissuggested that gene transfer to these regions was successfully
achieved via anterograde transport from the sciatic nerve
injection (Fig. 1A and B). We observed stronger fluorescencein the segments of L4 and L5 in the rat (Fig. 1C and D) and of
L5 to L7 in the marmoset (Fig. 1E and F). This difference in thesegmental distribution is considered reasonable for the species
difference in sciatic nerve anatomy. In the spinal cord, we foundstrong expression of Green Fluorescent Protein (GFP) not onlyin the DRGs, but also in the dorsal lemniscus (Fig. 1C-F), where
the proximal axon of DRG cells ascend toward the brainstem.
However, limited fluorescence was observed for DRGs that did
not accommodate sciatic nerve afferents (e.g., contralateral
DRGs). This result indicated a lower off-target effect for intranerve induction.
We further confirmed DRG-cell specific transductionof the exogenous GFP gene by quantification of the vector
genome copy number. Results are summarized in Fig. 1G.
These results demonstrated that expression of GFP was highly

III. RESEARCH RESULTS
This year, we built a new experimental environment inorder to proceed with the experiment more smoothly.
Specifically, the introduction of an automatic behavior analysis
system by machine learning and the introduction of a wireless
EMG measurement system. Furthermore, in order to investigate
the neural mechanism of muscle synergy plasticity, subduralelectrodes were implanted and the experiment was continued.
At the same time, we conducted research aimed at developing
an animal model that can intervene in sensory prediction error,
which is one of the driving factors of hyper-adaptation. The
results of this research are reported below.

Fig. 1. Intra-nerve injections of AAV-GFP vectors in the rat and marmoset

specific to DRGs as well as to the spinal segments thatinnervated the nerve where the virus vector was injected, but
without the off-target effect on tissues surrounding the site of
injection (i.e., the muscles). Figure 2A-D show examples of
immunostained sections containing DRG neurons transduced
by AAV6 (A, C) and AAV9 (B, D) vectors in the rat (A, B) andmarmoset (C, D). We immunostained with the antibodies, GFP(green) and neuron-sensitive marker (NeuN; red), to confirm
successful gene delivery into DRG neurons. In these examples,
we found a specific difference between the rat and marmoset.
In the rat, the DRG neurons transduced by the AAV9 vector
seemed to be larger than those transduced by the AAV6 vector.
However, in the marmoset, this contrast was less dominant.
Next, we compared the target specificity of the AAV6and AAV9 vectors in the rat and marmoset. Representative
examples are shown in Figure 2E–L. For this analysis, westained each DRG slice for NF200, a marker for myelinatedprimary afferents and DRG cells that convey somatosensory
signals other than nociception, and peripherin, a marker for
unmyelinated primary afferents and DRG cells that convey
nociceptive signals38. Subsequently, we counted the DRG cells
that exhibited double labeling for both GFP and NF200 or
peripherin.
In the rat, we confirmed the specificity for
nociceptive- and other somatosensory-related DRG neurons, asreported previously. For example, in the case of AAV6 vector
injection, GFP-positive cells were found to be more frequently
co-labeled for peripherin (Fig. 2F–F”) than for NF200 (Fig. 2E–E”; compare arrows in 2E” and F”). Conversely, in the case of
AAV9 vector injection, the co-labeled cells were found more
often for NF200 (Fig. 2G–G”) than for peripherin (Fig. 2H–H”)(compare arrows in Fig. 2G” and H”). Therefore, we
successfully reproduced the previous findings in rodents. Thisconfirmation is further supported by the transduction efficiency
results (expressed as the ratio of NF200- or peripherin-labeledcells to GFP-labeled cells; Fig. 2M, left and N, left). We foundthat the AAV6 vector exhibited higher transduction efficiency

into DRG neurons with unmyelinated fibers (peripherin labeled,Wilcoxon rank sum test, p < 0.05) and the AAV9 vector showed
higher efficiency into DRG neurons with myelinated fibers(NF200 labeled, Wilcoxon rank sum test, p < 0.05).
To test whether the comparable target specificity of
the AAV vectors to DRG neurons found in the rat is also
represented in the marmoset, we repeated the equivalent
examination in the marmoset and revealed two findings. First,
in the case of AAV6 vector injection, we found that a proportionof the double-labeled cells was observed within peripherinlabeled cells (Fig. 2J–J”) more frequently than within NF200labeled cells (Fig. 2I–I”). This profile was similar to thefindings in the rat (Fig. 2E–F”). Further population analysis of
the transduction efficiency of the AAV6 vector (Fig. 2M, right)
confirmed that the target specificity of the AAV6 vector wasbiased toward peripherin- over NF200-labeled DRG neurons inboth the rat and marmoset. Therefore, this result indicates thatthe target specificity of the AAV6 vector is represented in
nonhuman primates as well as in rodents. Second, we found thatthe target specificity of the AAV9 vector in the marmoset wasdifferent from that in the rat. For example, the GFP transduction
by AAV9 vector did not exhibit high preference to NF200labeled cells, as was the case in the rat (compare Fig. 2G” andH”). Instead, we found a comparable number of NF200-labeled(Fig. 2K–K”) and peripherin-labeled (Fig. 2L–L”) cells(compare Fig. 2K” and L”) in the marmoset. Again, theseresults were supported by a population analysis (Fig. 2N):
Although the AAV9 vector exhibited higher specificity to the
NF200-labeled cells in the rat, the efficiency of transduction did
not differ in the NF200- and peripherin-labeled cells in the
marmoset (Wilcoxon rank sum test, p > 0.05).
Overall, we found that although the AAV6 vector
displayed a comparable degree of target specificity (i.e., biased
toward peripherin-labeled cells) in both the rat and marmoset,
no clear target specificity of the AAV9 vector was detected in
the marmoset, unlike in the rat, which showed specificity biased
toward NF200-labeled cells.
IV. FUTURE PERSPECTIVE
Contrary to initial expectations, primates lost the selectivity of
AAV9s to myelinated nerves. We need to further explore theoptimal vector and/or promotor that is optimal to manipulate
the activity of myelinated fiber for accessing the sensory
prediction error. In the next fiscal year, we would like tocontinue the experiments which is currently ongoing, andclarify the neural basis behind the biphasic changes in muscle
synergies by analyzing the ECoG signal.
REFERENCES[1]

Fig. 2. Immunohistochemical evaluation and target-specific gene
expression in DRG neurons in the rat and marmoset

Kudo M, Wupuer S, Fujiwara M, Saito Y, Kubota S, Inoue K, , Takada
M, Seki K: Specific gene expression in unmyelinated dorsal root ganglion
neurons in nonhuman primates by intra-nerve injection of adenoassociated virus 6 vector. Molecular Therapy : Methods and ClinicalDevelopment, 6 Aug 2021

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 reveal neuralmechanisms in which body cognition and positive emotion, such as
motivation, facilitate motor learning in challenging situations("hyper-adaptation"). Our main achievements in this fiscal year
are 1) finding evidence that a sense of agency (a sense that "I am
the one causing an action," which is an aspect of body cognition)
facilitates motor learning in several behavioral experiments, 2)
confirming that high-definition non-invasive brain stimulation can
manipulate the relationship between sensorimotor informationand the sense of agency, 3) identifying subregions of the frontal
cortex to be involved in motivation by the non-invasive functional
interventions in monkeys, 4) establishing a device for investigating
sense of agency and motivation commonly in humans and monkeys,5) establishing a long-lasting electrocorticogram recording
method covering wide area of the monkey cerebral cortex.

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.
II. AIM OF THE GROUP
Our aims are 1) understanding mechanisms in which motivation
and body cognition facilitate motor learning in challenging
situations and 2) developing methods for facilitating motor
learning through artificial control of motivation and bodycognition. Our research activity will contribute tounderstanding the "hyper-adaptability" and future developmentof the methods for inducing and facilitating it.
III. RESEARCH TOPICS
A. Relationship sense of agency and motor learning
1) Effect of sense of agency on motor learning: A group of theprincipal investigator (Tanaka and Imamizu) and Prof. Izawa of
the B03-group have explored evidence that the sense of agency

facilitates motor learning. They identified a relationshipbetween spontaneous fluctuation in the sense of agency andefficiency in motor learning. In this fiscal year, they examined
if the experimental manipulation of the sense of agency(self/other attribution of movements) affects the efficacy ofshort-term motor learning. Two participants sat side by side andmoved a cursor to a target using a pen-tablet (Fig. 1A). In eachtrial, participants were instructed whether the cursor reflected
their movement (self-condition) or the other's movement (othercondition). The cursor movement was perturbed upward or
downward by a computer. We estimated the effect of theperturbation on the performance in the subsequent trial as ashort-term learning rate. As a result, the effect was significantly
more significant in the self-condition than in the other condition(Fig. 1B). However, the effect was significantly greater thanzero in the other condition. These results suggest that the sense
agency causes a modulatory (but not decisive) impact on thelearning efficacy.

Fig. 1 Manipulation of self/other attribution by an instruction toparticipants (A) effect of the instruction (B)2) Sensitivity to controllability and motor learning: A study on
this topic was published by the group of the principal
investigator Prof. Wen of the B03 [1].
B. The intervention of sense of agency with brain stimulation
A group of the principal investigator (Hiromitsu, Asai, andImamizu) started experiments on the intervention of sense ofagency with a high-definition transcranial alternating current
stimulation (HD-tACS). They hypothesized that the neuralconnection between the right inferior parietal lobe (rIPL) andthe right inferior frontal gyrus (rIFG) contributes to the
judgment of a sense of agency. Participants moved a cursor witha joystick to trace a target trajectory. The cursor movement was
mixed with the other's prerecorded movement with a specificratio (10, 50, or 90%). After their movement, participants rated
how much they felt the cursor movement seemed their own
movement (agency rating) and how much the rating wasconfident (confidence). The alternating current was applied to
the rIPL and rIFG during the movements under three
conditions: In-phase, Anti-phase, and No stimulation, Fig 2A).
As a result, we could not find a significant change in therelationship between the mix ratio of the other's movement andthe agency ratio. However, a correlation became significantly
more negative between the cursor-joystick distance (i.e.,
prediction error) and the agency rating in the anti-phasecondition compared to the no-stimulation condition (Fig. 2B).
It is known that an increase in the prediction error leads to a

Fig. 2 Stimulation method (A) and effects (B)decrease in the sense of agency (a negative correlation). The
negative correlation was prominent in trials with highconfidence in our data. These results suggest that thestimulation improved the agency rating based on the prediction
error.
C. Basic studies on sense of agency
A principal investigator group (Ohata, Asai, Imaizumi, andImamizu) wrote a paper on the sense of agency over speech,
and the manuscript was accepted [2]. A review paper on thesense of agency co-authored with Prof. Wen of the B03 was
accepted [3]. A group of the principal investigator developed amethod to predict the effects of a robot rehabilitation fromindividuals' initial states [4]D. Identificatin of motivation-related subareas in the frontal
cortex by use of non-invasive functional interventions
The co-investigator group (Tsutsui and Nakamura) identified
subregions in the frontal cortex to be involved in motivation byusing repetitive transcranial magnetic stimulation (rTMS) as ameans of neural intervention. To dissociate the factor ofphysiological drive, such as hunger, and cognitive motivation,
behavioral performance in “competitive food picking task” wasevaluated. After facilitating the neural activity of thedorsolateral prefrontal cortex (dlPFC), the stimulated monkey
reached out more frequently to the food close to the competitor,and consequently obtained larger number of food pieces, while
the performance under the non-competitive alone condition
remained unchanged. After inhibiting the neural activity of theventral medial frontal cortex (vMFC), the stimulated monkey
reached out less frequently to the food close to the competitor,and consequently obtained larger number of food pieces, while
the performance under the non-competitive alone condition

remained unchanged. These results indicate the differential
contribution of dlPFC and vMFC in cognitive motivation.

Fig. 3 The competitive foodpicking task. Off-line rTMS isapplied to one monkey
before the task performance.

E. Establishing a device for investigating sense of agency andmotivation commonly in humans and monkeys
The co-investigator group (Tsutsui and Nakamura) haveestablished a device for investigating sense of agency andmotivation commonly in humans and monkeys. A subject sees
his hands and target objects through a big LED screen; thegraphics engine can generate and display spatial or time
distortion to the visual image of the subject’s hands on the realtime basis. This device is expected to change the subject’s senseof agency parametrically. We have confirmed that monkeys
perform food picking through this system.
F. Establishing a long-lasting electrocorticogram recording
method covering wide area of the monkey cerebral cortex
By the improvement of electrode design and surgery procedures,the co-investigator group (Tsutsui and Nakamura) haveestablished an electrocorticogram recording methodology
which enables stable recording from a wider area of the monkey
cerebral cortex for a long period.
IV. FUTURE PERSPECTIVE
We made advances in developing a method to facilitate motor
learning through manipulation of sense of agency by
succeeding in facilitating motor learning due to experimental
manipulation and intervention of the sense of agency by using
non-invasive brain stimulation. We have identified multiple
areas in the frontal cortex being involved in the control of
cognitive motivation. We have established a methodologicalbackground to study sense of agency and motivation in acommon behavioral paradigm.
REFERENCES
[1]

[2]
[3]
[4]

Wen, W., Ishii, H., Ohata, R., Yamashita, A., Asama, H., Imamizu, H.
(2021) Perception and control: individual difference in the sense ofagency is associated with learnability in sensorimotor adaptation.
Scientific Reports, 11(1), e20542.
Ohata, R., Asai, T., Imaizumi, S., Imamizu, H. (in press) My voice,therefore I spoke: The sense of agency over speech is enhanced by hearing
self-voice, Psychological Science.
Wen, W. and Imamizu, H. (in press) The sense of agency in perception,
behaviour, and human-machine interactions, Nature Reviews Psychology.
Takai, A., Lisi, G., Noda, T., Teramae, T., Imamizu, H., Morimoto, J.
(2021) Bayesian Estimation of Potential Performance Improvement
Elicited by Robot-Guided Training. Front Neurosci., 15, e704402.

A04. Alteration of brain dynamics as underlying
mechanisms of hyper-adaptability in neurotransmitter
disorders
Kaoru Takakusaki1, Takashi Hanakawa2

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

1

Abstract - The present research project (A04) is designed to test
the hypothesis that the alteration of neural dynamics following
abnormal DA or ACh neurotransmissions may lead to the changeof “rule of the conduct” as an underlying mechanism of “hyperadaptation”. For this purpose, we employed both basic animalstudies and clinical human studies in elder persons. In the thirdyear, Takakusaki and colleagues has examined the role ofbrainstem ACh system and the brainstem-spinal cord pathways
involved in postural control in the cat. Hanakawa and colleagues
has been developing simultaneous EEG-fMRI for evaluating a
dynamic profile of functional connectivity between distinct
neural networks. They also examine neuromelanin MRI and DA
transporter (DAT) SPECT for evaluation of dopamine systems.
These studies will clarify relationship across cognitive functions,neural network dynamics and neurotransmitters underlying thegeneration of hyperadaptation.

I. INTRODUCTION
The brain as a part of the body experiences various changes
during senescence. Neurotransmitters such as dopamine (DA)and acetylcholine (ACh) are reduced by aging, resulting in
Parkinson's disease (PD) and Alzheimer's disease (AD),respectively. The goal of this research project (A04) is
elucidating brain-connectivity dynamics in the cortical andsubcortical neural structures that underly the generation of
hyperadaptation in elder persons due to decline of the abovementioned neurotransmitters. To achieve the above goals, basic
experiments in animals and clinical studies in humans are
performed.
II. AIM OF THE GROUPThe Takakusaki group (PI; AMU) was in charge of animal
experiments and examines the role of ACh and DA systems inexecutive posture-gait control. This year, we studied thepostural control mechanism by the brainstem ACh system andthe brainstem-spinal cord descending pathways. The reticulospinal tract (RST; load bearing) and vestibulospinal tract (VST;
extending the body and balance control), which haveantigravity capability, are crucial to maintaining the uprightstanding posture [1]. Because the brainstem ACh system isimpaired in aging and neurodegenerative disorders, there is aneed to formulate a working hypothesis that can explain therelationship between changes in the brainstem postural control
system and those in postural deformities. We also attempt toafford substantial data to construct postural control
mathematical models based on experimental findings.

Hanakawa (a research collaborator) and his colleagues in
Kyoto University are conducting clinical studies. They aim at
discovering relationship among brain functions and dynamics
of brain activity-connectivity in association with senescence.
To this end, the Hanakawa lab will take advantages of the
PADNI cohort [2] which is a longitudinal study involving
healthy elderly people as well as patients with PD and AD.
They have started to acquire simultaneous EEG-fMRI from theparticipants of the PADN cohort. They also have developed anadvanced method to analyze relationship between cognitive
functions and dynamic profiles of functional connectivity
among distinct neural networks. The Hanakawa lab also started
basic research on neuromelanin MRI to assess DA productionin the substantia nigra (SN) in addition to DA transporter(DAT) SPECT [3] for evaluating the activity of the DA system.
This year, they developed high-resolution MRI of the SN in the
postmortem human brain.
III. RESEARCH TOPICS
A. Role of the brainstem ACh system in postural control
We examined functional connections between the pedunculopontine nucleus (PPN), the origin of the brainstem AChsystems, pontomedullary reticular formation (RF), and thelateral vestibular nucleus (LVN) in decerebrate cats. We
observed that there are excitatory connections between the PPN
muscle tone inhibitory area, where the ACh neurons exist, andthe inhibitory region of the RF. Moreover, the PPN-AChsystem excited more than half of the reticulospinal neurons andone-third of the vestibulospinal neurons. Therefore, both RST
and VST can regulate postural muscle tone by the PPN-AChsystem (Fig.1A). In PD, severe Lewy body pathology appears
in the brainstem, including the PPN, RF, and LVN, in addition
to the DA system. Together with this pathological nature, thepresent findings suggest that the impairment of the inhibitory
RST and VST is more severe than the excitatory RST in PD.
Therefore, we propose the working hypothesis that (1) the
facilitation of the excitatory RST evokes co-contraction ofextensor and flexor muscles, resulting in muscular rigidity, and(2) the decrease in VST activity causes flexion posture in PD(Fig.1B-C). We will prove this hypothesis by investigating therole of DA-ACh interaction at the brainstem in postural control.
Moreover, there is a need to clarify how impairments in the
cerebral cortex, basal ganglia, cerebellum, and limbic system in
PD and AD disable postural control by acting on the brainstem
posture control mechanisms.

cognitively impaired elderly showed the sparse FC condition
than the healthy elderly and cognitively normal PD (Fig. 2).
C. DA imaging in humans
The Hanakawa lab has continued to use neuromelanin MRI
for the potential assessment of DA synthesis in the SN, in
addition to DAT SPECT for the assessment of presynaptic DA
terminals in the striatum in the elderly. The team has also
aimed to combine the MRI method with the histochemicalassessment in specimen. This year, the team has conductedhigh-resolution MRI of the midbrain including the SN,
prompting the understanding of the MRI signals of SN (Fig. 3).

Fig. 1. Brainstem ACh system and brainstem-posture control mechanism
A; A hypothetical network of posture control by brainstem ACh system,
RST, and VST. B-C. Activities in these descending tracts (B) and upright
posture in normal and PD patients (C).

Figure 3: High-resolution T1W and T2W MRI of the midbrain of apostmortem human brain.

IV. FUTURE PERSPECTIVE
B. Non-invasice multimodal measurement of dynamicchanges of brain activity and connectivity in humans
Yoshinaga (Kyoto University) and Hanakawa have beenworking on developing simultaneous EEG-fMRI and restingstate fMRI [4] to measure dynamic changes of brain activity
and connectivity non-invasively in humans. This year,
Yoshinaga et al. applied independent component analysis(ICA) to fMRI data from four groups: healthy elderly,
cognitively normal patients with PD, cognitively impaired
patients with PD symptoms, and patients with AD. Internetwork correlation of fMRI time-series (functional
connectivity, FC) was retrieved from the default mode network,
dorsal attention network, salience network, and central
executive network. Dynamic changes of FC were assessed foreach time window. Yoshinaga et al. found four internetwork
states: a dense FC condition, a sparse FC condition and twointermediate conditions. Yoshinaga et al. compared the
dynamic network states among the four groups. Yoshinaga et al.
found that cognitively impaired patients with PD and AD, i.e.,

Takakusaki's group identified the basic principle of optimal
postural control during voluntary movements [5]. In the nextyear, they will examine cortical and subcortical mechanisms of
the optimal postural control concerning the function of the DAACh system. This year, Hanakawa’s group advanced analysis
technique for evaluating a dynamic profile of functional
connectivity. They have also started to measure simultaneousEEG-fMRI from participants in the PADNI cohort study, while
performing basic studies on this technique and DA imaging. In
the PADNI cohort, DAT-SPECT will also be employed todetect a decrease in striatal DA. However, since many unclear
points exist in the contrast expression in neuromelanin MRI,
which is considered to reflect the production of DA in the SN,
basic studies including postmortem studies, will be conductedin parallel.
REFERENCES
[1]
[2]
[3]

[4]
[5]
Figure 2: Comparison of 4 dFC patterns (dense, sparse, and twointermediate connections) across Healthy elderly (HC), Parkinson’s
disease (PD), PD with cognitive disturbance (PDD/DLB) and Alzheimer’s
disease (AD).

Takakusaki K, Takahasi M, Noguchi T, Chiba R. Neurophysiologicalmechanisms of gait disturbance in advance Parkinson’s diseas. Neurol
Clin Neurosci. (in press)
N. Wakasugi N and T. Hanakawa: It is time to study overlapping
molecular and circuit pathophysiologies in Alzheimer's disease andLewy body disease spectrums. Front Systems Neurosci 15:777706, 2021.
J. Ikezawa, F. Yokoch, R. Okiyama, S. Kumada, M. Tojima, T.
Kamiyama, T. Hanakawa, H. Matsuda H, F. Tanaka, Y. Nakata, E.
Isozaki E: Is Generalized Dystonia Accompanied with Impairments of
Dopaminergic System? Front Neurol 12:751434, 2021.
花川 隆：Q&A-神経科学の素朴な疑問: fMRI で測定されるゆっく
りした脳活動の変動による脳領域間機能連関の神経基盤はどこまで解明されているのでしょう？Clin Neurosci 39(8): 1043, 2021.
Takahasi M, Nakajima T, Takakusaki K. Preceding postural control
during forelimb reaching movements in cats. Front Sys Neurosci 2022;
15:792665

A05-1Annual report of research projectShin-ichi Izumi

Graduate School of Biomedical Engineering, Tohoku University
Abstract—This study aims to elucidate the hyper-adaptation
mechanism of upper limb (UL) recovery in stroke patients. We
expect the elucidation of the adaptation mechanism underlying
UL recovery after stroke could contribute to understanding thehyper-adaptation mechanism of body-brain system. In this study,
we measured the real-world arm use and body-specific attention
longitudinally in subacute stroke patients using accelerometersand psychophysical methods, respectively, and investigated therelationship between them during the recovery process. The
results showed a significant positive correlation between theamount of change in body-specific attention up to 1 month after
enrollment and the long-term amount of change in real-worldarm use up to 6 months after enrollment ． This findings
contribute to a new rehabilitation strategy to increase the realworld arm use. In addition, to understand the characteristics ofthis body-specific attention in more detail, we measured bodyspecific attention to the hands and feet in healthy subjects. As aresult, we clarified the relationship between body-specificattention to the hands and feet and sensory-motor functions．
Furthermore, we investigated the effect of a decrease in bodyownership, one of the body consciousness, on sensory function(tactile sensitivity threshold). We found a significant negative
correlation between loss of body ownership and tactile sensitivity
threshold. These results contribute to a multifacetedunderstanding of the pathogenesis of UL paralysis in stroke
patients from the perspective of body consciousness．

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 toestablish 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 andtiming of combination is not clear. In order to overcome these
problems, we have been working with researchers in systems
engineering and brain science to understand the adaptive
mechanisms of the neural basis that mediates between the brain
and the body (body representation in the brain) using
mathematical models and to develop rehabilitation treatment
based on these models. In this study, we developed a method toquantify body-specific attention as a marker of body
consciousness, and found that body-specific attention was

lower in chronic stroke patients with longer time since stroke
onset and lower hand function[1]. This is the first finding tomeasure learned non-use in chronic stroke patients from the
aspect of body consciousness. However, it is not clear how
body-specific attention changes from the onset of stroke to thechronic phase, and how it relates to the real-world arm use. In
addition, the relationship between sensory-motor functions andbody consciousness, including body-specific attention to hands
and feet and a sense of body ownership, has not been fully
elucidated.
II. AIM OF THE GROUP
1)This study aims to elucidate the hyper-adaptation
mechanism of UL recovery in stroke patients. We
longitudinally investigate relationship between real-world arm
use and body-specific attention in stroke patients by using
accelerometers and psychophysical methods, respectively. We
expect the elucidation of the adaptation mechanism underlying
UL recovery after stroke could contribute to understanding thehyper-adaptation mechanism of body-brain system. 2) In
addition, the aim of this study is to clarify the characteristics
of body-specific attention to the hands and feet, and the effectsof loss of body ownership on sensory functions in healthy
subjects. These will contribute tobasic findings forunderstanding the pathology of hemiplegic stroke patientsfrom various perspectives of body consciousness.
III. RESEARCH TOPICS
A. Relationship between body-specific attention to a paretic
limb and real-world arm use in stroke patients: A
longitudinal study.
We conducted a longitudinal prospective observational studyof 25 patients with first-ever subacute stroke．Measurements
were taken at baseline (TBL), 2 weeks (T2w), 1 month (T1M), 2
months (T2M), and 6 months (T6M) after enrollment. UL
function was measured using the Fugl‒Meyer Assessment(FMA) and Action Research Arm Test. Real-world arm use
was measured using accelerometers on both wrists. Bodyspecific attention was measured using a visual detection task.
The UL function and real-world arm use improved up to T6M.
Longitudinal changes in body-specific attention were mostremarkable at T1M. Changes in body-specific attention up to
T1M correlated positively with changes in real-world arm use
up to T6M, and from T1M to T6M, and the latter more strongly
correlated with changes in real-world arm use. Changes in realworld arm use up to T2M correlated positively with changes in

FMA up to T2M and T6M. No correlation was found between
body-specific attention and FMA scores. Thus, these results
suggest that improved body-specific attention to the paretic
limb during the early phase contribute to increase long-term
real-world arm use, and that increased real-world use isassociated with recovery of UL function. Our results may
contribute to the development of rehabilitation strategies toenhance adaptive changes in body representation in the brain
and increase real-world arm use after stroke. [2]B. Body-specific attention to the hands and feet in healthy
adults.
Body-specific attention to the hands has been examined but
not to the feet. We aimed to confirm the existence of bodyspecific attention to the hands and feet, and examine its relation
to motor and sensory functions from a behavioral perspective.
The study included two groups of 27 right-handed and rightfooted healthy adults, respectively. Visual detection tasks wereused to measure body-specific attention. We measured reaction
times to visual stimuli on or off the self-body and calculated
the index of body-specific attention score to subtract thereaction time on self-body from that off one. Participants wereclassified into low and high attention groups based on each left
and right body-specific attention index. For motor functions,
Experiment 1 comprised handgrip strength and ball-rotation
tasks for the hands, and Experiment 2 comprised toe grip
strength involved in postural control for the feet. For sensory
functions, the tactile thresholds of the hands and feet were
measured. The results showed that, in both hands, the reaction
time to visual stimuli on the hand was significantly lesser thanthat offhand. In the foot, this facilitation effect was observed in
the right foot but not the left, which showed the correlation
between body-specific attention and the normalized toe
gripping force, suggesting that body-specific attention affected
postural control. In the hand, the number of rotations of the ball
was higher in the high than in the low attention group,
regardless of the elaboration exercise difficulty or the left or
right hand. However, this relation was not observed in thehandgripping task. Thus, body-specific attention to the hand is
an important component of elaborate movements. The tactile
threshold was higher in the high than in the low attention group,
regardless of the side in hand and foot. The results suggested
that more body-specific attention is directed to the limbs withlower tactile abilities, supporting the sensory information
reaching the brain. Therefore, we suggested that body-specificattention regulates the sensory information to help motor
control. [3]
C. Decreased tactile sensitivity induced by disownership: An
observational study utilizing the rubber hand illusion.
Long-term non-use of parts of the body due to physicaldysfunction may disturb multisensory integration, resulting
in a decreased sense of body ownership. The rubber hand
illusion (RHI) is an experimental method of manipulating thesense of ownership (SoO). In this illusion, subjects feel as if
the rubber hand in front of them were their own hand. The
RHI elicits the disownership phenomenon; not only does therubber hand feels like one’s own hand, but one’s own handdoes not feel like one’s own hand. The decrease of ownershipof one’s own body induced by the bodily illusion isaccompanied by neurophysiological changes, such asattenuation of somatosensory evoked potential and decreases
in skin temperature. If the loss of the SoO is associated withdecreased neurophysiological function, the dysfunction ofpatients complaining of the loss of ownership can beexacerbated; appropriate rehabilitation prescriptions areurgently required. The present study attempted to induce asense of disownership of subjects’ own hands using the RHI
and investigated whether the tactile sensitivity threshold wasaltered by disownership. Via questionnaire, subjects reported adecrease of ownership after the RHI manipulation; at the sametime, tactile sensitivity thresholds were shown to increase intactile evaluation using the Semmes-Weinstein monofilaments
test. The tactile detection rate changes before and after the
RHI were negatively correlated with the disownership-scorechanges. These results show that subjects’ sense of
disownership, that their own hands did not belong to them, led
to decreases in tactile sensitivity. The study findings alsosuggest that manipulating of illusory ownership can be a toolfor estimating the degree of exacerbation of sensory
impairment in patients. Consideration of new interventions
that optimize the sense of body ownership may contribute to
new rehabilitation strategies for post-stroke sensory
impairment. [4]
IV. FUTURE PERSPECTIVE
In the recovery process of paretic UL, body-specificattention was found to be related to real-world arm use. We
believe that the results will contribute to the construction ofrehabilitation strategies to enhance body consciousness andreal-world arm use in stroke patients. In the future, we will
investigate the neural basis of use-behavior and body-specificattention, and clarify the long-term changes in the brain
functional and structural networks during the recovery processof stroke patients.
REFERENCES[1] N. Aizu, Y. Oouchida, and S. Izumi, “Time-dependent decline of bodyspeci fi c attention to the paretic limb in chronic stroke patients,”
Neurology, vol. 91, pp. e751-758, 2018.
[2]

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,” Front. Syst. Neurosci., vol. 15, 2022.

[3]

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,” Front. Syst. Neurosci., vol. 15, Jan. 2022.

[4]

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

A05-3Annual report of research project
Riki Matsumoto

Division of Neurology, Kobe University Graduate School of Medicine
Abstract—In order to elucidate the Hyper-Adaptability
mechanism of motor function under aging and pathologicalcondition, it is essential to understand hyper-adaptability of the
premotor cortex that integrates the information top down fromthe prefrontal cortex and bottom up from the parietal lobe. We
record electrocorticogram (ECoG) in epilepsy patients who
undergo intracranial electrode implantation in the frontal andparietal lobes for preoperative evaluation of epilepsy surgery.
We probe neural signatures of higher-order motor control byrecording wide-band ECoG activities during higher-order motor
tasks. In order to understand the brain network associated withmotor control and hyper-adaptic reorganization, we made anelectrophysiological connectome by using cortico-cortical evoked
potentials (CCEPs) as an index of effective connectivity, whichwere obtained by systemic evaluation of the whole implanted
electrodes. We explored the network property of higher brain
functions such as the premotor and language areas, and theeffect of epilepsy upon the network integrity. For its clinicalapplication, we validated the safety and feasibility of CCEP bothfor acute and chronic evaluations with intracranial electrodes,
and proposed the intraoperative CCEP protocol.

I. INTRODUCTION
In order to elucidate the Hyper-Adaptability mechanism of
motor function under aging and pathological condition, it isessential to understand hyper-adaptability of the premotor
cortex that integrates the information top-down from theprefrontal cortex and bottom-up from the parietal lobe. For
epilepsy surgery, it is crucial to fully resect the epileptic focus
to cure the disease. At the same time, it is also important topreserve brain functions. As a part of presurgical evaluations
for intractable partial epilepsy, patients undergo chronic
implantation of subdural electrodes when the focus is not welldetermined by non-invasive evaluations or the focus is locatedaround the important functional cortices. For functional
mapping, we usually record neural activities (e.g., ERPs, highgamma activities) while patients complete a task, and thenlocate the cortex responsible for a particular task bydelineating 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 theresection of the premotor area by simulating the virtual lesionusing the electrophysiological connectome, and comparing itwith the movement disability and its recovery after surgery. In
Year 2021, because of the ongoing COVIC-19 pandemic thatprevented us from recruiting many patients, we focused on
making the electrophysiological connectome by means ofcortico-cortical evoked potentials (CCEPs) since theunderstanding of the macroscopic human brain connectome isessential to tangle the hyperadaptability of the premotor areaand human brain in general. We investigated the network
characteristics proper to the higher brain functions and its
plastic alternation due to pathology, namely, epilepsy.
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 and gave
written consent to the research protocols IRB#C533, 443 and1062.
In order to understand the brain network associated withmotor control, we make an electrophysiological connectomeby using CCEP as an index of effective connectivity, whichwere obtained by systemic stimulation and evaluation of thewhole implanted electrodes. We extract structural features
such as cluster coefficient or centrality, and attempt to clarify
modifications of the connectome by epilepsy pathology orvariations by different higher-order motor task strategies.
In order to delineate the mode of hyper-adaptability in eachindividual patient who underwent brain resection for epilepsy
or tumor surgery, we need to establish the CCEP protocol
under acute (intraoperative) and chronic evaluations withintracranial electrodes to make individual CCEP connectome.
We validated the safety of CCEP investigation, and exploredthe effect of general anesthetics upon CCEP.
III. RESEARCH TOPICS
We have carried out the following three research projects.
A. Investigation of electrophysiological connectome todelineate network configuration proper to higher
functions and its alternation due to pathology
Electrophysiological connectome, namely, CCEP connectome
was generated by means of systemic low-frequency electricalstimulation to all the implanted electrodes to record CCEP.
The network property of higher functions was investigatedusing the CCEP connectome, and the part of the findings(connectivity from the medial parietal cortices) is currently
under review (Togo, Matsumoto et al.).
The part of the ventral premotor area (negative motor area
[NMA]) is known to show a negative motor response (arrestsof repetitive movement of the tongue, hands, and feet) upon
ECS. In the group analysis of 8 patients, NMA showedsignificantly larger outbound connections compared to theprimary motor cortex and the language areas while thelanguage area showed larger inbound connections. With
regards to pathology, namely, epilepsy, N1 inboundconnections and N2 outbound connections were found to bealtered within the epileptic focus.
We now have constructed the CCEP connectome based
upon the data from 25 patients, and the network property andpathologic alternation will be presented in the coming annual
meeting of the Japanese Society of Neurology in Spring, 2022
(Togo et al.). In the future, we will compare individual
connectome with group data to clarify the transformation ofconnectome due to epilepsy pathology.
B. Validation of its safety and feasiblity under undergeneral anethesia and proposal of CCEP protocol forclinical application of CCEP connectome
For clinical application of CCEP connectomic approach for
hyper-adaptability, the safety is of utmost importance. A totalof 1283 stimulation was analyzed in 29 consecutive patients
who underwent chronic subdural electrode implantation and
CCEP recording for the presurgical evaluation.
Afterdischarges (ADs, 0.94%) and clinical seizures (0.39%)
were rarely induced by low-frequency stimulation, and thus
CCEP is regarded as a safe procedure. Although rare, theclinical seizure was more frequently triggered within the
seizure onset zone. Stimulation intensity did not seem to be arisk factor of ADs and clinical seizures [4].
With regards to the effect of general anesthesia, the effect ofpropofol, the most common anesthetics we use for awake
craniotomy, was systematically investigated in 14 patients
who underwent intraoperative CCEP evaluation. The
distribution of large CCEP response was marginally affectedby propofol. The CCEP N1 amplitude increased from generalanesthesia to awaking. These findings indicate that CCEP
allows testing of the functional network such as dorsal
language white matter pathway even under general anesthesia[5]. Taking account of these recent findings, we proposed theintraoperative CCEP protocol in an open-access manner [6, 7].
Besides, as a leading-expert in CCEP, the PI gave a talk invarious domestic and international meetings on the concepts,
development and future of CCEP [8-13], and wrote a chapterof the Handbook of Clinical Neurophysiology [14].
C. Modification of connectome by surgical resection andhyper-adaptability of network
This prospective study was not well performed due to ongoing
COVID-19 pandemic in year 2021. When we have to resect apart of the premotor area due to clinical need for epilepsy ortumor surgery, we will reconstruct the premotor areaconnectome based on the comprehensive CCEP responses tosimulate the structural changes in the network after resection.
We then compare this simulation results with actual
behavioral dysfunction that would occur immediately after
surgery with potential subsequent recovery, and clarify the
network-level hyper-adaptation by elucidating “hyperadaptive” indicators that incorporate indexes such as cluster
coefficient and centrality. Furthermore, based oncomprehensive connectome information, we will promotecollaborative researches with theory group B to seek forconstructing mathematical model of hyper-adaptation after
resection of the premotor area.

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 the large
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 at bothindividual and group levels. We delineated network
configuration proper to the premotor and other higher
functional areas and their alteration due to epilepsy pathology.
In the next grant application in this group, we plan to carry out
the initial proposal, namely, simulate the postoperative CCEP
connectome by using the CCEP connectomic data to clarify
the network-level Hyper-Adaptivity. We plan to compare the
network changes to the movement disability after surgery andits recovery, and try to extract the“hyper-adaptive” indicators.
We also plan to keep collaborating with theory group B to seek
for constructing mathematical model of hyper-adaptation.
References[1]

Togo M, Matsumoto R, Nakae T, et al. Characteristics of intercortical
networks created with late cortico-cortical evoked potential. 62th JSN
meeting, May 19, 2021. Candidate of the best presentation award.
[2] Togo M, Matsumoto R, Nakae T, et al. Connectivity strength
modification in interareal cortical networks from the seizure onset zone:
a cortico-coritical evoked potential study. JES54 meeting, Sept.23,
2021[3] Togo M, Matsumoto R, Nakae T, et al. Modification of effective
connectivity strength in interareal cortical networks from the seizure
onset zone: a cortico-cortical evoked potential study. The 13th AOEC
meeting, June 12, 2021.
[4] Kobayashi K, Matsumoto R, Usami K, et al. Cortico-cortical evoked
potential by single-pulse electrical stimulation is a generally safe
procedure. Clin Neurophysiol 132:1033-1040, 2021.
[5] Yamao Y, Matsumoto R, Kunieda T, et al. Effect of propofol on
cortico-cortical evoked potentials: findings of intraoperative dorsal
language pathway monitoring. Clin Neurophysiol 132:1919-1926, 2021.
[6] Yamao Y, Matsumoto R. Intraoperative cortico-cortical evoked
potential recording for monitoring the arcuate fasciculus; Feasible undergeneral anesthesia? Clin Neurophysiol S1388-2457(21)00737-9.
[7] Yamao Y, Matsumoto R, Kikuchi T, Yoshida K, Kunieda T, Miyamoto
S. Intraoperative brain mapping by cortico-cortical evoked potential.
Front Hum Neurosci, 15:55, 2021. doi: 10.3389/fnhum.2021.635453[8] 松本理器．皮質皮質間誘発電位：着想から臨床応用への道のり.
第 27 回日本脳神経モニタリング学会，2021 年７月３日[9] 松本理器．脳機能マッピングの過去･現在･未来．15 回日本てんかん学会関東甲信越地方会, 2021 年 11 月 27 日
[10] 松本理器．皮質皮質感誘発電位(CCEP)：歴史と展望．第 51 回日本臨床神経生理学会学術大会， 2021 年 12 月 16 日
[11] Matsumoto R. Intraoperative Brain Mapping by Cortico-Cortical
Evoked Potential. ON-LINE Meeting Spanish Society of Clinical
Neurophysiology (SENFC 2021), Oct 16th, 2021
[12] Matsumoto R. Cortico-cortical evoked potential: its past, present andfuture. Grand Round, Cleveland Clinic Epilepsy Center, June 4, 2021
[13] Matsumoto R. Physiology and pathology of the higher brain function:
insights from intracranial EEG recordings. Chaucer Club (webseminar), MRC Cognition and Brain Sciences Unit. University ofCambridge, Apr 29, 2021
[14] Hallett M, DelRosso LM, Elble R, et al. (松本 9 番目) Chapter 1.
Evaluation of movement and brain activity. Clinical Neurophysiologyof Movement Disorders, 2nd Edition Handbook of Clinical
Neurophysiology,
Clin
Neurophysiol
132:2608-2638.
doi:
10.1016/j.clinph.2021.04.023. (review)A05-5. Development of non-invasive brain stimulation
techniques that can icrease recruitment of the corticospinal
motor indirect pathway during acquisition of hand motor skills
1.
2.

Mitsunari Abe1 and Kazumasa Uehara2

Department of Advanced Neuroimaging, IBIC, National Center of Neurology and Psychiatry
Division of Neural Dynamics, National Institute for Physical Sciences

Abstract—Hand dexterity is a remarkable ability
characterizing higher primates, including humans. Evidence
from humans revealed that hand movement is controlled byprimary motor cortices (M1s) in bilateral hemispheres. Our
results suggested recruitment of the corticospinal networks
between M1s and the spinal cord during hand movement that
sends the influences from the contralateral M1 on the spinal cord
(contralateral M1-SC network) or that converge the influencesfrom bilateral M1s onto the spinal cord (bilateral M1s-SC
network). It has been proposed that bilateral M1s-SC network
may play a role in functional recovery of the impaired hand
movements. Our motivation is to develop the non-invasive
stimulation protocols to recruit the bilateral M1s-SC network infunctional recovery in patients who disable to perform hand
movements. During the fiscal year 2020-2022, we found that lefthand movement involved the bilateral M1s-SC network morethan right-hand movement. Furthermore, we found higher
neuronal activity in the spinal cord at the level of C3-C4
segments during left-hand movement. Previous evidence
indicated that the propriospinal neurons located at the level of
C3-C4 segments may serve functional recovery of the impaired
hand movement. We hypothesized higher recruitment of thebilateral M1s-SC network by inducing the neuronal plasticity inthe propriospinal neurons. We are developing the stimulation
protocols that induce the timing-dependent plasticity in theseneurons.

I. INTRODUCTION
Human studies unveiled that hand movement involved not
only contralateral M1 but also ipsilateral M1. A
neurophysiological report suggested existence of the bilateral
M1s-SC network in humans. However, no studies
demonstrated whether right-hand movement or left-handmovement recruited the bilateral M1s-SC network. We thus
developed the functional magnetic resonance imaging
techniques which enabled simultaneous measurement ofneuronal activity in M1 and spinal cord at the segments where
motoneurons innervating hand muscles reside. During the
fiscal year 2020-2022, we tested whether right-hand movementof left-hand movement involved the bilateral M1s-SC network.
Our results suggested that left-hand movement more likely
involved the bilateral M1s-SC network than right-handmovement. Furthermore, we found higher neuronal activity inthe spinal cord at the level of C3-C4 segments during left-handmovement. We hypothesized increase of recruitment of thebilateral M1s-SC network by inducing long-term potentiationlike plasticity in the neurons at the level of C3-C4 segments.

The stimulation protocols have been proposed that may induce
plasticity in the propriospinal neurons, assumedly located at thelevel of C3-C4 segments. We assumed that the propriospinal
neurons are implemented in bilateral M1s-SC network. We areconstructing the experimental settings to test our hypothesis.

II. AIM OF THE GROUP
Our long-term goal is to develop higher recruitment of thebilateral M1s-SC network by inducing the neuronal plasticity
in the propriospinal neurons at the level of C3-C4 segments.
III. RESEARCH TOPICS
Here we described the following what we achieved and what
we are preparing as follows.
A. Higher neuronal activity in the spinal cord at the level of
C3-C4 segments during left-hand movement .

We examined whether right-hand movement or left-handmovement recruited the bilateral M1s-SC network. Our results
suggested that left-hand movement more likely involved thebilateral M1s We constructed the bilateral M1s-SC networkmodel in which influences from bilateral M1s modulated
activity in the spinal cord (Fig. 1). Furthermore, we foundhigher neuronal activity in the spinal cord at the level of C3-C4
segments during left-hand movement than during right-handmovement (Fig. 2 and Fig. 3). Evidence from monkeys
indicated the propriospinal neurons that relay signals from M1s.
We interpreted recruitment of the neuronal activity in C3-C4
observed during left-hand movement.

modulation of motor evoked potentials when the double
stimulation of the motor cortex and the peripheral nerve wereapplied. Notably this modulation was most evident when the
peripheral stimulation was applied at the timing 7 ms before
the cortical stimulation. These results suggested collision of
stimulations from M1 and the peripheral nerve in the
propriospinal neurons. We hypothesized the propriospinal
neurons located at C3-C4 that are active using the double
stimulation technique. We are preparing the experiential settingthat enable measuring MRI activity in C3-C4 when the double
stimulation technique is given.
IV. FUTURE PERSPECTIVE
In the fiscal year 2020-2022, we discovered evidence
suggesting recruitment of neurons in C3-C4 during left-handmovement that are implemented in the bilateral M1-SC
network. To facilitate the bilateral M1-SC network, we are on
the way to develop the double stimulation protocols thatinduce plasticity in the propriospinal neurons located at theC3-C4 segment.
REFERENCES

We developed the pipelines of the imaging data by using
combination of the following widely distributed software(https://fsl.fmrib.ox.ac.uk/fsl/fslwiki[FSL],
https://spinalcordtoolbox.com/en/stable/[SpinalCord
Toolbox]). Now we achieved to perform statistical mapping
analysis by voxel by voxel in the brain and the spinal cord.
B. Development of stimulation protocol that induce thetiming-dependent plasticity in the propriosepinal neurons
at the segment of C3-C4
We assumed that the propriospinal neurons are
implemented in the bilateral M1-SC network. We designed theprotocol that induces the timing-dependent plasticity in the
propriospinal neurons. These neurons converge signals derived
from M1s, and also integrate signals from M1 and afferents
from peripheral neurons. Previous literatures observed

[1]
[2]

[3]
[4]

[5]

Isa T, Kinoshita M, Nishimura Y. Role of Direct vs. Indirect Pathways
from the Motor Cortex to Spinal Motoneurons in the Control of Hand
Dexterity. Front Neurol. 2013;4:191.
高澤 英嗣, 阿部 十也, 飯塚 伯, 設楽 仁, 高岸 憲二, 筑田 博隆, 花川
隆. 脊椎疾患のニューロイメージングの近未来 脳脊髄機能的 MRIによる皮質脊髄路の神経機能評価法とその展望. 日本整形外科学会雑誌 92(2) S335-S335 2018 年
高澤 英嗣, 阿部 十也, 飯塚 伯, 設楽 仁, 高岸 憲二, 花川 隆. 脳脊髄機能的 MRI による皮質脊髄路の機能評価法の開発. Journal of
Spine Research 7(9) 1366-1372. 2016 年
Hanakawa T, Mima T, Matsumoto R, Abe M, Inouchi M, Urayama S,
Anami K, Honda M, Fukuyama H. Stimulus-response profile during
single-pulse transcranial magnetic stimulation to the primary motor
cortex. Cerebral Cortex. 19(11):2605-15. 2009.
Abe M, Fukuyama H and Mima T. Water diffusion reveals networks thatmodulate multiregional morphological plasticity after repetitive brain
stimulation. Proceedings of the National Academy of Sciences of theUnited States of America. 111(12):4608-13. 2014.

A05-6 The cerebellum–rubral pathway is involved in the function
recovery by rehabilitation after intreacerebral hemorrhage
Hideki Hida

Nagoya city University Graduate School of Medical Sciences

Abstract— In the FY2021, we performed selective blockage inthe cerebellum-rubral pathway using double-virus infection
method, following to confirmation of optimum infection condition.
In the skilled reaching test, CNO treatment caused the inhibitionof the functional recovery that is induced by forced-limb use after
intracerebral hemorrhage. The CNO effect to the cerebellumrubral pathway is also supported by the electrophysiologicalanalysis.
.

Confirming EGFP-positive cells as the marker which
expression is induced by Cre, it was revealed that we canefficiently transfect the virus vectors into both the cerebellum
and red nucleus road as shown in Table 1.

I. INTRODUCTION

Table 1 Virus infection condition into the cerebellum-rubral pathway

To clarify the mechanism of CIMT that is used as arehabilitation method after intracerebral hemorrhage (ICH),
we have previously revealed that 1) forelimb motor function isrecovered by forced-limb use (FLU) after ICH, 2) the corticorubral pathway has a causality to the functional recovery of
FLU after ICH, and 3) the cortico-reticular pathway has apotency to substitute for the functional recovery byrehabilitation. In this study, using a rat ICH model, we arechallenging to clarify motor regulatory mechanism of thecerebellum in the recovery by FLU.
II. AIM OF THE GROUP
The final purpose of our study is to clarify the mechanism of
the recovered forelimb function by FLU after ICH using
selective blockade in the cerebellum-rubral pathway.
As we could establish the efficient double-transfection into the
cerebellum and the red nucleus in last year, we are challenging
three projects as follows.
In ICH model rats that recovered forelimb function by FLU, 1)
we transfect AAV-DJ-EF1-DIO-hM4D(Gi)-mCherry into thelateral nucleus of the cerebellum and FuGE-MCSV-Cre into
the red nucleus parvocellular part, 2) we assessed forelimb
function in the pellet reaching test with DREADD method by
CNO, and 3) we measure the change of the nerve activity inthe cerebellum-rubral pathway by multi-electrode electrodemethod as collaborating evidence for the inhibition by CNO.
III. RESEARCH TOPICS
A. Optimization of the double-virus infection method into the
cerebellum – rubral pathway
To optimize the virus vector infusion method, we injected
AAV-DJ-CAGGS-FLEX-EGFP into the cerebellum and FuGE-MSCV-Cre into the red nucleus changing infusion
conditions (quantity, the number of times, speed).

Red
NucleusCerebellum

both
side
1 area2 areasvolume

AD

ML

DV

0.8μℓ
/4min
0.6μℓ
/3min

－5.2

±1.6

－11.0
－11.4

±3.5
±3.6

脳表
7.5
4.4
4.3

Verticalangle
5 degree0 degreeB. Evaluation of forelimb function under selective block inthe cerebellum-rubral pathway using the DREADD
methodAfter pre-training for 2 weeks in 6 weeks-old Wistar rat, FuGE-MSCV-Cre was injected into the red nucleus at 8 weeks ofage and then AAV-DJ-EF1-DIO-hM4D(Gi)-mCherry was
injected into the lateral nucleus of cerebellum one week later.
We evaluated skilled reaching test at 2-3 day just before
ICH and at 12 day after ICH. The reaching test was also
performed at 30 min after CNO treatment at 16 and 20 day
after ICH, followed by the test at 28 day after ICH without
CNO administration (Figure 1).

Figure 1. Selective blockade of the cerrebello-rubral pathway by CNO
administration

As a result, it was revealed that the improvement effect of
FLU after ICH on the forelimb function was disappeared by the
CNO administration. In addition, the improvement effect by
FLU was observed without CNO treatment at 28 day after ICH.
On the other hand, the CNO action disappears three hours after
the treatment, confirming that the improvement by FLU after
ICH is related to the cerebellum-rubral pathway.

IV. FUTURE PERSPECTIVE
C. Confirmation of the electrophysiologicall response in the
red nucleus to the stimulation in the lateral nucleus of thecerebellum
The technique of electrophysiological experiment and theanalysis using the multi-point electrode probe were obtained inlast year.
As the recovery effect by FLU after ICH is inhibited by theselective blockade in the cerebellum-rubral pathway, we arechallenging the analysis of the response in the red nucleus
against electric stimulation in the cerebellum as thecollaborating evidence for the selective blockade.
The lateral nucleus of the cerebellum (AP: − 13.2, ML:3.5,
DV:4.5 with 20 degree angle posterior to vertical line）was
stimulated by various condition (intensity: 10~200μA,
duration: 0.1msec, interval: 3.3msec, 10 trains） .
Although both saline-treated control group and CNO-treatedgroup is n= 1, the field potential in the red nucleus against
cerebellum stimulation was stable during 1 hour. On the other
hand, about 35% decrease of the potential is shown at 30 mins
after CNO administration.
Although it is necessary to increase the number of experiments,
the electrophysiological collaborating evidence of the nerve
block effect of the cerebellum-rubral pathway by CNO
treatment could suppose collaborating evidence.

Finally, the results in FY2021I was summarized and our future
plan was written as fokllows.
In this year, we revealed that forelimb functional recovery by
FLU after ICH was significantly inhibited by selective
blockade in the cerebellum-rubral pathway with CNO
administration, which is shown by behavioral andelectrophysiological experiments.
In the next year, we are planning to investigate the detailmechanism in motor regulatory system mediated by the
cerebellum-rubral pathway, analyzing 1) time course ofactivation switch in the cerebellum-rubral pathway, 2) detailed
forelimb movement by markerless method using DeepLabCut
and its the relationship with the cerebellum-rubral pathway.
REFERENCES[1][2]

C C Joyal, C Strazielle, R Lalonde Effects of dentate nucleus lesions on
spatial and postural sensorimotor learning in rats. Behav Brain Res.
2001; 122(2): 131-37.
A Ishida, K Kobayashi, Y Ueda, T Shimizu, N Tajiri, H Hida Dynamic
Interaction between Cortico-Brainstem Pathways during TrainingInduced Recovery in Stroke Model Rats. J Neurosci. 2019; 39(37),7306-20..

A05-7Annual report of research project
Hiroyuki Miyawaki

The Osaka City University, Graduate School of Medicine
Abstract—Due to fearful memories of an excess aversive
experience, animals fall into maladaptation states in which theanimals take inadequate behaviors (e.g. freezing in safe
environments). The animals recover adaptive behavior throughthe following hyper-adaptation process induced by extinction
learning. It is well established that the amygdala is essential forfear-related maladaptation and hyper-adaptation. In addition,
recent studies indicate other brain regions, such as the ventral
hippocampus and prefrontal cortex, are also involved in themaladaptation and hyper-adaptation processes. However, itremains unclear how these brain regions interact with each otherand whether the interaction changes with transitions tomaladaptation or hyper-adaptation states. To investigate thesepoints, we performed multi-regional large-scale electrophysiology
in fear-conditioned rats. We found that cell-ensembles, groups ofsynchronously activated neurons, in the amygdala, ventral
hippocampus, and prefrontal cortex are simultaneously activatedduring sleep periods following the fear-conditioning. Such
synchronous activation was not detected in sleep preceding theconditioning. We also revealed that the triple-activationaccompanies fast network oscillations in the involved regions.
These findings suggest that the development of inter-regional
interaction supports the transition to mal-adaptation states, andfast network oscillations may have important roles in the interregional interaction. Further studies are warranted to elucidate
how inter-regional ensemble coactivations are involved in themaladaptation and hyper-adaptation process.

I. INTRODUCTION
As a well-established animal model of human post-traumaticstress disorder (PTSD), Fear-conditioning has been intensively
studied. Fear-conditioned animals are in maladaptation states
where the animals take inadequate behaviors such as freezing insafe environments. The fear-conditioned animals recover theirappropriate behavior through hyper-adaptation induced byextinction learning, whereas the fear memories themselves aremaintained [1]. The acquisition and extinction of fear memories
involve the amygdala, ventral hippocampus, and prefrontal
cortex [2]. However, it remains unclear how inter-regional
networks across these regions change through transitions tomaladaptation/hyper-adaptation states and how these changes
are regulated. Revealing these points would provide
fundamental information on memory- and emotion-related
mental disorders such as PTSD and anxiety disorders.
Furthermore, the information would be crucial for developing
neurophysiology-based cure procedures for these disorders.
II. AIM OF THE GROUP
This research project aims to clarify how inter-regional brain
networks changes during hyper-adaptation from fear-memory
induced maladaptation states. Furthermore, this project would

also reveal regulation mechanisms underlying the changes in theinter-regional networks. To obtain these goals, we first elucidate
how the network changes through the fear-memory-induced
maladaptation, which would give an important clue tounderstand what is compensated through the hyper-adaptation
process. In this year, we focused on the transition tomaladaptation states, and we aimed to clarify the dynamics ofinter-regional networks that involve multiple brain regions
during the maladaptation process and its regulation mechanisms
in each brain region.
III. RESEARCH TOPICS
A. Multi-regional large-scale electrophysiology in three brain
regions and identification of cell ensembles
By utilizing multi-regional large-scale electrophysiologicalrecordings on freely moving rats, we obtained spike activities ofhundreds of neurons and local field potentials (LFPs)
continuously for ~17 hours from basolateral amygdala (BLA),
ventral hippocampus CA1 region (vCA1), prelimbic cortex
layer 5 (PL5) in the prefrontal cortex (Fig. 1). During therecording, fear-conditioning and extinction-learning were
performed in which electrical stimulation through eyelid
electrodes was used as the unconditioned stimulus [3]. Then, we
identified cell-ensembles in each brain region and theirinstantaneous activation strength based on independent
component analyses (ICA) [4].

Fig. 1. A representative example of continuous large-scale
electrophysiological recording from vCA1, BLA, and PL5.

B. Synchronized activation of cell-ensembles across threebrain regions emerged after fear-conditioning.
We observed that cell-ensembles in vCA1, BLA, and PL5
were activated synchronously during sleep epochs following

activation events were detected by thresholding the
instantaneous triple-activation strength traces. To examine
whether characteristic network activity patterns are associatedwith the identified tripe-activation events, event-triggered
averages of LFP wavelet power were obtained (Fig. 4). The
event-triggered average of wavelet power had strong peaks
around 100 – 300 Hz in each brain region (Fig4), whichcorresponds to amygdalar high-frequency oscillations (HFOs)[5], hippocampal sharp-wave ripples (SWRs) [6], and cortical
ripples (cRipples)[7]. These results imply the tight relationshipbetween fast network oscillations, such as HFOs, SWRs, andcRipples, and inter-regional synchronous ensemble actives thatemerged after the transition to the maladaptation states.

vCA1 wideband
BLA wideband
PL5 wideband
0.5 mVBLA HFO
0.1 mV
10 z

50 ms

0.02
0
0.01
-200
-200

<0
0

200

vCA1 - PL5 (ms)

0

300
100
30
10
3
1

BLA

300
100
30
10
3
1

400 -4000
400 -400
Time from triple-activation (ms)

PL5

>2
1

0
< -0.50400

60IV. FUTURE PERSPECTIVE
In this year, we revealed that cell-ensembles in various brain
regions started to activate synchronously after the transition to
the maladaptation states. Furthermore, our results indicate thatfast network oscillations, such as HFOs, SWRs, and cRipples
are involved in the synchronous activations of cell-ensembles.
As the next step, we would like to analyze network changes
induced by extinction learning and compare them with ones
caused by fear-conditioning to clarify compensation
mechanisms that enable hyper-adaptation.

ActualSurrogates

40

REFERENCES%> 0.03

Triple CCG (a.u.)

BLA - PL5 (ms)Fig. 2. Representative example of synchronous activation across three brain
regions during NREM following fear-conditioning.

200

-400

vCA1

Fig. 4. BLA–CA1–PL5 triple-activation event-triggered average of LFP
wavelet power in a representative animal.

vCA1 SWR

PL5 cRipple

300
100
30
10
3
1

20

[1]

0
0

0.1

0.2

Peak (a.u.)

[2]
[3]

Fig. 3. Example of triple-CCG analysis.

C. Fast oscillations on the local field potentials hosted
synchronous ensemble activation across three brain
regions
Next, we sought what kind of network activity patterns
hosted the synchronous ensemble activation across the CA1,
BLA, and PL5. First, we obtained instantaneous triple-activation
strength as products of z-scored instantaneous activation
strength with the optimal time shift, then individual triple-

[4]
[5]

[6]
[7]

Power (z)Frequency (Hz)fear-conditioning (Fig. 2). To quantify this observation, we
defined tripe cross-correlation (CCG) analysis by expanding
CCG analyses (Fig 3). The subset of ensemble triplets showed aprominent peak on its triple CCG. The significance of the peak
was examined with random shuffling analysis. This analysis
revealed that the proportion of ensemble triplets with significant
peaks on their triple CCG during non-rapid-eye-movement(NREM) sleep following the fear-conditioning was larger thanthose in NREM preceding the fear-conditioning. It indicates thatensembles in BLA, vCA1, and PL5 become activatedsynchronously through the transition to the fear-memoryinduced maladaptation state.

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.
Johansen, J.P., et al., Neural Substrates for Expectation-Modulated Fear
Learning in the Amygdala and Periaqueductal Gray. Nat Neurosci, 2010.
13(8): p. 979-86.
Giri, B., et al., Hippocampal Reactivation Extends for Several Hours
Following Novel Experience. J Neurosci, 2019. 39(5): p. 866-875.
Ponomarenko, A.A., T.M. Korotkova, and H.L. Haas, High Frequency(200 Hz) Oscillations and Firing Patterns in the Basolateral Amygdala
and Dorsal Endopiriform Nucleus of the Behaving Rat. Behavioural Brain
Research, 2003. 141(2): p. 123-129.
Buzsaki, G., Hippocampal Sharp Wave-Ripple: A Cognitive Biomarker
for Episodic Memory and Planning. Hippocampus, 2015. 25(10): p. 1073188.
Khodagholy, D., Gelinas, J.N., and Buzsáki, G. Learning-Enhanced
Coupling Between Ripple Oscillations in Association Cortices andHippocampus. Science 2017 358, 369-372.

Annual report of research project A05-8
Takaki Maeda

Department of Neuropsychiatry, Keio University School of Medicine

Abstract

II. AIM OF THE GROUP

Our research project aims to reveal neural mechanisms of senseof agency (SoA) from the standpoint of functional connectivity.
Moreover, we intend to study pathophysiology of neurologicaland psychiatric illnesses from the stand of neural dysconnection.
Then, we try to recover those illness through reorganization ofneural systems on the SoA in order to facilitate hyperadaptability to living environment. Actually, we have developed amethod 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 showed abnormal learning patterns of prediction
system in patients with psychiatric disorders including
schizophrenia, ADHD and ASD. 2) We have released theapplication for agency tuning: the Agency Tuner, for general use
in US as well as Japan [1]. 3) In order to evaluate hyperadaptability, we reported passive way of measuring QOL/WellBeing levels using smartphone log [2].

Our research project aims to reveal neural mechanisms ofsense of agency (SoA) . Moreover, we intend to study
pathophysiology of neurological and psychiatric illnesses fromthe stand of neural disconnection. And then, we try to develop
methods for cognitive rehabilitation of SoA in order to tune up
precision of SoA. We have released the application for agency
tuning: the Agency Tuner, for general use in Japan (Fig. 2 and
3). We hypothesize that this tuning of SoA could reorganize
neural systems and achieve hyper-adaptation of patients withthose illnesses in their living environment.

I. INTRODUCTION
We aims to reveal neural mechanisms of sense of agency (SoA)

from the standpoint of functional connectivity. Moreover, weintend to study pathophysiology of neurological and psychiatricillnesses from the stand of neural dysconnection. Then, we try torecover those illness through reorganization of neural systems
on the SoA in order to facilitate hyper-adaptability to living
environment.
We have reported neural substrates on the SoA as the“Agency Network” (Fig. 1). We expect that development ofmethods for reorganization of dysconnectivities of the“Agency Network” in neurological and psychiatric illnesses
leads to the recovery from those illnesses.

Fig.2 “Agency Tuner” for Agency Tuning

Fig.3 Evaluation of learning processof the prediction system of SoA
III. RESEARCH TOPICS

Fig.1 The Agency Network

A. Abnormal learning patterns of prediction system inpatients with psychiatric disorders including
schizophrenia, ADHD and ASD
We showed abnormal learning patterns of prediction system
in patients with psychiatric disorders including schizophrenia,
ADHD and ASD (Fig4-6).

perspectives may help in the primary prevention of mental
illness and the comprehensive measurement of mental health.
In this study, we used users' smartphone logs to build a model
to estimate whether the scores on three types of questionnaires
related to quality of life and well-being would increase
compared to the previous week (fluctuation model) andwhether they would be higher compared to the average for thatuser (interval model). Sixteen participants completed threequestionnaires once per week, and their smartphone logs wererecorded over the same period. Based on the results,
estimation models were built, and the F-score ranged from0.739 to 0.818. We also analyzed the features that theestimation model emphasized. Information related to “physicalactivity,” such as acceleration and tilt of the smartphone, and“environment,” such as atmospheric pressure and illumination,
were given more weight in the estimation than information
related to “cyber activity,” such as usage of smartphone
applications. In particular, in the Positive and Negative Affect
Schedule (PANAS), 9 out of 10 top features in the fluctuation
model and 7 out of 10 top features in the interval model wererelated to activities in the physical world, suggesting thatshort-term mood may be particularly heavily influenced bysubjective activities in the human physical world.

Fig.7
IV. FUTURE PERSPECTIVE

B.

Agency Tuning by“Agency Tuner“（on Google Play in US）

We have released the application for agency tuning: theAgency Tuner, for general use in US as well as Japan [1], inorder to collaborate with US researchers.
C. Passive Way of Measuring QOL/Well-Being Levels
Using Smartphone Log
In order to evaluate hyper-adaptability, we reported passive
way of measuring QOL/Well-Being levels using smartphonelog [2](Fig.7).
Research on mental health states involves paying increasing
attention to changes in daily life. Researchers have attempted
to understand such daily changes by relying on self-reporting
through frequent assessment using devices (smartphones);
however, they are mostly focused on a single aspect of mental
health. Assessing the mental health of a person from various

We have made advances in developing an original method
for cognitive rehabilitation of the SoA: Agency Tuning. We
will advance clinical experiments of patients, and analyze theirleaning processes of predictive models on the SoA for
establishing feasible method of cognitive rehabilitation of the
SoA, and support patients with neurological and psychiatricillnesses for recovery, that is, hyper-adaptability to living
environment.
REFERENCES
Takaki Maeda：”AGENCY TUNER”（APP on Google Play in US）,
Aug, 2021.
https://play.google.com/store/apps/details?id=en.re.agencytuner.blp&hl=
en&gl=US[2] Wenhao Yao, Kohei Kaminishi , Naoki Yamamoto, Takashi Hamatani,
Yuki Yamada, Takahiro Kawada, Satoshi Hiyama, Tsukasa Okimura,
Yuri Terasawa, Takaki Maeda, Masaru Mimura and Jun Ota: Passive
Way of Measuring QOL/Well-being Levels Using Smartphone Log.
Frontier in Digital Health 2022.842460.
https://doi.org/10.3389/fdgth.2022.780566[1]

A05-9 The role of inhibitory neurons related toskilled hand movements after spinal cord injury.
Takahiro Kondo

Department of Physiology, Keio University School of Medicine,
Abstract—In this year, we established the analysis method inmouse and marmoset spinal cord injury (SCI) models. To
understand motor deficits and reconstructive processes after SCI,
we analyzed joint movement coordination patterns controlled by
the central nervous system (CNS). As a result, we found that thedegree of impaired coordination in joint movement correlatedwith the degree of histological damage in the spinal cord.

I. INTRODUCTION
During the acquisition of new motor skills or during
recovery after brain or spinal cord injury, the brain undergoes
functional
reorganization.
GABA,
aninhibitory
neurotransmitter, is thought to play an important role in theregulation of this plasticity. It has been reported that during
human motor learning, a decrease in GABA concentration in
the early stages of learning is strongly correlated with themagnitude of subsequent learning [1]. In addition, it is known
that functional reorganization occurs in the motor cortex after
CNS disorders such as stroke and spinal cord injury [2], butlittle is known about the relationship between these changes
and inhibitory control.

markerless method of 2D kinematic analysis to automaticallytrack joint positions. We found that a relatively small number(< 200) of manually labeled video frames was sufficient totrain the network to extract trajectories. The mean test error
was on average 3.43 pixels in intact mice and 3.95 pixels in
SCI mice, which is comparable to the manual tracking error(3.15 pixels, less than 1 mm). Thereafter, we extracted 30 gait
kinematic parameters and found that certain parameters such as
step height and maximal hip joint amplitude distinguishedintact and SCI locomotion [3].

II. AIM OF THE GROUP
The specific purpose of this study is to observe the activity ofGABAergic neurons in the motor cortex by calcium imaging
using a marmoset spinal cord injury model, and to follow thechanges in the spatiotemporal pattern of these neurons during
motor function recovery.
III. RESEARCH TOPICS
A. Markerless analysis of hindlimb kinematics in spinal cordinjured mice through deep learning
Rodent models are commonly used to understand theunderlying mechanisms of SCI. Kinematic analysis, an
important technique to measure dysfunction of locomotion
after SCI, is generally based on the capture of physical markers
placed on bony landmarks. However, marker-based studies
face significant experimental hurdles such as labor-intensive
manual joint tracking, alteration of natural gait by markers, andskin error from soft tissue movement on the knee joint.
Although the pose estimation strategy using deep neuralnetworks can solve some of these issues, it remains unclear
whether this method is adaptive to SCI mice with abnormal
gait. In the present study, we developed a deep learning based

Fig. 1

B. Functional reorganization of locomotor kinematicsynergies reflects the neuropathology in a mouse model ofspinal cord injury
SCI disrupts motor commands to modular structures of the
spinal cord, limiting the ability to walk. Evidence suggests thatthese modules are conserved across species from rodent tohuman and subserve adaptive walking by controlling
coordinated joint movements (kinematic synergies). Since SCI

causes uncoordinated joint movements of the lower limbs
during walking, there may be a disorder of the modular
structures that control them [4]. To gain insights into thiscomplex process, we recorded the kinematics of intact and SCI
mice when walking on a treadmill and applied principal
component analysis to extract kinematic synergies. Most SCI
mice walked stably on the treadmill, but their kinematicsynergies were generally different from those of intact mice.
We classified the kinematic synergies of SCI mice into threegroups based on the similarity of the extracted first threesynergy components. We found that these three groups had
different degrees of spinal cord damage. This suggests thatdifferences in kinematic synergies reflect underlying SCI
neuropathology. These results may help guide the development
of different rehabilitation approaches and future physiologicalexperiments to understand the mechanisms of motor control
and recovery [5].

planar covariance is disrupted in patients with stroke or SCI,
and that improvement in planarity correlates with recovery inwalking ability after rehabilitation. In this study, quadrupedal
marmosets were able to walk without loss of balance even after
SCI; the different balance needs of bipedal and quadrupedal
walkers may lead to differences in planar covariation. Our
results show that planar covariation was preserved at all time
points after the cervical unilateral hemisection [6].

Fig. 3
IV. FUTURE PERSPECTIVE
Fig. 2

C. Preserved Intersegmental Coordination During
Locomotion after Cervical Spinal Cord Injury in Common
Marmosets
It is known that primates including human regain some
locomotor function after a partial spinal cord injury, but thelocomotor pattern is different from before the injury. Althoughthese observations have many implications for improving
rehabilitative strategies, these mechanisms are not wellunderstood. In this study, we used a common marmoset
hemisection SCI model to examine temporal changes inlocomotor pattern, in particular, intersegmental coordination ofleft hindlimb. Marmoset showed loss of detectable function in
the left forelimb and hindlimb after left unilateral hemisectionof cervical spinal cord. At two weeks after injury, weightbearing of the left forelimb during locomotion was limited, but
the left hindlimb was able to plantar step. Then marmosets
showed gradual recovery in walking ability, but kinematics
analysis showed differences in the endpoint trajectory and joint
angle movement. Furthermore, intersegmental coordination inleft hindlimb represented by planar covariation was preserved
over time after the injury. Previous studies have reported that

This year, we established a gait analysis system in mice andmarmoset spinal cord injury models, and reported it in a paper[3,5-6]. These analyses and the rehabilitation model
established in the previous year will be used for further
validation.
REFERENCES
[1]
[2]
[3]

[4]
[5]

[6]

J. Kolasinski, E. L. Hinson, A. P. Divanbeighi Zand, A. Rizov, U. Emir,and C. J. Stagg, “The dynamics of cortical GABA in human motor
learning,” J. Physiol. 2019 Jan;597(1):271-282.
T. Isa, “Dexterous Hand Movements and Their Recovery After Central
Nervous System Injury,” Annu Rev Neurosci. 2019 Jul 8;42:315-335.
Sato Y, Kondo T, Shinozaki M, Shibata R, Nagoshi N, Ushiba J,
Nakamura M, Okano H, “Markerless analysis of hindlimb kinematics inspinal cord-injured mice through deep learning” Neurosci Res. 2021 Sep
8;S0168-0102(21)00203-0.
Y P Ivanenko 1 , G Cappellini, I A Solopova, A A Grishin, M J
Maclellan, R E Poppele, F Lacquaniti, “Plasticity and modular control oflocomotor patterns in neurological disorders with motor deficits”
Sato Y, Kondo T, Shibata R, Nakamura M, Okano H, Ushiba
J,“Functional reorganization of locomotor kinematic synergies reflects
the neuropathology in a mouse model of spinal cord injury” Neurosci
Res. 2021 Dec 15:S0168-0102(21)00248-0.
Sato Y, Kondo T, Uchida A, Sato K, Yoshino-Saito K, Nakamura M,
Okano H, Ushiba J,“Preserved Intersegmental Coordination During
Locomotion after Cervical Spinal Cord Injury in Common Marmosets”
in press.

A05-11Annual report of research project
Rieko Osu

Faculty of Human Sciences, Waseda University

Kento Hirayama

Graduate school of Human Sciences, Waseda University

Taiki Yoshida

Graduate school of Human Sciences, Waseda University andFaculty of Rehabilitation, School of Health Sciences, Fujita Health University
Abstract—Hand choice is an unconscious decision that we
frequently make in our daily lives. It has been shown that theprobability of hand choice was biased by modulating neuralactivity in the posterior parietal cortex by magnetic or electricalstimulation using the transcranial magnetic stimulation or thetranscranial direct current stimulation. However, the effect oftranscutaneous electrical stimulation from peripheral body
regions such as the wrist on hand selection has not been
investigated. In this study, we focused on hand choice at theequilibrium point of the left and right-hand choice during aquick reach to a target in front of us and examined whether
sensory stimulation by electrical stimulation to unilateral hand
immediately before hand choice facilitates or inhibits subsequent
hand choice. Electrical stimulation of the wrist significantly
increased the choice of the stimulated hand. These results suggestthat the prior sensory stimulation facilitates subsequent action
selection.

I. INTRODUCTION
To recover from the functional impairment caused by astroke or other injury to the central nervous system andmaintain its function, it is necessary not only to restore thefunction of the injured body part itself, but also to let thepatients to pay attention to the affected side of the body and todevelop a preference to actively use the affected body parts indaily life. In other words, it is important to improve theorientation and preference toward the body and space of theaffected side of the body, which is often disliked or ignored.
Therefore, this research project aims to clarify the method andmechanism to reveal and activate the neural circuits ofpreference to the affected side of the body using
neuromodulation by brain stimulation, manipulation of space
using VR technology, and manipulation of motivation.
This year, we tested if haptic electrical stimulation to wrist
can implicitly change the probability of hand use.

II. AIM OF THE GROUP
Flexible adaptation to the outside world mandates
appropriate action selection. Hand choice—deciding whichhand to use to reach for targets—is an example of a daily

unconscious action selection. Previous studies argued that theposterior parietal cortex (PPC) accumulates sensory
information to evaluate the appropriate action selection and,thus, plays a critical role in this process. Fitzpatrick et al. [1]reported that while functional magnetic resonance imaging(fMRI) revealed bilateral increases in PPC activity duringhand-choice tasks, this increase is enhanced in the PPC
contralateral to the selected hand. Oliveira et al. [2] showedthat the right-hand choice was suppressed following thedisruption of the left PPC with single-pulse transcranial
magnetic stimulation (TMS) just prior to the execution of thereach, but not vice versa, thus, indicating more dominant
involvement of the left PPC in hand choice than the right PPC.
In our study last year, we increased or decreased the corticalexcitability of PPC using tDCS and examined its online andresidual effects on the hand-choice probability and choice
reaction time. The results demonstrated that the probability ofleft-hand choice increased and that of right-hand choicedecreased significantly after stimulation when left PPC was
stimulated with cathode and right PPC with anode. The
decrease in the excitability of the left PPC and the increase in
the excitability of the right PPC are essential to enhance lefthand choice [3]. Considering the clinical application to thestroke patients, however, tDCS is not a good solution tofacilitate affected hand in daily life. Therefore, in the present
study, we examined if the simple electrical stimulation to thewrist can bias the choice or not.
III. RESEARCH TOPICS
We recruited 14 right-handed healthy people to participate
in this study. The Participants were asked to reach the target
using eighter left or right hand as quickly and accurately as
possible. The targets were presented at nine random positions
on the semicircular (Fig. 1). We consisted of the psychometricfunction about the hand preference for each participant. We set
the unilateral stimulus condition (right and left), the bilateral
stimulus condition (as the control condition) and the no
stimulus condition. The stimulation electrodes attached to each
wrist (Fig. 1). The electrical stimulation was applied at target
the target presentation (0 ms) or 300 ms, or 600 ms before
target presentation randomly (Fig. 2).

-8

0

8

-25

25

-45

45

-75

75

Fig. 2 stimulation site and target settings

Start position

300 msPSE (degrees)20[ Stimulation conditions ]• Left-hand ES• Right-hand ES• Bilateral ES• None ES

Unimanual

Bimanual

10
0−10

Fixation−20
No−Es

0, 300, or 600 ms

Left−Es

Right−Es

Bi−Es

Es−condition~ 650 ms

Fig. 4 choice probability and PSE[ Unimanual trial ]Choice hand, then,reaching for the target

NoES: no stimulation LtES：left wrist stimulation, RtES: rightwrist stimulation, BiEs: bilateral wrist stimulation♪success or ♪missIV. FUTURE PERSPECTIVE
Fig. 2 procedure

Using logistic regression, we estimated the point ofsubjective equality (PSE): the virtual point at which
participants would have an equal probability of using the rightor the left hand for the reach. The effect of the stimulation
condition of the PSE was investigated using one-way
ANOVAs with repeated measures. Significant main effect wasobserved in the stimulation condition (F (1, 10) = 11.3, P <0.01, partial η2 = 0.55). Multiple comparisons between eachpair of stimulation conditions showed that PSE in the left
stimulation condition (PSE = 4.40°±10.46°) was significantly
larger than that in the other stimulation conditions (adj P <0.05), and PSE in the right stimulation condition (PSE = 2.04°±9.97°) was significantly smaller than that in the otherstimulation conditions (adj P < 0.05). There was no significant
difference between PSE in the bilateral stimulation condition
and that in the no stimulation condition (adj P = 1.00). These
results showed that the probability of hand choice wasincreased with the stimulated hand (Fig. 3).
Furthermore, to test the effect of the timing of the stimuli (0,300, 600 ms) prior to the target presentation on the handselection, the 90% confidence intervals of PSE for eachstimulus timing were calculated with the bootstrap by 10000
resampling the data to compensate for the small number of data.
The results showed that the closer the stimulus timing to thetarget presentation, the more likely the stimulated hand waschosen.

The present study demonstrated that the electricalstimulation of the wrist significantly increased the choice of thestimulated hand. These results suggest that the prior sensory
stimulation facilitates subsequent action selection.
The results of the current study may be applied in theincrease of paretic-hand use in patients with hemiparetic stroke
whose quality of life is significantly diminished by strokeinduced difficulty in using paretic limbs effectively. Althoughrehabilitation has been demonstrated to improve limb function
to some extent, patients often only use their non-paretic limbs
after discharge. This learned non-use remains an unresolved
issue in rehabilitative practice. The results of this study havedemonstrated that simple, cheap, and easy to use electricalstimulation can bias the choice. We will continue developmentof our methods and their application to promote the use ofparetic upper limbs in the rehabilitation of patients with stroke.
REFERENCES
[1]
[2]

[3]

A.M. Fitzpatrick, N.M. Dundon, and K.F. Valyear, "The neural basis ofhand choice: An fMRI investigation of the Posterior Parietal
Interhemispheric Competition model," Neuroimage, 185, 208-221, 2019.
F.T. Oliveira, J. Diedrichsen, T. Verstynen, J. Duque, and R.B. Ivry,"Transcranial magnetic stimulation of posterior parietal cortex affects
decisions of hand choice," Proc Natl Acad Sci U S A, 107, 17751-6,
2010.
K. Hirayama, T. Koga, T. Takahashi, and R. Osu, "Transcranial direct
current stimulation of the posterior parietal cortex biases human handchoice," Sci Rep, 11, 204, 2021.

A05-12 Hyper-adaptability from inducing synapse
connection and regulation of extracelluar matrix.
- Spinal cord injury and AI-based motion capture Kosei Takeuchi

Dept. of Medical Cell Biol., Sch. of Med., Aichi Medical University

Abstract—The main results of this year are the following
three items. 1) Synapse connector “CPTX” is an synthetic

chimeric protein inspired by a synaptic organizer ”Cbln1”, andhas rapid and strong synaptic formation ability by crosslinking pre-and post-synaptic molecules. We demonstrated

the significant recovery from chronic phase of spinal cord
injury (SCI) by a synthetic synapse organizer protein,
CPTX. Since such a robust recover from chronic phase of
SCI has never been achieved, it is a great finding in our
project. 2) Moreover, we obtained the results of recovery
from acute or subacute phase of SCI by the application ofanti-sense oligo(ASO) of N-acetylgalactosaminyltransferase-1(T1), a key gene in CS biosynthesis. ASO
improved the microenvironment for functional recovery.
3)We constructed the AI motion capture system torigorously evaluate the recovery from SCI. Using thissystem, we succeed in the characterization of the
behavioral trait of CPTX induced hyper-adaptability. Tosummarize, it is now possible to guide the artificial superadaptation of the neural circuit after the damage of thecentral nervous system, and to evaluate the behavioral
outputs during the recovery.
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 aims of this research are (1) the remodeling of theregeneration field after spinal cord injury (SCI) by
inactivating a regeneration inhibitor, Chondroitin sulfate(CS), (2) the re-construction of the functional circuits byintroducing an artificial synapse-forming factor, synapse
connector CPTX, and (3) the establishment of AI-based
evaluation system to capture the parameters of hyperadaptive animals in which rehabilitation is added to theadministration of CS inhibitor and CPTX.
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 apresynaptic molecule (Nrx: neurexin) and a postsynaptic
molecule (AMPAR: AMPA receptor)(Fig1). It was createdas an artificial chimeric protein (CPTX) in which Nrx
binding domain of Cbln1 and AMPAR binding domain of
NP1 are joined.

This year, we tried if CPTX is effective to chronic phase of
SCI. It is well known that recovery of chronic phase of SCI isextremely difficult. Despite the difficulty, majority of patients
suffering from SCI are clinically chronic phase. Thus,
overcoming the chronic phase of SCI is an important mission.
CPTX restored the chronic phase of SCI(Fig 2). CPTX
injection activated the hind leg movement within a few days,
which otherwise showed the permanently poor movement. At
2 weeks after injection, the effect of CPTX became maximum
to the extent that some animals smoothly stepped. These
results suggest that CPTX boost the dormant neural pathway
into the active circuit. We are now attempting the more robust
recovery combined with rehabilitation.

in university of Electro-communications of the B02
group.Two-dimensional position is calculated by
DeepLabCut from the video data obtained by four highspeed cameras. This system enabled us to study theaccurate tracking of motor function during recovery
process. We succeeded in extracting the characteristic
behavioral pattern during recovery process of hyperadaptability mouse in which CPTX drive the remodeling of
nervous circuit.

III. FUTUREU PERSPECTIVE
B. Improvement of regeneration environment by
suppression of nerve regeneration inhibitor chondroitin
sulfateWe reported that chondroitin sulfate (CS) KO mice, which
are the strongest inhibitors of nerve regeneration after
spinal cord injury, show dramatic post-injury recovery [3].
CS is proposed to be one of the most ideal targets for SCI
treatment [4]. Last year, we developed and applied anantisense oligo (ASO) for knocking down of CS expression
in a tissue specific manner. This year, we applied ASOs to
SCI animals and found that our oligos restored thelocomotory function of acute and subacute phase of SCI.
C. Construction of AI trace system and capture of
behavioral parameter in hyper-adaptability mouse
We are promoting the introduction of AI into the evaluationof functional recovery to obtain the correlation between thequantitative motor function, nerve reorganization, andmotor function of the super-recovery model after spinal
cord injury. Last year, we first focused on the hind limb
stepping down (footfall analysis), which was generally used
for quantification of post-injury recovery. This year, we
further promoted the system construction and the analysis
to extract the characteristics of walking during the recovery
process (Fig. 4). This research was a joint research withProfessor Yanagihara in Tokyo university, who is also a
research collaborator of the A05-12 group (and the B02
planning group), and it was supported by Professor Funato

This year, we were able to obtain the results of functional
recovery from chronic phase of SCI through CPTX
application. We also showed that ASOs for knocking downof CS expression restored the locomotory function of acuteand subacute phase of SCI. Since the time window and themechanism of CPTX and ASOs for SCI recovery are different,
the combination of both strategies would be useful. We
succeed in the characterization of the behavioral trait of CPTX
induced hyper-adaptability by AI trace. Development of next

generation synapse connector that bridges inhibitory
synapse is also in progress. Such novel tool combined with
AI trace system will allow us to study super-adaptation andto conduct artificial intervention for functional recovery.
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)

A05-16Annual report of research project
Noriyuki Higo

Human Informatics and Interaction Research Institute,
National Institute of Advanced Industrial Science and Technology (AIST)

Abstract— In the present research project, we haveinvestigated neuronal plastic changes, which underlie motor
recovery after brain damage, using macaque models. In thestudies reported here, we examined both functional andstructural changes after focal infarction of the macaque internal
capsule. Brain imaging using functional near-infrared
spectroscopy (fNIRS) suggested compensatory increase of brain
activity in the ventral premotor cortex during voluntary
movements after motor recovery. Voxel-based morphometry(VBM) analysis using T1-weighted magnetic resonance imaging(MRI) and immunohistochemical staining using SMI-32 antibody
suggested compensatory structural changes in pyramidal
neurons in the ventral premotor cortex of the contralesional
hemisphere. In the next step, we plan to elucidate the changes ofneuronal projections that occur after brain damage. Specifically,
we will perform diffusion tensor imaging (DTI) using MRI after
recovery in order to clarify the projection changes underlying
functional compensation. In addition, compensatory projectionchanges will be investigated at cellular level using anatomical
tracers. We will also perform pharmacological inactivation
experiments to investigate a causal relationship between the
anatomical changes and functional recovery.

I. INTRODUCTION
When the brain is damaged, such as in stroke patients, brain
function is impaired. However, rehabilitation can induce
dynamic plastic changes in the nervous system and restore lost
brain functions. This is a typical example of "hyper-adaptivity"in the brain. Elucidating these changes will lead to innovative
neurorehabilitation technologies that induce appropriatechanges 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,
holding a small objects between the tips of the thumb andindex finger [1]. In addition, our brain imaging analysis
suggested that changes of brain activity occur in uninjured
motor areas during recovery of precision grip after the primary
motor cortex lesions [2]. However, clinically, cerebral

hemorrhage or infarction in the subcortical white matter isoften a problem. In this study, we will apply the experimental
techniques we have used in the primary motor cortex injury
model to macaque monkeys with focal infarction in the internal
capsule where motor output fibers run, in order to obtainknowledge that can be seamlessly applied to clinical practice.
III. RESEARCH TOPICS
A. Compensatory brain activity chnages after focal infarctionof the macaque internal capsule
We established a macaque monkey model with infarcts inthe posterior internal capsule, which is a common site of stroke
and involved in motor output from the cerebral cortex [3]. We
also established a technique for measuring motor cortex
activity in macaque monkeys using functional Near-Infrared
Spectroscopy (fNIRS) [4], and measured motor cortex activity
during a grasping task. Before infarction, there was an increasein activity in the hand region of the primary motor cortex,
whereas during functional recovery, there was an increase inactivity in the ventral premotor cortex (Fig. 1) [5]. The ventral
premotor cortex in the cortex contralateral to infarcts plays agreater role in recovery when damage by stroke is more severe.
When muscimol, a GABAA receptor agonist, was administered
to the ventral premotor cortex of the hemisphere with elevatedactivity to temporarily inactivate the activity, the upper limb
motor deficits recurred. This suggests that the area of increased
activity may be involved in compensating for the function of
the infarcted area. These results are consistent with the resultsof clinical studies reported in stroke patients, and also indicatethat this experimental system is optimal for investigating themechanism of functional recovery after stroke, as well as thepossibility of developing technology for monitoring therecovery of brain function using fNIRS.
Compared to magnetic resonance imaging (MRI), fNIRS isless expensive and can measure brain activity during exercise
without restraining the body, making it suitable for measuring
brain activity during rehabilitation. On the other hand, it has a
disadvantage that only brain surface activity can be measured
due to its measurement principle. To solve this problem, weapplied diffuse optical tomography (DOT) to the fNIRS data
and tried to estimate three-dimensional images of thefunctional hemodynamic response [6]. In the intact macaquemonkeys performing voluntary movements, the DOT analysis
estimated the activity of motor-related cortical areas, including
those in the brain sulci, and the temporal and spatial changes

were consistent with previously known physiological findings(Fig. 2). By using this method with the fNIRS data, we canidentify the changes in brain activity that occur duringfunctional recovery after stroke in more detail.

antibody to stain pyramidal cells in our established macaquemodel of capsular infarcts [3]. The VBM results suggested anincrease in gray matter in the ventral premotor cortex of thecontralesional hemisphere during the period of post-infarction
motor function recovery. Immunohistochemical analysis
showed shrinkage of pyramidal cells in layer V of the primary
motor cortex of the ipsilesional hemisphere. This may be due
to retrograde neurodegeneration after internal capsuleinfarction. On the other hand, pyramidal cells in layer V of theventral premotor cortex showed an increase in dendritic
arborization. These results suggest that compensatory structuralchanges in motor output cells in the contralesional ventral
premotor cortex are involved in motor function recovery after
capsular infarction, and that structural changes in dendrites
underlie the signal changes observed in the VBM analysis.
IV. FUTURE PERSPECTIVE

Fig. 1fNIRS imaging of bain activation during voluntary hand
movements before infarcts (A, C) and after motor recovery from the internal
capsular infarcts in the macaque (B, D). Before infarcts, focal activation wasobserved in the hand area of the primary motor cortex (arrowhead in A, C).
After motor recovery, increased activation of the premotor area was identified(arrowhead in B, D). The cortex contralateral to the stroke plays a greater role
in recovery when lesions are more severe (D)..

Studies in this project have elucidated some of the functional
and anatomical changes that occur during functional recovery
after capsular infarctions. In the next step, we plan to elucidate
the details of the anatomical changes that occur after brain
damage at the neural circuit level. Specifically, we will
perform diffusion tensor imaging (DTI) using MRI after
recovery in order to clarify the projection changes underlying
functional compensation. In addition, compensatory projectionchanges will be investigated at cellular level using anatomical
tracers. In particular, we will focus on the projection pathways
formed by the contralesional hemisphere to carry out the
movements of the ipsilateral hand. We will also analyze thebehavioral changes when the brain region where the new
projection pathway is formed is inactivated to prove a causal
relationship between the anatomical changes and functional
recovery.
References
[1]

[2]

Fig. 2. Brain activation during voluntary hand movements in the inatact
macaque. Color map of t-values (activated-area map) for HbO that exceededth significance level of 0.5%. Plots are based on the reconstruction results
using a DOT algorithm.

B. Structural changes after focal infarction of the macaqueinternal capsule
An appropriate structural basis is necessary for brain
function to occur. Therefore, structural changes in neurons may
underlie the compensatory changes in brain activity after
infarcts in the internal capsule described above. Therefore, we
performed voxel-based morphometry (VBM) analysis using
T1-weighted MRI and immunohistochemistry using SMI-32

[3]
[4]

[5]

[6]

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
T. Yamada, H. Kawaguchi, J. Kato, K. Matsuda, N. Higo, Functional
near-infrared spectroscopy for monitoring macaque cerebral motor
activity during voluntary movements without head fixation. Scientific
Reports 8: 11941, 2018
J. Kato, T. Yamada, H. Kawaguchi, K. Matsuda, N. Higo, Functional
near-infrared-spectroscopy-based measurement of changes in corticalactivity in macaques during post-infarct recovery of manual dexterity.
Scientific Reports, 10 (1), Article number: 6458, 1-12, 2020
R. Hayashi, O. Yamashita, T. Yamada, H. Kawaguchi, N. Higo, Diffuse
optical tomography using fNIRS signals measured from the skull surfaceof the macaque monkey, Cerebral Cortex Communications, in preA05-17 Establishement and circuit manipulation of ananimal model of ulateral spatial neglect in marmosets
Masatoshi Yoshida

The center for human nature, artificial intelligence, and neuroscience, Hokkaido University
Abstract— Unilateral spatial neglect (USN) is a phenomenon
characterized by an inability to response to the contralateral
stimuli in patients with damages in the (mainly right) brain. Ourgroup (A05-17) aims to establish an animal model of USN bymaking lesions in the ventral attention network of common
marmosets. In this fiscal year, we tried to establish 1) the target
and the method of the lesion, 2) how to evaluate their behavior
and 3) how to measure the brain activity. For 1), we identified
area TPO using ECoG. For 2), we established how to evaluatesaliency-guided eye movements during free-viewing. For 3), we
succeeded in measuring Ca signals simultaneously from bilateralposterior parietal cortices.

surgical lesion in the right superior temporal gyrus (rSTG)(Fig.1, left, magenta), which is the homologous brain region to
the human ventral attention network.

I. INTRODUCTION
Unilateral spatial neglect (USN) is a phenomenon
characterized by an inability to response to the contralateral
stimuli in patients with damages in the (mainly right) brain.
The mechanism of USN is still largely unknown but recent
studies of human neuroimaging suggest that USN occurs as aconsequence of imbalance in attentional networks. To
understand the brain mechanism of USN, establishment of ananimal model of USN is necessary. Our group (A05-17) started
a project to establish an animal model of USN by making
lesions in the ventral attention network of common marmosets.

Fig. 1. Candidate of the lesion site

II. AIM OF THE GROUP
The aims of the group are as follows. 1) Based on ourprevious findings using macaques, we will establish an animalmodel of USN by making a surgical lesion in the ventral
attention network of common marmosets. 2) Then we willmeasure the neuronal activities using an endoscope-type Ca
imaging system before and after the lesion and evaluate how
the local circuits are affected by the lesion. 3) Then we willmodel these changes in the brain using a computational model.
Finally, 4) based on the prediction made by the computationalmodel, we will manipulate the brain circuit using electricalmicro-stimulation, pharmacological technique, or optogenetics.
Using this strategy, we expect that we are able to test whether
and how the dorsal and ventral attention networks are causally
involved in the symptoms of USN and to develop methods forfunctional recovery from USN.

One of the candidates of the lesions is area TPO (Fig.1,
right, magenta) because it is the homologous brain region to
the human ventral attention network. To identify area TPO
physiologically, we implanted 96-channel ECoG electrodes tomarmosets [1] and measured brain activities during a visual
mismatch task (Fig.2, left) and auditory mismatch task to findbrain regions associated with surprise.

Fig. 2. Visual mismatch task

III. RESEARCH TOPICS

In the visual mismatch task (Fig.2, left), orientation grating
stimuli were presented in front of marmosets with their head
fixed. In the oddball condition (Fig.2, top), grating stimuli with
45-degree orientation were presented in 1/8 of ON periods andgrating stimuli with 135-degree orientation were presented in
7/8 of ON periods. In many standards condition (Fig.2, bottom),grating stimuli with eight different orientations were randomly
presented in equal probabilities (1/8). By comparing the
responses to the deviant stimuli in both conditions, we
identified area TPO that is associated with both visual andauditory surprise [3]. Thus, we established how to identify theventral attention network using a neurophysiological technique.

A. the target and method of the lesion
Our previous findings using macaques succeeded in
inducing neglect-like behavior in macaques by making a

As a method of the lesion, we choose injection ofendothelin-1, which is reported to be effective in marmoset [2].
Currently, we are planning to start a lesion experiment in thelate February.

B. how to evaluate their behavior
Our previous findings using macaques succeeded in
identifying neglect-like behavior using eye-tracking techniqueduring free-viewing, in which the gazes of the animals after thelesion showed biases gazes toward the right side of the screen,
which is ipsilateral to the lesion in the right STG.
Our group measured eye movements during free-viewing in
the head fixed condition and succeeded in simultaneous
measurement of both eyes and evaluated saccade properties [4].

measure local circuit activities using an endoscope type Ca
imaging system (nVoke, Inscopix).
In the last fiscal year, we injected adeno-associated virus(AAV) and introduced the Ca sensor GCaMP6 in the PPC of a
marmoset. The TET-off system specifically designed formarmosets was used to amplify expression of GCaMP6. We
succeeded in measuring Ca signals from the PPC of a
marmoset during free-viewing.
In this fiscal year, we measured Ca signals form bilateral
PPCs by using nVoke and nVista (Fig. 5). We identified visual
responsive neurons (Fig. 5, left) and saccade-related neurons.

Fig. 5. Ca imaging from bilateral PPCs

Fig. 3. Free-viewing task

We also found that the gazes during free-viewing of
marmosets are attracted to visually salient stimuli, which wasenhanced by subanesthetic dose of ketamine (Fig. 3) [5].
C. how to measure the brain activity
Our previous findings using macaques found that acrossarea correlation of BOLD signals (what is called ‘functional
connectivity’) in resting-state fMRI changed across the timecourse of the brain lesion and functional recovery.

We also evaluated correlation between the Ca signals
during free-viewing (Fig. 5, right). Both correlations of the Ca
signals within hemispheres and between hemispheres wereweakly and positively correlated.
IV. FUTURE PERSPECTIVE
In this fiscal year, we developed methods for establishingand evaluating an animal model of USN using marmosets. We
are ready to test these methods and plan to make a lesion inarea TPO in this fiscal year.
REFERENCES
[1]
[2]

[3]
[4]

Fig. 4. Ca imaging from bilateral PPC

To understand the detailed brain mechanism of the neglectlike behavior, our group’s strategy is to use marmosets and[5]

Komatsu, M., Kaneko, T., Okano, H., and Ichinohe, N. (2019). Chronic
Implantation of Whole-cortical Electrocorticographic Array in the
Common Marmoset. J. Vis. Exp. 221, 4–9.
Matsui H., Komatsu M., Kaneko T., Okano H., Ichinohe N., Yoshida M.
(2022) Visual and auditory mismatch negativities from whole-corticalelectrocorticogram (ECoG) arrays in common marmosets. The 11th
annual meeting of Japan marmoset society.
Teo L, Bourne JA. (2014) A reproducible and translatable model of
focal ischemia in the visual cortex of infant and adult marmosetmonkeys. Brain Pathol. 24(5):459-74.
Chen, C.-Y., Matrov, D., Veale, R.E., Onoe, H., Yoshida, M., Miura, K.,
and Isa, T. (2020). Properties of visually-guided saccadic behavior andbottom-up attention in marmoset, macaque, and human. J. Neurophysiol.
437–457.
Zlata P., Komatsu M., Yamamori T., Yoshida M. (2022) The effect ofketamine on the saccadic amplitudes and pupil size during free-viewingof images in common marmosets. The 11th annual meeting of Japanmarmoset society.

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 and 11 proposed
research projects in the group. Please refer to the report of eachresearch project for their research outcomes in the fiscal year.

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.
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.
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.
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.

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

Proposed Research Groups
B05-1: Elucidation of the mechanism of motor synergy emergence in deep reinforcement learning
Principal investigator: Mitsuhiro Hayashibe (Tohoku U)
B05-2: Adaptation ability of human postural control system
revealed by a closed-loop electrical muscle stimulation system
Principal investigator: Daichi Nozaki (U Tokyo)

B05-3: Mechanism underlying the hyper-adaptation of bipedal
locomotion to the evolutionary change of the foot.
Principal investigator: Naomichi Ogihara (U Tokyo)
B05-4: Understanding neural manifold of the movements using
human neuroimaging and non-invasive brain stimulation
Principal investigator: Isao Nambu (Nagaoka U of Tech)
B05-5: Development of motor learning model that can reuse
partial dynamics based on estimation of transformation between mappings
Principal investigator: Yuichi Kobayashi (Shizuoka U)
B05-6: Shared-control of teleoperated robot maintaining operator’s embodiment under intervention of AI
Principal investigator: Yasuhisa Hasegawa (Nagoya U)
B05-7: Systems engineering approach for understanding
supraspinal mechanisms of the intermittent feedback control
during human upright stancePrincipal investigator: Taishin Nomura (Osaka U)
B05-8: A reinforcement learning model with dynamic state
space that enables adaptation to indefinite environments
Principal investigator: Kazuhiro Sakamoto (Tohoku Med
and Pharma U)
B05-9: Attention control training based on tailor-made neurofeedback system for facilitating motor learning in elderly
Principal investigator: Takeshi Sakurada (Ritsumeikan U)
B05-10: Modeling of the motor recovery process and optimization of rehabilitation strategy using VR
Principal investigator: Tetsunari Inamura (NII)
B05-11: Developmentalhyper-adaptability of sensorimotor dynamics under rapid growth
Principal investigator: Hoshinori Kanazawa (U Tokyo)
III. ACTIVITIES
Activities mainly organized by the members in Group B are
described as follows:• September 7th, 2021, Group B meeting (Online)
The Group B meeting was held online (Zoom). All theprincipal investigators and most of the co-investigators
participated. Approximately 45 participants attended at
all times, and held deep discussions including futurecollaboration.
• December, 7th, 2021, HMS2021 Organized session (Online)
Due to the spread of COVID-19, the international symposium was held online. Prof. Hayashibe gave plenary talk.
In the organized session, Dr. Kaminishi gave keynote talkand 6 researchers in the research project presented theirrecent research results.
• January 20-22nd, 2021, Organized session at the symposium on Distributed Autonomous Systems (Online), 9
oral presentations.

IV. F UTURE PLAN
In the next fiscal year, we summarize the research topics inthe planned and proposed research projects by classifying the
topics from the targeted subjects and the modeling methodologies points of view. We continue deep discussion on the topics.
Moreover, we shall proceed fruitful collaborations with GroupA (Neuroscience group).

B01Annual report of research project
Toshiyuki Kondo
Tokyo University of Agriculture and Technology
t_kondo@cc.tuat.ac.jp

Abstract— To understand the adaptability mechanism of alarge-scale and complex network system such as the brain,
constructive approach is indispensable, where a phenomenon
can be modeled with the minimum degrees of freedom, andbehavior of the model is verified by computer simulations.
This research project aims to realize systems modeling of
hyper-adaptability mechanism with functional ”dis-inhibition”
observed in the impaired brain, especially from the viewpointof reconstruction of neural structure.

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 thathas long been unutilized through 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,
we apply an interdisciplinary approach that integrates themathematical modeling technology of systems engineeringwith neuroscience.
II. A IM OF THE GROUP
This research group aims to realize systems modeling of
hyper-adaptability mechanism with functional dis-inhibition
observed in the impaired brain, especially from the viewpointof “reconstruction of neural structure.” To clarify the underlying adaptability mechanism of a large-scale and complex
network system such as the brain, the constructive approachis indispensable, in which a phenomenon can be modeledwith the minimum degrees of freedom, and behavior of themodel is verified by computer simulations.
The research group concretely performs the following
three research topics. (1) By applying the probabilistic latentvariable modeling methods to long-term multi-modal datasuch as monkey and human brain/muscle activities andbehaviors provided from the groups A01/A02, we attempt
to interpret/visualize the physiological structure behind thesedata (Fig. 1). In addition, we develop constructive models ofbrain networks, and evaluate their appropriateness throughdata assimilation approach. (2) To elucidate the deterioration
mechanism of functional inhibition which seems different

Fig. 1.

Tensor decomposition and TVGL with GGM for brain modeling.

Fig. 2.

Constructive modeling approach.

between young and elderly, we build a gray-box model ofthe brain network by considering the findings in clinicalmedicine such as resource allocation between motor andcognitive function, and by assuming unknown parameterssuch as resource limitation and inhibition strength (Fig. 2).
By integrating this brain network model and musculoskeletal
model, we construct a posture control simulator in cooperation with B04. We estimate unknown model parameters by
incorporating the results of posture control experiments ofhuman subjects. (3) By developing experimental systems that
can arbitrarily change the relationship between the brain andbody using VR/robot technology, we perform collaborative
motor learning experiments in healthy young and elderly.
According to the findings, we will develop appropriate
visuomotor tasks which can promote the reconstruction ofneural structure in the brain, in collaboration with A01.
By integrating the knowledge, we aim to realize a modelof hyper-adaptability that can estimate the reconstruction ofneural structures in the process of recovery from a disorder
or disease, and to obtain knowledge for effective treatment
and training.
III. R ESEARCH OUTCOMES
A. Probabilistic latent variable model for analyzing longterm multi-modal data
Prof. Kondo (Tokyo University of Agriculture and Technology) and Dr. Yano (Toyota Motor Cooperation) havedeveloped a statistical technique to quantify the time-varying
structural change in the brain networks behind the hyperadaptability, with respect to simultaneously observed EEG–
EMG data. This year, they focused on the development ofconstructive models of brain networks and data assimilation.
They proposed a Collective Almost Synchronization (CAS)model for predicting EEG time-series by using a weaklycoupled network of dynamical neurons (e.g., HindmarshRose model or Kuramoto model), and demonstrated that the
network model can be used as a superior feature extraction
model for EEG pattern classification [1]. Moreover, they
proposed a novel reinforcement learning algorithm, and confirmed the superior performance in black-box optimization
tasks with constraints [2]. Furthermore, to understand therelationship between brain activities and motor control, they
analyzed EEG data measured during hand grasping movements under various experimental conditions (e.g., force
levels, with/without visual feedback). As a result, they foundthat an EEG feature (event-related desynchronization, ERD)
reflects the brain process for motor planning [3].
B. Integration of computational brain network and musculoskeletal models
Prof. Chiba (Asahikawa Medical University) and his colleagues put on the goal to estimate the factors which cause
differences in results of dual task between young and elderly
by differences of parameters in postural control simulator.
In this year, they focused on the motion at a gait initiationfrom a standing posture in order to see further differences inthe elderly. It has been reported that the amount and time ofcenter of pressure (CoP) movement at the gait initiation are
shorter than those of healthy young people. They hypothesizethat this is due to "increased muscle tone during standing
posture and insufficient inhibition at the gait initiation".
They proposed a neural controller for standing posture andgait, and investigated its validity. By using of a musculoskeletal model to take into account the muscle tone, with parameter adjustment by optimization, appropriate standing postureand gait were achieved. In addition, when co-contractions ofantagonist muscle groups were applied to simulate gait, falls
due to stumbling were observed [4]. This indicates that falls
in the elderly may be caused by the increased muscle tone.
They also proposed a neural controller for the gait initiation motion. It is confirmed that simple switching fromstanding postural control to gait control causes falls in computational simulations. Therefore, they proposed a motionof transition from a standing posture to a different postureand then switching to gait at an appropriate timing. The

transition posture and the timing of the switch were obtainedby optimization. As a result, a movement that leads to gait
was obtained, although the forward tilt of the upper body wasslightly large. In this motion, an approximate movement of
the CoP mentioned above was observed. This may be an
anticipatory postural adjustment to gait, and it is interestingthat this was observed even though the motion design was
not intended for the CoP movement.
C. Motor learning experiments with VR/Robot technologies
enhancing hyper-adaptability
Prof. Kondo and his colleagues investigated human–
human cooperative motor learning experiments with VR
and haptic robot technologies. They found that skill-level
matching in the cooperative motor task would be significant
for enhancing their adaptability to others [5], [6]. Moreover,they developed an immersive VR system for visuomotor
adaptation experiment, and showed that enhanced visualfeedback can facilitate impaired-side usage in simulated
hemiplegic participants [7].
IV. F UTURE PERSPECTIVE
In consideration of future collaborations with neuroscience
research groups, we proposed several methodologies formodeling the hyper-adaptability from both statistical andconstructive modeling standpoints. Moreover, we studied
cooperative motor learning with robotic interventions, toidentify motor tasks that can enhance adaptability.
Next fiscal year, we will continue to deepen the modelingmethodology, and we further apply the models to actual neurophysiological data (data assimilation). Moreover, we will
investigate the motor tasks that can induce ”reconstructionof neural structure” with dis-inhibition in the brain of elderly
people under the frailty state.
R EFERENCES[1] Nguyen T.M.P., Minh K.P., Hayashi, Y., Baptista, M.D.S., and Kondo,
T., Collective Almost Synchronization Modeling Used for Motor
Imagery EEG Classification, Proc. the 43rd Annual International
Conference of the IEEE Engineering in Medicine and Biology Society(EMBS), Virtual Conference, (11/1-5, 2021).
[2] Miyashita, M., Kondo, T., and Yano, S., Reinforcement learning withconstraint based on mirror descent algorithm, Results in Control andOptimization, 4, 100048, 2021.
[3] Nakayashiki, K., Tojiki, H., Hayashi, Y., Yano, S., and Kondo, T.,
Brain Processes Involved in Motor Planning Are a Dominant Factor
for Inducing Event-Related Desynchronization, Frontiers in Human
Neuroscience, 15:764281, 2021.
[4] Etoh H., Omura Y., Kaminishi K., Chiba R., Takakusaki K., Ota
J., Proposal of a Neuromusculoskeletal Model Considering Muscle Tone in Human Gait, 2021 IEEE International Conference on
Systems, Man, and Cybernetics (SMC), pp. 289-294, 2021, doi:
10.1109/SMC52423.2021.9658889.
[5] Nishimura, K., Saracbasi, O.O., Hayashi, Y., and Kondo, T., Cooperative Visuomotor Learning Experience with Peer Enhances Adaptability
to Others, Advanced Robotics, 35, 13, pp.835–841, 2021
[6] Saracbasi, O.O., Harwin, W., Kondo, T., and Hayashi, Y., Mutual Skill
Learning and Adaptability to Others via Haptic Interaction, Frontiersin Neurorobotics, 2021.
[7] Sakabe, N., Altukhaim, S., Hayashi, Y., Sakurada, T., Yano, S., andKondo, T., Enhanced Visual Feedback Using Immersive VR Affects
Decision Making regarding Hand Use with a Simulated Impaired limb,
Frontiers in Human Neuroscience, 15:677578, 2021.

B02Annual 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. Muscle
activity and brain waves during ﬁnger movement were measured
at the same time, and the direction of movement was estimatedusing muscle synergies. In addition, the signal source wascalculated from EEG, and the brain region which is related tothe identiﬁcation of the ﬁnger movement direction was searched.
2. We analyzed the muscle synergy before and after tendon
transfer using a mathematical model. Results suggested thatslow changes in muscle synergy and fast adaptation of temporal
patterns caused the change in temporal patterns obsearved in
the experiments.

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. Muscloskeletal Model of Monkey with Tendon Transfer
Analysis
We constructed a musculoskeletal model of the upper limb
for investigating the changes in the nervous system associatedwith tendon transfer (Fig. 1). The model was constructed on
the musculoskeletal simulation software OpenSim by scaling
the human upper limb model MoBL-ARMS [1] to matchthe monkey’s measurement data, and editing the skeleton andmuscle attachment positions based on the monkey’s anatomical
data [2]. The motion data of the monkey’s reaching movement
was reproduced by the movements of the musculoskeletal
model with inverse kinematics analysis. Then, the optimal
muscle activities to reproduce the generated movements were
estimated by inverse dynamics analysis and Static Optimization, and 41 muscle activities of the musculoskeletal model
were estimated.
On the constructed musculoskeletal model, we replaced theextensor digitorum communis (EDC) and ﬂexor digitorum superﬁcialis (FDS) muscles involved in ﬁnger ﬂexion and extension (similar to muscle reassignments performed in monkeys),and estimated muscle activity to reproduce the same motion
data as before transferring using the model after transferring.
Furthermore, we compared the muscle activity and the muscle
synergy of the models before and after the reassignment. As
a result, we found that the temporal pattern of activity inthe musculoskeletal model did not change before and after
the muscle transfer, but the combination of simultaneously
activating muscles were changed (Fig. 2). This result wasdifferent from the experimental results of monkeys [3], inwhich the combination of muscles hardly changed after muscle
transferring.
In the model, the optimal muscle activity in the standpoint

Fig. 1. Muscloskeletal modelBefore Tendon Transfer

After Tendon Transfer

learning the velocity-dependent force ﬁeld, the results wereobtained by alternating between the channel and no channel
conditions, and as in the human experiment, adaptation to
the force ﬁeld remained in the channel condition. The change

Fig. 2. Muscle synergies of the model before and after tendon transferof the mechanical features of the musculoskeletal system wasestimated, whereas in the monkey, the neural structures ofmuscle synergy were involved in the muscle activity. Therefore, there is a difference in the presence or absence of neuraldemands (constraints) on the activity between the experimentand the model. In the future, we will explore the contributionof neural structures to muscle transfer by investigating theconstraint that reproduce the experimental conditions using
the model.
B. Analysis of adaptation process by gray box model
We examined how to adapt to multiple force ﬁelds usinga gray box model that separately learns a forward model thatpredicts the future state of the body, a feedback controllerthat compensates for the difference between the motion target
and the current state, and an inverse model that generates
motor commands to stabilize the motion target[4]. Adapta-

Fig. 4. Result of adaptationin the output of each module was examined, and the outputof the feedback controller was different between the two
conditions. In addition, when compared with the condition of
being clamped or not clamped at the end after the velocitydependent force ﬁeld, the output of the inverse model showed
differences between the conditions when clamped at the end.
Thus, we were able to simulate the process of adaptation to theforce ﬁeld and ﬁnd the difference in the role of each module,
which leads to a more detailed model.
IV. F UTURE PERSPECTIVE
In this year, we used computer simulations to construct a
musculoskeletal model that realizes muscle rearrangement inmonkeys and to verify whether it can be adapted to multiple
force ﬁelds. As a result, we conﬁrmed that the muscle synergy
is different from the actual experimental results in monkeys
and that it can reproduce well the experimental results inhumans.
We are planning to continue joint research with other groups
in the next year and beyond.
R EFERENCES

Fig. 3. Gray-box model for adaptation of force ﬁeld

tion to a velocity-dependent force ﬁeld, a position-dependent
divergence force ﬁeld, a velocity-dependent force ﬁeld anda channel constraining the motion, and a velocity-dependent
force ﬁeld and a clamp at the end were simulated respectively,
and the output of each module was compared before and after
adaptation. After the adaptation, motion trajectories reproduced well the human motion trajectories and characteristics
that have been investigated so far. An example for a channel
constrainig adaptation is shown in Fig. 4. In this ﬁgure, after[1] K. R. Saul, “Benchmarking of dynamic simulation predictions in two
software platforms using an upper limb musculoskeletal model”, Comput.
Method Biomec, 18(13), 1445-58, 2015.
[2] N. Ogihara, H. Makishima, S. Aoi, Y. Sugimoto, K. Tsuchiya, M
Nakatsukasa, “Development of an Anatomically Based Whole-Body
Musculoskeletal Model of the Japanese Macaque (Macaca fuscata)“, Am
J Phys Anthropol, 139, 323-338, 2009.
[3] N. Uchida, R. Philipp, T. Oya, T. Funato, K. Seki. “Muscle synergy
analysis of forearm muscles in macaque monkey after cross-transfer
surgery”, Neural Control of Movement Conference, 1-LB-106, 2019.
[4] H. Kambara, A. Takagi, H. Shimizu, T. Kawase, N. Yoshimura, N.
Schweighofer, and Y. Koike, “Computational reproductions of external
force ﬁeld adaption without assuming desired trajectories”, Neural Networks, vol. 139, no. July, pp. 179–198, 2021.

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 the Bayesian
integration model for the sense of agency; 2) examined therelationship between body consciousness and slow/fast dynamics
of motor learning; 3) we developed an evaluation model toexamine hemiplegic patients’ recovery.

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.

III. RESEARCH TOPICS
B03 accomplished the following three studies in the pastyear.
A. The Bayesian Integration Model for Sense of Agency
First, Asama and Wen’s group (The University of Tokyo)
proposed the Bayesian integration model to explain the senseof agency, which is one of the most important aspects in body
consciousness [1] (Fig. 2). We also designed a behavioral
experiment to further verify this model and to determine theparameters. The Bayesian integration model suggest thathumans have prior distributions regarding the sensory input at
both the cognitive and sensorimotor level. When receiving
actual sensory inputs, the probability of the self is calculated atboth the cognitive and sensorimotor levels, and are integrated
into the sense of agency with different weights. The weights
for integration are considered to be related with the variance ofthe prior distribution. This model can well explain individual
differences and other factors such as social influence. We
conducted a behavioral experiment to find out the integration
principle and to find out the method of describing individual
difference with parameters of the model. The interim results
confirmed the input at both levels, and highlighted thedominance of the sensorimotor level in the integration.

II. AIM OF THE GROUP
Figure 1 shows the outline of our approach. We aim toquantitatively measure body consciousness, emotion, and thehyper-adaptation of body motor control, establishing amathematical model that is able to predict optimized
intervention method for rehabilitation.

Fig. 2 The Bayesian integration model of the sense of agency

Fig. 1 Processes of intervening hyper-adaptation via body
consciousness and emotion, and quantitative measures.

Furthermore, Asama and Wen’s group examined theinfluence of self-voice on spatial localization [2]. This study
focused on the influence of self-awareness on the perceptual
processing of voice stimulus. The results showed thatcomparing to other-voice, the distance of the self-voice wasover-estimated [2]. Moreover, this over-estimation effect ofself-voice was correlated with participants’ schizotypal scores,indicating the potential link between schizophrenia and the
distortion in voice processing. People tend to over-estimate a
distortion in their voice, and such distortion can be evenmagnified in schizophrenia. Further, B03 group iscollaborating with A05-8 group on a schizophrenia study. This
ongoing project sheds lights on the sub-processes underlying
patients’ sense of agency, and aims to design a novel diagnose
for schizophrenia from the perspective of sense of agency.
At last, Asama and Wen’s group investigated thecharacteristics of the EEG power spectrum during motor
learning in order to construct a neuromodulation method tofacilitate motor learning [3]. We measured the EEG during ahand motor learning task because we thought that
understanding the changes in the EEG power spectrum during
motor learning could identify the appropriate frequency bands
and stimulation areas for neuromodulation intervention.
Increased activity in the gamma band was observed in thefrontal, lateral parietal, and occipital areas, and these areas
were identified as candidates for neuromodulation intervention.
B. Bodily-self is composed of fast-self and slow-selfMotor recovery and motor learning are driven at least by
two interacting systems with different dynamics: fast and slow
systems. In the fast system, the memory is updated quickly but
forgotten fast, whereas, in the slow system, it is updated
gradually but sustained long. Since we are interested in how
the bodily self which is composed of body ownership andagency, facilitates the hyper-adaptation phenomenon. To figure
this out, Izawa (University of Tsukuba)’s group immerse theparticipant in VR space and ask them to manipulate the body of
the avatar when the visual distortion of reach movement wasinserted between the actual hand movement and the virtual
body movement. Meanwhile, we asked them about feelings ofbody ownership and agency. We divided the participants into 3
groups. One group called 1PP-ipsi manipulated the right arm of
the avatar with their right arm. 2nd group called 1PP-contmanipulated the left arm of the avatar by their left arm. 3rd
called 3PP, is the same as 1PP-ipsi except that they view theiravatar from the third-person perspective. By thesemanipulations, these groups exhibited different learning
curvatures. We decomposed the body ownership and agency
scores to slow and fast systems to determine how theselearning speeds relate to body recognition. As a result, wefound that the fast system drives the body ownership, but theslow system drives the agency. This suggests that the minimum
unit of self is slow and fast selves [4]. In addition, we foundthat motor exploration is critical for de-novo learning, whichappeared in neurorehabilitation [5].
C. Evaluation for Recovery Process of Hemiplegic Patients
An’s group (Kyushu University) investigated the hyperadaptation process of recovery in hemiplegic patients with
impaired motor function. We hypothesized that there would bea difference between patients with better ADL ability and thosewith worse ADL ability at the time of hospital discharge.
Patients within 3 months of onset of illness were divided into
two groups, severe and moderate, based on FIM scores atdischarge, and muscle synergy structures and kinematic indicesof standing were compared.

Four muscle synergies were extracted from both severe andmoderate groups, and it was found that these spatial patterns ofmuscle synergies (coordinated muscle activation) were similar
in both groups. However, the peak of muscle synergy 3
contributing to body extension was later in the moderate groupthan in the severe group. This phenomenon is thought to be dueto the fact that the moderate group used the paralyzed side,which lengthened the movement time and delayed the peak ofmuscle synergy 3.
Although there was no difference in motor function
between the two groups by 3 months of stroke onset, themoderate group, which used the paralyzed side, recovered
more ADL ability at the time of discharge. In other words, theuse of the paralyzed limb in standing, especially from hip rise
to body extension, resulted in better recovery of ADL abilityby the time of hospital discharge [6]. This result is useful for
promoting Hyper-Adaptation in functional recovery ofhemiplegic patients.
IV. FUTURE PERSPECTIVE
In the past year, we proposed the computational modelfor the sense of agency, and designed a behavioral experimentto verify the model the determine the parameters. We also
reported a novel effect of self-awareness on the spatial
processes of voice, and revealed the link between this effectand the schizophrenia. We further collaborated with A05-8
group to design efficient diagnose focusing the sense ofagency for schizophrenia. Furthermore, we examined theeffect of body ownership on motor learning in a virtual reality
experiment, to clarify the mechanism of slow and fast
dynamics in motor learning. At last, we analyzed the muscle
synergies of standing-up motion in paralyzed patients, foundout significant differences in muscle synergies for patientswith difference recoveries. Those results are useful forunderstanding the hyper-adaptation processes in hemiplegicpatients.
REFERENCES
[1]
[2]
[3]

[4]
[5]
[6]

Wen, W. & Imamizu, H. “The sense of agency in perception, behaviourand human–machine interactions.” Nature Reviews Psychology,(accepted).
Wen, W., Okon, Y., Yamashita, A., & Asama, H. “The over-estimationof distance for self-voice versus other-voice.” Scientific Reports, 2022,
vol. 12, article no. 420.
Hamada, H., Wen, W., Kawasaki, T., Yamashita, A. & Asama, H.
“Characteristics of EEG Power SpectraInvolved in the Proficiency ofMotor Learning.” Proceedings of the 34th SICE DAS Symposium,
Online, 2022.
Ishikawa, R., Ayabe-Kanamura, S., & Izawa, J. “The role of motor
memory dynamics in structuring bodily self-consciousness.” iScience,
2021, 24.12: 103511.
Dal’Bello, L. R. & Izawa, J. “Task-relevant and task-irrelevant
variability causally shape error-based motor learning.” Neural Networks
2021, 142: 583-596.
Kogami, H., An, Q., Yang, N. Wang, R., Yoshida, K., Hamada, H.,
Yamakawa, H., Tamura, Y., Shimoda, S., Yamasaki, H., Sonoo,
M., Alnajjar, F., Hattori, N., Takahashi, K., Fujii, T., Otomune,
H., Miyai, I., Yamashita, A., & Asama, H. “Analysis of muscle synergy
and kinematics in sit-to-stand motion of hemiplegic patients in subacuteperiod.” Advanced Robotics, 2021, vol. 35, pp. 867-87

B04Annual 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 20 coinvestigators (Shirafuji, Kaminishi, Takamido, Omura, Ishii,
Hamada, Kohno, Kishimoto, Yuine, Ishibashi, Etoh, Miyata,
Osaki, Kanaya, Kawano, Kawai, Sonoda, Mori, Hasegawa,
Makino).

with the reticulospinal tract, and studied the effect of its
presence or absence [1]. We newly introduced control into theneural controller model that we have developed, based on thefollowing findings: 1) It coordinates muscle activations of theentire body; 2) It maintains the body in a vertical position; 3) It
receives mainly vestibular sensation in the vestibular nucleus in
the brainstem; 4) It has an excitatory effect on extensors and an
inhibitory effect on flexors (Fig. 1).

Fig. 1. The neural controller model with the control that mimics thevestibulospinal tract.

We performed simulations using the neural controller modeland a musculoskeletal model with 19 degrees of freedom ofjoints and 94 muscles to maintain a standing posture. In almost
all muscle tone conditions, the value of the center of footpressure velocity in the case with the vestibulospinal tract was
smaller than that in the case without the vestibulospinal tract(Fig. 2). This result is in line with the results of experiments onpatients with unilateral vestibular dysfunction, and confirms
the validity of the neural controller model.

II. RESEARCH RESULTS AND FUTURE PLANS
A. Verification of the influence of the control introduced into
the neural controller model that mimics the descending
pathways
Ota and colleagues build a mathematical model thatconsiders the role of neurotransmitters in posture control with
Prof. Takakusaki (A04) and Prof. Chiba (B01).
In this year, we aimed to introduce into our computationalmodel a control that mimics the vestibulospinal tract, adescending tract that is responsible for postural control along

Fig. 2. Muscle tone index ||uff|||2 and center of pressure velocity in theanterior-posterior direction. Green shows the results without vestibulospinal
tract and Orange shows the results with vestibulospinal tract. * p < 0.05,** p < 0.01.

In addition, simulations using the neural controller model haveconfirmed the possibility that increased muscle tone in thereticulospinal tract leads to the bent posture seen in Parkinson's
disease. In the future, we will use this neural controller model
to represent diseases such as Parkinson's disease, in which thefunction of the descending tract is increased or decreased. We
will also investigate the relationship between the model
parameters and the visualized neurotransmitters in the brain
and the characteristics of actual standing posture.
We analyzed the data of the standing posture maintenanceexperiment and tried to extract the important factors formodeling. We conducted an experiment in which participants
were made to stand on a sliding table, and showed that muscle
tone may affect the postural control strategy during posture
recovery [2]. We also used machine learning methods toclassify and cluster the standing data of stroke patients, andshowed that the posture and the magnitude of sway may differ
among the patients of subcategories of cerebral infarction andcerebral hemorrhage [3, 4].
B. Evaluation of the role of neurotransmitters in posturalcontrol with multitask
Yozu et al. evaluated the role of neurotransmitters inpostural control with multitasking.
1) The effects of neurotransmitters on multitasking inpatients with Parkinson’s disease
Two Parkinson’s disease patients were assessed. The
participants were asked to perform (i) a standing task, (ii) a
multitasking of the standing task and the arithmetic task, and(iii) the standing task. For the arithmetic task, the participants
were asked to recite serial subtractions by seven. They wereassessed before and after drug administration to evaluate theeffect of dopamine on the multitasking test. As a result, there
were fluctuations in symptoms over the course of one day, andthe center of pressure and electromyography changed partiallyconcordantly. Our results were accepted to an international
journal [5].
2) Analysis of voluntary sway in standing posture in
healthy individuals
We analyzed voluntary sway in a standing posture as abasic study aimed at elucidating postural control in Parkinson’s
disease patients. Ten healthy participants were included. We
measured the joint angles of the trunk and lower limbs; thetrajectory of the center of pressure; and the surface EMG of thetrunk and lower limbs. The measurement condition was
voluntary weight shifting in the anterior-posterior in a standing
posture; the speed was divided into ten levels. As a result, wefound that the joint angles of the trunk and knee used for
postural control differed depending on the speed of weightshifting. We are preparing to submit to an international journal.
We will analyze Parkinson’s disease patients to evaluate therole of neurotransmitters in postural control.
C. Relationship between motor Functional Independence
Measure gain and nonparalyzed side skeletal muscle masschange in rehabilitation after stroke
This study investigated the impact of control adaptation andphysical recovery on stroke patients. We examined therelationship between skeletal muscle mass change on the
nonparalyzed side and motor Functional Independence
Measure (mFIM) gain in stroke patients. The group withmaintenance of and gain in muscle mass on the nonparalyzed
side at the time of discharge was defined as the maintenanceand gain group (GG), and the group with a decrease in skeletal
muscle mass on the nonparalyzed side at the time of discharge
was defined as the loss group (LG), and mFIM gain wascompared. Multiple regression analysis was performed withmFIM gain as the objective variable and hospitalization days,
mFIM at admission, muscle mass at admission, andnonparalyzed side muscle mass as explanatory variables. In themultiple regression model, nonparalyzed side muscle mass was
not significantly associated with mFIM gain. We found no
relation between mFIM gain and the maintenance and gain or
the loss of muscle mass on the nonparalyzed side during
rehabilitation after stroke. It was assumed that poststroke
functional recovery was not mainly due to a gain in muscle
mass.
D. Development of a unilateral spatial neglect (USN) mousemodel
Unilateral spatial neglect (USN) is a disorder ofhigher brain function that occurs after a brain injury, such as
stroke. We induced a focal infarction in the right medial
agranular cortex (AGm) of mice using photothrombosis andcreated a mouse model of USN. After the induction of cerebral
infarction, USN was evaluated. The left-side selection rate wascalculated as a symptom of USN. After the final evaluation of
the USN (18 days after cerebral infarction), the brain wasremoved and cryosectioned. The sections were observed to
examine the infarct volumes, the extent of the lesions, and thecentroid of the lesion area. In addition, a ladder rung walking
task was performed to evaluate motor function. The findings
suggest that recovery from ipsilesional spatial bias requires
neural plasticity within the anterior AGm. Motor function
evaluation is currently under analysis. This conditional mousemodel of ipsilesional spatial bias may be used to develop
effective treatments for unilateral spatial neglect in humans.
REFERENCES
[1]
[2]
[3]

[4]

[5]

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, accepted.
K. Kaminishi, R. Chiba, K. Takakusaki, and J. Ota, “Increase in muscle
tone promotes the use of ankle strategies during perturbed stance,” Gait
& Posture, vol. 90, pp. 67-72, 2021.
D. Li, K. Kaminishi, R. Chiba, K. Takakusaki, M. Mukaino, and J. Ota,
“Evaluating quiet standing posture of post-stroke patients by classifying
cerebral infarction and cerebral hemorrhage patients,” Advanced
Robotics, vol. 35, no. 13-14, pp. 878-888, 2021.
D. Li, K. Kaminishi, R. Chiba, K. Takakusaki, M. Mukaino, and J. Ota,
“Evaluation of Postural Sway in Post-stroke Patients by Dynamic Time
Warping Clustering,” Frontiers in Human Neuroscience, vol. 15, 731677,
2021.
A. Yozu, K. Kaminishi, D. Ishii, Y. Omura, A. Matsushita, Y. Kohno, R.
Chiba, and J. Ota, “Effects of medication and dual tasking on postural
sway in Parkinson’s disease: A pilot case study.” Advanced Robotics,
vol. 35, pp. 889-897 , 2021

B05-1 Elucidation of the mechanism of motor
synergy emergence in deep reinforcement learning
Mitsuhiro HayashibeTohoku University

Abstract—Deep reinforcement learning (DRL) offers a promising model-free approach for handling high-dimensional control
problems. It still remains an open problem on how we cancreate human-like synergetic motion using learning algorithms.
In this study, we apply DRL for managing full-dimensionalarm manipulation. Although synergy information has never been
encoded into the reward function, the synergy naturally emerges
along with feedforward control, leading to a similar situation as
human motion learning. To the best of our knowledge, this is apioneer study demonstrating the error and energy optimization
issue exists behind the motor synergy employment.

I. I NTRODUCTION
The ultimate goal for bionic arms is to realize human-likenatural control. To perform an energetically efficient motion
as in human control, the so-called mathematical optimizationbased approach is a state-of-the-art approach for solving
redundancy problems in robotics and neuroscience. Such an
optimization approach can provide an optimal solution when
the prior dynamics information of the manipulator and environment is explicitly given. For the manipulation task, thedynamics conditions are always unknown or change with thecontact situation and the hand load. Importantly, deriving
a dynamics equation for a full-dimensional human arm isalready challenging.
In order to manage a physically complex, high-dimensional
problem, applying deep learning techniques to reinforcement
learning (RL), i.e., deep reinforcement learning (DRL) offers
a promising model-free approach. It enables the robot tolearn directly from the interaction with the environment, basedon the actions and states, instead of predefined dynamic
conditions. DRL is gaining prominence in robotic research andhas obtained impressive results for arm manipulation tasks.
However, we should note that the current DRL algorithm
does not account for synergetic behaviors, which can tend to
produce unnatural and awkward control results even if we referto learned motions available on OpenAI’s website.
Previous method was not possible to apply to the fulldimensional reaching problem as the algorithm could nothandle high-dimensional redundancy problems. We apply deep
reinforcement learning for managing a full-dimensional arm
manipulation, and then, verify the relations among motion
error, energy, and synergy emergence in the learning process
to reveal the mechanism of employing motor synergy.
To the best of our knowledge, it still remains an open
problem on how we can create human-like synergetic reachingmotion by using the DRL algorithm. This study provides thefirst demonstration that the error-energy optimization issue exists behind the motor synergy employment in full-dimensionalarm manipulation.
II. PDRL C ONTROL F RAMEWORK
Here, two types of DRL control are studied. When DRL wasapplied to the arm model, the action command was learned as
an open-loop command to be given to the agent as shown by
the blue line in Fig. 1(a). This is a standard style of DRL
framework. Thus, the action from DRL can be considered asa feedforward signal. In addition, we study a second type ofcontrol, where DRL is applied while having a feedback control
loop, represented by the other black lines. We name thissecond version as ”PDRL” which stands for a PD controller
combined with a DRL controller.
For the neurological interpretation of the proposed control,
we can basically apply the so-called internal model theory
in the cerebellum. The climbing fiber input to the Purkinjecells carries error signals in the motor command coordinates,
and their temporal waveforms can be well reproduced using
the dynamics model. This neurological function corresponds
to the DRL box that develops a predictive neural networkwith the state feedback during the learning process to form
the feedforward signal. We can consider that the DRL box
develops as an internal model. The contribution shift between
the feedback and feedforward controls in the process of
motor learning has been well demonstrated by obtaining theinternal model in the cerebellum. Feedforward movements are
generated without sensory feedback, showing the predictive
nature of the given dynamics. Feedback control, in contrast,
senses the state of the system and uses the information of error
detection for modifying the current movement.
The control architecture is formulated as illustrated in theblock diagram in Fig. 1(a), which comprises these separated
elements in the loop.
• The feedback force error indicates the intention to track
the target, which is represented by the direction toward
the target, using the proportional (p) error between thetarget and current endpoint.
• The feedback force error is mapped into the joint spaceby using the Jacobian transpose of the arm.
• Here, local PD control mainly represents a local reflex
loop as a function of the muscle spindles. This part helps
to smoothly change the joint angle.
• The term displayed as the gray box corresponds to theaction command from DRL algorithm.

Task space

Joint space(Shoulder, Elbow and Wrist)

Feedforward
Feedback Force Error
𝑟

+

-

P

Motor-command Error

DRL

J T (θ)Force to TorqueEndpoint error Mapping
△𝑝

PD

-

State

𝒂𝒕

+

+

+ 𝜏1

+ 𝜏2

Joint

𝜃, 𝜃 ሶ Joint Angle and Angular Velocity𝑥EndpointFKaShoulder Joint (1 DOF)

3 DOF

Wrist Joint (1 DOF)

2 DOF
Target trajectory

Elbow Joint (1 DOF)

2 DOF

Target point

Fingertipb
Fig. 1. a) Block diagram of motor learning, where P represents proportional, D represents derivative, and at is the action from DRL algorithm. b) The
configuration of our simulated agents.

IV. C ONCLUSION
Automatic generation of human-like synergetic motion inhigh redundant 7 degrees of freedom is an unsolved issue by
conventional approach. Although we did not explicitly encode
the synergy between joints in our reward function, the synergy
phenomenon naturally emerged during the learning process.
The result implies that the error-energy optimization issue
exists behind the synergy. The synergy may be a means for
optimizing the error-energy criterion.
Moreover, the results of the proposed feedback-augmented
DRL controller demonstrate better capability over DRL in

PD

DRL

12

12

10

10

8

8

E-E index

E-E index

III. M OTOR C ONTROL R ESULT
To evaluate the performance of the proposed scheme, thetracking result is compared among 1) PD feedback controller,2) DRL, and 3) PDRL controller. PD feedback controller
corresponds to the black lines in Fig. 1(a). DRL corresponds
to the blue lines. PDRL controller involves all the lines.
Human motor control takes into account also the energy
efficiency. A new measure is required to correctly evaluate therate of motion accuracy per the energetic effort. We proposed
previously a simple criterion to evaluate the coupled index
over both error and energy. We name it as E-E index, whichmeans 1/Error/Energy. It is simply the tracking accuracy rate
per energy consumption.
Finally, we observe gradually optimized performance in the
E-E index of both DRL and PDRL. The result of experiments
assessing statistically the E-E index with PD, DRL and PDRL
control are visualized in Fig. 2. All of the bar plots are
generated by averaging the results of five experimental trials
for 3- (Fig. 2(a)) and 7-DOF (Fig. 2(b)) agent. The standard
deviation are added as error bars. As can be seen, withguidance from a feedback controller, the E-E index of PDRL
is generally larger than that of DRL for all cases.

6

6

4

4

2

2

0

0 kg

0.4 kg

PDRL

0

(a)

0 kg

0.8 kg(b)

Fig. 2. Bar plots showing the result of the E-E index with PD, DRL and
PDRL control under different hand load for (a) 3-DOF robotic arm case (b)7-DOF robotic arm case.

terms of synergy development and error-energy index. Thisimplies that feedback control can support the feedforward term
development by avoiding unnecessary random exploration
which leads to better search efficiency. We have also applied
the concept to Soft Robot adaptation and Spiking Neural
Network toward energy efficient motor control.
R EFERENCES[1] J. Chai and M. Hayashibe, Motor Synergy Development in HighPerforming Deep Reinforcement Learning Algorithms, IEEE Robotics andAutomation Letters, vol.5, no.2, pp.1271–1278, 2020.
[2] J. Han, J. Chai and M. Hayashibe, Synergy Emergence in Deep Reinforcement Learning for Full-dimensional Arm Manipulation, IEEE Trans. on
Medical Robotics and Bionics, vol.3, no.2, pp.498-509, 2021.
[3] T. Sugiyama, K. Kutsuzawa, D. Owaki and M. Hayashibe, Individual Deformability Compensation of Soft Hydraulic Actuators through Iterative
Learning-Based Neural Network, Bioinspiration and Biomimetics, vol.16,
056016, 2021.
[4] K. Naya, K. Kutsuzawa, D. Owaki and M. Hayashibe, Spiking Neural
Network Discovers Energy-efficient Hexapod Motion in Deep Reinforcement Learning, IEEE Access, Vol.9, pp.150345-150354, 2021.

B05-2 Adaptation ability of human postural control
system revealed by a closed-loop electrical muscle
stimulation system
Daichi Nozaki1, Shota Hagio2

Graduate School of Education, The University of Tokyo
Graduate School of Human and Environmental Studies, Kyoto University
closed-loop EMS system to investigate the adaptation process
Abstract—Last year, we investigated how the postural control
and to alter the movement pattern.
12

system adapts to a new dynamical environment using a closedloop electrical muscle stimulation (EMS) system. This year, we
pursued the possibility of the closed-loop EMS system to alter thehuman movement pattern through the adaptation process.

I. INTRODUCTION
To artificially alter the dynamics of the human body during
standing, we have developed an experimental system to impose
ankle joint torque depending on the position and/or velocity of
the center of body mass (CoM). Most importantly, our systemdoes not use a special perturbation device but electricallystimulates the tibialis anterior muscle to generate the ankle
joint torque (Fig.1A). Modulating the magnitude of electricalmuscle stimulation (EMS) with the CoM measured by a laser
displacement sensor enabled to alter the dynamics of the body.
Furthermore, the postural control system develops the control
strategy depending on the type of the imposed dynamics(Fig.1B).
II. AIM OF THE RESEARCH
Conventionally, a special device like a robotic manipulandumis necessary to investigate how the motor learning system
adapts to a new dynamical environment. Thus, the target
movement is restricted to a simple movement (e.g., reaching
movement). Our method can remove this limitation and can beused to investigate the adaptation process of more complicatedmovements. This year, we performed new experiments using
the closed-loop EMS system. We also tried to develop a newA

B

Width: 0.5ms

PC
EMG

Laser
sensorES pulse
Pulse train: 20Hz

Modulated withpostural sway

Stimulator

EMG

AD
deviceCoM pos
CoM velCoP

DA
deviceCalculate
ES intensity

Force plate

Fig.1 Experimental system to impose a new dynamical environment
during standing posture using the electrical muscle stimulation (A). The
postural response to a step perturbation while adapting the quiet stance toposition and velocity dependent EMS (B).

III. RESEARCH TOPICS
A. Adaptation of reaching movement to EMS
The EMS was applied to the biceps brachii muscle during
reaching movements. When the induced muscle contraction
perturbs the movement in the subsequent trial in the direction
opposite to the perturbation, it is evident that the motor systemcorrects the movement in the next trial in the direction opposite
to the perturbation. Thus, in the present study, we focused on
the movement correction induced by the unpredicted muscle
contraction itself by constraining the hand trajectory with theerror clamp method.
The EMS of about 20mA (2 biphasic pulses, pulse
width:250µV, 100Hz) induced the maximal force of 4N at thehandle. This force magnitude was comparable with the force
field perturbation created by the ordinary robotic
manipulandum. Importantly, since we used the error-clamp
method, this force did not induce any movement error.
Furthermore, the subjects were unable to tell the detail of theperturbation.
Nevertheless, in the subsequent error-clamp trial, weobserved the force output at the handle directing in the
direction opposite to the perturbation. This result indicates that,even when the perturbation did not induce any task-relevant
movement error, the motor system detected the unpredictedmuscle contraction and tried to correct the motor command.
B. Adaptation of walking movement to EMS
While the subject walked on a treadmill, the EMS wasapplied to the tibialis anterior (TA) and the biceps femoris (BF)
during the swing and stance phase (biphasic pulse; pulse width:
500µV; 20Hz). The movement of the lower limb and theground reaction force were measured by a motion capturesystem. We investigated how the walking movement patternchanged during 10 min walking under the presence of the EMS
and how the adaptive change (i.e., aftereffect) persisted after
the EMS was turned off.
In the initial EMS phase, not surprisingly, the EMS
changed the movements around the joints spanned by thestimulated muscles (i.e., ankle joint by TA and knee joint by
BF). The adaptation to the EMS did not restore the original
walking movement pattern. Instead, the walking movement

pattern converged to a new pattern while globally changing all
lower limb joints movement patterns. Furthermore, such
adaptive change persisted for a long time (~10 min) after the
EMS was turned off.
These results indicate that there is no ideal lower limb
movement pattern, and the motor system adopts different
norms to be optimized. This study also implied that the EMS
system developed here can alter the walking movement
patterns without using expensive robotic devices [2].
C. Adaptation of standing posture to galvanic vestibular
stimulation
The stability of standing posture is accomplished bymultiple sensory information, including visual, vestibular, andproprioceptive information. However, it remains unknown how
the sensory information of these different modalities is
integrated to stabilize the standing posture.
This study investigated how proprioceptive and vestibular
information is integrated to control standing posture by
artificially creating a novel relationship between bothinformation. To this end, we used galvanic vestibular
stimulation (GVS): Applying weak electrical current throughthe electrodes placed behind both ears stimulates the vestibularorgans and resultantly the body is swayed in the mediolateral(ML) direction. We tried to modulate the magnitude of GVS
depending on the CoM velocity in the anterior-posterior (AP)direction measured with a laser displacement sensor.
In this experimental setup, the CoM velocity in the AP
direction was always accompanied by the postural sway in the
ML direction, for which the postural control system needs tocompensate. When the 1cm/s velocity of the COM was set to
induce 1mA GVS, the magnitude of GVS was restricted below
1.5mA and the subjects did not feel dizziness.
Maintaining the standing posture under the GVS might
induce the following 3 possible adaptation responses. First, thepostural control system ignored the GVS because the threat

induced by the GVS was not critical. Second, the posturalcontrol system could try to reduce the AP sway to minimize theinfluence of GVS. Third, the postural control system could
actively compensate for the ML sway by predicting the GVS
associated with the AP sway. We are currently performing theexperiment to examine which possibility is correct.
D. Development of a system to modulate EMS with movement
patterns
The results of projects A and B described above strongly
indicate that the EMS modulated with the movement couldchange the movement patterns through the motor adaptation
process. We have developed an EMS system to alter the
movement pattern, which is applicable for various movements,
including throwing, walking, running, kicking, jumping.
We adopted a relatively inexpensive sensor (C-Stretch
sensor, Kisco, Japan) to measure the angles of the upper andlower limbs. This sensor is stretchable and outputs the voltage
signal depending on the stretching magnitude. We havesucceeded in applying EMS without any delay.
IV. FUTURE PERSPECTIVE
The closed-loop EMS system can perturb the movement
without using expensive special devices. This system is useful
to investigate the adaptation of movements to a novel
dynamical environment and to alter the movement pattern
through the adaptation ability of the motor system. We need toverify the validity of this method further in future research byusing the system that we have just finished developing(Research projects C and D).
REFERENCES[1][2]“Balance, Gait, and Falls” Eds. Day B. L. and Lord S. R. Elsevier, 2018.
I. Cajigas, A. Koenig, G. Severin, M. Smith, and P. Bonato. “Robotinduced perturbations of human walking reveal a selective generation of
motor adaptation”. Sci Robotics 2, eaam7749–10, 2017

B05-03: Mechanism underlying the hyper-adaptation of
bipedal locomotion to the evolutionary change of the footNaomichi Ogihara

Department of Biological Science, Graduate School of Sciences, The University of Tokyo
Abstract— Foot is the most drastically altered part of thehuman body in the process of the human evolution. The fact thatthe human foot is highly specialized not only indicates thatmodification of the foot structure is essential for acquisition of
stable, efficient bipedal locomotion, but also suggests that thehuman nervous system possesses an ability to adaptively
reorganize itself so that the change in the body morphology canbe recognized and effectively utilized to facilitate the control of
bipedal locomotion. In this study, we try to elucidate such hyperadaptive mechanism of human bipedal locomotion based on aneuro-musculoskeletal forward dynamic simulation.

I. INTRODUCTION
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. Among these differences, thoseof the foot are particularly important, as it is the most distalsegment of the body that directly interacts with the ground.
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 foot structure directly
affect the way the foot mechanically interacts with the ground,and should drastically alter coordinated dynamics of bipedal
walking, more likely to disturb successful generation of stablebipedal locomotion. The fact that the selective pressure wasapplied to the humanlike foot structure during the evolution ofhuman bipedal locomotion strongly indicates that the nervous
system possesses an ability to spontaneously reorganize itself
in such a way to adaptively make use of the morphologicalchange in the foot structure to accomplish more stable, robustand efficient bipedal locomotion. If the neuronal mechanism

underlying such “hyper-adaptability” of human locomotion to
the change of the body structure can be elucidated, the findings
will not only contribute to clarifying the neural basis of theevolution of human bipedal locomotion, but also provides
implications for effective therapeutic or rehabilitativeinterventions to restore walking ability in old adults who sufferfrom decline of bodily and neurological functions.
II. AIM OF THE GROUP
In this study, we aimed to clarify the neuronal mechanism
underlying the “hyper-adaptability” of human locomotion to
the alteration of the foot structure in a constructive approach
using a forward dynamic simulation. Specifically, we analyzed
the process of reorganization of the neural control system dueto the alteration of the foot structure in a bipedal gait
simulation of the Japanese macaque based on a neuromusculoskeletal model.
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 maximum forces of the muscles wereassumed to be proportional to the physiological cross-sectional
area (PCSA) of the corresponding muscle group.
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 ishypothesized to contribute to the generation of actual
locomotion. Recent studies have suggested that the CPG
consists of two layers: a rhythm generation (RG) layer thatgenerates oscillatory signals and a pattern generation (PG)
layer that generates muscle activity patterns based on the phasesignal from the RG layer. Therefore, in the present study, amathematical model of the CPG consisting of the RG and PG
layers was constructed. The RG layer was modeled by twophase oscillators corresponding to the phase signals for the left
and right legs. The PG layer then generated the activation
pattern of each muscle represented by a combination of two
Gaussian basis functions of the phase signal. The RG layer in
the CPG is known to modulate its basic rhythm by producing
phase shifts and rhythm resetting based on sensory information.
To take this into account, we reset the oscillator phase based on
foot–ground contact events. In addition, we assumed a simple
feedback control for postural control of the trunk segment.
To generate bipedal walking, an appropriate activation
pattern must be determined for each of the 10 muscles. In thepresent study, we used a genetic algorithm for tuning a total of
60 parameters defining the sequence of muscle activation
patterns so as to minimize the gross metabolic cost of transportestimated based on the mechanical work done by the muscles
and basal metabolic energy [6].

alteration of the foot affects kinematics, kinetics, andenergetics of bipedal locomotion. The present nervous model
incorporated only the mathematical model of the CPG, but thisis not sufficient to explain the mechanism of “hyperadaptability”. The reticulospinal tract that involves in posturalcontrol and locomotion, and the vestibulospinal tract
responsible for vestibulospinal reflex necessary to maintainpostural balance against external perturbations should beincorporated in the present nervous model towards deeper
understanding mechanisms of “hyper-adaptability” of bipedal
locomotion.
A

1
2

When the foot morphology was altered, the vertical groundreaction force profile shifted from a single- to a double-peaked
profile as in human walking (Fig 2C). As the calcaneal point
was inferiorly translated, the cost of transport gradually
decreased compared with that of the intact condition. The
double-peaked vertical ground reaction force profile is one of
the most distinctive characteristics of human bipedal walking,and it is known to be functionally related to the energetic
efficiency of walking. Our results therefore suggested thatevolutionary changes in the foot morphology were important
for the acquisition of human-like efficient bipedal walking [6].
V. FUTURE PERSPECTIVE
We constructed a computer simulation of bipedal
locomotion in the Japanese macaque based on aneuromusculoskeletal model to evaluate how structural

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.

IV. RESULTS AND DISCUSSIONSThe kinematics of the simulated bipedal locomotion in theJapanese macaque generally agreed with the measured data.
The simulated gait also captured the main features of theground reaction force profiles in the Japanese macaque.
Simulation framework successfully reproduced the basic
kinematic and dynamic features of bipedal locomotion in theJapanese macaque (Fig 2A) [6].
We evaluated how virtual manipulation of foot morphology
affects the kinematics, dynamics, and energetics of bipedal
locomotion (Fig 2B). Specifically, we investigated how thealteration of the foot from digitigrade to plantigrade by theinferior translation of the calcaneal tuberosity, which allows
heel strike bipedal locomotion, affects the kinematics,
dynamics, and energetics of bipedal locomotion in the Japanesemacaque using computer simulation.

B

C

150
ｖGRF (N)A

B

alteredintact0
0

1

RL

Gait cycle (x100％)

Fig. 2. Simulation results. A: Stick diagram of the simulated locomotion. B:
Virtual alteration of foot morphology. C: Comparisons of the changes invertical ground reaction force (vGRF).

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.

B05-4Annual report of research project
Isao Nambu

Nagaoka University of Technology
Abstract—This project aims to find low-dimensional space inthe brain networks and manipulate its state using non-invasive
brain stimulation to understand the mechanism of the “hyperadaptability.” This year, we continued to develop a method to
identify the low-dimensional state based on the time-varying
graphical lasso. In addition, we tried to develop a method to finddirected graph information using auto-regressive models andKalman filter. We tested these two methods for different datasets

I. INTRODUCTION
Recent studies suggest that the movement is represented byposition or state in the low dimensional neural subspacecomposed of the population of the neural activities (i.e., Neuralmanifold) in the phase of movement planning and execution[1]. Neurophysiological studies (e.g. [1, 2]) showed theimportance of identifying low dimensional subspaces. On theother hand, not only one or two motor-related areas but also
several brain regions are involved in generating movements.
However, it is unclear how these regions and the connectivity
between the regions are related to the movement. It ishypothesized that low dimensional neural subspace in theplanning/execution is influenced by multiple brain regions. In
addition, we think that such different neural subspace
representations are based on “hyper-adaptability.” Therefore, inthis study, we try to identify low dimensional neural subspace
related to the movements in the whole-brain levels as statespace dynamics.
II. AIM OF THE GROUP
The purpose of this research is to examine neuralrepresentation (manifold) related to movement (variability) in
humans. To achieve this purpose, we are going to conduct an
EEG (electroencephalogram) experiment with motor tasks andidentify the low-dimensional representation of EEG activity
during the movements. Especially, we examined theeffectiveness of the methods using the TVGL-based methodand directed graph (AR-based method).

example, a method to calculate a correlation between regions
or identify connectivity based on degrees of phasesynchronization [8]. On the other hand, brain activity or EEG
signals change dynamically, and connectivity between regions
also changes depending on the time. Therefore, it is required todevelop models that consider the dynamic property of thesignals. In a previous study, Time-Varying Graphical Lasso(TVGL) has been proposed and applied to the resting-state
brain activity measured by functional magnetic resonanceimaging [9]. This is an extension of Graphical Lasso, which
considers time-varying components and may capture
dynamical connectivity between brain regions.
Therefore, in the present study, we tried to identify the low
dimensional subspace for EEG data using a method based on
TVGL, which was developed in the previous fiscal year. In thisfiscal year, we continued to test the method for several datasets.
The first dataset was public datasets for the BCI
competition IV [8]. In this dataset, data consists of trials withthe four motor imagery tasks (left-hand, right-hand, foot, andtongue). When we applied the TVGL-based method to thisdataset, the difference between the tasks seemed to be
separated, indicating the possibility of visualization of thestates. In addition, when we applied this to the data with resting
and motor execution, the transitions from two states (from rest
to left-hand task or from rest to right-hand task) were observed(Fig. 1). These results suggest a possibility that time-varying
functional brain networks can be identified by this method.
Next, we also applied this method to data for the reachingexperiment [9]. In this experiment, the subject performed thearm reaching movement for the target. Three types of target
size were prepared and the subject was asked to reach therequired target. Also, this data consists of rest, preparation (cueperiod), and execution period.

III. RESEARCH TOPICS
A. Development of the method to identifying low dimensional
subspace for EEG data using TVGL-based method
First, this study examined the method to identify low
dimensional subspace for the brain networks to understandhyper-adaptability. Specifically, we focused on theElectroencephalogram (EEG) data and examined a method
using graphical models. In general, there are several ways tofind connectivity between brain regions using EEG. For

Fig. 1. Task transitions in the low dimensional states for BCI competition
datasets.

Fig. 3. Two-dimensional (left) and three-dimensional representation of thestates using AR-based models for the reaching data.

IV. FUTURE PERSPECTIVE
Fig. 2. Task transitions in the low dimensional states for the reaching data.

The result applying the TVGL-based method showed that
preparation and execution data were largely overlapped in the
low-dimensional space (Fig. 2) and transitions from rest to
preparation or preparation to execution are not observed clearly.
B. Development of the method to identifying low dimensional
subspace for EEG data using AR-based method
Next, we considered the method using a directed graph. In
this study, we developed an auto-regressive (AR) model
combined with a Kalman filter to identify low-dimensional
space for EEG data.
The abovementioned TVGL-base method is a model togenerate undirected graphs that have bidirectional connections
between two nodes (i.e., two EEG channels). However, it isdifficult to understand how information is transmitted between
the regions. Therefore, here we considered AR models, which
are commonly used to generate a directed graph. We also
adopted the Kalman filter to find multiple weights of the AR
models. After calculating the weight, the distances were
calculated. The distances were used to identify the lowdimensional space using manifold learning.
The result for artificial data confirmed that this AR-based
method can detect time-varying directed connectivity. Then weapplied it to the reaching data described above. In the singletrial analysis, there is a possibility that resting and movement
states can be separated in the low dimensional space (Fig. 3)
while This result depended on the selection of manifold
learning. On the other hand, preparation states are widelydistributed and no clear separation was observed. Because thisresult was obtained from the single-trial analysis, we need tofurther examine the effectiveness of the AR-based method.

In this study, we tried to develop a method to identify the low
dimensional subspace for EEGs in humans. We proposed two
methods by extending TVGL and AR models. The possibilityof the proposed method for identifying low dimensional spacefor real EEG data is suggested. However, there are several
issues to be examined in future works, including a comparisonof the two methods. Therefore, further investigation into thesemethods is required. Also, collaboration with other groups is
expected to deeply understand the low dimensional neuralsubspace and hyper-adaptability．
REFERENCES
[1]
[2]

[3]
[4]

[5]

[6]
[7]
[8]

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.
N. Mizuguchi, S. Uehara, S. Hirose, S. Yamamoto, and E. Naito.
Neuronal substrates underlying performance variability in well-trained
skillful motor task in humans. Neural plasticity, 2016.
R. L. Perri, M. Berchicci, G. Lucci, D. Spinelli, and F. Di Russo. “Whydo we make mistakes? Neurocognitive processes during the preparation–
perception–action cycle and error-detection”. Neuroimage, vol. 113, pp.
320-328, 2015.
H. Yokoyama., I. Nambu., J. Izawa, and Y. Wada. Alpha Phase
Synchronization of Parietal Areas Reflects Switch-Specific Activity
During Mental Rotation: An EEG Study. Frontiers in humanneuroscience, vol. 12, pp. 259, 2018.
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.
M. Tangermann, "Review of the BCI competition IV", Frontiers
Neuroscience, vol. 6, no. 55, pp. 1-31, 2012
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, 2018

B05-05 Development of motor learning model thatcan reuse partial dynamics based on estimation oftransformation between mappings
Yuichi Kobayashi
Shizuoka University, Faculty of Engineering, Department of Mechanical Engineering

Abstract—In this research, we aim to construct a model thatcan explain the reuse of partial control knowledge in humanmotor learning process. We developed a learning model based on
the estimation of partial physical relationships of the target motor
control system and a method for estimating the transformation
between partial relations using the learning method. We assume
that the task is to control the hand position of two arms
with symmetrical kinematics. We proposed a motor learning
framework that can explain the process of reusing partial
information in the controllers of the left arm for recovery of
the right arm controller. Assuming a twin-arm ﬁngertip position
control task with symmetrical kinematics, we proposed a motor
learning framework that can account for the process of reusing
partial information in the controllers of the left and right arms.
It was shown that the coordinate transformation can be obtainedby a distributed computation method.

Joint angle

Hand position
Joint torqueMuscle length

Muscle force

End effector
force

Hand
position

Joint torque

Joint angle

Muscle force

MusclelengthFig. 1. An example of partial physical causality in human upper limb control

I. I NTRODUCTION
Human adaptability includes the ability to adaptively regainfunction by reusing previously acquired neural circuits in theevent of partial dysfunction of the body or brain. For example,
as an example of neural circuit substitution, it has been shownthat when one hand is paralyzed, that hand can be controlled
by a different neural circuit than the normal one [1]. In order
to explain the aspect of ‘reuse of neural circuits,’ which has
not been adequately represented in previous models of humanmotor learning, this study constructs a motor learning modelthat can explain the process of reusing a part of the motor
control model once it has been acquired, depending on thesituation. In this study, we construct a motor learning modelthat can explain the process of reusing parts of a once-acquired
motor control model according to the situation.
In the motor control of the upper limb shown in Fig.1,
the state can be represented by multiple sensor variables(somatosensory information) with different modalities, suchas muscle length, muscle contraction force, joint torque, joint
angle, and hand position. Furthermore, there are several causal(dependency) relationships between these sensor variables,such as kinematics that relates joint angle and hand position,and equations of motion that relate joint angular torque andjoint angular acceleration. In this research, we propose amodel for automatically generating controllers by estimating
dependencies among various sensor signals in motor control
based on past research results [2], and develop a motor
learning model that explains the process of reusing partial
causal relationships in controllers acquired in the past by

Fig. 2. Transformation estimation to discover the mirror image relationshipbetween the left and right arms.

introducing a new mechanism called transformation estimation
between mappings to the model.
II. ACHIEVEMENTS
As an example of motor function recovery through partial
causal transformation, we consider a symmetrical 2-DOF arm
as shown in Fig.2. The joint angles of the left arm and the rightarm are deﬁned symmetrically, with the direction in which
the arm closes being positive and the direction in which it
opens being negative. On the other hand, the hand positions
are measured in a common Cartesian coordinate system. In
this case, the two arms are mirror images of each other, andthe hand positions for the same joint angle values of the left
and right arms are represented by a relation with positive andnegative inversions and offsets.

Fig. 3. Coordinate transformation estimation using adaptive grids.

In these left and right arms, a controller is constructedindependently based on the method proposed in [2]. Oneof the partial physical causal relationships that is acquired
and used in this controller is the relation of inverse differential kinematics (Jacobian inverse). There is a mirror image
relationship between the left and right inverse differential
kinematics (functions), which is represented by the transformation of the input G. In other words, the problem of reusing
the internal information of the controller is formulated as aproblem of estimating the input transformation such that thetwo functions are matched. For the representation of the input
transformations, we use an adaptive grid arrangement method
as shown in Fig.3.
For a grid point position vector, we deﬁne an energyfunction and estimate the transformation such that the function
matches after the input transformation by minimizing theenergy function. To evaluate the agreement of the functions,the energy function is constructed as a sum of terms that
evaluate the agreement of the zero-order and ﬁrst-order local
approximations of the functions, and a term corresponding
to the regularization that evaluates the smoothness of thegrid point arrangement. The minimization of this energyfunction corresponds to the estimation of the desired input
transformation.
As a method for minimizing the energy function, we
implemented the genetic algorithm (GA)[3]. The location
information of the grid point group X is encoded as a gene,and multiple individuals are generated by random numbers.
These individuals are selected based on the evaluation function
E(X), and the selected individuals are crossed and mutatedto search for a better solution. As an example, we tested thematching of functions representing the kinematics of a twodegree-of-freedom manipulator.
Fig.4 shows how a two-dimensional grid starts from a random initial position and eventually ﬁnds the desired function
matching. (a) in the ﬁgure shows the initial conﬁgurationof the grid and the conﬁguration where the ﬁnal matchingshould be done. In such a case where the grids have different
orientations, the continuous variation of the gradient method
alone cannot achieve proper matching because it is trappedin the local solution. It can be seen that the optimizationby the proposed genetic algorithm succeeds in estimating the

Fig. 4. Snapshots of grids for matching acquisition in GA evolution.

coordinate transformations that can perform proper matchingfor coordinate systems that are set in different directions at
the beginning of the search.
III. S UMMARY AND FUTURE PLAN
Aiming at a motor learning model that enables the reuseof the information of motor controllers in the motor learning
process, we proposed a method for estimating the transformation between partial causal relations by matching functions
with an adaptive grid. We proposed a method of mapping
between coordinates using a distributed algorithm with nodes
distributed in a grid, and an implementation method thatsearches for matching between functions while using thesmoothness of the transformation as a regularization term by
an energy function. As one of the implementation methods,
we veriﬁed the optimization using the genetic algorithm andconﬁrmed that the desired transformation can be estimated in
the function matching problem that imitates the kinematics ofleft and right arms in a mirror image relationship.
In addition, we formulated the reuse of information in a
motor controller from the viewpoint of feedback control, i.e.,‘input transformation of a function whose input is a state.’
The construction of a model from such a perspective is also
considered to be a future issue.
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 and Y. Kobayashi, A Grid-Based Estimation of Transformation of Partial Dynamics using Genetic Algorithm for Motor
Learning, Proc. of The 32nd 2021 International Symposium on MicroNanoMechatronics and Human Science, MP2-2-3 (2021)

B05-6 Annual report of research project
Yasuhisa Hasegawa, Tomoya Morita
Dept. of Micro-Nano Mechanical Science and Engineering, Nagoya University

Abstract—The goal of this research is to enable an operator toenhance robotic embodiment through robot operation, which isunder a shared control with an AI robot controller. As a result,the operator could operate skillful manipulation remotely, using
the robot as if it were a part of his or her own body. In thisyear, we constructed an experimental environment to evaluate
effects of intervention of the robot controller with AI on theoperator’s sense of agency under different auditory feedback
conditions and then conducted experiments to evaluate them.
In this environment, an operator sends motion commands toa robot for object handling through an intuitive teleoperation
system with VR devices and then the robot controller adjusts
the operator’s commands to achieve a target task if necessary.
Through experiments, we confirmed that auditory feedback
during robot operation affects the operator’s sense of agency
when manipulation intervention occurs. It was also suggested thatthe simultaneous presentation of operator’s sounds and sounds
around the robot may obscure the judgment criteria related tothe sense of agency.

Fig. 1. Manipulation intervention to maintain or change arm velocity

I. I NTRODUCTION
Shared control is expected to reduce an operator’s workload
and improve safety in task execution with a tele-operated
robot. This enables both real-time autonomous operation, in
which the robot makes its own decisions and actions basedon information obtained from the work site, and intelligent
operation, in which the operator makes advanced cognitive andjudgmental decisions based on the information from the worksite. In addition, in order to ensure the execution of tasks andcollision avoidance with obstacles, shared control is required,
which gives strong authority to the robot side, such as shiftingfrom human operation to autonomous operation by the robot.
On the other hand, the sense of immersion in the workspace
is a major factor in the realization of the task, and further
improvement in operability can be expected by promoting“robotic embodiment,” in which the robot’s motions can beplanned and manipulated as if they were its own physicalmotions. One of the factors that promote this phenomenon is
the sense of agency (the sense of recognition that the motionof the observed object is caused by himself or herself [1]).
However, when manipulation intervention occurs, the sense ofagency is expected to be degraded. Since this degradation is
expected to cause the operator to feel uncomfortable and tolose motivation to work, it is necessary to realize new sharedcontrol that does not degrade the sense of agency.
II. R ESEARCH TOPICS
In this research, we will develop a manipulation interface forteleoperation of a robot arm which enables robot manipulation
with self-agency while maximizing human adaptability without degrading robotic embodiment. Specifically, we will search(a) Operator (1F)(b) HSR (2F)

Fig. 2. Experimental environment

for methods to suppress the cognitive functions that distinguishbetween self and other robot operations, as well as conditions
that promote the adaptability of the robot operator. Based onthese methods, we will develop a manipulation interface. Our
goal is to develop a manipulation interface which enables theoperator to manipulate the robot as if it were a part of his or
her own body, while realizing skillful manipulation throughthe intervention of the robot controller.
III. ACHIEVEMENTS
A. Manipulation intervention using intuitive teleoperation system
In order to investigate the effect of manipulation intervention on the sense of agency during robotic teleoperation, weused an intuitive teleoperation system [2] that we have beendeveloping. This system uses a VR device (HTC Vive Pro
Eye) to remotely control TOYOTA HSR (Human Support
Robot [3]). The position and posture of the operator’s left
hand are synchronized with the tip of HSR arm, and thehead camera posture of HSR is controlled in real time tocorrespond to the operator’s head posture. In addition, bypresenting parallax images to the HMD, the system provides
an immersive teleoperation experience.
A manipulation intervention to maintain or change the arm
tip velocity of HSR and move it to the target position is
introduced in this system. As shown in Fig. 1, when theFig. 3. System configuration for teleoperation between 1st and 2nd floors

distance between the arm tip and the target position becomesless than a certain value, the arm tip moves to the target
position at a constant velocity automatically according to theequation (1).
xarm (n) = xarm (n − 1) + vxarm (ts ) · (1 + a) · ∆ts ,(1)

where xarm is the commanded position of the arm [m], vxarm
is the velocity of the arm in the depth direction [m/s], ts isthe start time of the correction [s], a is the rate of change invelocity, and ∆ts is the time required for one loop at the startof the correction [s].
B. Evaluation of the effect of auditory feedback on the senseof agency
We investigated the effects of manipulation interventions on
the sense of agency under four different auditory feedback
conditions. None: no sound is presented during operation.
Beep: a sound source (monotone) is always played, and thepitch is changed according to the velocity of the controller
in the depth direction. Surround: a microphone mounted on
HSR collects sounds around the robot and plays them back.
Both: both operation sounds and robot peripheral sounds arepresented.
In this experiment, the operator tele-operated the robot on adifferent floor (Fig. 2). The system configuration of the interfloor teleoperation is shown in Fig. 3.
In this experiment, HSR arm tip was remotely controlled
only in the depth direction by seven subjects. The task
was to move the arm from the initial arm position to a
position where it could grasp a pole located 25 cm away in
3 seconds. The conditions with or without correction werechanged randomly. For comparison, we changed a in sevensteps (a = 0, ±0.1, ±0.2, ±0.3) in each auditory feedback
condition.
The evaluation indices were the sense of agency toward arm
manipulation and the awareness of correction. We asked theparticipants to rate on a 5-point scale after each trial whether
their own operations were reflected in the movements of therobot arm. The experimental result is shown in Fig. 4.

Fig. 4. Average of differences in agency ratings in each auditory feedback
condition

In the None condition, the effect of weak manipulationintervention on the sense of agency was small, while in the
Beep/Surround condition, the effect of manipulation intervention was large, and the sense of agency decreased significantlyin the strong intervention condition.
In Both conditions, the decrease in the sense of agency
was greater in the weak intervention condition than in theother conditions, but the decrease in the sense of agency was
suppressed the most in the strong intervention condition. This
result suggests that synchronization of visual and auditory
stimuli and synchronization of motor and auditory stimuli may
obscure the criteria for decreasing the sense of agency.
IV. F UTURE PERSPECTIVE
In this year’s study, we introduced manipulation interventions to maintain or change the arm velocity under different
auditory feedback conditions, and investigated the effectsof these interventions on the sense of agency through theexperiment. The experiment showed that the presentation of
the robot arm movement sound and the controller operation
sound did not suppress the decrease in the sense of agency
caused by the strong intervention, and that the simultaneouspresentation of both sounds may reduce the effect of the strong
intervention.
In the future, we will redesign the type of sound and thepresentation method according to the operator’s operation,
aiming for manipulation interventions that are hardly noticed
by the operator.
R EFERENCES[1] S. Gallagher, “Philosophical conceptions of the self: implications forcognitive science,” Trends in Cognitive Sciences, vol. 4, no. 1, pp. 14–21,
2000.
[2] J. Nakanishi, S. Itadera, T. Aoyama, and Y. Hasegawa, “Towards thedevelopment of an intuitive teleoperation system for human support robot
using a vr device,” Advanced Robotics, vol. 34, no. 19, pp. 1239–1253,
2020.
[3] T. Yamamoto, K. Terada, A. Ochiai, F. Saito, Y. Asahara, and K. Murase,“Development of human support robot as the research platform of adomestic mobile manipulator,” ROBOMECH Journal, vol. 6, no. 1, p. 4,
2019.

B05-7 Systems Engineering Approach for
Understanding Neural Mechanisms of
the Intermittent Control during Human Stance
Taishin Nomura

Graduate School of Engineering Science, Osaka University
Abstract—Beta rebound after execution and/or suppression of
movement has been studied in the context of motor decisionmaking processes. However, fewer study focused on the beta
rebound during postural control in upright stance. Here, we
examined beta rebound of electroencephalogram (EEG) activity
during perturbed upright stance to investigate supraspinalcontributions to postural stabilization. To this end, EEG signals
were acquired from nine healthy young adults in response to abrief support-surface perturbation, together with the center ofpressure, the center of mass and electromyogram (EMG) activities
of ankle muscles. Event-related potentials (ERPs) and eventrelated spectral perturbations were computed from EEG datausing the perturbation-onset as a triggering event. After shortlatency ERPs, our results showed a decrease in high-beta band
oscillations (event-related desynchronization), which was followed
by a significant increase (event-related synchronization) in thesame band, as well as a decrease in theta band oscillations. Unlike
during upper extremity motor tasks, the beta rebound in this case
was initiated before the postural recovery was completed, andsustained for as long as three seconds with small EMG responses
for the first half period, followed by no excessive EMG activities
for the second half period. We speculate that those novel
characteristics of beta rebound might be caused by slow posturaldynamics along a stable manifold of the unstable saddle-type
upright equilibrium of the postural control system without active
feedback control, but with active monitoring of the postural state,in the framework of the intermittent control.

I. INTRODUCTION
In this study, we examined Event-Related Spectral
Perturbations (ERSPs) during the long-lasting postural recovery
process of upright equilibrium in response to small impulsive
step-like support-surface perturbations. Particular interest was toinvestigate the timing and the duration of beta band EventRelated Desynchronization (ERD) and Event-related
Synchronization (ERS) responses, and to quantify them.
Because a postural recovery process from a perturbed posture to
the upright equilibrium is transient, and it settles down
eventually to the post-recovery equilibrium state, the postrecovery state could be regarded as a status quo for postural
maintenance. Therefore, similarly to the studies for upper limbs(e.g., Engel and Fries 2010), we hypothesized that beta ERD
during the recovery response and the subsequent post-movement(post-recovery) beta rebound (beta ERS) would be present in the
EEG cortical activities following upright stance perturbations. If

we could observe beta ERD and beta rebound as hypothesized,
the next objective would be to clarify in the temporal profiles of
the appearance of beta activities within the long-lasting
biomechanical postural recovery response. In the simplest
possible situation, beta ERD would appear persistently during
the postural response while the muscles are active, and then abeta rebound would appear after the postural response iscompleted, i.e., as a post-movement rebound after a few seconds
required for the upright posture to be fully recovered. This
scenario is based on beta ERD and ERS during upper extremity
tasks (Engel and Fries 2010), with possible variations in timeintervals due to the longer-lasting postural response. However,
if we observe beta ERD and ERS with qualitative differences in
temporal characterizations from those during upper extremity
tasks, they could lead to shedding new light on mechanistic
causes of the beta rebound. Indeed, the final phase of the postural
recovery has a particular meaning for the intermittent control
hypothesis that has received attention in recent years (Bottaro etal. 2008; Asai et al. 2009; Suzuki et al. 2020). That is, the final
phase of postural recovery takes place with a small postural tilt,
during which, according to the intermittent control hypothesis,
the active feedback control is switched off, and the postural state
point approaches slowly the upright posture along a stablemanifold of the unstable saddle-type upright equilibrium in thestate space of postural control system. Because the switchingaction of the active feedback control, such as deciding tomaintain the switch off or to turn the switch on, would involve
active information processing, we expected to find corticalactivities associated with it.
II. AIM OF THE GROUP
According to the intermittent control hypothesis, the central
nervous system (CNS) inactivates (switches OFF) and activates(switches ON) the active contraction of medial gastrocnemius
at a sequence of appropriate timings that depend on the state of
posture (i.e., the tilt angle and angular velocity of the standing
posture) as somatosensory information that is conveyed to thesupraspinal brain with a large transmission delay time. That is,
the active postural feedback control is switched OFF and ON
alternately in accordance with time-delayed sensory
information. In the intermittent control model, a timing ofswitch OFF the activation of gastrocnemius plays a key role forthe postural stabilization. This seemingly paradoxical property
allows the model to exhibit postural fluctuation with a long-range correlation. In other words, the core hypothesis of theintermittent control, in terms of its neural mechanisms, is an
automated selection either activation (Go) or inactivation(NoGo) of the neural circuitry in the brainstem that innervatesthe medial gastrocnemius, depending on the mechanical state of
upright posture. The cortico-basal ganglia loop is a coremechanism for such information processing, regulating activityof direct (corresponding to Go signal) and indirect(corresponding to NoGo signal) pathways.
The goal of 2021 was to characterize the beta rebound (beta
ERS) that appears in response to a support surface perturbation
during quiet stance. Beta rebound has been known for motor
control of upper extremities, which appears after motor
execution or suspension of motor execution (NoGo response in
Go/NoGo tasks). Here, we hypothesize that the beta ERS forthe postural recovery reflects a neural process for selecting
either activation (Go) or inactivation (NoGo) of medial
gastrocnemius along with active monitoring of the latest phaseof the postural recovery, which might correspond to aprocessing of reafferent sensory motor information.

neurophysiological interpretation with the long-lasting highbeta ERS (beta rebound) at the late phase of the postural
recovery, in conjunction with the intermittent control model.
Specifically, we speculate that the long-lasting high-beta ERS
identified in this study, together with the theta ERD, areassociated with the long-lasting neural expression of the bodyFig. 1. Postural responses to the support-surface
perturbation. (A) CoM position, (B) ERSP, (C) ITC, and(D) ERP at Cz electrode in resp.

III. RESEARCH TOPICS
A. The long-lasting beta rebound in the late phase of Postural
Recovery in Response to a support-surface perturbation
Standing posture, electromyography (EMG), and EEG
signals were measured under the following two conditions: quiet
standing (control) and support-surface perturbation (perturbed).
Postural sway during quiet stance in the control condition wasmeasured to determine the equilibrium posture withaccompanied EEG signals (baseline EEG) to be compared withpostural recovery process, particularly in the period a few
seconds prior to each perturbation. We showed two novel eventrelated clusters of powers in ERSP for latencies longer than 1.0s, which were the main findings of this study. One was the ERS
at high-beta band (high-beta ERS, beta rebound), whichappeared after the high-beta ERD (See Fig. 1). The high-beta
ERS sustained over a relatively long duration with its peaklatency located at about 3 s. The high-beta ERS was observed
mainly at Cz electrode. The averaged powers across participantsat the high-beta band in the latency of 1<t<4 s were significantly
greater than the resting potential, i.e., the beta rebound sustained
for as long as three seconds. During the appearance of the highbeta ERS, the CoP and the CoM became close to each other inthe very late phase of the recovery process. Comparison between
ERSP and EMGs showed that activations of SO and MG
muscles had become small compared to those of short latency(<0.3 s), but they were still slightly larger than those during quiet
stance for the early period (1<t<2.5 s) of the high-beta ERS.
However, activations of all muscles were the same as thoseduring quiet stance for the later phase (2.5 <t<4 s) of the highbeta ERS. That is, the high-beta ERS in its later phase was
generated with no perturbation-induced excessive muscular
activations.
Neural information processing to accomplish the postural
recovery observed in this study can be interpreted according to
the neuroanatomical model of higher-order regulation of
postural control (Takakusaki et al, 2017). We associate such aschema of postural verticality at S1 and the posteroparietal
cortex and a process of matching between the body schema andreafference of the postural state, where they are required to belong-lasting due to slow mechanical dynamics of postural
recovery, perhaps along the stable manifold of the unstablesaddle-type upright equilibrium point. That is, those circuitries
might fulfill a role of active monitoring (including matching
between the efference copy and the afference information)
ongoingly, either with (middle phase of the recovery after thevestibular-spinal reflex) or without (late phase of the recovery)
EMG outputs, in order to be always ready for switching ON or
OFF the intermittent feedback controller.
IV. FUTURE PERSPECTIVE
It is expected that a similar beta rebound could be observed
during quiet stance, because the intermittent control hypothesis
claims that the active feedback is switched OFF during “micro
postural recovery” from every “micro-fall” of postural sway.
REFERENCES
[1]
[2]
[3]

[4]

[5]

Engel, A. K., and Fries, P. (2010). Beta-band oscillations—signalling thestatus quo? Curr. Opin. Neurobiol. 20, 156–165.
Bottaro, A., Yasutake, Y., Nomura, T., Casadio, M., and Morasso, P.
(2008). Bounded stability of the quiet standing posture: An intermittent
control model. Hum. Mov. Sci. 27, 473–495.
Asai, Y., Tasaka, Y., Nomura, K., Nomura, T., Casadio, M., and Morasso,
P. (2009). A Model of Postural Control in Quiet Standing: Robust
Compensation of Delay-Induced Instability Using Intermittent Activationof Feedback Control. PLOS ONE 4, e6169.
Suzuki, Y., Nakamura, A., Milosevic, M., Nomura, K., Tanahashi, T.,
Endo, T., et al. (2020). Postural instability via a loss of intermittent control
in elderly and patients with Parkinson’s disease: A model-based and datadriven approach. Chaos Interdiscip. J. Nonlinear Sci. 30, 113140.
Takakusaki K. (2017). Functional Neuroanatomy for Posture and Gait
Control. J. Mov. Disord. 10, 1-17.

Annual report of research project B05-8
Kazuhiro Sakamoto
Faculty of Medicine Tohoku Medial and Pharmaceutical University, Tohoku University Graduate School of Medicine
Abstract—The real world is an indefinite environment inwhich the probability space is not specified in advance. Here, wedeveloped a learning model in which the state space is expanded
so that it refers to the arbitrary length of previous states, basedon two criteria: experience saturation and decision uniqueness ofaction selection. The model was tested by a behavioral task called
a two-target search task, and compared with ordinary infinite
hidden Markov models (iHMMs). In contrast to the iHMMs, ourmodel performed well by generating only necessary states withhigh reproducibility. The proposed model will serve as a basis
hyper-adaptability to an indefinite environment by using thesecriteria defining the appropriateness of state expansion.

I. INTRODUCTION
Uncertainty is classified into two types. The first is where thestate of the environment is defined and fixed, and we canutilize this prior knowledge. The other is the case where eventhe state space is neither given nor hypothesized in advance.
The latter type is defined as an indefinite environment, andadaptation to such an environment is a critical issue for living
systems.
Reinforcement learning is a form of learning in which theagent learns to take an action in an uncertain environment
based on the state at the previous time-step to maximize thecumulative reward. However, the current state of functions asprior knowledge in conventional reinforcement learningmodels. Therefore, they may not achieve high learning
performance in indefinite environments.
II. AIM OF THIS RESEARCH
Here, we propose a reinforcement learning model with adynamic state space, and tested its performance by a twotarget search taskm previously used in a physiologicalexperiment with monkeys (Fig.1) [1,2]. Subjects wererequired to gaze at one spot from among four identical stimuli.
If the correct spot (designated by green in Fig. 1) was selected,
a reward was delivered. After training, the subjects learned tosaccade alternately to two targets in a valid pair, and received
a reward for several correct trials in a row (the exploitation
phase). If the valid pair was changed without instruction, they
started searching for a new valid pair after making errors (theexploration phase). In this task, by simply hypothesizing thatthe previous state is the previous trial, the agent cannot
maximize the total reward. To do so, the agent must consider
the two previous trials together as the previous state by itself.
III. RESEARCH TOPICS
Our proposed model is given no prior knowledge of the task
structure other than the action of gazing at one of the four

spots. Instead, it starts learning using the immediately
preceding trial as the starting state, and expands and contracts
the state space in the direction of previous trials based on thecriteria of experience saturation and the decision uniquenessof the action selection (Fig. 2). These two criteria evaluate themagnitude of the change in Q-value with experience, and how
close the Q-table is to a unique behavioral decision,respectively.
N-2th trial

N-1thcorrect

correct

Nth

N+1th

N+2th

N+3th

N+4th

…
…

N+5th

correcterror
error
correct correct correct
Pair switch<------Exploitation phase-------> <-------Exploration phase--------> <--Exploitation phase--

2nd trial in exploration phaseFig. 1. Overview of the two-target search task. Schematic of several trials
before and after a valid pair change. The pair change triggers the transition
from the exploitation phase to the exploration phase. Dashed lines, empty
arrows and green spots denote valid pairs, gazes and correct targets,
respectively. Note that the subjects were not instructed to move their eyes by
the green spot before gaze shift.

AB

rt-1 o?,t-1

Start
Step1
Update Q-table and Update_KLD

Working memory

Step2

Environment

Agento?,t-1o?,t-2..
Dynamic
sate

s1 … si

Qj->i

If Update_KLD < ζ
… sn

sj->i

new state
Experience saturation
Decision uniquenessAction selection
at

false

Step3
true
Calculate Decision Uniqueness_KLD(D_KLD)
Step4
If D_KLD < η

true

false
Step5
false
If D_KLD < Parent D_KLD + bias
Step6
true
Save the D_KLD as Parent D_KLD
Step7
Expand state space
Step6’
Prune the current stateEnd

Fig. 2. Schematics of the proposed model. (A) Our dynamic state scheme
hypothesizes that the agent receives only partial information from theenvironment. However, unlike POMDP, these observations are temporarily
stored in working memory and serve to generate a new state not prepared apriori, based on the two criteria experience saturation and decision uniqueness.
(B) Expansion and contraction of the state space. Flowchart of the expansion
and contraction process.

0% 1

IV. FUTURE PERSPECTIVE
One trial of the two-target search task includes multiple task
events. Currently, we are developing a learning model that canlearn appropriate actions for each task event as well as decision
making based on the history of previous trials, and aresubmitting the first report. We are also developing a model thatcan learn delayed response tasks and can perform tasks that arecharacteristic of the frontal lobe, especially the premotor andsupplementary motor areas. These preliminary results will bepresented at next year's conference. Through developing thismodel, we want to pioneer a new field of computational
higher-order dysfunction studies in the future.

Trial No

105

Dirichlet process

Trial No

105

E

No of States
Trial No

105

C

1

0

1

Trial No

105

0Trial No

105

F

8000

Count of Pair Switch

8000

0

0% 1
800

No of States
1

Count of Pair Switch

We also performed a time-frequency analysis of local fieldpotential (LFP) recorded from the monkey lateral prefrontal
cortex (lPFC) during the execution of a shape manipulation
task. In the ventral part of the lPFC, θ-δ waves during thedelay period changed in response to the transformed shape tobe temporarily memorized. On the other hand, in the dorsal
part, the γ wave in the shape presentation periods changed in
response to the action-manipulation correspondence thatswitched every few dozen trials. These results suggest that thelPFC is functionally differentiated between ventral and dorsal
parts, and that each region dynamically recruits informationnecessary for task execution via LFPs in different frequency
bands [5].

0

D

100%

B

80

These observations indicates that the two criteria included in
the proposed model, decision uniqueness and experiencesaturation define the purpose of state expansion and the timingof state expansion, respectively. The proposed model is
expected to be a basis of learning models that can adapt to anindefinite environment by including these criteria defining theappropriateness of state expansion.

Proposed model

Correct Response Rate

Furthermore, we compared the proposed model with infinite
hidden Markov models (iHMMs) that dynamically generatestates without prior knowledge by hierarchically using a
Dirichlet process (Fig. 3D-F). In contrast to our model, thesemodels generated too many states unnecessary to perform thebehavioral task tested, and showed much less reproducibility.

In addition to above topic, "a prototype of a neural-net
reinforcement learning model for learning one-target search
Tasks" and "a meta-analysis of cognitive behavioral tasks inleptin and leptin receptor-deficient mice" reported last year
were published in [3] and [4], respectively.

A

100%

Correct Response Rate

The model not only achieved high performances (Fig. 3A),
appropriate number of states (Fig.3B) and large number ofvalid pair switches (Fig. 3C) with a high reproducibility, whichwere comparable to the ideal model given prior knowledge ofthe task structure (data not shown), but also performed well ona task that was not envisioned when the models weredeveloped (data not shown).

1

Trial No

105

Fig. 3. Comparison of the proposed model and the iHMM in terms of the
reproducibility of two-target search task learning. (A–C) Time courses of thecorrect response rate (A), increase in number of states (B), and increase in the
cumulative number of target pair-switches (C) exhibited by the proposedmodel. (D–F) Identical plots of the Dirichlet process version of the iHMM.
Black arrows indicate the same calculation of poor performance.

REFERENCES
[1]

[2]
[3]

[4]
[5]

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.
K. Sakamoto, “The golden mean in dynamic reinforcement learning,”
The 31th Annual Conference of the Japanese Neural Network Society,P2-08, September, 2021.
K. Sakamoto, H. Okuzaki, A. Sato, H. Mushiake, “Experience resetting
in reinforcement learning facilitates exploration-exploitation transitions
during a behavioral task for primates,” bioRxiv 10.1101/2021.09.30.
462676, September 2021.
T. Watanabe, K. Sakamoto, "Meta-analysis of cognitive and behavioral
tests in leptin- and leptin receptor-deficient mice," Neurosci. Res., Vol.
170, pp. 217-325 September 2021.
K. Sakamoto, N. Kawaguchi, H. Mushiake, “Multidimensional Analysis
of Task Relevance of Theta Oscillations in Monkey Lateral Prefrontal
Cortex during Shape Manipulation Task,” The 44th Annual Meeting of
the Japan Neuroscience Society, 3P-095, July, 2021.

B05-9 Annual report of research project
Takeshi Sakurada

College of Science and Engineering, Ritsumeikan University
Abstract—The research project B05-9 aims to establish tailormade EEG-based neurofeedback (NF) training system toimprove motor and cognitive functions by quantifying an
individual’s ability to control attention using brain activity.
Furthermore, we try to demonstrate that a protocol considering
individual cognitive traits contributes to the facilitation of NF
training efficacy. The achievements in this year are as follows: 1)
Our NF system successfully modulated somatosensory area
activity through training over multiple days. Especially, we foundsuch marked neuromodulation in individuals who had a higher
cognitive ability for directing their attention to body movements.
2) With the neuromodulations in the somatosensory area,
movement accuracy also improved during a rhythmic hand
movement task. The current results suggested that tailor-madeprotocol is helpful to overcome individual differences in NF
training efficacy.

I. INTRODUCTION
In the previous academic year, we developed a noiselessvibrotactile stimulator for eliciting steady-state somatosensory
evoked potentials in NF training and implemented a real-time
estimation method of the individual attentional state. In thisacademic year, in order to demonstrate the usefulness of the
NF training system, we evaluated neural activity change (i.e.,
neuromodulation) reflecting attention function improvement,
and the effect of neuromodulation on motor function. Here, toachieve the improvement of the attention function, it isnecessary to overcome the problem that NF training efficacy
widely varies among individuals [1]. In this study, we focusedon the individual differences in the ability to direct attention tobody movements [2]-[4] as a factor that influence the NF
training efficacy. By clarifying the relationship between
individual differences in cognitive function and NF training
efficacy, we try to propose a new training protocol thatappropriately sets the goal of neural circuits to be reconstructed for each individual.
II. AIM OF THE GROUP
The present research aims to promote the reconstruction of
an individual's neural circuit for attention control and facilitate
the improvement of motor and cognitive functions throughtailor-made NF training that shows the individual's estimatedattention state in real-time. Furthermore, we aimed to identify
the factor (i.e., explanatory variable) that determines theindividual NF training efficacy, and to proposed a prediction
model of NF training efficacy based on individual differencesin the brain function characteristics. In this way, we try toindividually realize the maximum improvement of brain
function by a tailor-made protocol instead of a uniform
protocol.

III. RESEARCH TOPICS
A. Individual differences in somatosensory responses in the
proposed neurofeedback training
We used steady-state somatosensory evoked potentials(SSSEP) and steady-state visual evoked potentials (SSVEP)
observed from the somatosensory and visual cortices toestimate individual attentional state during NF training. Here,
we mainly report the findings regarding SSVEP responses.
We randomly assigned the participants to Real or Shamgroups and the participants performed NF training seven days
in total. Individuals in the Real group were received actual
attentional state feedback based on their own SSSEP responses,
and those in the Sham group were received dummy feedback
based on the prerecorded SSSEP responses of another person,
respectively. Electrodes for recording EEG were placed in thebilateral somatosensory area, and we measured SSSEP while
presenting vibrotactile stimuli (Stimulus frequencies were
22Hz on left fingertips and 25Hz on right fingertips,
respectively). For raw EEG data, we calculated frequency
spectrum density by a fast Fourier transform applying to thebuffered (5s) and filtered EEG signals. Then, the participant’s
attentional state (i.e., the strength of attention to vibrotactile
stimuli) was quantified by calculating the signal-to-noise ratioof SSSEP. Speakers to present sounds reflecting the attentional
state were placed in front of the participants, and the value ofthe signal-to-noise ratio of SSSEP was converted into the
speaker volume. Note that, the strength of the attention to theleft vibrotactile stimuli on the left hand was reflected as thevolume of the left speaker, and that to the right hand wasreflected as the volume of the right speaker, respectively. We
required the participants to direct their attention to the left
vibrotactile stimuli. In addition, the participants were also
explained when the participants properly directed theirattention to the left stimuli, the volume from the left speaker
would be loud. If the proposed NF training system can improve
the attention ability, it is expected that the SSSEP response in
the right somatosensory area will be increased during thetraining process.
Figure 1 shows typical frequency spectrums in the bilateralsomatosensory areas (Upper panels: left somatosensory area,
Lower panels: right somatosensory area). The left, center, andright columns show an individual with higher attention ability
to body movements (Real group), an individual with lower
attention ability to body movements (Real group), and an
individual in the Sham group, respectively. Large individual
differences were observed in SSSEP response and its
modulation. Specifically, in the individual with high attention
ability, the 22Hz SSSEP response in the right somatosensory

area was increased as expected. By contrast, in the left
somatosensory area, decreased 25z SSSEP response wasobserved (Fig. 1a). On the other hand, the individual with low
attention ability and that in the Sham group did not realize
SSSEP modulation corresponding to the NF training purpose(Figs. 1bc). Based on the frequency spectrum shown in Fig. 1,
Fig. 2 shows the changes in the signal-to-noise ratio of eachindividual.

training minus before training). In the individual whose SSSEP
response in the right somatosensory area was increased by NF
training (individual corresponding to Fig. 1a), the amplitude
variability reduced only in the left hand to be trained. On theother hand, for individuals who did not achieve the desired
SSSEP responses (individuals corresponding to Figs. 1bc), nochange in movement accuracy corresponding to the attention
training to the left hand was observed.

Fig. 1 Frequency spectrum of the SSSEP

Fig. 2 Changes in SSSEP response in NF training

B. NF training effect on motor function
To evaluate the effect of improvement of attention ability
by NF training on motor function, we prepared rhythmic andreciprocal reaching movement task before and after NF training.
Participants held a digitizing pen on a drawing tablet with theirright hand and were instructed to reciprocate the pen
continuously on the longitudinal direction for 60s according to
the sound presented at a constant rhythm (1Hz). Furthermore,they were also required to keep their movement amplitude
constant. The participants performed the rhythmic reachingtask with both the left and right hands, and the amplitude
variability was evaluated for each hand. If the sensory
sensitivity of the left hand is strengthened as the attention
ability is improved by NF training, it is expected that theparticipants can easily maintain the movement amplitude, andas a result, the variability of movement amplitude becomes
small (i.e., movement accuracy is improved).
Figure 3 shows the difference of movement amplitude
variabilities evaluated before and after the NF training (afterFig. 3 Motor variability before and after NF training

IV. FUTURE PERSPECTIVE
In B05-9, we evaluated the SSSEP modulation in thesomatosensory area associated with NF training and the effectof neuromodulation on motor function. As a result, we
confirmed that individuals with higher attention ability to body
movements were more likely to obtain the NF training efficacy.
These findings indicate that the proposed NF training system
may be useful for improving not only cognitive function but
also motor function. Furthermore, we suggest the importanceof a tailor-made training protocol that considers individual
brain characteristics rather than traditional protocol withuniform training goals. However, we have not proposed a
prediction model of NF training efficacy based on individual
differences in brain function characteristics. In the future, we
aim to propose a model for the purpose of maximizing the NF
training efficacy by collecting further experimental data.
REFERENCES
[1]
[2]

[3]

[4]

K.C. Kadosh, and G. Staunton, “A systematic review of thepsychological factors that influence neurofeedback learning outcomes,”
NeuroImage, vol. 185, pp. 545-555, 2019.
T. Sakurada, M. Hirai, and E. Watanabe, “Optimization of a motor
learning attention directing strategy based on an individual’s motor
imagery ability,” Experimental Brain Research, 234(1), pp.301-311,
2016.
T. Sakurada, T. Nakajima, M. Morita, M. Hirai, E. Watanabe,“Improved motor peformance in patients with acute stroke using the
optimal individual attentional strategy,” Scientific Reports, Vol.7, 40592,
2017.
T. Sakurada, M. Yoshida, and K. Nagai, “Individual optimal attentional
strategy in motor learning tasks characterized by steady-statesomatosensory and visual evoked potentials,” Frontiers in Human
Neuroscience, 15: 784292, 2022.

B05-10: Analysis of the effect of the successive illusory

movement stimuli of the self-body for the adaptation ability
Tetsunari Inamura

National Institute of Informatics / The Graduate University for Advanced Studies (SOKENDAI)
Abstract— This study focuses on the optimal rehabilitation
strategy for generating illusory visual stimuli in situations where
the difference between the actual motion and the illusory-induced
visual stimuli varies step by step over time. I discuss how to
generate the illusory visual stimuli that have an intervention on
proprioceptive sense, taking into account the learning effect on
stimulus variability.

I. INTRODUCTION
One of the problems in rehabilitation using visual stimuli
that induce motion illusions (e.g., [1]) is that there are no clear
criteria for judging how much difference should beincorporated into the images when creating illusion images thatdiffer from reality. A significant induction effect can be
expected if the difference is large, but it may adversely affectthe sense of motion subjectivity and body possession. On theother hand, if the difference is slight, the sense of agency andbody ownership are maintained, but the induction effect is not
obtained.
In addition, it would be effective to dynamically vary the
degree of difference in the illusory stimuli according to thesituation, rather than just presenting the same degree ofdifference during the whole rehabilitation period. However,there is no specific adaptive model to determine the specificvariation strategy.
II. AIM OF THE STUDYThis study focuses on the optimal strategy for generating
illusory visual stimuli in a situation in which the difference
between the actual motion and the illusion-induced visual
stimuli is varied step by step over time.
This study aims to identify a suitable strategy for generating
illusory visual stimuli that can maintain a large amount ofintervention on the proprioceptive sense without adversely
affecting the sense of agency and body ownershipIII. METHODAs shown in Fig. 1, two targets are displayed on the desk inthe VR environment. The participants are asked to slide thetargets back and forth on the desk according to a metronomethat sounds at 1 Hz. After 90 seconds, the screen goes dark tohave a 20-second break. In the break time, participants are
instructed to touch the target with their left hand, and the righthand is required to be placed symmetrically to the left hand.
Oculus Quest 2 is used as a device to present the VR
environment. The position and the bending angle of fingers
are measured, and the results are used to visualize the virtual
hand of the left hand. The virtual hand of the left hand isdisplayed only during the left-right reciprocating motion, andthe virtual hand is not shown during the 20-second break formeasuring the proprioceptive sense. The virtual hand of theright hand is not displayed at all times. Here, during the leftright reciprocating motion, the actually measured forearm
angle θreal is exaggerated and adapted to the virtual hand angle
θvirtual, as shown in the right side of Figure 1.
The 90-second session is repeated 11 times. The
exaggeration coefficients are varied for each session, as shownin Table 1. Two conditions were set up: Group A, in which thecoefficients fluctuated from frequent to infrequent changes,
and Group B, in which the coefficients changed from
infrequent to frequent changes. At the end of 11 sessions, thesubjects were asked to answer a questionnaire regarding theload on the VR experience based on VRNQ[2], the sense ofagency, and the sense of body ownership.
sessionGroupAGroupB

Table 1: Exaggeration coefficiences through the experiment
1
1.0
1.0

2
2.0
2.0

3
2.0
0.5

4
2.0
2.0

5
0.5
0.5

6
0.5
2.0

7
0.5
0.5

8
2.0
2.0

9
0.5
2.0

10
2.0
2.0

11
1.0
1.0

IV. EVALUATION AND DISCUSSION

Fig.1: VR environment. The participants are asked to move theirhand between two targets.

The following three parameters were used as indicators to
express the subject's adaptive ability. 1) The drift of the
proprioceptive sense of the left-hand position D(t) (the amountof drift between the target displayed in the VR and the actual
position of the left hand.) 2) The drift of the proprioceptive
sense of the right-hand position, D'(t) (the difference between
the position of the target displayed for the left hand and theactual position of the right hand). 3) Adaptive inertia G(t)(cumulative difference between the position of the target andthe position of the virtual hand at the moment the sound isplayed, i.e. at the moment when the target should be touched,during the first 5 seconds of each session.)
To exclude the learning effect that occurs throughout asession, I also considered the change in the exaggeration
coefficient in each session, Cdiff(t), and the total amount ofchange in the exaggeration coefficient experienced up to thatsession, Cacc(t). The relationship between Cdiff(t), the amountof drift change, Cdiff(t), and Cacc(t) was investigated. Figure 2
shows the results for D(t). Group A consisted of two subjects,
and Group B consisted of three subjects.

In Group A, D(t) for the first half decreased 3.94 degree tothe latter half. In Group B, D(t) for the first half decreased
only 1.39 degrees to the latter half. The results suggest that the
subjects in Group A were strongly affected on the
proprioceptive sensation in the latter half of the session evenlearning effect is strong.
Although the small number of subjects does not allow forstatistical discussion, the general trend suggests that Group A
has a stronger ability to influence the amount of drift change,even when the learning effect is taken into account. In otherwords, starting from a situation in which the exaggeration
coefficient fluctuates moderately, as in Group A, suggests thatthe degree of intervention on the proprioceptive sense isstronger than starting from a situation in which theexaggeration coefficient fluctuates violently, as in Group B.
Although comparisons of individual differences must be made,when intervening with visual illusions using VR, it issuggested that the difference between the actual and thedifference should be allowed to fluctuate gently and that
presenting a large change in the difference will weaken theimpact of the intervention.
Table 3: Drift of the proprioceptive sense at the first and latter half of thesessionFig. 2 : Drift of the proprioceptive sense for each session

Cdiff(t) and Cacc(t) for groups A and B are as follows. If thecoefficient changes from 0.5 to 2.0 from session t to t+1, it isinterpreted as a two-step change, and Cdiff(t) is set to 2. If thecoefficient changes from 1.0 to 0.5 or from 2.0 to 1.0, it isinterpreted as a one-step change and Cdiff(t) is set to 1. Cacc(t)is the accumulation of Cdiff(t) experienced up to time t.
Table 2: Change of the drift D(t), change of the coefficience Cdiff(t), andaccumulation of Cdiff(t), that is Cacc(t)sessionD(t) A
D(t) B
Cdiff(t)A
Cacc(t)A
Cdiff(t)B
Cacc(t)B

1

2

3

4

5

6

7

8

9

10

1
1
1
1

0
1
2
3

0
1
2
5

2
3
2
7

0
3
2
9

0
3
2
11

2
5
2
13

2
7
0
13

2
9
0
13

1
10
1
14

2.19
6.5

2.46
9.76

5.41
2.5

4.67
1.39

1.95
3.11

3.12
2.24

4.76
1.52

2.9
1.26

0.43
2.82

3.71
3.25

We can see that similar combinations of Cdiff(t) and Cacc(t)values appear in the first and latter half of the session forGroup A and Group B as shown by the gray area in Table 2.
Therefore, by examining D(t) at these two areas, we cancancel out the learning effect where the drift does not changewith time and make a comparison. The following table shows
the results of the average drift values of both groups A and B
in the first half and the latter half.

Group AGroup BFirst half (t: 3～5)
12.03
6.99

Latter half (t:7～9)
8.09
5.60

V. FUTURE PERSPECTIVE
Because of the delay in planning the subject experiments
due to the COVID-19 disaster, only preliminary experimental
results are reported in this document.
Thus, after increasing the number of subjects, I plan to
conduct a comprehensive analysis that considers adaptive
inertia, sense of agency and body ownership. I also plan toanalyze the process of visual experience and the variation ofadaptive capacity in VR.
REFERENCES
[1]

[2]

M. Okawada, F. Kaneko, K. Shindo, M. Yoneta, K. Sakai, K. Okuyama,
K. Akaboshi, and M. Liu. "Kinesthetic Illusion Induced by Visual
Stimulation Influences Sensorimotor Event-Related Desynchronizationin Stroke Patients with Severe Upper-Limb Paralysis: A Pilot Study."
Restorative Neurology and Neuroscience 38 (6): 455–65, 2021.
P. Kurtesis, S. Collina, L. A. A. Doumas, and S. E. MacPherson. 2019.
"Validation of the Virtual Reality Neuroscience Questionnaire:
Maximum Duration of Immersive Virtual Reality Sessions Without the
Presence of Pertinent Adverse Symptomatology." Frontiers in Human
Neuroscience 13: 417.

B05-11. Developmental hyper-adaptability of
sensorimotor dynamics under rapid growth
Hoshinori Kanazawa

The Graduate School of Information Science Technology, The University of Tokyo,

Abstract— From early developmental phase, human infants
exhibit complex and various spontaneous whole-body movements.
It is often assumed that sensorimotor experiences evoked by suchkinds of spontaneous movements have an essential role fordevelopment of sensorimotor coordination. In the same phase,they also exhibit acute physical growth, which would affectsensorimotor interactions. Although developmental changes ofmotor patterns have been well characterized, how a humaninfant develops their sensorimotor coordination in the midst of
drastic changes remains unclear. Here, we conductedmusculoskeletal dynamic simulation to understand how
maximum muscle power and joint angle restriction affect tosensorimotor interaction during infantile spontaneous movement.
Furthermore, we develop the soft skin and uterine model tosimulate the physical interaction in twins.

I. INTRODUCTION
It has been proposed that the mutual dynamics among
brain-body-environment, induced by sensorimotor experienceduring early infancy, contributes to the development of humancognition and behavior in later life. While such a concept has
been proposed for decades, it has not been concretely verified
how sensory input and motor output are utilized for behavior
acquisition in the early developmental stage, when the brain,
body, and environment all show rapid and drastic changes.
In this project (B05-11), we aim to deepen theunderstanding of the mechanisms of developmental behavioral
changes with rapid physical growth of the musculoskeletalbody.
In FY2021, we conducted simulations of infantile
spontaneous movement assuming for preterm infants withmuscle weakness, hypertonia, and joint angle restriction toconfirm the effects on the sensorimotor information structure.
In addition, we developed the infant musculoskeletal modelwith soft tissues for the implementation of developmental
simulation.
II. AIM OF THE GROUP
From early developmental phase, human infants exhibit
complex and various spontaneous whole-body movements. It
is often assumed that sensorimotor experiences evoked bysuch kinds of spontaneous movements have an essential role
for maturation of sensorimotor modules. Although motor
output patterns have been well characterized, whether and how
a human infant acquires and augments these types of motor

modules remains unclear. In addition, it is still unclear how
neuronal disorders affects spontaneous movements andsensorimotor information structures in early development. In
this project, we simulated the spontaneous movement using
infantile musculoskeletal model and neural oscillators to makeclear how muscle power and joint restriction affects
sensorimotor information structures.
In this study, we investigate the developmental mechanism
by combining measurements from experiments with actual
infants and from physical dynamic simulations.
III. RESEARCH TOPICS
A. Sensorimotor information structure and neuraldisorder[1,2]Infants with neuronal injury, preterm infants, and low-birthweight infants sometimes have muscle weakness or joint angle
restriction, which would affect sensorimotor experiences
through early spontaneous movements. From preventive
perspective, relaxations, passive joint movements, or stretches
are provided in clinical care. However, it is unclear how suchkinds of muscle abnormality and joint angle restriction during
the early infancy affect the sensorimotor experience. Here, we
examined the effects of muscle weakness and strengthening
and joint angle restriction on sensorimotor information
structure using musculoskeletal simulation.
First, we generated infantile whole-body spontaneous
movements with neural oscillators applying α-motoneurons
activities to each muscle of infantile musculoskeletal bodymodel (gestational age: 32 weeks, number of skeletal muscles:
390, number of body parts: 21, number of joints: 20, number ofdegrees of freedom of joints: 36). Second, we examined theeffect of increasing or decreasing the maximum muscle force
for sensorimotor information structure. The standard muscle
force (100%) was estimated based on the physiological crosssectional area (6.3 kg/cm2). The muscle force ratio wasincreased or decreased every 10% from 10% to 200%
(Experiment (i)). Third, we examined the effect of joint angle
restriction for sensorimotor information structure. The standard
joint angle (100%) was defined based on the infant data. The
joint angle was restricted every 2% from 0% to 40%(Experiment (ii)).
We calculated the correlation coefficients among muscle
activities and proprioceptive inputs during spontaneous
movement simulations for each of the conditions (Experiment(i) and (ii)). Then, we estimated the number of coordinatedmuscle modules using an infinite relation model. Finally, we
confirm how muscle power and joint restriction affects thenumber of coordinated muscle modules by approximated withcubic function.

B. Development of the soft skin and uterine model[3]The spontaneous movement simulations conducted in
Section A employed a rigid skin model, which may notadequately reproduce the tactile sensory inputs of real fetuses
and infants with soft skin. Therefore, we developed a flexible
skin model and realistic 3D uterine model to simulate infantile
sensorimotor interaction in a stretchy soft skin or uterine
environment. Consequently, the implementation of the flexible
skin model makes the tactile information received by the fetus
more enrich, and realistic 3D uterine model enabled us tosimulate interaction in twins (Fig. 2).

Fig. 1. Overview of module estimation in infantile spontaneous movement.

In experiment (i), we found that there are upward peaks ofthe number of coordinated muscle modules around 50% forboth muscle activity and proprioceptive inputs (Fig. 2, top),indicating the existence of adequate muscle power to acquire
the muscle modules. In experiment (ii), we found monotonic
increase of the number of coordinated muscle modules formuscle activity, whereas upward peaks around 30% forproprioceptive inputs (Fig. 2, top), indicating the complex
effects of joint restriction together with the adequate joint range.

Fig. 3. Flexible uterus model (top) and twin model (bottom).

IV. FUTURE PERSPECTIVEIn FY2021, we simulated infantile spontaneous
movements with muscle hypotonia and hypertonia and joint
restriction and developed soft tissue in dynamic bodily
simulation. In the future, we aim to investigate a learningmodel to explain the developmental changes of thesensorimotor information structure.
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Fig. 2. Effect of muscle power (top) and joint restriction (bottom) on thenumber of coordinated muscle modules.

[2]

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Journal Papers
1.

Dongdong Li, Kohei Kaminishi, Ryosuke Chiba, Kaoru Takakusaki, Masahiko Mukaino, Jun Ota,
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2.

Lin Chingszu, Ogata Taiki, Zhong Zhihang, Kanai-Pak Masako, Maeda Jukai, Kitajima Yasuko,
Nakamura Mitsuhiro, Kuwahara Noriaki, & Ota Jun, Development of robot patient lower limbs to
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4.

H Kambara, H Ogawa, A Takagi, D Shin, N Yoshimura, Y Koike, Modulation of wrist stiffness causedby adaptation to stochastic environment, Advanced Robotics, 35(29):1-17, 2021

5.

H Kambara, A Takagi, H Shimizu, T Kawase, N Yoshimura, Y Koike, Computational reproductions ofexternal force field adaption without assuming desired trajectories, Neural Networks, 139, 179-198,
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Nishimura K, Saracbasi OO, Hayashi Y, Kondo T, Cooperative Visuomotor Learning Experience with
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7.

Hiroki Kogami, Qi An, Ningjia Yang, Ruoxi Wang, Kazunori Yoshida, Hiroyuki Hamada, Hiroshi
Yamakawa, Yusuke Tamura, Shingo Shimoda, Hiroshi Yamasaki, Moeka Sonoo, Fady Alnajjar, Noriaki
Hattori, Koji Takahashi, Takanori Fujii, Hironori Otomune, Ichiro Miyai, Atsushi Yamashita, andHajime Asama, Analysis of muscle synergy and kinematics in sit-to-stand motion of hemiplegicpatients in subacute period, Advanced Robotics, 35(5):1-11, 2021

8.

Kazunori Yoshida, Qi An, Hiroyuki Hamada, Hiroshi Yamakawa, Yusuke Tamura, Atsushi Yamashita,and Hajime Asama, Artificial Neural Network that Modifies Muscle Activity in Sit-to-Stand Motion
Using Sensory Input, Advanced Robotics, 35(13-14):858-866, 2021

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Amemiya K, Naito E, Takemura H, Age dependency and lateralization in the three branches of thehuman superior longitudinal fasciculus, Cortex, 139, 116-133, 2021

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functional morphology of human and chimpanzee feet based on three-dimensional finite element
analysis, Frontiers in Bioengineering and Biotechnology, 9, 760486, 2022
100. 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
101. Takahashi M, Nakajima T, Takakusaki K, Preceding Postural Control in Forelimb Reaching
Movements in Cats, Front Syst Neurosci, 15, 792665, 2022

102. Obara K, Chiba R, Takahashi K, Matsuno T, Takakusaki K, Kee dynamics of the take-off and landingof the spike jump in volleyball players with patellar tendinopathy, J Phys Ther Sci, in press, 2022
103. Takakusaki K, Abnormality in neurotransmitters in Synucleinopathy and behavioral alteration in
relation to awake-sleep states, Clin Neurphysiol, in press, 2022
104. Takakusaki K, Mirai T, Noguchi T, Chiba R, Neurophysiological mechanisms of gait disturbance inadvanced Parkinsonʼs disease, Neurology and Clinical Neuroscience, in press, 2022
105. Sato Y, Kondo Y, Uchida A, Sato K, Yoshino-Saito K , Nakamura M, OkanoH, Ushiba J, Preserved
intersegmental coordination during locomotion after crvical spinal cord injury in common marmosets,
Behav Brain Res, in press, 2022

International Conference
1.

Imamizu, H. and Tsutsui, K, Neuroscientific approach to body cognition and emotion that induce"hyper-adaptability", The 1st International Symposium on Hype-Adaptability (HypAd2021), Online,
2021

2.

Ohata R, Asai T, Imaizumi S, and Imamizu H, My voice therefore I spoke: Sense of agency over speechenhanced in hearing self-voice, The 1st International Symposium on Hype-Adaptability (HypAd2021),
Online, 2021

3.

Ohata R, Asai T, Imaizumi S, and Imamizu H, My voice therefore I spoke: Sense of agency over speechenhanced in hearing self-voice, The 2021 APS (Association for Psychological Science) Virtual
Conference, Online, USA, 2021

4.

Ota Jun, Science of hyper-adaptability: An Overview, Scientific Lectures in South China University ofTechnology, Online, China, 2021

5.

K Kaminishi, Understanding the role of neurotransmitters in human postural control using aneuromusculoskeletal model, 32nd 2021 International Symposium on Micro-NanoMechatronics andHuman Science, Online, 2021

6.

Ota Jun, Hyper-adaptability for overcoming body-brain dysfunction and development of a new
rehabilitation device, Workshop program KSU (King Saud University) & U Tokyo, Online, 2021

7.

J Chai, M Hayashibe, Quantification of Joint Redundancy considering Dynamic Feasibility using Deep
Reinforcement Learning, IEEE Int. Conf. on Robotics and Automation (ICRA2021), Online, 2021

8.

L Guanda, J Shintake, M Hayashibe, Deep Reinforcement Learning Framework for Underwater
Locomotion of Soft Robot, IEEE Int. Conf. on Robotics and Automation (ICRA2021), Online, 2021

9.

J Chai, D Owaki, M Deep Reinforcement Learning with Gait Mode Specification for Quadrupedal
Trot-Gallop Energetic Analysis, 43rd Annual International Conferences of the IEEE Engineering in
Medicine and Biology Society (EMBC2021), Online, 2021

10. M Hayashibe, Motor Synergy Emergence in Redundancy through Deep Reinforcement Learning, 32nd
2021 International Symposium on Micro-NanoMechatronics and Human Science, Online, Japan, 2021
11. Isa T, Functional recovery after brain and spinal cord injuiries, JANUBET Primate Neurobiology
School, Online, Japan, 2021
12. Isa T, How the brain works for recovery from spinal cord injury, Mongolian Neuroscience Society
Meeting, Online, Mongol, 2021

13. Isa T, Neural circuit mechanism of functional recovery after brain and spinal cord injury, S.Hagiwara
Memorial Lecture, Online, 2021
14. Naito E, Hyper-adaptation in a para-athlete, Ｈyper-adaptation in the brain The Symposium on 44th
Annual Meeting of the Japan Neuroscience Society, Kobe, 2021
15. K Saitoh, I Nambu, Y Wada, The Effect of the Coefficient of Viscosity on the Reaching Movement in
Virtual Space Using VR, Online, 2021
16. K Saitoh, I Nambu, Y. Wada, The Effect of the Rotary Inertia on the Reaching Movement in VirtualSpace Using VR, 43rd Annual International Conferences of the IEEE Engineering in Medicine andBiology Society (EMBC2021), Online, 2021
17. D Nishiura, . Nambu, Y Maruyama, Y Wada, Improvement of human error prediction accuracy insingle-trial analysis of electroencephalogram, 43rd Annual International Conferences of the IEEE
Engineering in Medicine and Biology Society (EMBC2021), Online, 2021
18. M Yasuhara, I Nambu, Y Maruyama, Y. Wada, Decoding Individual Finger Movements from Single
Trial EEG of Motor Execution and Imagery Using CNN, 43rd Annual International Conferences of
the IEEE Engineering in Medicine and Biology Society (EMBC2021), Online, 2021
19. Seki K, Sensory gain modulation at the primate cuneate nucleus; top-down and bottom-up neuralmechanisms, NCM2021, Online, 2021
20. Seki K, Elucidation of neural mechanisms of hyper-adaptabilityto body change, 1st International
Symposium on Hyper-Adaptability, Online, 2021
21. Masaya Togo, Riki Matsumoto, Takuro Nakae, Katsuya Kobayashi, Kiyohide Usami, Akihiro
Shimotake, Takayuki Kikuchi, Kazumichi Yoshida, Masao Matsuhashi, Takeharu Kunieda, Susumu
Miyamoto, Ryosuke Takahashi, Akio Ikeda, Modification of effective connectivity strength in interareal
cortical networks from the seizure onset zone: a cortico-cortical evoked potential study, 13th AOEC,
Online, 2021
22. H Etoh, Y Omura, K Kaminishi, R Chiba, K Takakusaki, and J Ota, Proposal of a Neuromusculoskeletal
Model Considering Muscle Tone in Human Gait, 2021 IEEE International Conference on Systems,
Man, and Cybernetics (SMC), Online, Australia, 2021
23. S Nakamura and Y Kobayashi, A Grid-Based Estimation of Transformation of Partial Dynamics using
Genetic Algorithm for Motor Learning, The 32nd 2021 International Symposium on MicroNanoMechatronics and Human Science, Online, 2021
24. Isa T, Dynamics of global brain networks for recovery from spinal cord injury, The 7th CiNet
Conference: New horizons in brain mapping, Online, 2022

25. Isa T, Induction of large-scaled adult brain plasticity for neuronal repair, The 25th Thai Neuroscience
Society Conference 2022 (TNS25), Online, Thailand, 2022

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 (Section Chief, 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, Kyushu University)

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 (Section Chief, CiNet)
Funded co-investigator Hidenori Aizawa (Professor, Hiroshima University)
Funded co-investigator Minoru Asada (Specially Appointed Professor, Osaka University)
Co-investigator

Onoe Hirotaka (Project-specific Professor, Kyoto University)

Co-investigator

Tomohiko Takei (Program-specific Associate Professor, Kyoto University)

Co-investigator

Reona Yamaguchi (Project-specific Assistant Professor, Kyoto University)

Co-investigator

Ryo Sasaki (Assistant Professor, Kyoto University)

Co-investigator

Satoko Ueno (PhD Student, Kyoto University)

Co-investigator

Masahiro Mitsuhashi (PhD Student, Kyoto University)

Co-investigator

Kaoru Isa (Technical Stuff, Kyoto University)

Co-investigator

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

Co-investigator

Satoshi Hirose (Associate Professor, Otemon Gakuin University)

Co-investigator

Nodoka Kimura (Technical Staff, CiNet)

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

Hideki Nakano (Associate Professor, Kyoto Tachibana University)

Co-investigator

Yoshito Masamizu (Professor, Doshisha 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

Tomomichi Oya (Section Chief, NCNP)

Co-investigator

Roland Phillipp (Postdoctoral Fellow, NCNP)

Co-investigator

Amit Yaron (Postdoctoral Fellow, NCNP)

Co-investigator

Shinji Kubota (Postdoctoral Fellow, NCNP)

Co-investigator

Akito Kosugi (Postdoctoral Fellow, NCNP)

Co-investigator

Satomi Kikuta (Postdoctoral Fellow, NCNP)

Co-investigator

Yuki Hara (Lecturer, University of Tsukuba)

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

Toshi Nakajima (Assistant Professor, 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

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

Hiroki Togo (Postdoctoral fellow, NCNP)

Co-investigator

Toma Matsushima (Undergraduate Student (Research Student),
Tokyo University of Agriculture and Technology (NCNP))

Research Project A05-01: Elucidation of the hyper-adaptation mechanism of 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/Occupational therapist, Tohoku University)

Co-investigator

Tamami Sudo (Researcher (Part-time Lecturer),
Oouchi Hospital(Tohoku University))

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 A05-03: Mechanism of Hyper-Adaptivity of the human premotor area: electrophysiologicalconnectome analysis with electrocorticogram
Principal investigator

Riki Matsumoto (Professor, Kobe University)

Co-investigator

Akihiro Shimotake (Assistant Professor, Kyoto University)

Co-investigator

Takayuki Kikuchi (Assistant Professor, Kyoto University)

Co-investigator

Kiyohide Usami (Assistant Professor, Kyoto University)

Co-investigator

Hirofumi Takeyama (Assistant Professor, Kyoto University)

Co-investigator

Masaya Togo (Assistant Professor, Kobe University)

Co-investigator

Kozue Hayashi (Graduate Student, Kyoto University)

Co-investigator

Kento Matoba (Graduate Student, Kobe University)

Research Project A05-05: Development of non-invasive brain stimulation techniques that can increase
recruitment of the corticospinal motor indirect pathway during acquisition of hand motor skills.
Principal investigator

Mitsunari Abe (Director, IBIC)

Co-investigator

Kazumasa Uehara (Assistant Professor, NIPS)

Research Project A05-06: Analysis of motor control system in the recovery of forelimb function byrehabilitation after intracerebral hemorrhage
Principal investigator

Hideki Hida (Professor, Nagoya City University)

Co-investigator

Naoki Tajiri (Associate Professor, Nagoya City University)

Co-investigator

Takeshi Shimizu (Lecturer, Nagoya City University)

Co-investigator

Kenta Kobayashi (Associate Professor, NIPS)

Co-investigator

Shinya Ueno (Assistant Professor, Nagoya City University)

Research Project A05-07: Regulatory mechanisms of inter-regional network changes underlying hyperadaptation from mal-adaptation state caused by fear memory.
Principal investigator

Hiroyuki Miyawaki (Assistant Professor, Osaka City University)

Research Project A05-08: Facilitating hyper-adaptation in neurological and psychiatric diseases thoroughimproving precision on the sense of agency
Principal investigator

Takaki Maeda (Assistant Professor/Senior Assistant Professor, Keio University)

Co-investigator

Yuichi Yamashita (Section Chief, NCNP)

Co-investigator

Tsukasa Okimura (School of Medicine, Keio University)

Co-investigator

Hiroki Oi (School of Medicine, Keio University)

Research Project A05-09: The role of inhibitory neurons related to skilled hand movements after spinal cord
injury.
Principal investigator

Takahiro Kondo (Assistant Professor, Keio University)

Co-investigator

Yuta Sato (Ph.D Student, Keio University)

Research Project A05-11: Activating preference network for affected side by neural and behavioral
modulation.
Principal investigator

Rieko Osu (Professor, Waseda University)

Co-investigator

Taiki Yoshida (Ph.D Student, Waseda University)

Co-investigator

Kento Hirayama (Ph.D Student, Waseda University)

Research Project A05-12: Hyper-adaptability from inducing synapse connection and regulation of
extracelluar matrix. -Spinal cord injury and AI-based motion capturePrincipal 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

Yuki Morioka (Research Technician, Aichi Medical University)

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 Science and 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: Animal model of unilateral spatial neglect in marmosets
Principal investigator

Masatoshi Yoshida (Specially Appointed Associate Professor,
Hokkaido University)

Co-investigator

Hiroshi Matsui (Postdoctoral Fellow, Hokkaido University)

Co-investigator

Polyakova Zlata (Postdoctoral Fellow, Hokkaido University)

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)
Co-investigator

Koji Ito (Emeritus Professor, Tokyo Institute of Technology)

Co-investigator

Yoshikatsu Hayashi (Associate Professor, University of Reading)Co-investigator

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

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

Dai Yanagihara (Professor, The University of Tokyo)

Co-investigator

Shinya Aoi (Lecturer, Kyoto University)

Co-investigator

Kazuo Tsuchiya (Emeritus Professor, Kyoto University)

Co-investigator

Soichiro Fujiki (Assistant Professor, Dokkyo Medical University)

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, Kyushu University)
Co-investigator

Masafumi Yano (Professor Emeritus, Tohoku University)

Co-investigator

Atsushi Yamashita (Associate Professor, The University of Tokyo)

Co-investigator

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

Co-investigator

Ren Komatsu (Project Assistant Professor, The University of Tokyo)

Co-investigator

Ningjia Yang (Project Researcher, The University of Tokyo)

Co-investigator

Yukio Honda (Project Researcher, The University of Tokyo)

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

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

Co-investigator

Hitohiro Etoh (Master Student, The University of Tokyo)

Co-investigator

Huyutake Makino (Master Student, The University of Tokyo)

Co-investigator

Kota Sonoda (Master Student, The University of Tokyo)

Co-investigator

Tomoki Mori (Undergraduate Student, 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

Hironobu Osaki (Assistant Professor, Tokyo Womenʼs Medical University)

Co-investigator

Moeko Kanaya (Assistant Professor, Tokyo Womenʼs Medical University)

Co-investigator

Michihiro Kawano (Professor, Saku University)

Co-investigator

Yoshihide Kanai, (Lecturer, Saitama Medical University)

Research Project B05-01: Elucidation of the mechanism of motor synergy emergence in deep reinforcement
learning
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 B05-02: Adaptation ability of human postural control system revealed by a closed-loop
electrical muscle stimulation system
Principal investigator

Daichi Nozaki (Professor, The University of Tokyo)

Co-investigator

Syota Hagio (Lecturer, Kyoto University)

Research Project B05-03: Mechanism underlying the hyper-adaptation of bipedal locomotion to theevolutionary change of the foot.
Principal investigator

Naomichi Ogihara (Professor, The University of Tokyo)

Research Project B05-04: Understanding neural manifold of the movements using human neuroimaging andnon-invasive brain stimulation
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 B05-05: Development of motor learning model that can reuse partial dynamics based on
estimation of transformation between mappings
Principal investigator

Yuichi Kobayashi (Associate Professor, Shizuoka University)

Co-investigator

Taisei Matsuura (Undergraduate Student, Shizuoka University)

Co-investigator

Sota Nakamura (Undergraduate Student, Shizuoka University)

Research Project B05-06: Shared-control of teleoperated robot maintaining operatorʼs embodiment underintervention of AI
Principal investigator

Yasuhisa Hasegawa (Professor, Nagoya University)

Co-investigator

Tomoya Morita (Nagoya University)

Research Project B05-07: Systems engineering approach for understanding supraspinal mechanisms of theintermittent feedback control during human upright stancePrincipal investigator

Taishin Nomura (Professor, Osaka University)

Co-investigator

Yasuyuki Suzuki (Lecturer, Osaka University)

Co-investigator

Matija Milosevic (Assistant Professor, Osaka University)

Co-investigator

Akihiro Nakamura (Ph.D Student, 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 B05-08: A reinforcement learning model with dynamic state space that enables adaptation
to indefinite environments
Principal investigator

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

Co-investigator

Hajime Mushiake (Professor, Tohoku University)

Co-investigator

Satoshi Zuguchi (Ph.D Student, Tohoku University)

Research Project B05-09: Attention control training based on tailor-made neurofeedback system forfacilitating motor learning in elderly
Principal investigator

Takeshi Sakurada (Assistant Professor, Ritsumeikan University)

Research Project B05-10: Modeling of the motor recovery process and optimization of rehabilitation strategy

using VR
Principal investigator

Tetsunari Inamura (Associate Professor, National Institute of Informatics)

Co-investigator

Fuminari Kaneko (Project Associate Professor, Keio University)

Research Project B05-11: Developmentalhyper-adaptability of sensorimotor dynamics under rapid growth
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)