2020 Annual report"Hyper-adaptability for overcoming body-brain dysfunction:
Integrated empirical and system theoretical approaches"
Program Director: Jun Ota (The University of Tokyo)

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

Contents
Program Overview and Activities of Steering Committee
Principal investigator, Jun OTA (Professor, The University of Tokyo)
Activities of Group A (Experiment and Analysis)
Leader of Group A, Tadashi ISA (Professor, Kyoto University)
Annual report of research project A01
Principal investigator, Tadashi ISA (Professor, Kyoto University)
Annual report of research project A02
Principal investigator, Kazuhiko SEKI (Director, NCNP)
Annual report of research project A03
Principal investigator, Hiroshi IMAMIZU (Professor, The University of Tokyo)
Annual report of research project A04
Principal investigator, Kaoru TAKAKUSAKI (Professor, Asahikawa Medical University)
Annual report of research project A05-1
Principal Investigator, Shinichi IZUMI (Professor, Tohoku University)
Annual report of research project A05-2
Principal Investigator, Kenichi AMEMORI (Associate Professor, Kyoto University)
Annual report of research project A05-3
Principal Investigator, Riki MATSUMOTO (Professor, Kobe University)
Annual report of research project A05-4
Principal Investigator, Kazuto KOBAYASHI (Professor, Fukushima Medical University)
Annual report of research project A05-5
Principal Investigator, Mitsunari ABE (Section Chief, NCNP)
Annual report of research project A05-6
Principal Investigator, Hideki HIDA (Professor, Nagoya City University)
Annual report of research project A05-7
Principal Investigator, Hiroyuki MIYAWAKI (Assistant Professor, Osaka City University)
Annual report of research project A05-8
Principal Investigator, Takaki MAEDA(Assistant Professor/Senior Assistant Professor, Keio University)
Annual report of research project A05-9
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 A05-13
Principal Investigator, Fumino FUJIYAMA (Professor, Hokkaido University)
Annual report of research project A05-14
Principal Investigator, Yoshito MASAMIZU (Deputy Team Leader, RIKEN)
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 B05-1
Principal Investigator, Mitsuhiro HAYASHIBE (Professor, Tohoku University)
Annual report of research project B05-2
Principal Investigator, Daichi NOZAKI (Professor, The University of Tokyo)
Annual report of research project B05-3
Principal Investigator, Naomichi OGIHARA (Professor, The University of Tokyo)
Annual report of research project B05-4
Principal Investigator, Isao NAMBU(Associate Professor, Nagaoka University of Technology)

Annual report of research project B05-5
Principal Investigator, Yuichi KOBAYASHI (Associate Professor, Shizuoka University)
Annual report of research project B05-6
Principal Investigator, Yasuhisa HASEGAWA (Professor, Nagoya University)
Annual report of research project B05-7
Principal Investigator, Taishin NOMURA (Professor, Osaka University)
Annual report of research project B05-8
Principal Investigator, Kazuhiro SAKAMOTO(Associate Professor, Tohoku Medical and Pharmaceutical University)
Annual report of research project B05-9
Principal Investigator, Takeshi SAKURADA (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

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

call this learning cycle in a new control space as“reconstitution of sensorimotor control rules”, i.e. neuralcomputation 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. New members of publicly
projects join the program from April, 2020 (Fig. 1); A05
group (15 projects): Izumi, Amemori, Matsumoto, K.
Kobayashi, Abe, Hida, Miyawaki, Maeda, Kondo, Osu,
Takeuchi, Fujiyama, Masamizu, Higo, and Yoshida. B05
group (11 projects): Hayashibe, Nozaki, Ogihara, Nambu, Y.
Kobayashi, Hasegawa, Nomura, Sakamoto, Sakurada,
Inamura, and Kanazawa). We adopt two new analytical
approaches: (a) Robotic-interventional neuroscience, i.e.
combinatory use of well-controlled robotic technologies andbiological 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 statistical

When a person experiences acute/chronic impairment or
disorder due to aging, the brain reorganizes neural networks
by disinhibiting pre-existing neuralnetwork that is normally suppressed and
Neuro
A04
A03
A01
A02
searching for latent but available
Science
Takakusaki
network that has long been unutilized
ImamizuIsa
Seki（U Tokyo） （Asahikawa（Kyoto U）（NCNP）
through course of evolution andMedikal U）
A
development. We call this process ofExperimental
functional
compensationasanalysis“reconstruction of neural structure”, i.e.
a neural entity that achieves hyperReconstitution ofadaptability. In order to implement
Reconstruction of
sensorimotor
practical functions to this reconstituted
neural structurescontrol rules
neural network, the network should
Mathematicalacquire a new control policy of motor
models andeffectors based on precise recognition ofinterventions
the present states of the brain and the
body. Here, the brain has to activate the
B03
B04
B01
B02
Systems Kondo
new network by repeatedly performing
Koike
Asama
Ota
neural computations and updates theEngg B （TUAT） （Tokyo Tech） （U Tokyo） （U Tokyo）
network based on prediction error. We

A05

Izumi (Tohoku U)
Kondo (Keio U)
Amemori (Kyoto U)
Osu (Waaseda U)
Matsumoto (Kobe U)
Takeuchi
K. Kobayashi(Aichi Medical U)(Fukushima Medical U)
Fujiyama
Abe (NCNP)(Hokkaido U)
Hida (Nagoya City U)
Masamizu (RIKEN)
Miyawaki (Osaka City U)
Higo (AIST)
Maeda (Keio U)
Yoshida(Hokkaido U)

B05

Hayashibe (Tohoku U)
Sakurada
Nozaki (U Tokyo)(Ritsumeikan U)
Ogihara (U Tokyo)
Inamura (NII)
Nambu
Kanazawa(Nagaoka U Tech)(U Tokyo)
Y. Kobayashi(Shizuoka U)
Hasegawa (Nagoya U)
Nomura (Osaka U)
Sakamoto(Tohoku Med Pharm U)

Fig. 1 Composition of groups & research items

methods.
III. GROUP MEMBERS
Members of the management group consists of one
principal investigator (Ota), three funded co-investigators (Isa,
Kondo, and Funato), and fifteen co-investigators (Seki,
Imamizu, Takakusaki, Koike, Asama, Naito, Hanakawa,Izawa, Tsutsui, Aizawa, Chiba, Wen, An, and Yozu).
IV. ACTIVITIESFollowing events were held by management group.
A. Activities organized by the project- 4th management meeting
Date: May 15th, 2020. 12:00-13:00
Place: Online
Contents: members of the management group discussed about
the operation method, symposiums, and counterplans against
Covid-19, etc.
- 1st public symposium
Date: Oct. 10th, 2020. 12:00-12:50
Place: Online
Contents: The theme was "Hyper-adaptation in a postcoronavirus society". The symposium focused on the decline
in motor functions due to the restricted activities caused by thecorona disaster, the resulting maladaptation in the body-brain
relationship, and the prospects for solutions. After an
overview by Ota, seven lectures were given by Isa, Takakasagi,
Tsutsui, Maeda, Asama, and Hanakawa, followed by a panel
discussion on the theme of post-corona and hyper-adaptation.
- 5th management meeting
Date: Oct. 10th, 2020. 12:00-12:50
Place: Online
Contents: members of the management group discussed about
the operation method, symposiums, publications, etc.
- B group meeting
Date: Nov. 7th, 2020. 10:00-17:00
Place: Online
Contents: A group meeting was held for the purpose of
exchange within and outside Group B. After an explanation of
the purpose of the meeting by the group leader (Kondo), groupmembers took presentations of their researches and researchplans. The meeting was attended by more than 45 researchers
at all times, and active discussions were held.
- A group meeting
Date: Nov. 13th and Nov. 14, 2020.
Place: Online
Contents: A group meeting was held for the purpose of
exchange within and outside Group A. 24 group members
gave 10-minute presentation and 10-minute Q&A sessions inblocks of 3 members each. A "virtual coffee break" using the
Zoom's breakout room function of Zoom was held between
each block. Through the meeting, active exchanges took place.
- 2st plenary conference
Date: March 5th - 6th, 2021

Place: Online
Contents: summary of the researches in the year was presented
by project representative and researchers in the project.
- 6rd management meeting
Date: Marge 5th, 2021. 11:40-13:00
Place: Online
Contents: members of the management group discussed about
the operation method, management of International
symposium, etc.
B. Activities for publications- Newsletters
We published two newsletters on May, 2020 and on Jan, 2021.
Th newsletters are published from the following website.
https://www.hyper-adapt.org/newsletter/
V. ACTIVITIES BY YOUNG RESEARCHERS
The Young Scientists' Group, chaired by Dr. Qi An(Kyushu University), has been organized to promote theactivities of young scientists in this field. This year's activities
are listed below.
A. Organizing International Workshop
On July 18, 2020, we held an online workshop at theinternational conference EMBC2020. Prof. Ota, Prof.
Takakusagi, Prof. Chiba, Prof. Shirafuji, Prof. Hayashi, andProf. An participated in the workshop, and presented theirresearches and had discussions with the participants.
B. Literature Study GroupA study session on "Control and Adaptation of Body
Movements" by Koji Ito was held from October to November
2020. Each chapter was explained and discussed by young
researchers in the research field. The materials and movies of
the workshop are shared on the website of the research area,
and we are trying to expand the common knowledge base.
C. Organizing Speciall Issue in International Journal
We planned a special issue on our field, "HyperAdaptability for Overcoming Body-Brain Dysfunction," inAdvanced Robotics, an international journal published by the
Robotics Society of Japan. We have received submissions
from inside and outside the field, and we are preparing for
publication in July 2021.
D. Information Sharing Infrastructure using SlackWe have started to use Slack to promote discussion andinformation sharing among young researchers in this area, andplan to expand it to the entire area in the next year.
VI. FUTURE PERSPECTIVE
As main activities of the next fiscal year, the management
group plans an International symposium on May 2021 (online)
and 3rd plenary conference on March 2022.

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
the adaptation mechanism of the neural systems. “Hyperadaptation” operates as the biological responses to thesevere acute insults such as brain and spinal cord injury or
chronic dysfunctions of the brain and spinal cord causedby aging and frailty, far beyond the ordinary adaptation.
The Group A (Neurosciences) will aim at clarifying themechanism of “Reconstruction of neural structures” and“Reconstruction of sensorimotor control rules” associatedwith Hyper-adaptation through experimental studies
mostly in the field of neurosciences. However, justlooking at the experimental data, it is difficult to getinsight into the principles of the neural system operation
underlying the obtained data. Therefore, the Group A will
promote the researches in collaboration with the Group 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
from brain and spinal cord injury (experiments on
nonhuman primates), or aging (experiments on humans)
and at elucidating its neuronal mechanisms in rodents. In
the nonhuman primate studies, Isa and colleagues clarified
that the interhemispheric interaction between the bilateralmotor and premotor cortices switches from inhibitory tofacilitatory during the recovery process from thesubhemisection injury of the spinal cord. Naito andcolleagues showed the decline of interhemisphericinhibition in aged subjects by measuring the negative
BOLD signal of the fMRI.
Group A02 (Seki) investigated the adaptation of the central
nervous system to the acute change of the body schema bycross union surgery of forearm muscles in the macaquemonkeys. They clarified that the muscle synergies once
switch soon after the surgery, but later changes again to theoriginal one. In collaboration with the Group B, they are
analyzing the phenomenon from the viewpoint of the costfunction.
Group A03 (Imamizu, Tsutsui) is trying to understandmechanisms in which motivation and body cognition
facilitate motor learning in challenging situations, and todevelop the methods for facilitating motor learning
through artificial control of motivation and body cognition.
Imamizu and colleagues demonstrated that the short-term
learning efficiency is enhanced by the sense of agency inthe preceding trials compared with the cases without it.
Tsutsui and colleagues used the transcranial magnetic
stimulation and suppressed the local brain activity in thefrontal cortex and investigated the effects on theperformance in the motor learning task. They found thatsuppression of the anterior cingulate cortex caused
deterioration of the task performance caused by decline ofmotivation.
Group A04 (Takakusaki and Hanakawa) is investigating
how the change in the dynamics of brain activity caused by

the dynamic change in the neurotransmitter systems suchas dopamine and acetylcholine/ For that purpose,
Takakusaki and colleagues set up the experimental system
to analyze the shift of the center of the gravity of catsduring the reaching movements.
Hanakawa andcolleagues are developing the experimental system tomeasure the brain activity and \dynamics of theirconnectivity using the simultaneous recordings of theelectroencephalography and fMRI.
In addition, 15 members (below) joined as the A05 group,
who were selected through the applications.
Shin-ichi Izumi “Elucidation of the hyper-adaptation
mechanism of upper limb recovery in stroke patients”
Kenichi Amemori “Synchronization of neural oscillation
among primate limbic structures and the striatum during
recovery from anxiety like state”
Riki Matsumoto “Mechanism of hyper-adaptivity of thehuman premotor area: electrophysiological connectome
analysis with electrocorticogram”
Kazuto Kobayashi “Neural circuit rearrangement
mechanisms underlying the recovery from learning deficits
in Parkinson’s disease model animals”
Mitsunari Abe “Development of non-invasive brain
stimulation techniques that can induce recruitment of thecorticospinal motor indirect pathway during acquisition ofhand motor skills”
Hideki Hida “Analysis of motor control system in therecovery of forelimb function by rehabilitation after
intracerebral hemorrhage”
Hiroyuki Miyawaki “Regulatory mechanisms of interregional network changes underlying hyper-adaptationfrom mal-adaptation state caused by fear memory”
Takaki Maeda “Facilitating hyper-adaptation inneurological and psychiatric diseases through improving
precision on the sense of agency”
Takahiro Kondo “The role of inhibitory neurons related toskilled hand movements after spinal cord injury
Rieko Osu “Activating preference network for affectedside by neural and behavioral modulation”
Kosei Takeuchi “Hyper-adaptability from inducing
synapse connection regulation of extracellular matrix. –Spinal cord injury and AI-based motion capture-“
Fumino Fujiyama “Reconstruction of basal ganglia byaging and neurodegenerative disease”
Yoshito Masamizu “Development of techniques toimprove brain functions by using hyper-adaptability”
Noriyuki Higo “Adaptive mechanism occurring in bothhemispheres after unilateral brain damage”
Masatoshi Yoshida “Animal model of unilateral spatial
neglect in marmosets”
III. ACTIVITIESHe whole Group A meeting was held on November 13(Sat) – 14 (Sun) by online with Zoom software.
IV. FUTURE PLAN
We will soon organize the 1st Group A 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 existence of disinhibitioninterhemispheric pathway in the monkeys with spinal cord injury
using electrophysiology and aged humans using fMRI. We arealso studying the involvement of dopamine system in the global
disinhibition in 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 record the movement-related activity inthe sensorimotor cortices and the cortical and muscle
responses to electrical stimulation through the corticalelectrodes longitudinally before after the spinal cord injury inmonkey to clarify the mechanism of disinhibition across thelarge-scaled brain network. In rodents, Aizawa Group will
perform the activity measurement and optogeneticalstimulationofmonoaminergic
systems
including
dopaminergic and serotonergic neurons, to supply the
information about the global disinhibition in the cortex. In
humans, Naito Group will capture the chronic disinhibitory
state of the aged people by fMRI and propose the effective
training methods of the brain to improve the rain functioms
using the disinhibitory state as a measure of progression of
aging-related frailty.

III. RESEARCH TOPICS
A. Global dinsinhibition after spinal cord injury in monkeys
As reported in the last year, in a macaque monkey with subhemisection at the mid-cervical level, dropped from 100% to
0%, but success ratio of the immature grasping guided by theslit edge recovered to around 90 % in 1-2 months. We useddirect electrical stimulation with ECoG electrodes toinvestigate the connectivity between motor-related areas andmuscles for the affected side before and after the subhemisection. Fig.1 shows the induced muscle twitches in theaffected forelimb by the electrical stimulation of contralesional
PM. Muscle twitches started being induced in proximal
muscles from the contralesional PM/M1 after around Day 16.
Twitch responses gradually spread from the proximal to distal
muscles including digits almost simultaneously as the monkey
started grasping. Similar results were confirmed in the 2ndmonkey. Furthermore, we confirmed that interhemisphericinhibitory interaction in the intact state was changed tointerhemispheric facilitation during the recover. These results
suggested that global disinhibition spread across a large
cortical network along with the recovery.
[1] 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, in press.
[2] Koshimizu Y, Isa K, Kobayashi K, Isa T (2021) Double viral vector
technology for selective manipulation of neural pathways with higher level of
efficiency and safety. Gene Therapy, doi: 10.1038/s41434-020-00212-y.
[3] 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, doi: 10.1093/cercor/bhaa399.
[4] Takakuwa N, Isa K, Onoe H, Takahashi J, Isa T (2020) Contribution of
the pulvinar and lateral geniculate nucleus to the control of visually guided
saccades in blindsight monkeys. Journal of Neuroscience, JN-RM-2293-20.
doi: 10.1523/JNEUROSCI.2293-20.2020.
[5] Tokuoka K, Kasai M, Kobayashi K, Isa T (2020) Anatomical andelectrophysiological analysis of cholinergic projections from theparabigeminal nucleus to the superficial superior colliculus. Journal of
Neurophysiology, 124(6):1968-1985.
[6] Chen C-Y, Matrov D, Veale R, Onoe H, Yoshida M, Miura K, Isa T(2020) Properties of visually-guided saccadic behavior and bottom-up
attention in marmoset, macaque, and human. Journal of Neurophysiology,
125:437-457.
[7] Isa K, Sooksawate T, Kobayashi K, Kobayashi K, Redgrave P, Isa T(2020) Dissecting the tectal output channels for orienting and defense
responses. eNeuro, 7(5): ENEURO.0271-20.2020.
[8] Vancraeyenest P, Arsenault JT, Li X, Zhu Q, Kobayashi K, Isa K, Isa T,
Vanduffel W (2020) Selective mesoaccumbal pathway inactivation affects

motivation but not reinforcement-based learning in macaques.
Neuron, 108:568-581.
[9] Suzuki M, Onoe K, Sawada M, Takahashi N, Higo N, Murata Y, Tsukada
H, Isa T, Onoe H, Nishimura Y (2020) The ventral striatum is a key node ofcortical reorganization required for functional recovery of finger dexterity after
spinal cord injury in monkeys. Cerebral Cortex, 30: 3259-3270.

B. Visualization of development and age-related deteriorationof human interregional inhibitory systems using negative
BOLD as an indicator (Naito and Asada)
In collaboration with Asada group (Osaka University), Naito
group (NICT, CiNet) visualized development and age-related
deterioration of human interregional inhibitory systems using
Negative BOLD, which can be measured by functional MRI,
as an index during a right-hand motor task. In young adults,
we identified interhemispheric inhibition in the hand sectionof the ipsilateral (right) motor cortex, cross-somatotopic
inhibition in the feet and face sections, crossmodal inhibitionin visual and auditory
cortices, as well as inhibitionin the default mode network.
We found that over alifespan,allof these
inhibitions develop fromchildhood to adulthood anddiminishing or disappearing
with age (Figure from Morita
et al. 2021). Regarding the interhemispheric inhibition, we
found that inhibition of the hand/arm section of the ipsilateral(right) motor cortex during a right hand movement was
associated with right hand\finger dexterity, and thatelementary school children with more developed the inhibition
had more developed right hand/finger dexterity as assessed by
the peg test (Naito et al. 2020). In contrast, in the elderly, theinhibition of the hand/arm section of the ipsilateral (right)
motor cortex was attenuated or lost, and this was related totheir deterioration in right hand/finger dexterity. We further
found 2-months bilateral digital training could improve thisinhibition, which in turn improved their hand/finger dexterity.
[1] Naito E, Morita T and Asada M Importance of the primary motor cortex indevelopment of human hand/finger dexterity. Cerebral Cortex
Communications 1: 1-12, https://doi.org/10.1093/texcom/tgaa085, 2020.
[2] Morita T, Asada M and Naito E Examination of the development andaging of brain deactivation using a unimanual motor task. Advanced Robotics
accepted 2021.

C. Elaboration of the rodent model of hyper-addaptation anda role of dopamine
Aizawa group (Hiroshima University) addressed a role ofextracellular dopamine in modification of the mice behavior toadapt to the tail suspension condition. Mice exhibited
alternation between despair and struggling behaviors under the
tail suspension stress (Cui et al., 2020). Upon real-timemeasurement of extracellular dopamine in the nucleus
accumbens, transient decrease of the extracellular dopamine
release was observed immediately before the transition to thestruggling from despair state. To address a causal effect of
dopamine dip in the nucleus accumbens on initiation of
struggling, optogenetic suppression of the accumbal dopamine
was performed in collaboration with Isa group. Results
showed that photoinhibition of the accumbal dopamine releaseinduced struggling under the tail suspension stress. Consistent
with this, knocking out of the dopamine type 1 receptor in theregion also facilitated the struggling behavior, suggesting thatthe dopaminergic modulation in the nucleus accumbens has an
instructive role in stress coping behavior for adaptation under
the acute stress.
The group also established an experimental setup to
enable physiological analysis on interhemispheric inhibition asa basis of global disinhibition suggested in hyper-adapting
brain in human and non-human primate model in Isa andNaito group, based on the electrophysiological recording of
the murine cortical activity (Aizawa et al., 2020). Furthermore,
mouse model of systemic inflammation was analyzed toaddress the mechanism underlying hyper-adaptation to theglobal stress such as systemic inflammation (Kikutani et al.,2020; Giga et al., 2020).
[1] Giga H, Ji B, Kikutani K, Fukuda S, Kitajima T, Katsumata S, Matsumata
M, Suhara T, Yamawaki S, Shime N, Hosokawa K, Aizawa H.
Pharmacological and Genetic Inhibition of Translocator Protein 18 kDa
Ameliorated Neuroinflammation in Murine Endotoxemia Model. Shock.
2020 in press[2] Kikutani K, Giga H, Hosokawa K, Shime N, Aizawa H. Microglialtranslocator protein and stressor-related disorder. Neurochem Int. 2020
Nov;140:104855.
[3] Ito H, Nozaki K, Sakimura K, Abe M, Yamawaki S, Aizawa H. Activationof proprotein convertase in the mouse habenula causes depressive-likebehaviors through remodeling of extracellular matrix.
Neuropsychopharmcology. 2021 46(2):442-454.
[4] Cui W, Aida T, Ito H, Kobayashi K, Wada Y, Kato S, Nakano T, Zhu M,
Isa K, Kobayashi K, Isa T, Tanaka K, Aizawa H. Dopaminergic Signaling
in the Nucleus Accumbens Modulates Stress-Coping Strategies during
Inescapable Stress. J Neurosci. 2020 Sep 16;40(38):7241-7254.
[5] Aizawa H, Sun W, Sugiyama K, Itou Y, Aida T, Cui W, Toyoda S, Terai
H, Yanagisawa M, Tanaka K. Glial glutamate transporter GLT-1 determines
susceptibility to spreading depression in the mouse cerebral cortex. Glia.
2020 68(12):2631-2642.

IV. FUTURE PERSPECTIVE
Isa Group aim will publish an article on the current results,
and will work on the more fundamental neuronal mechanismsof global dininhibition in collaboration with Aizawa group.
Naito Group will conduct additional experiments tosummarize our findings in the elderly, and at the same time, incollaboration with the Asada group, will attempt to develop amathematical model of the development and deterioration of
E/I balance.
Aizawa group would analyze roles of monoamines in theinterhemispheric inhibition and adaptation to the global stressusing neuropharmacology and optogenetics based on thefindings they acquired in this fiscal year.

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

Abstract—In the FY2020, our experimental studies werebasically halted under the effect of the Covid-19. However, we’ve
made significant progresses for data analysis as well an writing
manuscripts.

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.
III. RESEARCH TOPICS
A. Data Analysis
Aftercompletingthebehaviouralandelectromyography (EMG) recordings in a second monkey
which underwent a procedure of cross-connecting the forearm
tendons of the flexor digitorum superficialis (FDS) andextensor digitorum communis (EDC) and which lasted from
December to March of the last year, we focused on the analysisof the collected data (n=2).
We first analysed the behavioural data recorded by
two high-speed cameras from two different angles by
calculating the times required for reaching and grasping each of
the four objects. For monkey A, data from 40 days before
surgery to 3 months after surgery, and for monkey B, data from

3 days before surgery and 2 months after surgery were taken
into account. In both monkeys A and B, reaching and graspingtimes were temporarily and significantly prolonged after
surgery, however, recovered to preoperative times about 30
days after surgery (early phase), and changes thereafter were
gradual (late phase). These results strongly suggest that thecentral nervous system adaptation to functional muscle
repositioning, introduced by tendon cross-transfer surgery,should be complete within about one month after surgery.
Therefore, as an index for the adaptive evaluation of thenervous system, we analysed the electromyographic activity of
the rearranged muscles and the changes over time in muscle
synergies including non-relocated muscles.
In each case, recovery was evaluated by crosscorrelation analysis between the pre-operative recordings of
EMG/muscle synergy and the same muscle for each postoperative measurement date. Firstly, as a result we found thatthe decrease in the mutual correlation coefficient in the major
muscle synergies of the rearranged as well as the non-relocatedmuscles became the largest at the same time as the recovery of
the above evaluated behavioural index. On the other hand, theintercorrelation coefficients with the repositioned muscle andmuscle synergy were maximized at the same time. Therefore,
the tendon transfer procedure revealed that the re-positioned
muscle exhibits the temporal activity of the original muscle
activity/muscle synergy at its new destination. Thisphenomenon was observed in both the FDS and EDC muscles,
suggesting that the central nervous system uses a strategy toswap the activity of the repositioned flexor and extensor
muscles and flexor/extensor synergy, thereby improving the
behavioural performance.
However, the cross-correlation coefficients of EMG
activity and synergy of the repositioned muscles, which had
once decreased, increased again in the two monkeys about 60
days after surgery and returned to their preoperative values.
This trend was observed in both the FDS and EDC muscles,
indicating a two-stage adaptation of the central nervous system
in which muscle activity and muscle synergy are "replaced" inthe first early phase and then "restored" in the second late phase.
Next, more detailed movement observations were made toinvestigate the functional significance of the late phase
adaptation. An ataxic movement patterns, which were notobserved preoperatively, were observed up to the late phase
adaptation, but disappeared after the late phase. These results
suggest that the changes in motor control strategies in thecentral nervous system during the early phase are not optimal
from the point of view of smooth execution of movements, andthat before the late phase, ataxic control strategies were used toachieve successful movements, probably at a high cost.

Therefore, it was considered that the smooth movement wasachieved by returning to the conventional/original control
style/strategy instead of using the temporarily adopted control
style/strategy. In the future, quantitative evaluation of theabove-mentioned cost and further analysis of the difference
between the two control systems (late phase and the control)
will be necessary.
B. Modular organization of spinal motor output
The human hand has 27 muscles and 18 joints, which
our nervous system is able to coordinate for complex
movements. However, the number of combinations -- ordegrees of freedom -- is so large that attempting to artificiallyreplicate this control and adjustment of muscle activity during
movement in real time taxes even a modern supercomputer.
While the method used by the central nervous system to reduce
this complexity is still being intensely studied, the “motor
module” hypothesis is one possibility. Under the motor module
hypothesis, the brain recruits interneuronal modules in the
spinal cord rather than recruiting individual muscles directly tocreate movement; wherein different modules can be combined
to create specific movements. Nearly 40 years ago, research infrogs showed that simultaneously recruiting two modules ofneurons controlling leg muscles created the same pattern of
motor activity that represents a “linear summation” of the twocomponent patterns.
In this paper, we attempted to determine if this motor
control method is also present in the primate spinal cord. If

validated, it would provide new insight into the importance ofspinal interneurons in motor activity and lead to new ideas inmovement disorder treatments and perhaps even a method to“reanimate” a limb post-spinal injury. We implanted a small
array of electrodes into the cervical spinal cord in threemacaque . Under anesthesia, different groups of interneurons
were recruited individually using a technique called intraspinal
microstimulation (ISMS). We found that, as in the frog leg, theforce direction of the arm at the wrist during dual-site
simulation was equal to the linear summation of theindividually recruited outputs. However, unlike the frog leg, theforce magnitude output could be many times higher than that
expected from a simple linear summation of the individual
outputs. When we examined the muscle activity, they found that
this supralinear summation was in a majority of the muscles,
particularly in the elbow, wrist, and finger.
This is a very interesting finding for two reasons. First,
it demonstrates a particular trait of the primate spinal cord
related to the increased variety of finger movements. Second,
we now have direct evidence primates can use motor modules
in the spinal cord to control arm movement direction and force
magnitude efficiently and independently.
In effect, using paired stimulation in the primate spinal cord not
only directly activate two groups of interneurons, INa and INb,
which recruit their target muscle synergies, Syn-a and Syn-b, to
set the arm trajectory, but can also activate a third set ofinterneurnons that can adapt the motor activity at the spinallevel to change the force of the movement, group INc. This
would let the brain plan the path the arm should take while thespinal cord adapts the muscle activity to make sure that pathhappens. One example of this “plan and adapt” approach to
motor control is the deceptively simple act of drinking from acan of soda. The brain can predetermine the best way to lift the
can to your mouth for a sip, but the actual amount of soda in thecan -- and therefore the can’s weight -- is perhaps unknown.
Once your brain has determined the trajectory the can shouldtake -- in this case INa and INb -- the amount of force needed
to complete that action can be modulated separately in INc,
rather than redetermining which sets of muscles will be needed.
This study experimentally proves for the first time that primate
arm movements may be efficiently controlled by motor
modules present in the spinal cord. Based on the results of thisresearch, it is expected that the analysis and interpretation ofhuman limb movements based on the motor module hypothesis
will further advance in the future.
IV. FUTURE PERSPECTIVE
We are preparing to start the planned experiment again. We willaddress neural representation of the early and late adaptation.
REFERENCES[1]

Yaron A, Kowalski D, Yaguchi H Takei T, Seki K : Forelimb force
direction and magnitude independently controlled by spinal modules in
the macaque. PNAS 117(44): 27655 - 27666, 15 October 2020

Annual report of research project A03
Hiroshi Imamizu1 and Ken-Iichiro Tsutsui2
1

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

Abstract— Our research project aims to 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 relationships between sense of agency (a sense that
"I am the one causing an action," which is an aspect of bodycognition) and motor learning in several experiments, 2) revealing
fundamental mechanisms of sense of agency, 3) establishing astandard procedure to evaluate the motivational level of animals(monkeys), and 4) specifying a brain region in the cerebral cortex
responsible for motivation.

I. INTRODUCTION
Previous studies in neuroscience and psychology haveinvestigated how feedback information from the external world(such as motor error and reward prediction error) contribute to
motor learning. Recently, by contrast, many researchers haveinterest in the contribution of internal information, such as
motivation and body cognition, to motor learning. For instance,
the level of 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
a theoretical 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 (Mr. Tanaka and Imamizu) and Prof.
Izawa of the B03-group started an experiment, investigating
how self-attribution of the movement facilitates motor learningin the last fiscal year. They collected data from 20 participantsin this fiscal year. They revealed that the self-attribution ofactions in the preceding trial
significantly facilitates ashort-term
efficacyoflearning rate in the next trial(Fig. 1).
2) Sense of agency andcontrol as a reward: A groupof the principal investigator(Mr. Tanaka, Dr. Ohata, andImamizu) and Prof. Izawa ofFig. 1 Difference in learning rate
the B03-group started adue to action attribution
psychological experimentexamining if successful manipulation of an object gives aperformer a subjective reward. Participants moved a cursor to
the left or right target. A variance of a noise imposed to thecursor was different between the left and right movements.
They investigated if participants preferred the action with small
noise (high controllability) even when the rewards for the left
and right actions were equalized to each other. In collaboration
with Prof. Tsutsui (co-investigator), they also designed anexperiment to investigate a change in neural signatures ofmotivation (activity of neurons in the medial and dorsal
prefrontal regions) according to the controllability of devices.
3) Relationship between sensitivity to controllability andefficacy of motor learning: The principal investigator and Prof.
Wen of the B03 group investigated the individual difference insensitivity to change in controllability of an object. They foundthat an individual's sensitivity to increasing control is correlatedwith his/her efficacy in motor learning. The principal
investigator revealed that an increase in proprioceptive acuity,which is essential for the perception of controllability,
facilitates motor learning effectiveness [1].
B. Understanding mechanisms of sense of agency
1) Sense of agency over speech: It has been suggested that
deficits in the sense of agency over speech lead to auditory
hallucinations. However, little is known about the agency overspeech. A group of the principal investigator (Dr. Ohata andImamizu) has investigated a sense of agency during anaturalistic speech paradigm. They found a critical role of thesign of self in acoustic quality, as well as action-outcome
causality, in the sense of agency over speech.
2) Cognitive psychological approach to sense of agency: Agroup of the principal investigator (Dr. Ohata and Imamizu) andProf. Wen of the B03-group investigated cognitive mechanisms

of sense of agency. They established a two-stage model ofactive sensing of control difference [2]. They also found acategorial information processing in the perception of control[3].
3) Mathematical model of sense of agency: The principal
investigator, Prof. Kondo and Dr. Yano of the B01-group, andDr. Maeda of the B05-8 established a statistical learning modelin the sense of agency. They revealed that the sense of agency
could be formalized as the likelihood of an internal model ofthe environment [4].
4) Brain network related to individual differences in the senseof agency: The principal investigator, Dr. Yamashita, and Dr.
Maeda of the B05-8 found that networks including the caudate
nucleus and the right parietal regions are associated with theindividual difference in the sense of agency. The principal
investigator and Dr. Yamashita published a paper on afundamental method for investigating brain networks. [5].
C. Understanding the brain mechanisms of motivation
1) Establishing a methodology of evaluating the level ofmotivation in animals (monkeys): A group of co-investigator
Tsutsui and his colleague (Dr Nakamura) has established astandardized method of evaluating the level of motivation of amokey, by using a modified Brainkman Board Test. This
procedure enables the evaluation of motivation with small
number of trials and short time duration [6].
2) Evaluating the effect of repetitive transcranial magnetic
stimulation (rTMS) on the cortical excitability andelectrocorticogram signals as a mean of neural intervention: Agroup of co-investigator Tsutsui and his colleague (DrNakamura) has studied the impact of rTMS on the corticalexcitability indicated by motor evoked potentials (MEP) andthe electrocorticogram (ECoG) signals. Stimulation frequency
conditions were set in a parametrical manner (0.5, 1, 2, 5, 10,
and 20 Hz). Out of these conditions, 1Hz rTMS induced largest
inhibitory effect on the cortical excitability, accompanied by thedecrease of the power of relative low frequency bands (theta,alpha, and beta) in the ECoG signal, whereas 10 or 20 Hz rTMS
induced largest excitatory effect on the cortical excitability,
accompanied by the increase of the gamma frequency band in
the ECoG signal. Thus, we found that 1 Hz and 10 or 20 Hz,
respectively would be the optimum rTMS frequency forinhibitory and facilitatory neural interventions, and thatchanges of ECoG signals can be indications of the efficacy ofrTMS interventions on the stimulated brain area [7].
3) Finding the cortical area responsible for the generation of
motivation: A group of co-investigator Tsutsui and hiscolleague (Dr Nakamura) has studied whether the motivation
measured by the modified Brinkman Board Test can be alteredby an inhibitory neural intervention of 1 Hz rTMS targeting
various regions of the frontal cortex. It was found that the
inhibition of neural activity of the subgenual or pregenual

anterior cingulate cortex alters the performance, whereas theinhibition of any other regions of the frontal cortex does not.
The observed change of performance was that the monkeys
completed less number of sessions in the “difficult” condition,
whereas no change was observed in the “easy” condition. (Taskdifficulty was manipulated by changing the width of the foodwell on the board.) The result indicates the critical involvementof the subgenual or pregenual anterior cingulate cortex in thegeneration of motivation. Furthermore, in addition to thechange of performance in Brinkman Board Test, those monkeys
showed reduced sociability and increase of blood cortisol level,
which can be indications of a mild depressive state [8].
IV. FUTURE PERSPECTIVE
We made advances in the investigation of the relationshipbetween the sense of agency and motor learning. Our
experiments have demonstrated that a sense of agency
facilitates motor learning. We also revealed fundamental
mechanisms of sense of agency in cognitive framework andbrain networks. Concerning motivation, we have established amethodology of evaluating the level of motivation in animals(monkeys), and specified the brain area critically involved in
the generation of motivation. In collaboration with Prof. Izawa
of the B03-group, we will investigate neural and psychologicalmechanisms in which sense of agency plays a role of a reward
in motor learning in monkey and human experiments.
REFERENCES
[1]

[2]

[3]

[4]

[5]

[6]
[7]

[8]

Chiyohara, S., Furukawa, J. I., Noda, T., Morimoto, J., & Imamizu, H.
(2020). Passive training with upper extremity exoskeleton robot affects
proprioceptive acuity and performance of motor learning. Scientificreports, 10(1), e11820.
Wen, W., Shibata, H., Ohata, R., Yamashita, A., Asama, H., & Imamizu,
H. (2020). The Active Sensing of Control Difference. iScience, 23(5),101112.
Wen, W., Shimazaki, N., Ohata, R., Yamashita, A., Asama, H., &Imamizu, H. (2020). Categorical Perception of Control. eNeuro, 7(5),ENEURO.0258-20.2020.
Yano, S., Hayashi, Y., Murata, Y., Imamizu, H., Maeda, T., & Kondo, T.
(2020). Statistical Learning Model of the Sense of Agency. Frontiers in
Psychology, 11, e539957.
Yamashita, A., … （中 21 人）… Imamizu, H. (2020). Generalizable
brain network markers of major depressive disorder across multiple
imaging sites. PLoS biology, 18(12), e3000966.
Nakamura, S, Ogawa, K, Tsutsui, KI. Modified Brinkman board test forevaluating motivational state in monkeys. (in prep.)
Honda, Y., Nakamura, S., Ogawa K., Tobler, P., Nishimura Y., Tsutsui
KI. Changes in beta and high-gamma power in resting-stateelectrocorticogram induced by repetitive transcranial magnetic
stimulation of primary motor cortex in unanesthetized macaque monkeys.
Neuroaceince Research (under revision)
Nakamura, S., Kishimoto Y., Sekino M., Nakamura, M., Tsutsui, KI.
Depression-like state induced by low-frequency repetitive transcranial
magnetic stimulation targeting ventral medial frontal cortex in monkeys.
(in prep.)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)
Kyoto University Graduate School of Medicine/National Center of Neurology and Psychiatry (NCNP)
1

2

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 second
year, Takakusaki and colleagues examined the role of the parietal
cortex in the optimization of predictive postural control duringmulti-task voluntary movements (from gaze fixation in quiet
standing to postural preparation and forelimb reaching) in thecat. Hanakawa and colleagues used a 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 evaluationof dopamine systems. These studies will clarify relationshipacross cognitive functions, neural network dynamics andneurotransmitters underlying the generation 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 [1]. 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 GROUP
Takakusaki (a principal investigator) and his colleagues in
AMU aim to verify the mechanisms of optimal postural control
during voluntary movements in experimental animals (cats)with and without damages in DA/ACh systems. In the first year
(2019), we established paradigms of postural control of the catthat has learned the "multitask voluntary movements" fromquiet standing with fixed gaze position to forelimb reaching to
the target in various conditions (Fig.1). In the 2020 year, we
employed this experimental paradigm to examine the role ofthe parietal cortex in the manner of optimization in posturalcontrol preceding the forelimb reaching movement. For this,muscimol (10 µg/µl, per site), one of GABAA-agonist, wasmicroinjected into the various areas of the posterior parietal

cortex (PPC) so that the target areas were inactivated. Then,changes in the center of vertical pressure of the cat wasmeasured before, during, and after achievement of the reaching.
Hanakawa (a research collaborator) and his colleagues in
Kyoto University/NCNP have performed clinical studies. In theacademic year 2019-2023, they aim at discovering relationshipamong brain functions and dynamics of brain activityconnectivity in association with senescence. To this end, theHanakawa lab will take advantages of PADNI [1] which is alongitudinal cohort study involving healthy elderly people aswell as patients with PD and AD. In this year they developed amethod to examine relationship between cognitive functions
and dynamic profiles of functional connectivity among distinct
neural networks (simultaneous EEG-fMRI) of elder subjects in
a comprehensive manner so that they provide necessary data
for modelling to engineering research groups. The Hanakawa
lab also started a basic research on neuromelanin MRI to assess
DA production in the substantia nigra (SN) in addition to DA
transporter (DAT) SPECT for evaluating the activity of the DA
system.
III. RESEARCH TOPICS
A. Role of the parietal cortex in the optimal postural control
Takakusaki group established an experimental system for
examination of the visuomotor multi-tasks of the cat. (Fig. 1A).
The center of vertical pressure (CVP), which was calculated
from the ground reactive force exerted on each limb, was used
as a parameter of postural changes. As shown in Fig.1Ba,
0.2~0.4 s after gazing at the target, the cat started to change
posture (●), which shifted CVP with a speed of 0.2~0.6 m/s(●), and then, the cat lifted its left forelimb, which reached thetarget of 0.1~0.2 s later (●). Fig.1Ca shows changes in CVP
throughout the task. When the cat gazed at the target (🔹🔹), theCVP moved forward about 20 mm from the reference
coordinates (○), and before lifting the forelimbs (🔹🔹), it moved
about 30 mm to the right. However, the CVP hardly moves
from the forelimb lift to the end of reaching (🔹🔹). Namely, theoptimal postural control that precedes the voluntary movement
is considered to be "predicting and generating the postural stateof the end of the goal-directed movement before starting." We
also observed that muscimol injection into the medial PPC
prolonged postural control time, decreased CVP velocity,
reduced the CVP shift between the states, and restricted the
distribution range of coordinates of each state (Fig. 1Bb, Cb).
Muscimol injections into the medial PPC, on the other hand,Yoshinaga et al. applied this analysis to the previous data of the
PADNI study [1]. Yoshinaga et al. compared the dynamic
network states among patients with Parkinson’s disease, thosewith dementia with Lewy bodies (DLB) and healthy elderly
participants. Yoshinaga et al. found that patients with DLB
tended to show the sparse FC condition.

Fig. 1. Forelimb reaching task in the cat.
See text for details. M1; primary motor cortex, S1; primary sensory cortex,
PPC; posterior parietal cortex.

increased the frequency of target gaze and increased the timerequired for reaching. Therefore, the medial part of the PPC
may be involved in preceding postural control, and the lateralpart may contribute to the accurate control of forelimb reaching
movements.
B. Non-invasice multimodal measurement of dynamicchanges of brain activity and connectivity in humans
Yoshinaga and Hanakawa have been working on developing simultaneous EEG-fMRI and MEG [2] to measure dynamicchanges of brain activity and connectivity non-invasively inhumans. This year, Yoshinaga et al. applied independent
component analysis (ICA) to fMRI data and retrieved thedefault mode network, dorsal attention network, salience
network, and central executive network. Inter-network
correlation of fMRI time-series (functional connectivity, FC)
was assessed for each time window, yielding dynamic changesof FC. Yoshinaga et al. found at least two internetwork states: adense FC condition and a sparse FC condition (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 substantia
nigra, in addition to dopamine transporter (DAT) SPECT forthe potential assessment of presynaptic DA terminals in the
striatum. The team has also aimed to combine the MRI methodwith the histochemical assessment in specimen. In this year, by
utilizing a few structural MRIs, the team has discovered asynthesized MRI contrast image, which potentially reflects
substantia nigra and fibers connecting between the substantial
nigra and other basal ganglia nuclei.
IV. FUTURE PERSPECTIVE
Takakusaki's group found that the PPC was involved in the
optimal control of preceding posture adjustment and goaldirected movements. Considering that degeneration of theparietal cortex in AD in which cholinergic function is impaired,
the present findings suggest that postural dysregulation that
depends on higher brain function is one of the major causes ofthe frequent falls in AD. From the next fiscal year onward, they
will employ pharmacological and optogenetic approaches
combined with neurophysiological techniques to examine
cortical and subcortical mechanisms of optimal control ofpreceding postural adjustment with respect to the role of theDA-ACh systems. Hanakawa’s group developed simultaneousEEG-fMRI measurements for evaluating a dynamic profile offunctional connectivity alongside standard fMRI technique inPD [3]. They plan to apply this method to the PADNI cohort
study, while performing basic studies on this technique and DA
imaging. In the PADNI cohort, DAT-SPECT will also be
employed to detect a decrease in striatal DA. However, since
many unclear points exist in the contrast expression inneuromelanin MRI, which is considered to reflect the
production of DA in the substantia nigra, basic studies will be
conducted in parallel.
REFERENCES
[1]
[2]

[3]

Fig. 1. Dense and sparse states of functinal connectivity across default
mode network, salience network, dorsal attentional network, and central
executive network.

T. Hanakawa: Parkinson’s disease and Alzheimer’s disease Dimensional
Neuroimaging Initiative in Japan. Dementia Japan 35:39-44, 2021.
K. Yoshinaga, M. Matsuhashi, T. Mima, H. Fukuyama, R. Takahshi, T.
Hanakawa, A. Ikeda: Comparison of different phase synchronization
measures for identifying event-related functional connectivity in humanmagnetoencephalographic and simulated data. Front Neurosci 14:648,
2020.
D. Nishida, K. Mizuno, E. Yamada, T. Hanakawa, M. Liu, T. Tsuji: The
physiological mechanism of gait improvement with rhythmic soundstimulation in patients with Parkinson's disease – A functional MRI
study. Parkinson Relat Disord (in press).

Annual report of research project A05-1
Shin-ichi Izumi
Graduate School of Biomedical Engineering, Tohoku University
Abstract—This study aims to elucidate the hyper-adaptation
mechanism of upper limb recovery in stroke patients. We
longitudinally investigate relationship among frequency of upper
limb use, body-specific attention and brain activities in stroke
patients by using accelerometers, psychophysical methods, andfMRI, respectively. We expect the elucidation of the adaptation
mechanism underlying upper limb recovery after stroke could
contribute to understanding the hyper-adaptation mechanism ofbody-brain system. This year, we enrolled 30 patients with stroke,
and analyzed the measured data in the subacute phase andmeasured the longitudinal data until the chronic phase.

I. INTRODUCTION
The most common disability after stroke is upper limb
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 morethan 40% have residual disability in the chronic phase [1].
Upper limb paralysis affects the activities of daily living andsignificantly impairs the quality of life[2].
In order to establish effective rehabilitation for upper limb
paralysis, various treatment techniques based on plasticchanges in the central nervous system have been developed sofar. For example, CI therapy, which encourages the use of theparetic upper limb in daily life by restraining the non-paretic
upper limb and overcomes the learned non-use of the paretic
hand[3], repetitive facilitation exercises, action observation,
brain- computer interface (BCI), virtual reality (VR),kinesthetic illusion induced by visual stimulation (KiNvis),robotic training, neuromuscular electrical stimulation, andnoninvasive brain stimulation have been reported [4]. However,the pathophysiology and recovery process of stroke hemiplegia
are diverse, 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[5]. In order to overcome
these problems, we have participated in the scientific researchon innovative areas " Understanding brain plasticity on body
representations to promote their adaptive functions: Embodiedbrain systems science" (2014-2018) as leader of therehabilitation medicine group. And we have been working withresearchers in systems engineering and brain science tounderstand the adaptive mechanisms of the neural basis thatmediates between the brain and the body (body representationin the brain) using mathematical models and to develop
rehabilitation treatment based on these models. In this study,
we developed a method to quantify body-specific attention as amarker of body consciousness, and found that body-specificattention was lower in chronic stroke patients with longer timesince stroke onset and lower hand function[6]. This is the firstfinding to measure learned non-use in chronic stroke patientsfrom the aspect of body consciousness. However, it is not clear
how body-specific attention changes from the onset of stroke tothe chronic phase, and how it relates to the frequency of upper
limb use and brain activity.
II. AIM OF THE GROUP
This study aims to elucidate the hyper-adaptation
mechanism of upper limb recovery in stroke patients. We
longitudinally investigate relationship among frequency ofupper limb use, body-specific attention and brain activities instroke patients by using accelerometers, psychophysicalmethods, and fMRI, respectively. We expect the elucidation of
the adaptation mechanism underlying upper limb recovery
after stroke could contribute to understanding the hyperadaptation mechanism of body-brain system.
III. RESEARCH TOPICS
The study design was a longitudinal prospective
observational study. The subjects were 30 patients withsubacute stroke whose general condition had stabilized after 2
weeks of onset. The measurement time points were initial(after enrollment), 2 weeks, 1 month, 2 months, 6 months, and1 year. The Fugl-Meyer Assessment (FMA) was used toevaluate clinical upper limb function, and the Action Research
Arm Test (ARAT) was used to evaluate the ability tomanipulate objects. The frequency of upper limb use in daily
life was measured using an activity meter with a built-in 3axis accelerometer (wearable activity meter ActiGraph Link,
GT9X Acti Japan Co). The accelerometers were worn on bothwrists (Fig. 1A). The measurement period was 3 days, and thedaily wearing time was from waking up to going to bed. The
accelerometers were removed when the subjects were wet,such as when bathing. We measured the body-specificattention of the paretic hand in a visual stimulus detection task
as an index reflecting body consciousness [6]. To examine theamount of body-specific attention to the paretic hand, we
defined the amount of body-specific attention to the paretic
hand calculated by subtracting the average reaction time forthe paretic hand from that for the dummy hand (Fig. 1B).
Brain activity was confirmed by fMRI. The motor task
consisted of finger flexion and extension at a rate of 1 Hz. 30second block design was used (30-second rest, 30-second hand
movement). Three conditions were performed: paretic hand,
non-paretic hand, and both hands. The images were analyzed

A. Changes of function, frequency of upper limb use, andbody consciousness in subacute stroke patients.
First, data were analyzed to determine the relationshipbetween paretic upper limb function (FMA, ARAT), frequency
of upper limb use (activity meter), and changes in bodyspecific attention, a marker of body consciousness, in patientswith subacute stroke.
Results showed that paretic upper limb function improved
up to 2 months and was maintained up to 6 months. The
frequency of upper extremity use and body-specific attention
increased up to 1 month, and the change was gradual up to 6
months. There was a positive correlation between the amountof change in body-specific attention and the amount of change
in the frequency of upper limb use.
B. Changes of brain functional and structural networks insubacute stroke patients.
Second, functional brain imaging was used to examinechanges in brain activity regions during paretic hand
movements. In order to align the lesion side of the stroke
patients, the functional and structural images taken were
flipped for 2nd level analysis. The extensive brain activity
including the contralesional hemisphere, which was observedin the early stage during paretic hand movement, gradually
localized to the hand region.
Diffusion tensor imaging (DTI) was performed during theacquisition of functional brain images to evaluate the status ofstructural networks including corticospinal tracts due to brain
damage. TBSS was performed to confirm the changes of whitematter fibers in the whole brain. We also performed ROI
analysis using regions of interest, which are considered to beimportant for recovery after brain injury. DTI analysis has been
the subject of various discussions regarding damaged brains,
and we aimed to establish an optimal analysis method. In the
future, we will analyze the correlation between the obtained
functional and structural image data and the behavioral indicesat each period.

Dummy hand

Activity counts

using SPM12 (Statistical Parametric Mapping: Wellcome
Trust Center for Neuroimaging, University College London,
UK), a functional brain image analysis software, in a
MATLAB environment. Individual and group analyses were
performed using the preprocessed data (Fig. 1C).
For rehabilitation during the study period, the usual
occupational and physical therapies were performed according
to the general condition (early mobilization, ADL training,self-management guidance). The content and amount of upper
extremity exercises were controlled using a standardized upper
extremity program Graded Repetitive Arm Supplementary
Program (GRASP) for one hour per day[7].

Non-paretic
upper limb
非麻痺側上肢活動量

Paretic upper limb

Real hand (Paretic hand)

XYZ

Visual detection task (Psychophysical methods)

A. Frequency of upper limb use (Accelerometers)
Base line

1 month

B. Body-specific attention
2 months

6 months

Lesionside

C. Activation during paretic hand movements (fMRI) ※A case of longitudinal changes of brain activity
6ヵ月
ベースライン
2週 1
1ヵ月
1年
Base
line 2 weeks
month
2 2ヵ月
months6 months
1 year

計測時期
Measurement

Fig.1. Overview of this study. A：Wristwatch type 3-axis accelerometers(right) were worn on both wrists (left) , and the time series data of bilateralarm activities were recorded (bottom). B ： When a blue stimulus waspresented on the dummy hand or the paretic hand, the subject quickly pressed
a button with the non-paretic hand and the reaction time (RT) was measured.
we defined the amount of body-specific attention as calculated by subtracting
the RT for the paretic hand from that for the dummy hand．C：Brain
activities during the paretic hand movement were recorded using fMRI.
Changes of the brain activity were found in a representative case frombaseline to 6 months after the enrollment.

IV. FUTURE PERSPECTIVE
In the recovery process of paretic upper limb, bodyspecific attention was found to be related to frequency ofupper limb use. We believe that the results will contribute to
the construction of rehabilitation strategies to enhance body
consciousness and frequency of upper limb use according to
the stage and condition of stroke. In the next year, we will
analyze the correlation between the brain functional andstructural networks and behavioral indices of stroke patients.
REFERENCES
[1]

[2]

[3]

[4]

[5]

[6]

[7]

S. C. Cramer et al., “A Functional MRI Study of Subjects
Recovered From Hemiparetic Stroke,” Stroke, vol. 28, no. 12, pp.
2518 LP – 2527, Dec. 1997.
D. S. Nichols-Larsen, P. C. Clark, A. Zeringue, A. Greenspan, and S.
Blanton, “Factors influencing stroke survivors’ quality of life duringsubacute recovery,” Stroke, vol. 36, no. 7, pp. 1480–1484, 2005.
E. Taub, G. Uswatte, and T. Elbert, “New treatments inneurorehabiliation founded on basic research,” Nat. Rev. Neurosci.,
vol. 3, no. 3, pp. 228–236, 2002.
N. Takeuchi and S. I. Izumi, “Rehabilitation with poststroke motor
recovery: A review with a focus on neural plasticity,” Stroke
Research and Treatment. 2013.
N. Takeuchi and S. I. Izumi, “Combinations of stroke
neurorehabilitation to facilitate motor recovery: Perspectives on
Hebbian plasticity and homeostatic metaplasticity,” Front. Hum.
Neurosci., 2015.
N. Aizu, Y. Oouchida, and S. Izumi, “Time-dependent decline ofbody-speci fi c attention to the paretic limb in chronic stroke
patients,” Neurology, vol. 91, pp. e751-758, 2018.
J. E. Harris, J. J. Eng, W. C. Miller, and A. S. Dawson, “A selfadministered graded repetitive arm supplementary program(GRASP) improves arm function during inpatient stroke
rehabilitation: A multi-site randomized controlled trial,” Stroke, vol.
40, no. 6, pp. 2123–2128, 2009.

Annual report of research project A05-2
Ken-ichi Amemori

Institute for the Advanced Study of Human Biology (ASHBi), Kyoto University
Abstract—— Clinical studies have shown that patientswith anxiety disorders exhibited coactivation of limbic
cortices and basal ganglia. We hypothesized that excessivesynchronization of the neural oscillations of the corticalstriatal network could be involved in the induction of theabnormal anxiety states. To investigate this, we willproduce a monkey model of an anxiety disorder using theelectrical stimulation of the striatum. Using a multi-siteneural recording method, we will simultaneously record
neural activity in the network of the limbic cortex andstriatum. We will analyze how the activities of each region
synchronized during the pathologically anxiety state andrecovery phases.
I. INTRODUCTION
The functional alterations in the state of anxiety disorders
should be correlated with the abnormal communication in thelimbic structures. We hypothesized that excessivesynchronization of neural oscillations in the limbic system
could be correlated with excessive anxiety-like states. To
investigate this hypothesis, we will record the neuraloscillations from the multiple structures of the limbic cortex
and basal ganglia. Then we analyze whether the synchronous
activities among those structures changes depending on thepathologically anxiety-like state and the recovery phase.
II. AIM OF THE GROUPFirst, the specific objective of this research item is to clarify
the differences in neural activity between pACC andsubgenual cingulate cortex (SCC). We further focus on thesynchronization of the local field potentials (LFPs), whichhave been recorded by the representative regions using themulti-site recording method. We will clarify whether theneural oscillations recorded from the pACC and SCC cansynchronize. We adopted the approach-avoidance decisionmaking task to quantitatively analyze the anxiety-like state of
macaque monkeys. We then have found that the electricalmicrostimulation of the striatum can induce pessimistic
decision making resembling the anxiety-like state [1, 2]. The
striatal beta oscillation could be categorized into two majorgroups (Ap and Av groups), and the oscillation intensity of theAv group was correlated with the induced anxiety-like state.

III. RESEARCH TOPICS
Three specific results of this year's project are listed below.
We provide a summary for each section.
A. Anatomy of an anxiety-related
network originating from the pACC
First, we asked humans to perform aconflict task similar to that of macaquemonkeys and examined neural responses
by fMRI. We found that neural activity
related to the approach-avoidance
conflict was similar between macaquesand humans in various brain areas, Fig. 1. pACC
including the pACC [3]. These results projections to thesuggest that the network originating amygdala.
from the pACC is common between

Fig. 2. The connection originating from the pACC
and cOFC could consists anxiety network.
humans and macaques. We found that the pACC sends
projection to the striatal striosome compartment [4]. We
further found that the pACC and other anxiety-related
structure projects to the amygdala (Fig. 1)and construct a
reciprocally connected network. We summarize these results(Fig. 2) and submitted to a journal [5].

Fig. 3. Simultaneous recording from
pACC, SCC, and striatum wasconfirmed.

B. Construction of an experimental system from theventromedial prefrontal cortex
Second, we have recorded from the SCC [6], which is thecritical target of deep brain stimulation (DBS) to treat major
depression. We also have simultaneously recorded from thepACC. We thus investigated the relationship between thepACC and the SCC by focusing on the two areas' synchronous
activities (i.e., coherence). We further recorded the LFPs in the
striatum. The results were analyzed and published in a journal[7]. The synchronization of the beta oscillations in each area
can be examined by analyzing coherence. We will induce a
pessimistic state by striatal stimulation and examine whether
the coherence intensity changes with the change of theemotional states. If the membrane potential in the two regions
oscillates synchronously, then the influence of spikes in one
region to the other becomes more effective (communication
through coherence hypothesis)[8]. We will examine whether
the induction of anxiety can change the coherence between the
two areas. This year, we confirmed the positions of theelectrodes by histological examination. We confirmed that theelectrodes had been properly inserted into the SCC (Fig. 3).
C. Discovery of un-uniform inverse correlation between
striatal dopamine concentration and beta oscillations
Dopamine depletion in the brain is known to cause
Parkinson’s disease, associated with movement and mooddisorders. In the patients with Parkinson’s disease, an increasein the magnitude of the beta-range neural oscillations in the
striatum of the basal ganglia was observed. It is thus assumed
that the beta magnitude in the striatum is inversely correlatedwith the dopamine concentration. We performed simultaneousrecordings of the beta-range neural oscillations and dopamine
signals using Fast scan cyclic voltammetry (FSCV) whilemonkeys performed a behavioral task. We found that theinverse correlation between beta waves and dopamine appeared
depending on the task context, showing a more complex
feature than the previous assumption. The inverse correlation

between beta oscillation and dopamine was not uniform. This
new finding is expected to provide a new guideline fordiagnosing and treating Parkinson’s disease. We recently
published these results[9].
IV. FUTURE PERSPECTIVE
Finally, we will summarize the results of this year's
research and provide an outlook for the next year and beyond.
We calculated the degree of synchronization by calculating thecoherence between the pACC and the LFP of the SCC andfound that there is particularly strong synchronization(coherence) in the high-beta band (Fig. 4). We will continue toanalyze these data and further elucidate how the correlation
between pACC and SCC changes when anxiety is induced.
REFERENCES

[1]
[2]

[3]

[4]

[5]
[6]
[7]

[8]
[9]

Fig. 4. Example of coherence between SCC andpACC. We will investigate the relationship between
anxiety induction and coherence in detail.

Amemori K, Graybiel AM. Localized microstimulation of primate
pregenual cingulate cortex induces negative decision-making. Nat
Neurosci. 2012 May;15(5):776-85.
Amemori KI, Amemori S, Gibson DJ, Graybiel AM. Striatal
Microstimulation Induces Persistent and Repetitive Negative DecisionMaking Predicted by Striatal Beta-Band Oscillation. Neuron. 2018 Aug
22;99(4):829-841.e6.
Ironside M, Amemori KI, McGrath CL, Pedersen ML, Kang MS,
Amemori S, Frank MJ, Graybiel AM, Pizzagalli DA. ApproachAvoidance Conflict in Major Depressive Disorder: Congruent NeuralFindings in Humans and Nonhuman Primates. Biol Psychiatry. 2020
Mar 1;87(5):399-408.
Amemori S, Amemori KI, Yoshida T, Papageorgiou GK, Xu R,
Shimazu H, Desimone R, Graybiel AM. Microstimulation of primate
neocortex targeting striosomes induces negative decision-making. Eur J
Neurosci. 2020 Feb;51(3):731-741.
Amemori S, Graybiel AM, Amemori KI. Causal evidence for inductionof anxiety-like behavior in primates by the network including frontoorbital cortex and striosomes, Front. Neurosci. 2021 (under review).
Mayberg HS, Lozano AM, Voon V, McNeely HE, Seminowicz D,
Hamani C, Schwalb JM, Kennedy SH. Deep brain stimulation fortreatment-resistant depression. Neuron. 2005 Mar 3;45(5):651-60.
Amemori KI, Amemori S, Gibson DJ, Graybiel AM. Striatal Beta
Oscillation and Neuronal Activity in the Primate Caudate Nucleus
Differentially Represent Valence and Arousal Under ApproachAvoidance Conflict. Front Neurosci. 2020 Feb 7;14:89.
Fries P. Rhythms for Cognition: Communication through Coherence.
Neuron. 2015 Oct 7;88(1):220-35.
Schwerdt HN, Amemori K, Gibson DJ, Stanwicks LL, Yoshida T,
Bichot NP, Amemori S, Desimone R, Langer R, Cima MJ, Graybiel
AM. Dopamine and beta-band oscillations differentially link to striatal
value and motor control. Sci Adv. 2020 Sep 25;6(39):eabb9226.

Annual report of research project A05-3
Riki Matsumoto

Division of Neurology, Kobe University Graduate School of Medicine
Abstract—In order to elucidate the Hyper-Adaptabiility
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, we make an electrophysiological connectome byusing cortico-cortical evoked potentials (CCEPs) as an index ofeffective connectivity, which were obtained by systemic
evaluation of the whole implanted electrodes. We extract
structural features such as cluster coefficient or centrality, andattempt to clarify modifications of the connectome by epilepsy
pathology or variations by different strategies for higher-ordermotor tasks.

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 research area “Embodied-Brain Systems Science,”
we delineated the neural basis and compensation mechanismof praxis or sense of agency (SoA) embodied in the ventral
frontal lobe and the parietal lobe as physicians contributing toepilepsy surgery. In the present research area, “HyperAdaptability,” for investigating the acute and subacute (hyper)
adaptation at the network level, we investigate the impact of
the resection of the premotor area by simulating the virtual
lesion using the electrophysiological connectome, andcomparing it with the movement disability and its recovery
after surgery.
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.
We probe neural signatures of higher-order motor control
by recording wide-band ECoG activities during higher-ordermotor tasks. In order to understand the brain network
associated
with
motor
control,wemakeanelectrophysiological connectome by using cortico-corticalevoked potentials (CCEPs) as an index of effective
connectivity, which were obtained by systemic evaluation of
the whole 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 clarify how the brain hyper-adapts to theintervention to the premotor cortex, we investigate the impactof high-frequency electrical stimulation and brain surgery on
hyper-adaptation. We apply high-frequency electricalstimulation to the cortex for clinical functional corticalmapping. Stimulation of the premotor areas affects theexecution of higher-order motor tasks to a variable degree. To
investigate the immediate modulation, we use this opportunity
to study the relationship between the mode of behavioral
impairment and network impairment by combining
quantitative motion analysis and ECoG signal analysis during
stimulation.
III. RESEARCH TOPICS
We have carried out the following three research projects.
A. Electrophysiological connectome of individual patientand patients group
With subdural electrodes (more than 100 electrodes per one
patient), ECoG was recorded during motor tasks such as
simple movements (face, hands, shoulders, feet) and higherorder motor tasks such as tool-use pantomime, reach to grasp
movements, finger movements, and Go / NoGo tasks. The
cortical brain rhythms, ranging from infraslow to high gamma
activity, was explored and referred to as a localizer together
with the anatomical information of the electrodes. In the
NoGo task, task-specific event-related potentials (ERPs) wererecorded mainly in the medial frontal lobe and the dorsal
premotor area [1]. Next, low-frequency electrical stimulation
was applied through all electrodes, and the cortico-corticalevoked potential (CCEP) recorded via intercorticalconnections was used as an indicator of effective connection.
Electrophysiological connectomes were created in eachpatient.
CCEP during the inferior frontal gyrus stimulation showeda connectivity gradient. Pars Orbitalis and Pars Opercularis
stimulation elicited responses at the antero-inferior part of thetemporal lobe & angular gyrus, and the posterior part of thetemporal lobe & supramarginal gyrus, respectively, while Pars
Triangularis stimulation showed an intermediate connectivity
pattern between the two. [2].
The part of the ventral premotor area (negative motor area
[NMA]) was known to show a negative motor response(arrests of repetitive movement of the tongue, hands, and feetstops) upon ECS. In NMA, we showed significantly larger
outbound connections compared to the primary motor cortex
and the language areas [3]. In addition, we investigated how
epilepsy modulated brain networks, and revealed strengthened
connectivity in epileptic regions and weakened connectivity in
non-epileptic regions [4]. In the future, we will compareindividual connectome with group data to clarify thetransformation of connectome due to epilepsy pathology andstrategies. Besides, as a leading-expert in CCEP, the PI wrote
reviews and textbook chapters on intraoperative CCEP
function mapping [5] [6] .
B. Disturbance of network by high-frequency electricalstimulation and disability of higher-order motor
functionIn the Go / NoGo task, high frequency stimulation tended to
impair NoGo task performance when the stimulated electrodes
had broader CCEP connectivity. It was suggested that
disturbance of the NoGo task is caused by its impact to thewhole network including the cortical regions other than thestimulated area.
In addition, by carefully removing stimulus artifacts ofhigh-frequency stimuli, we were able to detect intrastimulus
discharges (ISD) that were induced in the remote corticalregions by ECS of the premotor area. ISD appeared within therange of physiological CCEP connectivity, but only a small
number of electrodes with ISD did show a negative motor
response upon ECS [7]. We will identify the mode of network
disturbance caused by ECS in the premotor area by
incorporating ECS-induced ISD and CCEP connectome
database. We then compare the network disturbance with thedegree of task impairment by combining subject’s
introspection and quantitative behavior analyses in order toclarify the dynamics of the brain to the acute intervention. We
attempt to generalize the brain dynamics to ECS byperforming the similar analyses in the language or visualrelated tasks (preliminary reports [8,9]).
C. Modification of connectome by surgical resection andhyper-adaptability of network
This prospective study is not well performed this year due to
COVID-19 pandemic. When we have to resect a part of the
premotor area due to clinical need for epilepsy or tumor
surgery, we will reconstruct the premotor area connectome
excluding the resection area based on the comprehensive
CCEP responses to simulate 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 elucidate“hyper-adaptive” 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, we focused on the human
premotor area and constructed preliminary electrophysiological connectomes related to the premotor area to
elucidate the network characteristics. Although it may bedifficult to recruit patients due to the COVID-19 pandemic, weplan to recruit five patients prospectively. We also
retrospectively incorporate 20 patients in whom higher motorrelated cortical rhythm measurements and comprehensive
CCEP evaluations were available. We will continue theaforementioned research plans in the next year. Findings
obtained in clinical systems neuroscience would contribute todelineate the "hyper-adaptability" mechanism as an essentialreference for model construction/verification and network
modification by rehabilitation intervention.

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

[7]
[8]
[9]

Takeyama H et al. In preparation
Nakae T, Matsumoto R, et al. Connectivity Gradient in the Human Left
Inferior Frontal Gyrus: Intraoperative Cortico-Cortical Evoked Potential
Study. Cereb Cortex, 30:4633-4650, 2020
十河正弥 , 松本理器ら．ヒト一次運動感覚野と高次運動/言語皮質における皮質間ネットワークの特徴の差異：皮質皮質間誘発電位を用いた検討. 第 43 回日本神経科学学会. July 2020
十河正弥 , 松本理器ら. 難治部分てんかん患者における皮質間ネットワークの特徴と臨床所見との関連：皮質皮質間誘発電位を用いた検討. 第 50 回臨床神経生理学会．Nov 2020
Yamao Y, Matsumoto R, Kikuchi T, Yoshida K, Kunieda T, Miyamoto
S. Intraoperative brain mapping by cortico-cortical evoked potential.
Front Hum Neurosci, accepted, doi: 10.3389/fnhum.2021.635453
Matsumoto R, Dionisio S, Staudt M, Bulacio J, Nair D. Functional
Mapping with Neurophysiologic Techniques (Chapter 78). In: Wyllie E,
Gidal B, Goodkin H, Loddenkemper T, Jehi L. Wyllie’s Treatment of
Epilepsy 7th edition, Lippincott Williams&Wilkins, Philadelphia,
pp891-904 2020、書籍（分担執筆）ISBN/ISSN 9781496397690
十河正弥 , 松本理器 , 下竹昭寛ら．高頻度皮質電気刺激による高次運動障害の発現機構の検討:皮質刺激誘発反応と行動解析の包括的検討．第 48 回臨床神経生理学会．Nov 2018
Usami K, Matsumoto R, et al. Neural processes during picture naming
are lateralized and category-biased in occipitotemporal areas. 第 61 回
日本神経学会学術大会. Aug 2020
宇佐美清英, 松本理器ら. 生物・非生物の視覚刺激は脳後方で異なる神経活動伝播をもたらす．第 50 回臨床神経生理学会．Nov
2020

A05-4 Neural circuit rearrangement mechanisms
underlying the recovery from learning deficits in
Parkinson’s disease model animals
Kazuto Kobayashi
Fukushima Medical University

Abstract—Model rats for Parkinson’s disease generated bydegeneration of nigrostriatal dopamine neurons impairs
the acquisition of sensory discrimination learning, but the
impairment is recovered gradually through the continuoustrials. To understand this recovery mechanism, ourresearch group aims to address the role of dopaminergic
neuronal pathway originating from the ventral tegmental
area (VTA) and projecting to the nucleus accumbens(NAc) in the learning recovery.
I. INTRODUCTION
Neural circuits in the central nervous system show a dynamic
rearrangement in response to impairment or injury of the brain
regions and play a key role in the recovery and compensationfrom the disturbed functions. The rearrangement of the
network is an important strategy for animal’s adaptation tocompensate and restore the impaired functions. Although thestudy of the mechanism underlying network rearrangement
has been performed by using models impaired motor functions,the mechanism for improving learning and cognitive
dysfunctions remains still unknown. Our research group has
been investigating the neural mechanism of cortico-basal
ganglia network that controls the acquisition and performanceof stimulus-response association during operant learning.
Especially, the learning deficits caused by impaired dorsal
striatal function were recovered gradually by continuous task
trials, and this recovery is dependent on the function of thenucleus accumbens (NAc). These findings suggest thepossibility that dysfunction in the dorsal striatum in
Parkinson’s disease may be compensated by the neural circuit
mediated through the NAc. Model rats for Parkinson’s disease
generated by pharmacological disruption of nigrostriatal
dopamine neurons impairs the acquisition of sensory
discrimination learning, but the impairment is recovered
gradually through the continuous trials. So far, one mechanismthat explains this recovery is considered to be originated fromregeneration of nerve fibers from the nigrostriatal system. In
this study, our group will examine the possibility that the NAc
circuit may be involved in the restoration of the learning
deficits in Parkinson’s disease model animals during therepetitive task trials.

II. AIM OF THE GROUP
In this study, our group focusses on dopamine system
originating from the ventral tegmental area (VTA) andprojecting to the NAc and to investigate the role of VTA-NAc
dopamine pathway in the learning recovery by using thetechnique of pathway-specific manipulation of neural activity.
To address this issue, a chemogenetic strategy is necessary to
suppress or stimulate the activity of specific neuronal types
during the process of learning recovery. Generally, a mutant
form of metabotropic receptors is used for chemogenetic
strategy, but this procedure requires endogenous second
messenger systems, which may lead to different responses
among individual neuronal types. Therefore, our group tried touse a chemogenetic approach with ionotropic receptors. For
inhibitory ionotropic receptors, glutamate-chloride ionicchannels that are stimulated by a specific ligand ivermectin are
known to be useful to suppress the activity of target neurons.
In contrast, because a stimulatory ionotropic receptor systemthat can induce a behavioral response has not yet beendeveloped, our group tried to establish this new approach.
Next, a transgenic rat strain that expresses Cre recombinase in
specific ell types is necessary to manipulate the activity of the
VTA-NAc dopamine system. Our group aimed to generateknock-in rats, in which Cre transgene is introduced into thegene encoding tyrosine hydroxylase (TH) by using the
genome editing technique.
III. RESEARCH TOPICS
A. Develpmeent of stimulatory ionotropic chemogeenetic
strategy: Drosophila ionotropic recepors (IRs) belong to theglutmate receptor superfamily, and IR84a and IR8a subunits
form the functional complex showing an excitatory respnse to
specific ligands, such as phenylacetoaldehyde andphenylacetate. In this technoloy, IR84a/IR8a complex isexpressed in neurons of interet and injection of a specific liand
into the brain region induces the activation of the target
neurons (Fig. 1A). Transgeic mice were generated that carry
GFP-IR84a-2A-IR8a gene cassette under the contrrol of TH
gene promoter. Transgene expresson was detected in the locus

coeruleus (LC) of the transgenic mice by immunohistochemistry and in situ hybridization methods. Brain slices
through the LC were pepared and used for in vitro
electrophysiology. The applicaton of phenylacetate solution(0.1%) induced the increased firing frequency and elevatedmembrane potential in LC neurons of the transgenic mice (Fig.
1B). In the in vivo electrophysology, pneumatic injection ofphenylacette solution into the LC caused an increase in firing
activity in LC neurons of the transgenic animals (Fig. 1C). In
addition, the microdialysis experiment indicated thatmicroinjection of phenylacetate into the LC stimulated therelease of noradrenaline in nerve terminal regions of the LC in
the transgenics. Furthermore, conditioned mice with taste
version were used for taste reactivity test, in which
conditioned stimulus (CS: sucrose solution) is intraorally
injected through the cathether. Microinjection of phenylacetate
into the LC markedly shortened the latency to express aversive
behavior to the CS in the transgenic mice (Fig. 1D), indicatingthat the stimulation of LC actvity enhances the rerieval
process of conditioned taste avesion memory. These results
highlight that our ionotropic chemogenetic strategy with IRs
enable to stimulate the activity of target neurons expressing
IR84a/IR8a complex, resulting in the chaned behavioral
response (reference 1).
B. Generation of knock-in rats expressing Cre recombinase in
dopamine neurons: The gene cassette contaning 2A-Cre-poly
A signal was introduced beween the C-terminal amino acidand stop codon in te rat TH gene by using a modified genome
editing termed the Combi-CRISPR method. Guide RNA,
edited DNA, ans Cas 9 were introduced into rat fertilized eggs
with electroporation, which were implanted into
pseudopregnant females. The offspring with a knock-in
mutation were ideentified with polymerase chain reaction(reference 2). Expression pattern of Cre recombinasetransgene was analyzed by immunohistochemistry with antiCre antibody. Cre transgene was detected in dopamine
neurons in the substantia nigra and VTA as well as
noradrenline neurons in the LC of the knock-in transgenic rats.
When viral vectors that expres GFP dependent on Cre-loxP
recombination were injected into the brain rgions, expressionof GFP transgene was observed in dopamine and noadrenaline
neurons in the knock-in rats. These data indicte that the knockin rat stran will be useful to introduce the transgenes
appropriate for the control of neuronal activity into dopamine
neuons by using the viral vector system.
IV. FUTURE PERSPECTIVE
Our research group successfully developed a new strategy forstimulatory ionotropic chemogenetic tool with insect IRs andgenerated the knock-in rat strain that expressed Cre transgenein dopamine neurons. These resources are necessary forstudying the role of VTA-NAc dopamine system in therecovery from learning deficits in Parkinson’s disease model.
Subsequently, our group will produce a disease model with the
TH-Cre knock-in rats and manipulate the activity of VTA-

NAc dopamine neurons with stimulatory and inhibitory
chemogenetic approaches to examine the involvement of
VTA-NAc dopamine system in the behavioral recovery inanimal models.

Figure 1. Stimultory ionotropic chemogenetic approach. (A)Experimental strategy. (B) Slice electrophysiology. (C) In vivo
electrophysiology. (D) Effect of ligand injection on the retrievl
process of conditioned tast aversion task. (modified fromreference 1)

REFERENCES
[1]

[2]

Fukabori, R., Iguchi, Y., Kato, S., Takahashi, H., Eifuku, S., Tsuji, S.,
Hazama, A., Uchigashima, K., Watanabe, M., Mizuma, H., Cui, Y.,
Onoe, H., Hikishima, K., Yasoshima, Y., Osanai, M., Inagaki, R.,
Fukunaga, K., Nishijo, T., Momiyama, T., Benton, R., and Kobayashi, K.
Enhanced retrieval of taste associative memory by chemogenetic
activation of locus coeruleus norepinephrine neurons. J. Neurosci. 40(43): 8367-8385 (2020).
Yoshimi, K., Oka, Y., Miyasaka, Y., Kotani, Y., Yasumura, M., Uno, Y.,
Hattori, K., Tanigawa, A., Sato, M., Oya, M., Nakamura, K.,
Matsushita, N., Kobayashi, K., and Mashimo, T. Combi-CRISPR:
combination of NHEJ and HDR provides efficient and precise plasmidbased knock-ns in mice and rats. Hum. Genet. 140 (2): 277-287 (2021).

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. Hand
movement is controlled mainly by the two corticospinal
pathways: the direct and indirect pathway which exert
monosynaptic or polysynaptic control from M1s over spinal
motoneurons. When the direct pathway was lesioned, the indirect
pathway played a role in functional recovery of the impaired
hand movements. Our motivation is to develop theneurorehabilitative protocols to increase recruitment of theindirect pathway in functional recovery in patients who disable toperform fractionated hand movements. Repetitive brain
stimulation protocols alter corticospinal conductivity. Our longterm goal is to develop the brain stimulation techniques thatenable to optimize recruitment of the indirect pathway duringexecution of hand movements. During the period of fiscal year
2020, we have finished development of the cortico-spinal
functional magnetic resonance (cs-fMRI) techniques to observe
activation of the indirect pathway. We are also developing thenovel techniques to estimate change in conductivity of theindirect pathway after the brain stimulation protocols.

I. INTRODUCTION
Recent studies in non-human primates indicatedinvolvement of the indirect pathway during dexterous hand
movement. No studies so far demonstrated recruitment of theindirect pathway during hand movements in humans. We
estimated recruitment of the direct and indirect pathway during
right-hand movement (RHM) and left-hand movement (LHM)
using cs-fMRI. We measured neuronal activity in M1 andspinal cord at the segments where motoneurons innervating
hand muscles reside. We assumed the direct and indirect modelthat exerted multi-synaptic influences from primary motor
cortex to the spinal cord (Figure 1). Our results suggested
involvement of the indirect pathway during LHM but notduring RHM. We will observe the effects of repetitive brain
stimulation on the indirect pathway using cs-fMRI techniques.
The effects of several brain stimulation protocols will becompared with respect to the following questions. Will thestimulation protocols increase recruitment of the indirect
pathway during LHM? Will the stimulation protocol alter
conductivity in the indirect pathway? To address the twoquestions, we decided to improve the cs-fMRI techniques.

II. AIM OF THE GROUP
Our goal is to determine the brain stimulation protocol that
can induce the greatest recruitment of the indirect pathway
during LHM. To compare the effects of several brain
stimulation protocols on the indirect pathway, we will employ
the cs-fMRI techniques. In this fiscal year 2020, we havedeveloped the modified version of the cs-fMRI techniques.
III. RESEARCH TOPICS
Here we described the following what we achieved and what
we are preparing as follows.
A. Dvelopment of the modified version of the cs-fMRI
techniques.
As we described in the introduction, we had developed thecs-fMRI techniques. We measured activity in M1 and thespinal cord only, as depicted in Fig.1. Note that the indirect
pathway includes relay circuits in the brainstem and the spinal
cord at the cervical segments C3-C4 that likely serve
integration of the signals from bilateral M1s. We did notobserve activity in the brainstem and the spinal cord. To
visualize activity in the brain, brainstem and the spinal cord.
We modified the MRI scanning protocol to cover all of thethree regions. We also 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. Here we presented the result of
single subject’s data (Fig. 2).

techniques to record neuronal activity in the spinal cord in
humans. We decided to develop the techniques to estimate
conductivity in the indirect pathway. In this experimental
setting, we will give test pulses of brain stimulation, measure
evoked activity in the brainstem and the spinal cord at thecervical segments C3-C4, and compute conductivity with useof the input and output measurements. To achieve this project,
we need to integrate brain stimulation and cs-fMRI techniques.
We also need to synchronize the timing of test pules of brain
stimulation and fMRI scanning. We have experiences toaccomplish integration of the brain stimulation and fMRI
techniques. We are preparing to reconstruct this system.
IV. FUTURE PERSPECTIVE
In this fiscal year 2020, we have developed the cs-fMRI
techniques to estimate activity and conductivity in the indirect
pathway. We conducted a pilot study, analyzed data anddiscussed the expected results. In the next fiscal year 2021, we
will perform a main experiment using the techniques that wedeveloped.
REFERENCES

[1]
[2]

[3]
[4]

B. Development of the novel techniques to estimate
conductivity between M1, brainstem and the spinal cord.
In the conventional techniques, we computed conductivity
with use of input and output activity. There are no non-invasive[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 Analysis of motor control system in the recovery of
forelimb function by rehabilitation after intracerebral hemorrhage
Hideki Hida

Nagoya City University Graduate School of Medical Sciences
Abstractt—In the FY2020, we established the methods for our
project such as multi-electrode analysis from the cerebellum anddouble-virus infection into dentate nucleus and red nucleus,
showing that we are ready to analysis for our project in near
future.

I.

INTRODUCTION

Constraint-induced movement therapy (CIMT) is known as
an effective rehabilitation by forcing the use of damaged
forelimb after the cerebrovascular disorder. Using model ratsthat show relatively big motor deficits with a small
hemorrhage near the internal capsule (ICH), we arechallenging to clarify the mechanism of forced-limb use(FLU) that represents CIMT in rats. We revealed that FLU
after ICH improves the recovery of disturbed forelimb
function by ICH, cortico-rubral pathway is involved in thefunctional recovery by FLU, and cortico-reticular pathway has
a potential to substitute the recovery of cortico-rubral pathway
when needed. However, dynamic change of the adaptive
mechanism via the cerebellum in the functional recovery by
FLU is still unknown.
II.

AIM OF THE GROUP

The final purpose of our project is to analyze adaptive
mechanism of motor control system in the cerebellum on therecovery process of the rehabilitation after cerebral
hemorrhage by the selective blockade in the recurrence
pathways among the red nucleus, the olive nucleus and the
cerebellum with double virus-vector infection method.
The purpose of this year for our final purpose is 1) to confirm
both inputs and outputs in the red nucleus (parvocellular part)that is supposed to be related to functional recovery by therehabilitation, 2) to investigate if we can successfully detect
the electric response from the cerebellum nucleus (dentate
nucleus) and Purkinje cell using the multi-electrode analysis,
and 3) to establish our experimental system for the final
purpose including the availability of double virus-infection
method into the red nucleus.
III.

RESEARCH TOPICS

The results of our experiment in this year were shown as
follows.

A. Confirmation of the inputs and the outputs in the red
nucleus parvocellular partWe inject a retrograde tracer Mini-Ruby into two places of
the red nucleus at first and examined the stainability of thecerebellar nucleus of contralateral side of the cerebellum,
ipsilateral side of the cingulate gyrus and ipsilateral side of the
motor area.
Although Mini-Ruby-positive cells were detected in thedentate nucleus, the interposed nucleus, and the fastigial
nucleus of the cerebellum, no significant difference in thepositive cells was shown between three groups of sham group,
ICH group, and ICH + FLU group. In other words, it was
revealed that the tracer infusion should be limited near the
parvocellular part of the red nucleus.
We next performed to inject Mini-Ruby into red nucleus
parvocellular part and the biotin dextran amine (BDA) into thedentate nucleus of the cerebellum.
Limited detection of Mini-Ruby-positive cells was observedin contralateral side of dentate nucleus with the injection into aplace (A: -5.2mm, L: 1.6mm, V: 7.5mm) of the red nucleus(Figure 1). In addition, the injection of an anterograde tracer
BDA into the dentate nucleus resulted in BDA-positive fibers
in the contralateral side of red nucleus. Furthermore, MiniRuby-positive cell bodies were shown in the purkinje cell
layer by the injection into the dentate nucleus.
From the above results, neural network of Purkinje cell dentate nucleus - red nucleus parvocellular part was confirmed.

B. Investigation of successful detection of the electric
response fron the cerebellum nucleus (dentate nucleus)
To measure the changes of electrical response in the motor
control system by the rehabilitation after ICH, we challenged
to detect electrical response in the Purkinje cell of control rat
with the multi-channel electrode method as the pre-stage.
（Solages et al, Neuron 2008）
As a result, we was able to record the spikes in the cerebellar
cortex that is related to forelimb movement in anesthesia
condition. In addition, we revealed that each response of
simple spike and the complex spike was detected by spike
analysis of the recorded spikes (Figure 2). In other words, wewere ready to measure the changes of electrical response in
the cerebellum Purkinje cell by the rehabilitation after ICH
with multi-channel electrode method.
On the other hand, we are now trying to investigate theelectrical response in the dentate nucleus of the cerebellum.

C. Confirmation of experimental plan for double virus
infection method
To block neuronal network between dentate nucleus and red
nucleus parvocellular part selectively with double-virus
infection method, we discussed the virus information with
Associate Professor Kenta Kobayashi of Natl. Inst. forPhysiological Sci. (research cooperator of this project) anddecided a future experiment plan as follows: adenoassociatedvirus vector (AAV-DJ-EF1-DIO-hM4D(Gi)-mCherry) should
be injected into the dentate nucleus with retrovirus vector(FuGE-MCSV-Cre) injection into red nucleus parvocellularpart.
Using AAV-DJ vector (there is no DREDD system)
developing only EGFP in response to Cre, we are now
examining optimization of the quantity of infusion of the virus
vector to a cerebellum dentate nucleus.
IV.

FUTURE PERSPECTIVE

To analyze adaptive mechanism of motor control system inthe cerebellum on the recovery process of the rehabilitation
after ICH, we performed 1) to confirm both inputs and outputs
in the red nucleus (parvocellular part), 2) to investigate if we
can successfully detect the electrical response from thecerebellum nucleus (dentate nucleus) using the multi-electrodeanalysis, and 3) to establish our experimental plan for the final
purpose including the availability of double virus-infection
method into the red nucleus and dentate nucleus.
Following to the optimization of virus infusion, we will
confirm the loss-of-function of DREADD system with doublevirus infection method in the group of ICH + FLU. We also
evaluate forelimb motor function by pellet reaching test.

References
[1]

[2]
[3]

Camille de Solages, Germán Szapiro, Nicolas Brunel, Vincent
Hakim, Philippe Isope, Pierre Buisseret, Charly Rousseau, Boris
Barbour, Clément Léna, High-frequency organization and synchrony ofactivity in the purkinje cell layer of the cerebellum, Neuron.
2008;58(5):775-88.
Richard Apps, Richard Hawkes, Cerebellar cortical organization: a onemap hypothesis., Nat Rev Neurosci. 2009;10(9):670-81.
Takahiro
Ishikawa, Saeka
Tomatsu, Yoshiaki
Tsunoda, Jongho
Lee, Donna S Hoffman, Shinji Kakei, Releasing dentate nucleus
cells from
Purkinjecellinhibition generates
outputfromthe cerebrocerebellum, PLoS One. 2014;9(10):e108774.

A05-7 Dynamics of Inter-regional networks
underlying hyper-adaptation from fear induced maladaptation states
Hiroyuki Miyawaki

The Osaka City University, Graduate School of Medicine
Abstract—Animals that have experienced strong aversive
experiences fall into mal-adaptation states in which they cannot
take adaptive behaviors due to the fearful memories. Through thefollowing hyper-adaptation process, the animals recover theiradaptive behaviors. Accumulating evidence suggests that multiple
brain regions such as amygdala, ventral hippocampus, andprefrontal cortex are involved in the fear memory relatedtransitions to the mal-adaptation states and ones to the hyperadaptation states, but it is still elusive how inter-regional networks
change through these state transitions. Furthermore, it is barely
known what kind of network activities regulates the changes. To
elucidate these points, we performed simultaneous large-scale
electrophysiological recording in the amygdala, ventral
hippocampus, and prelimbic cortex of fear-conditioned rats. We
found that amygdala–prelimbic cortex and ventral hippocampus–
prelimbic cortex cell ensemble pairs coactivated in post- but not in
pre-conditioning non-rapid eye movement sleep epochs. We also
observed that these inter-regional coactivations were hosted by
fast network oscillations. These findings suggest fast oscillation
may regulate inter-regional communications that are crucial forthe transition to mal-adaptation states. Further studies are
warranted to elucidate how inter-regional ensemble coactivations
are involved in the hyper-adaptation process.

I. INTRODUCTION
Fear conditioning is a well-established animal model ofhuman post-traumatic stress disorder (PTSD). Fear-conditioned
animals are in mal-adaptation states where the animals cannot
take adaptive behavior due to their fearful memories. On theother hand, extinction learning provokes a hyper-adaptation
process in which animals recover normal behavior by acquiring
new memory that suppress fear responses while fearful
memories themselves are maintained [1]. The acquisition andextinction of fear memories involve the amygdala, ventral
hippocampus, and prefrontal cortex [2]. However, it is still
elusive how inter-regional networks across these regions change
through transitions to mal-adaptation states and ones to hyperadaptation states and how these changes are regulated.
Revealing these points would provide fundamental information
for developing cure methods of memory and emotion related
mental disorders such as PTSD and anxiety disorders.
II. AIM OF THE GROUP
This research project aims to clarify changes in interregional brain networks which supports hyper-adaptation fromfear memory induced mal-adaptation. Furthermore, this project
also tries to reveal regulation mechanisms underlying thechanges in the inter-regional networks. To obtain these goals,
first, we elucidate what changes in the process of mal-adaptation,then try to understand the compensation mechanism that enables
hyper-adaptation. In this year, we focused on the transition tomal-adaptation states, and we aimed to clarify the dynamics ofinter-regional networks during the mal-adaptation process andnetwork mechanisms that support the dynamics.
III. RESEARCH TOPICS
A. Simultaneous recording from three brain regions using
multi-regional large-scale electrophysiology
By
performing
multi-regionallarge-scale
electrophysiological recordings on freely moving rats, we
obtained spike activities of hundreds of neurons and local fieldpotentials (LFPs) from basolateral amygdala (BLA), ventral
hippocampus CA1 region (vCA1), prelimbic cortex layer 5(PL5) in the prefrontal cortex (Fig.1). Neuronal activities were
continuously recorded for 17 hours, during which fear
conditioning, context and cue retention test, extinction learning,
and test for retention of extinction were administrated. To reduce
electrical noise, we used stimulation through eyelid electrodes
as the unconditioned stimulus [3].

Fig. 1. A representative example of continuous large-scale
electrophysiological recording from vCA1, BLA, and PL. Firing rates ofsimultaneously recorded neurons were calculated in 10-s bins. Each row
represents each cell. Hypnograms and timing of behavioral experiments wereshown on the bottom.

B. The inter-regional synchronous activity emerged in sleep
periods following fear conditioning
To evaluate inter-regional interactions of local cell
ensembles, we first determined cell ensembles in BLA, vCA1,
and PL5 during fear conditioning periods by using independent
component analyses and estimated instantaneous ensemble
activation strength of the identified ensembles in pre- and postconditioning sleep periods [4]. Next, we evaluated inter-regional
interactions of the cell ensemble activations by performing
cross-correlogram analyses. We found that subsets of BLA–PL5
and vCA1–PL5 ensemble pairs coactivated significantly in postconditioning non-rapid eye movement sleep (NREM) epochs(Fig. 2). In contrast, we observed virtually no significant
coactivations in pre-conditioning NREM epochs. These results
indicate BLA–PL5 and vCA1–PL5 synchronous activity
emerged after the transition to the fear-induced mal-adaptation
states.

SWR-peak triggered average of ensemble coactivation strength
showed transient enhancement around time 0. These results
imply that fast network oscillations such as HFOs and SWRs aretightly related to inter-regional synchronous ensemble actives
that emerged after the transition to the mal-adaptation states.

Fig. 3. BLA–PL5 and vCA1–PL5 coactivation triggered average of LFP
wavelet power. Wavelet powers were z-scored within each frequency scale.

IV. FUTURE PERSPECTIVE

Fig. 2. Cross-correlograms across instantaneous activation strength of cell
ensembles in different brain regions during pre- and post-conditioning NREM.
Each row represents each ensemble pair.

C. Fast oscillations on the local field potentials hosted interregional ensemble coactivation
Next, we sought network activity patterns during which theensemble coactivation among vCA1, BLA, and PL5 occurred.
First, we obtained instantaneous coactivation strength as
products of z-scored instantaneous activation strength with
optimal time shift which was determined based on the peak time
point of the cross-correlogram, then individual coactivation
events were detected by thresholding the instantaneous
reactivation strength traces. To examine whether characteristic
network activity patterns are associated with the inter-regional
ensemble coactivations, coactivation event-triggered averagesof LFP wavelet power were obtained (Fig. 3). At the BLA–PL5
and vCA1–PL5 coactivation, strong peaks around 100 – 300 Hz
were observed on BLA and vCA1 wavelet power, respectively.
These peaks presumably reflect amygdalar high frequency
oscillations (HFOs) [5] and hippocampal sharp-wave ripples(SWRs) [6]. Consistent with these observations, HFO- andIn this year, we revealed that inter-regional coactivations ofcell ensembles emerged after the transition to the mal-adaptation
states. Furthermore, our results indicate that fast network
oscillations such as HFOs and SWRs are involved in the interregional coactivations of cell ensembles. So far, we performed
pair-wise analyses of cell ensemble interactions, but we will
analyze how interactions among three brain regions change toobtain a comprehensive understanding of inter-regional network
dynamics associated with transitions to mal-adaptation states.
Furthermore, we will analyze network changes induced byextinction learning and compare them with ones caused by fearconditioning to clarify what kind of compensation enable hyperadaptation.
REFERENCES
[1]
[2]
[3]
[4]
[5]

[6]

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 anddorsal 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. 1073-188.

Annual report of research project A05-8
Takaki Maeda
Department of Neuropsychiatry, Keio University School of Medicine
Abstract
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. Our main achievements inthis fiscal year are as follows. 1) We revealed a Statistical
Learning Model of the Sense of Agency, 2) We showed the time
window for sense of agency in school-age children is different
from that in young adults, 3) We found the altered sense ofagency in children with developmental coordination disorder, 4)
We developed a method for cognitive rehabilitation of SoA(Agency Tuning).

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”. We expect that development of methods
for reorganization of dysconnectivities of the “Agency
Network” in neurological and psychiatric illnesses leads to therecovery from those illnesses.

the 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 hypothesize that this tuning of SoA
could reorganize neural systems and achieve hyper-adaptationof patients with those illnesses in their living environment.
III. RESEARCH TOPICS
A. Statistical Learning Model of the Sense of Agency
Humans have a natural tendency to generate prediction
models of the environment and adapt their models according tochanges in the environment. The SoA is associated with thedegree of the adaptation of the prediction models, e.g.,
insufficient adaptation causes low predictability and lowers the
SoA over the environment. Thus, identifying the mechanisms
behind the adaptation process of a prediction model related to
the SoA is essential for understanding the generative process of
the SoA. In the first half of the current study, we constructed amathematical model in which the SoA represents a likelihood
value for a given observation (sensory feedback) in a prediction
model of the environment and in which the prediction model is
updated according to the likelihood value. From ourmathematical model, we theoretically derived a testable
hypothesis that the prediction model is updated according to a
Bayesian rule or a stochastic gradient. In the second half of ourstudy, we focused on the experimental examination of thishypothesis. In our experiment, human subjects were repeatedly
asked to observe a moving square on a computer screen andpress a button after a beep sound. The button press resulted in
an abrupt jump of the moving square on the screen.
Experiencing the various stochastic time intervals between theaction execution (button-press) and the consequent event(square jumping) caused gradual changes in the subjects'
degree of their SoA. By comparing the above theoretical
hypothesis with the experimental results, we concluded that the
update (adaptation) rule of the prediction model based on the
SoA is better described by a Bayesian update than by astochastic gradient descent [1].
The time window for sense of agency in school-age
children is different from that in young adults
This study investigated the difference in the time window ofsense of agency between school-age children (N = 94, aged 6–
12 years) and young adults (N = 30, aged 21–23 years) byusing an agency attribution task and international standardized
manual dexterity test. The results showed that the time
window for sense of agency was significantly shorter inchildren compared to young adults. In addition, there was asignificant correlation between the time window for sense ofagency and manual dexterity only in children. The present
B.

Fig.1 The Agency Network
II. AIM OF THE GROUP
Our research project aims to reveal neural mechanisms ofsense of agency (SoA) . Moreover, we intend to study
pathophysiology of neurological and psychiatric illnesses from

results suggested that sensory-motor function greatly
contributes to sense of agency in school-age children [2].

examined with correlation analysis. Children with DCD hadsignificantly stronger visual bias than typically developingchildren. Overall correlation analysis showed that increased
visual bias was significantly correlated with poor manual
dexterity. Children with DCD had a strong visual bias, which
was associated with poor manual dexterity [4].
D. We developed a method for cognitive rehabilitation of
SoA (Agency Tuning).
We developed a method for cognitive rehabilitation of SoA(Agency Tuning) in order to tune up precision of SoA. We
hypothesize that this tuning method of SoA could reorganize
neural systems and achieve hyper-adaptation of patients withthose illnesses in their living environment [5][6].

Fig.2 Different SoA between children and adults
Altered sense of agency in children with developmental
coordination disorder
There is increasing evidence that children with
developmental coordination disorder (DCD) have deficits insensory-motor integration, but it is unclear whether the SoA
generated by sensory-motor integration is altered. Aims: To
investigate whether there is a difference in the time window
for SoA between children with DCD and typically developing(TD) children. Methods and Procedures: An agency attribution
task was used to quantitatively measure and compare the time
window for SoA in 15 children with DCD and 46 children in
the TD group. Variables that correlated with the time window
for SoA were also examined in both groups of children.
Outcomes and Results: The time window for SoA wassignificantly extended in children with DCD compared to TD
children. The time window for SoA in TD children was
significantly associated with manual dexterity, whereas thetime window for SoA in children with DCD was significantly
associated with depressive tendency. Conclusions and
Implications: The time window for SoA is altered in children
with DCD. The present results suggest that there may be abidirectional relationship between an internal model deficit
and depressive tendency and SoA in children with DCD [3].
Moreover, Previous studies have suggested that children with
DCD rely heavily on vision to perform movements, which
may contribute to their clumsy movements. However, few
studies have objectively and quantitatively investigated theperceptual biases of children with DCD. A visual-tactile
temporal order judgment (TOJ) task was used to measure andcompare the perceptual biases of 19 children with DCD and
19 age- and sex-matched typically developing children. The
point of subjective equality, which demonstrates when “visual
first” and “tactile first” judgment probabilities are equal (50%),
obtained by analyzing the results of the visual tactile TOJ task,
was used as an indicator of perceptual biases. Further,variables (age and manual dexterity in all participants; motor
function, autism spectrum disorder and attention deficit
hyperactivity disorder traits, and depressive symptoms inchildren with DCD) associated with perceptual biases wereC.

Fig.3 “Agency Tuner” for Agency Tuning
IV. FUTURE PERSPECTIVE
We have made advances in developing an original method
for cognitive rehabilitation of the SoA. In the next fiscal year,
we will advance clinical experiments of patients, and analyze
their leaning processes of predictive models on the SoA for
establishing feasible method of cognitive rehabilitation of the
SoA.
REFERENCES
[1]
[2]

[3]

[4]

[5]
[6]

Yano S., Hayashi Y., Murata Y., Imamizu H., Maeda T., Kondo T.:
Statistical Learning Model of the Sense of Agency. Frontiers in
Psychology 11 (539957), 2020.
Nobusako S., Tsujimoto T., Sakai A., Shuto T., Hashimoto Y.,
Furukawa E., Osumi M., Nakai A., Maeda T., Morioka S.: The time
window for sense of agency in school-age children is different from thatin young adults. Cognitive Development 54（ 100891 ）, 2020.
Nobusako S., Osumi M., Hayashida K., Furukawa E., Nakai A., Maeda
T., Morioka S.:Altered sense of agency in children with developmental
coordination disorder. Research in Developmental Disabilities 107(103794), 2020.
Satoshi Nobusako, Michihiro Osumi, Emi Furukawa, Akio Nakai,
Takaki Maeda, Shu Morioka: Increased visual bias in children with
developmental coordination disorder: Evidence from a visual-tactile
temporal order judgment task. Human Movement Science 75 (102743),
2020.
Takaki Maeda：「AGENCY TUNER」（APP on Google Play）,
Apr 6, 2020.（Patent：National application 2019-166203）
Takaki Maeda：PCT/JP2020/03448, Sep 11, 2020.

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—This year's results can be divided into the following
two major categories. (1) Establishment of a model ofcorticospinal tract injury in marmosets, (2) Establishment of arehabilitation model, and establishment of an evaluation system
for the model.

(described below) was 28.6% in the non-trained group (n=3).
On the other hand, the success rate recovered to 94.2% in the
rehabilitation group (n=2).

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
central nervous system (CNS) disorders such as stroke andspinal cord injury [2], but little is known about the relationshipbetween these changes and inhibitory control.
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.

Fig. 1 Effect of rehabilitation in marmosets after injury
In these models, we performed intracortical microstimulation(ICMS) of the motor cortex and found that the motor map of
the hand region expanded in the trained group, suggesting thatrehabilitation after spinal cord injury causes functional
reorganization of the marmoset brain (Fig. 2). Golgi staining
was performed to verify the structural changes in the regions
where these plasticities occurred, and dendrite elongation wasobserved in the regions where hand regions newly appeared in
the rehabilitation group, suggesting that structural
reorganization occurred (Fig. 3).

III. RESEARCH TOPICS
A. Establishment of marmoset dexterous rehabilitation model
Although marmosets cannot perform the skilled precision grip,
in which they pick up small objects with their thumb and index
finger like macaque monkeys, they can perform the scissor grip,
in which they forage by inserting their fingers into small gaps[3]. In this study, we developed a lever-pulling task formarmosets, referring to a study of squirrel monkeys in thesame New World monkeys [4]. We analyzed the kinematics ofthe hand movements during the lever-pulling task using a highspeed camera, and found that there were differences in aiming
and changes in finger angle in the case of small gaps.
B. Establishment of marmose tcorticospinal tract injury
model
Second, we established a model of corticospinal tract injury
at the level of the fourth cervical cord in marmosets. We foundthat the success rate of the marmosets in the lever-pulling task

Fig. 2 The wrist/digit region appears after rehabilitation
IV. FUTURE PERSPECTIVE

motor cortex by calcium imaging in this model, and follow thechanges in spatiotemporal patterns of these neurons during therecovery of motor functions.
REFERENCES
[1]
[2]
[3]

Fig. 3 Structural reorganization after injury
This year, we established a marmoset spinal cord injury modeland a rehabilitation model, and verified the functional andstructural reorganization in the motor cortex. In the next year,
we will observe the activity of GABAergic neurons in the[4]

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.
B. Tia, M. Takemi, A. Kosugi, E. Castagnola, A. Ansaldo, T. Nakamura,
D Ricci, J. Ushiba, L. Fadiga, A. Iriki,” Cortical control of objectspecific grasp relies on adjustments of both activity and effective
connectivity: a common marmoset study” J Physiol. 2017 Dec
1;595(23):7203-7221
R. J. Nudo, G. W. Milliken, “Reorganization of movement
representations in primary motor cortex following focal ischemic
infarcts in adult squirrel monkeys” J Neurophysiol . 1996
May;75(5):2144-9.

A05-11 Activating neural circuits that prefer affectedside of the body using neural modulation by brain
stimulation and behavioral technique
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 choices—deciding which hand to use to reachfor targets—represent continuous, daily, unconscious decisions.
The posterior parietal cortex (PPC) contralateral to the selected
hand is activated during a hand-choice task, and disruption ofleft PPC activity with a single-pulse transcranial magnetic
stimulation prior to the execution of the motion suppresses the
choice to use the right hand but not vice versa. These findings
imply the involvement of either bilateral or left PPC in handchoice. To determine whether the effects of PPC’s activity are
essential and/or symmetrical in hand choice, we increased or
decreased PPC excitability in 16 healthy participants usingtranscranial direct current stimulation (tDCS; 10 min, 2 mA,
5 × 7 cm) and examined its online and residual effects on handchoice probability and reaction time. After the right PPC was
stimulated with an anode and the left PPC with a cathode, theprobability of left-hand choice significantly increased andreaction time significantly decreased. However, no significant
changes were observed with the stimulation of the right PPC witha cathode and the left PPC with an anode. These findings, thus,
reveal the asymmetry of PPC-mediated regulation in hand choice.

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 neuromodulation by non-invasive
brain stimulation can implicitly change the probability of handuse [1].

II. AIM OF THE RESEARCH
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. [2]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. [3] 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.
While single-pulse TMS provided causal evidence bydisrupting an on-going neuronal process, it did not convey
whether neuromodulatory changes in PPC excitability causedby plasticity such as long-term potentiation (LTP) or
depression (LTD) affect the decision regarding hand choice. In
recent years, tDCS has garnered attention as a tool toinvestigate a causal link between the behaviour and theneuronal activity of stimulated brain areas in neuroscience. In
this study, we increased or decreased the cortical excitability of
PPC using tDCS and examined its online and residual effects
on the hand-choice probability and choice reaction time.
III. METHODS
Sixteen right-handed healthy participants were asked toreach a 4-cm-diameter target circle presented at one of nine
positions on a semicircle situated approximately 27 cm awayfrom the start position either with left or right hand as quickly
as possible within 650 ms of target presentation (Fig 1).

By fitting a logistic function to the choice probability data,
we determined the point of subjective equality (PSE), i.e. thelocation at which the participants were estimated to be equally
likely to use either the right or left hand. The change in PSE
was significantly larger under the LCRA condition relative to
the LARC condition after the stimulation. In contrast, the effectof stimulation was not significant under the LARC condition(Fig 5).

Direct electrical current
was delivered using aneuroConnDC
Brain
Stimulator Plus unit. Two
stimulation conditions wereassigned: (1) the left PPC was
stimulated with a cathode andthe right PPC with an anode(LCRA condition), and (2)the left PPC was stimulatedwith an anode and the right
PPC with a cathode (LARC
condition). A constant currentof 2 mA was applied for a
total of 10 min.

Figure 2 Setup

One block consisted of 108 unimanual reach trials (ninetargets on the semicircle displayed 12 times) and 12 control
trials presented in a pseudo-random order. The participants
performed two blocks before (PRE), during (DURING), andafter (POST) the stimulation (Fig 2).

Figure 5 The change in point of subjective
equality (PSE) relative to the PRE phase

Figure 3 Procedure

IV. RESUTLS
A logistic function was fitted to the probability of righthand choice and averaged across participants under the LCRA
and LARC conditions. In the LCRA condition (Fig. 4A), thelogistic function considerably moved toward the right in favourof the left-hand choice during and after the stimulation relative
to before stimulation. In contrast, in the LARC condition (Fig.
4B), the logistic function did not significantly move during andafter the stimulation.

V. FUTURE PERSPECTIVE
The present study demonstrated that the probability of lefthand choice increased and that of right-hand choice decreased
significantly after stimulation under LCRA condition. 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.
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 10 min of tDCS increased the probability ofleft-hand choice subsequently. Therefore, we recommend
continued development of our methods and their application topromote the use of paretic upper limbs in the rehabilitation ofpatients with stroke.
REFERENCES
[1]

[2]

[3]

Figure 4 Logistic function
fitted to the probability ofright-hand choice

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.
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.

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 results of this year are the following three
items. 1. We demonstrated the unprecedented earlyrecovery from acute and subacute phase of spinal cord
injury(SCI) by a synthetic synapse organizer protein, CPTX.
Moreover, we obtained the potential results of recovery
from chronic phase where no effective therapeutic method
has ever reported. 2. We applied the anti-sense oligo(ASO)of N-acetylgalactosaminyltransferase-1(T1), a key gene in
CS biosynthesis into rodent SCI model. ASO improved themicroenvironment for functional recovery. 3. We
constructed the AI motion capture system to rigorously
evaluate the recovery from SCI. We first tested an
automatic measurement system for hind limb stepping(footfall analysis) of mouse. To summarize, it is now
possible to guide the artificial super-adaptation of theneural circuit after the damage of the central nervous
system, and to evaluate the behavioral outputs during therecovery.
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 inactivation of theregeneration inhibitor chondroitin sulfate (CS) to improve afine regeneration environment for neural circuits and (2)the introduction of a synapse-forming factor synapse
connector to artificially connect synapses. Furthermore, by
combining these strategies with (3):rehabilitation, we aim
the further functional improvement after injury. In
particular, (1):we will try to reorganize the circuit using
nucleic acid medicine for future clinical application. In(2):we plan to apply various epoch-making synaptic
connectors to treat spinal cord injury. The technological
purpose of this study is to establish a mouse physiology
recovery evaluation system by introducing an AI tracesystem.
III. RESEARCH TOPICS
Followings are three outlines of this year’s project.
A. Creation of artificial chimeric protein synaptic
connector CPTX and application to spinal cord injury
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 spinal cord injury(Science. 2020)[1]. Although the effect of CPTX to Alzheimer's model
mice and cerebellar ataxia is transient, CPTX permanently
restored the motor function of SCI. These results indicatethat CPTX is a powerful tool for physiological recovery by
circuit reorganization and for future therapeutic
applications [2] [3].

CPTX is a novel synthetic molecule that crosslinks apresynaptic molecule (Nrx: neurexin) and a postsynaptic
molecule (AMPAR: AMPA receptor). It was created as an
artificial chimeric protein (CPTX) in the Cbln1 molecularregion that binds to Nrx in the cerebellum and the NP1
molecular region that binds to AMPAR (Fig. 1).

CPTX induces de novo synaptogenesis both in vitro and invivo. The analysis of spinal cord tissue with a superresolution microscope revealed that it surely localizes
between pre and post synapses. These expression patternsuggest that CPTX recruit the adaptive neural circuits forfunctional recovery. Consistent with this idea, we foundthat mice treated with CPTX one week after injury(subacute post-injury) also showed dramatic recovery [1](Fig. 3). In the future, we aim to dissect circuit
reorganization and its adaptation to physiological functions.
Furthermore, we aim to verify the spinal cord circuit with a
new version of CPTX (which enables connections ofinhibitory nerves, etc.).
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]. This year, we developed and applied anantisense oligo (ASO) for knocking down of CS expression
in a tissue specific manner. Although the ASO sequence
expected to be effective in mice and rats has already beenfound, it was confirmed that the mouse sequence ASO with
the new chemical modification has a physiologicalrecovery effect after spinal cord injury in mouse. In
addition, we were able to show the possibility of the effectof using it in combination with CPTX. Currently, we are
optimizing the appropriate administration timing and dose
and try to generate a super-recover model with ASO.
C. Construction of AI trace system
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. This year, we first focused on the hind limb
stepping down (footfall analysis), which was generally used
for quantification of post-injury recovery. Since we had torely on human evaluation, the automation and systemevaluation were performed (Fig. 4).

Mice in the process of recovery after spinal cord injury
walk freely on the grid for a certain period, and therecorded movies from different angles were analyzed. The
basement of the mouse tail is automatically tracked by AI
to extract parameters of the total walking movement
distance and speed (right). At the same time, the hind limbs
dropped from the grid (left) has been automaticallyrecognized and recorded. By combining these data, we
succeeded in fully automating the footfall analysis. Now, it
is possible to analysis our super-recover model to evaluate
the degree of recovery over time and by the amount ofadministered drug. Furthermore, in the future, we plan to
introduce rehabilitation and evaluate its effect with ourcollaborator, Dr. Yanagihara.
III. FUTUREU PERSPECTIVE
This year, we were able to obtain results of functional
recovery from SCI through CPTX application. CPTX
restored acute and sub-acute phase of SCI. We obtained thepreliminary data that CPTX is also effective to chronic
phase, which is beyond the initial plan. Since we havealready prepared ASO for CS suppression, we will create asuper-recovery model and expand the AI trace system inparallel. 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 and to conduct artificial
intervention for functional recovery.
REFERENCES[1]

Suzuki K, Elegheert J, Song I, Sasakura H,et.al., A synthetic
synaptic organizer protein restores glutamatergic neuronal
circuits. Science 369 ;6507 : eabb4853 (2020)[2]

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)
Takeuchi “Oligonucleotides Therapeutics for Spinal Cord Injury” 20th
The JapaneseSociety for Regenerative Medicine, Symposium(2021)

[3]

[4]
[5]

A05-13 Reconstruction of Basal Ganglia by Aging
and Neurodegenerative Disease
Fumino Fujiyama
Laboratory of Histology and Cytology
Faculty of Medicine and Graduate School of Medicine
Hokkaido University
Abstract—One of the inevitable changes faced by animals is the"aging" and the search for a desirable adaptation to the inevitablechanges is an unavoidable problem in the aging society, which is
becoming increasingly serious. Also, although "aging" is a risk
factor for many neurodegenerative diseases, few studies haveclearly delineated the relationship between aging and disease
factors. Is the decline in neurotransmitters as a result of
degeneration of certain nerve cells the only cause of
neurodegenerative disease in older people? Or does the age-related
decline in physical function affect the rearrangement of neuralcircuits? In other words, the possibility that neurons degenerate
gradually over time and that the neural pathways may berearranged to compensate for the effects of their slow decrease in
dopamine has also been completely ignored. In this study, weinvestigate how the basal ganglia pathways are adaptively
rearranged in young and old animals, isolating aging fromneurotransmitter effects.

II. INTRODUCTION
Frailty refers to a multifaceted situation in which not only
physical functions such as the musculoskeletal system or
cardiopulmonary function but also mental functions such as
cognitive functions including decreased motivation are observed
as a result of aging, and social life is generally impaired. In otherwords, with aging, both motor and brain functions are difficult
to say as a result of either cause or effect, and they are impaired
in a coordinated way. On the other hand, the incidence of manyneurodegenerative diseases increases with age. For example,
Parkinson's disease is caused by degeneration and loss of
dopaminergic neurons in the substantia nigra of the midbrain,with incidence and prevalence increasing with age. However,
the experimental animals used in most of the basic research todate are young adults, and the plasticity of neural pathways due
to aging or changes in dopamine levels is not completely
understood. To address this issue, we will explore how the basal
ganglia pathway is adaptively rearranged, separating aging fromneurotransmitter effects.

III. AIM OF THE GROUP
The aim of this study was to elucidate the anatomical andelectrophysiological plasticity of the basal ganglia due to aging
and the decrease in dopamine with aging.

IV. RESEARCH TOPICS
All experiment was conducted with research collaborators
Dr. Fuyuki Karube, Dr. Yasuharu Hirai, and Ms. Fuko Kadono.
The cooperation of Dept. Dermatology and Dept. Urology atthe Hokkaido University was got for acquisition and clinicalanalysis of the aged mouse.
All researchers conduct appropriate animal experiments incompliance with the Animal Welfare and Management Law,related standards, etc., and with the principle of 3R(Replacement Reduction Refinement), which is the basic
concept of animal welfare, and "National University
Corporation Hokkaido University Rules on Animal
Experimentation".
The following three results in this fiscal year are outlined.

A. Analysis of Motor Performance of Young and Aged Mice
Rotarod tests were performed toevaluate motor function (12-weekold male = 5; 60-70-week-old
males = 4, females = 5). The
rotarod test was conducted using amouse rotarod device (MK -610 A,
Muromachi), and the condition
setting was made with reference tothe mouse phenotype analysis
protocol (Rota-rod test v1) of
RIKEN BRC.
The running time of 60-weekold mice tended to be shorter thanthat of 12-week-old mice. However,since it cannot be ruled out that thisdifference may depend on weightFig1. motion analysis ofgain, it is necessary to compare
mouse
mice whose body weight haschanged at the same age in the future. There is currently nosignificant difference between males and females, but the
population needs to be increased.

B. Morphological Analysis of Young and Aged Mice
B-1. Subclass and distribution of dopamine neurons
Dopamine neurons are subclassified biochemically andgenetically into several subclasses. Are the subclasses of
dopamine neurons that degenerate with Parkinson's disease or
aging the same? Since the subclass that is preferentiallydenatured and lost by Parkinson's disease is known to becalbindin-negative dopamine neurons, we performed double
immunostaining of calbindin and tyrosine hydroxylase (TH), a
rate-limiting enzyme in the process of dopamine synthesis, inyoung and aged mice.

V. FUTURE PERSPECTIVE
This year, due to the transfer of principal researcher andresearch collaborators from Doshisha University to Hokkaido
University and restrictions on bringing in laboratory animals due
to COVID-19, we spent a lot of time preparing the research
environment and creating a valuable access route for aging
animals. With the research environment finally in place, thefollowing experiments will be conducted in next year (A) axon
labeling detects age-altered neural projections, (B) confocal
microscopic analysis reveals neurons with which these neuralprojections anatomically synapse; (C) changes in receptors at
optically identified synaptic sites are confirmed by confocal
microscopic analysis or electron microscopic analysis; and (D)anatomically identified synaptic connections are confirmed
electrophysiologically by patch-clamp cell recordings usingslice preparations. Thus, through this study, we plan to elucidate
in detail the changes in neural pathways due to aging, fromsynapses to macroscopic levels.

REFERENCES[1]

Fig.2 Dual immunohistochemistry of TH and calbindin in the
midbrain substantia nigra

B-2. Dopaminergic terminals in the striatum
We are now preparing to purchase cyclic voltammetry andmeasure dopamine levels electrochemically. However, we firstobserved dopamine projections from the substantia nigra of the
midbrain to the striatum by immunostaining for TH. Calbindin
immunohistochemistry was performed to visualize the patch(striosome) matrix compartments of the striatum.

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

Fig.3 Dual immunohistochemistry of TH and calbindin in the
striatumOkamoto S, Yamauchi K, Sohn J, Takahashi M, Ishida Y, Furuta T, Koike
M, Fujiyama F, Hioki H. Exclusive labeling of direct and indirect
pathway neurons in the mouse neostriatum by an adeno-associated virus
vector with Cre/lox system. Star Protocols, 2(1), 2021, 100230
Okamoto S, Sohn J, Tanaka T, Takahashi M, Ishida Y, Yamauchi K,
Masato Koike, Fumino Fujiyama, Hiroyuki Hioki. Overlapping
Projections of Neighboring Direct and Indirect Pathway Neostriatal
Neurons to Globus Pallidus External Segment. iScience, 2020, 101409
Karube F, Takahashi S, Kobayashi K, Fujiyama F: Motor cortex candirectly drive the globus pallidus neurons in a projection neuron typedependent manner in the rat. eLife. 2019, e49511
Fujiyama F, Unzai T, Karube F: Thalamostriatal projections andstriosome-matrix compartments. Neurochemistry International 125, 2019,
67-73
Nakano Y, Karube F, Hirai Y, Kobayashi K, Hioki H, Okamoto K,
Kameda H, Fujiyama F: Parvalbumin-producing striatal interneurons
receive excitatory inputs onto proximal dendrites from the motor thalamus
in male mice. J. Neurosci. Res. 96, 2018, 1186-1207
Unzai T, Kuramoto E, Kaneko T, Fujiyama F: Quantitative analyses ofthe projection of individual neurons from the midline thalamic nuclei tothe striosome and matrix compartments of the rat striatum. Cerebral
Cortex 27(2), 2017, 1164-1181
Oh Y-M, Karube F, Takahashi S, Kobayashi K, Takada M, Uchigashima
M, Watanabe M, Nishizawa K, Kobayashi K, Fujiyama F: Using a novel
PV-Cre rat model to characterize pallidonigral cells and their terminations.
Brain Struct. Funct. 222, 2017, 2359-2378
Mizutani K, Takahashi S, Okamoto S, Karube F, Fujiyama F: SubstanceP effects exclusively on prototypic neurons in mouse globus pallidus.
Brain Struct. Funct. 222, 2017, 4089-4110
Fujiyama F, Nakano T, Matsuda W, Furuta T, Udagawa J, Kaneko T: A
single-neuron tracing study of arkypallidal and prototypic neurons inhealthy rats. Brain Struct. Funct. 221(9), 2016, 4733-4740.

A05-14 Development of techniques to improve brain
functions by using hyper-adaptability
Yoshito Masamizu
RIKEN Center for Brain Science

Abstract—Previous studies have mainly focused on thetransplantation of cell suspension and have not established amethod to create the neural circuit. In this study, the aim is todevelop techniques to improve brain functions by using hyperadaptability. In details, the transplantation of neuronal fibers
could bypass the damaged areas of the brain. And then, we reveal
the neural activity during the recovery of brain function with invivo calcium imaging.

I. INTRODUCTION
Transplantation of neural stem cell suspension is a useful
method for the treatment of brain injury [1]. The transplanted
neural stem cells could recover brain functions of the damaged
area. Previous studies have mainly focused on thetransplantation of cell suspensions and have not established amethod to create the neural circuit. In this study, we aim todevelop techniques to construct novel neural circuits.
II. AIM OF THE GROUP
The aim is to develop techniques to improve brain functions
by using hyper-adaptability. In details, the transplantation ofneuronal fibers could bypass the damaged area of the brain(Fig. 1, left). And then, we reveal the neural activity during therecovery of brain function with in vivo calcium imaging (Fig.
1, right).

Fig. 1 Aim of the research

III. RESEARCH TOPICS
The significant results of this year are following two points.
A. Formation of neuronal fiber
We have established a system to produce a neuronal fiber witha double-coaxial laminar-flow microfluidic device [2]. The
microfluidic device was fabricated by assembling pulled glass
capillary tubes, rectangular glass tubes and connectors. The
microfluidic device has a three-layer coaxial laminar flow(core stream, shell stream, and sheath stream). First, a corestream containing neuronal cells was surrounded by a shellstream of sodium alginate solution. Next, a calcium alginate
shell is formed from the gelation reaction between sodium
alginate and calcium chloride (Fig. 2). We investigated how
long the neuronal fibers would survive. We also confirmed thespontaneous activity of the neuronal fiber with a calcium
sensor. Fluorescence Changes of the calcium sensor is known
to correlate with neural activity [3]. Furthermore, we
confirmed what types of cells the fiber was composed of byimmunostaining.

Fig. 2 Formation of a cell fiber with a microfluidic device

B. Motor task
We examined whether mice were able to perform motor tasks
in the head-fixed condition (Fig. 3). In the future, we plan to
examine whether damage to the cerebral cortex decreases the
success rate and the number of successful trials in motor tasks.

Fig. 4 Features of in vivo calcium imaging
REFERENCES

Fig. 3 Motor task
IV. FUTURE PERSPECTIVE
This year, we established a system to produce a neuronal fiberwith a microfluidic device and examined whether mice wereable to perform motor tasks in the head-fixed condition.
Next year, we plan to produce mice that have damage to the
cerebral cortex. We examine whether the damage decreases
the success rate and the number of successful trials in motor
tasks. Next, we develop a system for transplanting neuronalfibers into the brain. Furthermore, we reveal the neural activity
during brain function recovery with in vivo calcium imaging
established in previous research [4]. We observe the neuralactivity of both a transplanted neuronal fiber and cerebral
cortex with two-color calcium imaging of green and red
calcium sensors. Calcium sensors are based on cpEGFP(circularly permuted enhanced green fluorescent protein) or
cpRFP (circularly permuted red fluorescent protein), a
calcium-binding protein (calmodulin), and a binding peptide(M13). In the presence of calcium, CaM undergoes a
conformational change and associates with M13. The
conformational change results in increased fluorescencebrightness. [3, 5]. Using a glass window that we reported
previously [6], we aim to observe the neuronal activity in thesame population of neurons for a long time (Fig. 4).

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Shimoyama Y, Takeuchi S. Metre-long cell-laden microfibres exhibit
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Masamizu Y#, Tanaka YR#, Tanaka YH, Hira R, Ohkubo F, Kitamura K,
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Annual report of research project A05-16
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 both motor
recovery and pain syndromes after brain damage, using macaquemodels. In the studies reported here, we examined temporalchanges of macrophages and microglia (MΦ/MG) after focal
infarction of the internal capsule using a macaque model werecently established. Immunoreactivity for Iba1, a generalmarker for MΦ/MG, in the periinfarct core gradually increased
from 0 days to 2–3 weeks after infarction, and the increased
immunoreactivity continued at least until 6 months. We also
found that Iba1-positive MΦ/MG transiently increased in layer V
during several weeks after the infarction. Therefore, the timecourse of MΦ/MG activation differs between the perilesional
area and the remote brain area where secondary damage occurs
to tissue initially preserved after the infarct. Detailed analyses
using the functional phenotype markers as well as cytokines
released by cells with each phenotype suggest an antiinflammatory role for activated MΦ/MG both in the periinfarctcore during the chronic phase and in the primary motor cortex.
In addition, to investigate changes in neuronal structuresassociated with central post-stroke pain (CPSP), we performed
voxel-based morphometry (VBM) using T1-weighted magnetic
resonance imaging and immunohistochemical analysis with ourestablished CPSP monkey model. The present VBM analysis
revealed a decrease in gray matter volume in the pain-relatedareas after weeks. Furthermore, immunohistochemical staining
in the ipsilesional posterior insular cortex (ipsi-PIC), where thegreatest reduction in gray matter volume was observed in the
VBM result, displayed a significant reduction in both excitatory
and inhibitory synaptic terminals compared to intact monkeys.
Our results suggest that progressive changes in neuronalmorphology, including synaptic loss in the ipsi-PIC, are involvedin the pathogenesis of CPSP.

I. INTRODUCTION
Neural plasticity after brain damage is crucial for functional
recovery from deficits caused by brain damage. This plasticity
is exploited by rehabilitation for stroke survivors. On the other
hand, central post-stroke pain (CPSP) developed as a result ofmaladaptive plasticity after stroke in thalamus and other brain
regions involved in somatosensory processing. CPSP ischaracterized by not only spontaneous pain but also evokedpain in which normally innocuous stimuli are perceived as
painful, i.e., allodynia, and it decreases the quality of life andfrequently interferes with rehabilitation of the affected patients.
Therefore, Hyper-Adaptability after brain damage has both
good and bad sides.

II. AIM OF THE GROUP
So far, we have examined the process of functional recovery
after brain injury in the macaque monkey, as it has cerebral andmusculoskeletal structures in similar to those of humans. Our
behavioral analyses suggested that recovery of dexterous hand
movements can be induced by intensive postlesion training [1].
Moreover, our brain imaging analysis suggested that changesof brain activity occur in uninjured motor areas during recovery
of precision grip after the primary motor cortex lesions [2]. The
aim of the present research project is to investigate plasticchanges of neuronal functions and structures in the macaquemonkeys in which stoke was induced in the internal capsule orthalamus, by applying experimental design we have used forthe primary motor cortex lesioned monkeys.

III. RESEARCH TOPICS
A. Time- and area-dependent macrophage/microglial
responses after focal infarction of the macaque internal
capsuleInflammatory responses induced by macrophages/microglia(MΦ/MG), which are rapidly activated and proliferate after
stroke, can impede functional recovery. It should be noted,
however, that not all activated MΦ/MG are involved in the
inflammatory response; in contrast to the pro-inflammatory M1phenotype, the anti-inflammatory M2 phenotype may be
responsible for tissue repair. Knowing the phenotype istherefore important to understanding the pathogenic role ofMΦ/MG. We here examined temporal changes of MΦ/MG
after focal infarction of the internal capsule using a macaquemodel we recently established [3]. Immunoreactivity for Iba1,
a general marker for MΦ/MG, in the periinfarct core gradually
increased from 0 days to 2–3 weeks after infarction, and theincreased immunoreactivity continued at least until 6 months(Fig. 1); no study in rodents has reported increased Iba1immunoreactive cells for so long.

Fig. 1 Temporal changes of Iba1-immunoreactivity in the perilesional
area. (A, B) Lower- (A) and higher- (B) magnification immunostaining
images showing immunoreactivity of Iba1 in the periinfarct core from Early
stage to Half year after infarction. (C) Quantitative analysis of Iba1
immunoreactivity in the periinfarct core. Iba1 immunoreactivity in theperiinfarct core gradually increased from Initial stage to Middle stage.
Although the immunoreactivity then decreased, it remained significantly
elevated relative to the intact animals until Half year, the last time period
assessed after infarction. **P < 0.01, ****P < 0.0001 (Kruskal–Wallis oneway analysis of variance and Dunn’s post hoc test). Scale bars = 200 μm in A;
50 μm in B.

Retrograde atrophy / degeneration of neurons in layer V ofthe primary motor cortex, where the descending motor tract
originates, was seen as secondary damage. Here we found that
Iba1-positive MΦ/MG transiently increased in layer V during
several weeks after the infarction (Fig. 2). Therefore, the timecourse of MΦ/MG activation differs between the perilesional
area and the remote brain area where secondary damage occurs
to tissue initially preserved after the infarct. Detailed analyses
using the functional phenotype markers CD68, CD86, andCD206, as well as cytokines released by cells with eachphenotype, suggest an anti-inflammatory role for activatedMΦ/MG both in the periinfarct core during the chronic phaseand in the primary motor cortex. We have published the resultsof this study in an international journal [4].

the maladaptive plasticity of pain-related brain regions.
However, little is known about the cellular basis of the plasticchanges and how they are functionally related to pain. To
elucidate these issues, we recently established a non-humanprimate model of CPSP based on the ventral posterolateral
nucleus (VPL) of the thalamus using rhesus macaque monkeys[5]. The monkey model of CPSP faithfully reproduces lateonset allodynia / hyperalgesia observed among human patients.
Moreover, using functional MRI in the macaque model, we
previously reported increased cortical activity in theipsilesional posterior insular cortex (ipsi-PIC) [6].
Here, we performed voxel-based morphometry (VBM)
using T1-weighted magnetic resonance imaging andimmunohistochemical analysis with the monkey model of
CPSP. Several weeks after a hemorrhagic lesion to theunilateral ventral posterolateral nucleus of the thalamus, themonkeys exhibited behavioral changes that were interpreted asreflecting allodynia. The present VBM results revealed adecrease in gray matter volume in the pain-related areas after
weeks
ratherthan
early
periods.
Furthermore,immunohistochemical staining in the ipsi-PIC, where thegreatest reduction in gray matter volume was observed in the
VBM result, displayed a significant reduction in both
excitatory and inhibitory synaptic terminals. The results
suggest that progressive changes in neuronal morphology are
involved in the pathogenesis of CPSP.
IV. FUTURE PERSPECTIVE
We will investigate plastic changes of neuronal functions andstructures in the macaque monkeys in which stoke was inducedin the internal capsule. At present, we are applying VBM and
immunohistochemical analyses to the macaques with internal
capsular infarct.

REFERENCES[1]

Fig. 2. Time-dependent changes of Iba1-immunoreactivity in the primary
motor cortex. (A) Lower-magnification image of immunoreactivity for Iba1 at
Middle stage. (B, C) Higher-magnification images of immunoreactivity for
Iba1 in layers V (B) and III (C) in the intact tissue and at different time
periods after infarction. (D, E) Quantitative analysis indicated that Iba1immunoreactivity in layer V was significantly higher at Middle stage than in
the intact animals and animals at other periods after infarction (D), whereas noincrease was observed in layer III (E). ***P < 0.001, ****P < 0.0001(Kruskal–Wallis one-way analysis of variance and Dunn’s post hoc test).
Scale bars = 200 μm in A; 50 μm in B, C.

B. Structural changes of cortical gray matter revealed byvoxel-based morphometry and immunohistologicalanalyses in a macaque CPSP model
Second, we investigated changes in neuronal structuresassociated with central post-stroke pain (CPSP). Several brain
imaging studies among patients with CPSP demonstrate thatthe pathophysiological mechanism underlying this condition is

[2]

[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
J.Kato, Y. Murata, I. Takashima, N. Higo, “Time- and area-dependent
macrophage/microglial responses after focal infarction of the macaqueinternal
capsule”,
Neurosci
Res.
https://doi.org/10.1016/j.neures.2020.12.001
K. Nagasaka, I. Takashima, K. Matsuda, N. Higo, “Late-onset
hypersensitivity after a lesion in the ventral posterolateral nucleus of thethalamus: A macaque model of central post-stroke pain”. Scientific
Reports 7(1):10316, 2017
K. Nagasaka, I. Takashima, K. Matsuda, N. Higo, “Brain activity
changes in a monkey model of central post-stroke pain”. Exp Neurol 323,
113096

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 surgical lesions in the ventral attention network ofcommon marmosets. In this fiscal year, we tried to establish 1)
the target of the lesion, 2) how to evaluate their behavior and 3)
how to measure the brain activity. For 1), we identified temporoparieto-occipital association area using electrocorticogram(ECoG). For 2), we established the methods for eye-tracking
during free-viewing. For 3), we succeeded in measuring Ca
signals from the posterior parietal cortex.

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
surgical lesions in the ventral attention network of common
marmosets.

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 whole-cortex brain activity using a 96 channel
ECoG electrode before and after the lesion and evaluate how
the brain network is affected by the lesion. 3) 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. 4) 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.

III. RESEARCH TOPICS
A. the target of the lesion
Our previous findings using macaques succeeded in
inducing neglect-like behavior in macaques by making asurgical lesion in the right superior temporal gyrus (rSTG)(Fig.1, left, magenta), which is the homologous brain region to
the human ventral attention network.

Fig. 1. Candidate of the lesion site

Our group selected as a candidate of the surgical lesion thetemporo-parieto-occipital association area (rTPO) (Fig.1, right,
magenta) [1], which is the homologous brain region to thehuman ventral attention network.
We also used a neurophysiological technique to find thehomologous brain region to the human ventral attention
network. We implanted 96-channel ECoG electrodes tomarmosets [2] and measured brain activities during a visual
mismatch task (Fig.2) to find brain regions associated withvisual surprise.

Fig. 2. Visual mismatch task

In the visual mismatch task (Fig.2), orientation grating
stimuli were presented in front of marmosets with their head
fixed. The stimulus sequence is composed of 300 repetitions of
0.5sec ON and 0.5sec OFF periods. In the oddball condition(Fig.2, top), grating stimuli with 45-degree orientation were
presented in 1/8 of ON periods and grating stimuli with 135degree orientation were presented in 7/8 of ON periods. Inmany standards condition (Fig.2, bottom), grating stimuli witheight different orientations were randomly presented in equal
probabilities (1/8). By comparing the responses to the deviant
stimuli in both conditions, we are able to evaluate which brain
areas are associated with surprise. Using this method, we
identified in the temporal association areas a brain area that is
associated with surprise. Thus, we established how to identify
the ventral attention network using a neurophysiological
technique.

the temporal association areas and the posterior parietal cortex(PPC). These activities showed selectivity to the directions ofsaccades.
Based on these findings, we decided to measure Ca signals
from the PPC. By injecting adeno-associated virus AAV, theCa sensor GCaMP6 was introduced in the PPC of a marmoset.
The TET-off system specifically designed for marmosets [5]was used to amplify expression of GCaMP6. We succeeded inmeasuring Ca signals from the PPC of a marmoset (Fig. 4, left).

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, detection of saccades and the effectof ketamine on the saccade amplitudes (Fig. 3).
Fig. 4. Ca imaging from PPC

We measured Ca signals during free-viewing (Fig. 4, right)
and identified signals related to stimulus onsets and saccade
onsets.
IV. FUTURE PERSPECTIVE

Fig. 3. Detection of saccades

In collaboration with Isa group in Kyoto University, we
also found that the gazes during free-viewing of marmosets are
attracted to visually salient stimuli [4], using a method
developed for studying gazes of macaques [3].
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.
To understand the detailed brain mechanism of the neglectlike behavior, our group’s strategy is to use marmosets andmeasure whole-cortical activities using 96-channel ECoG
electrodes [2] and measure local circuit activities using an
endoscope type Ca imaging system (nVoke, Inscopix).
Concerning the 96-channel ECoG recording, we succeeded
in measuring activities not only during the visual mismatchtask but also during free-viewing. We calculated the saccadetriggered averaging of the ECoG signals and found thatsaccade-related activities were found in the prefrontal cortex,

In this fiscal year, we developed methods for establishingan animal model of USN using marmosets. From the next
fiscal year onward, we will continue to add more experimental
data for establishing methods to enable surgical lesions inmarmosets.
REFERENCES
[1]

[2]
[3]

[4]

[5]

Majka, P., Bednarek, S., Chan, J.M., Jermakow, N., Liu, C., Saworska,G., Worthy, K.H., Silva, A.C., Wójcik, D.K., and Rosa, M.G.P. (2021).
Histology-Based Average Template of the Marmoset Cortex With
Probabilistic Localization of Cytoarchitectural Areas. Neuroimage 226,
117625.
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.
Yoshida, M., Itti, L., Berg, D.J., Ikeda, T., Kato, R., Takaura, K., White,
B.J., Munoz, D.P., and Isa, T. (2012). Residual attention guidance inblindsight monkeys watching complex natural scenes. Curr. Biol. 22,
1429–1434.
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.
Sadakane, O., Masamizu, Y., Watakabe, A., Terada, S.-I., Ohtsuka, M.,
Takaji, M., Mizukami, H., Ozawa, K., Kawasaki, H., Matsuzaki, M., etal. (2015). Long-Term Two-Photon Calcium Imaging of Neuronal
Populations with Subcellular Resolution in Adult Non-human Primates.
Cell Rep. 13, 1989–1999.

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.
B01 Systems modelling of hyper-adaptation mechanism forreconstruction of neural structure
Principal investigator: Toshiyuki Kondo (TUAT), Funded
co-investigator: Ryosuke Chiba (Asahikawa Med Univ)
Research Outline: This research group aims to realize
systems modeling of hyper-adaptability mechanism with functional dis-inhibition observed in the impaired brain, especiallyfrom the viewpoint of reconstruction of neural structure. To
clarify the underlying adaptability mechanism of a large-scaleand complex network system such as the brain, the constructive approach is indispensable, in which a phenomenon can bemodeled with the minimum degrees of freedom, and behaviorof the model is verified by computer simulations.
In this fiscal year, (1) they proposed a combined use of
tensor decomposition for dimensionality reduction and TimeVarying Graphical Lasso (TVGL) with Gaussian graphicalmodel (GGM) for analyzing multimodal time-series data.
(2) They improved the simulator to explain the changes of
postural control in the dual task. (3) They found that a skilllevel matching algorithm for adjusting the degrees of robotic
intervention would be promising for enhancing the adaptabilit
B02 Modeling of ultra-adaptive to body change
Principal investigator: Yasuharu Koike (Tokyo Tech),
Funded co-investigator: Tetsuro Funato (UEC)
Research Outline: This research group aims at the modelingof the mechanism underlying the recovery of the motor
function after the alteration of the body, especially from theviewpoint of the reconstruction of a neural structure in hyperadaptability.
In this fiscal year, (1) they estimated the muscle activities by
inverse dynamics analysis from the measured human reachingmotion, and simulated the reaching motion using the estimatedmuscle activities as control inputs. (2) They investigated therelationship between muscle activities and brain waves during
finger movements, and identified the brain region relating
finger movement direction.
B03 Systematic understanding and realization of hyperadaptive phenomena focusing on cognition and emotion
Principal investigator: Hajime Asama (U Tokyo), Funded
co-investigator: Jun Izawa (U Tsukuba), Wen Wen (U Tokyo),
An Qi (Kyushu U)
Research Outline: This research group focuses on rehabilitation, the mechanism of hyper-adaptability is investigated froma systematical approach, and new rehabilitation methods aredeveloped based on the investigation of effect of cognition andemotion on behavior adaptation and motor learning.
In this fiscal year, (1) they developed VR motor rehabilitation system for stroke patients, and found visual intervention
enhanced the sense of agency and showed positive effect on
motor motivation. (2) They investigates the muscle activity ofhemiplegic patients using surface electromyography. (3) Theydeveloped miniature robotic manipulandum for rodend studies.
B04 Modeling of hyper adaptability in human postural control
considering the role of neurotransmitters
Principal investigator: Jun Ota (U Tokyo), Funded coinvestigator: Arito Yozu (U Tokyo)

Research Outline: The research group aims to verify thefollowing hypothesis from the viewpoint of reconstitution of
sensorimotor control rules of the hyper-adaptation functions:
Neurotransmitters (such as dopamine; DA), whose levels are
reduced in patients with neurodegenerative disorders, adjust
the activity levels in various brain areas and coupling strength
between neuronal circuits as well as control the multitasking
function.
In this fiscal year, (1) they investigated the role of arms
in maintaining human standing posture. (2) They executed
postural control experiments, and evaluated the role of neurotransmitters in the postural control with multitask. (3) Theydeveloped unilateral spatial neglect mouse model.
Proposed Research Groups
From this fiscal year, 11 proposed research projects joined
the group. Please refer to the report of each research project
for their concrete research outcomes.
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:• July, 18th, 2020, IEEE EMBC2020 Workshop (Online)
Due to the spread of COVID-19, the international workshop was held online. Prof. Ota and 5 researchers in the
research project presented their recent research results
and discussed.
• November 7th, 2020, Group B meeting (Online)
The first Group B meeting was held online (Zoom).
All the principal investigators and most of the coinvestigators participated, thus approximately 45 people
attended at all times, and deeper discussions were held,
including future collaboration.
IV. F UTURE PLAN
Towards the mid-term evaluation, in the next fiscal year, wesummarize the research topics in the planned and subscription
research projects by classifying the topics from the targeted
subjects and the modeling methodologies points of view. Then
we start deeper discussion on some of the topics. Moreover,
we shall proceed fruitful collaborations with Group A (Neuroscience group).

Annual report of research project B01-1
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 disinhibiting pre-existing neural network that is normallysuppressed and searching for latent but available network thathas long been unutilized through course of evolution and development. We call this process of functional compensation
as “reconstruction of neural structure”, i.e. a neural entitythat achieves 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 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.

Fig. 1.

Tensor decomposition and TVGL with GGM for brain modeling.

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 multimodal 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, to quantify the long-term changeof the extensive disinhibition structure in the brain, wedevelop a simultaneous analysis method by integrating muscle activities and cortical electroencephalography. (2) To

Fig. 2.

Constructive modeling approach.

elucidate the deterioration mechanism of functional inhibition which seems different between young and elderly,
we build a gray-box model of the brain network by considering the findings in clinical medicine such as resource
allocation between motor and cognitive function, and byassuming unknown parameters such as resource limitationand 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 theresults of posture control experiments of human subjects.
(3) By developing experimental systems that can arbitrarilychange the relationship between the brain and body using
VR/robot technology, we perform the collaborative motor
learning experiments in healthy young and elderly. Based on
the findings, we will develop appropriate visuomotor tasks

which can promote the reconstruction of neural structure in
the brain, in collaboration with A01. By integrating thesefindings, we aim to realize a model of hyper-adaptability
that can estimate the reconstruction of neural structures in
the process of recovery from a disorder or disease, and toobtain knowledge for effective treatment and training.

legs, and it was necessary to simplify the instructions. This
preliminary experiment enabled measurement experiments
for the elderly, and was ready to collect knowledge for model
construction and data for verification of models.

III. R ESEARCH OUTCOMES

Prof. Kondo and his colleagues investigated cooperative
motor learning tasks that can enhance their adaptability after
the training with VR and haptic robot technologies. They
found that a skill-level matching algorithm for adjusting
the degrees of robotic intervention would be promising forenhancing the adaptability [3].

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) havebeen developing a statistical technique to quantify the timevarying structural change in the brain networks behind thehyper-adaptability, with respect to simultaneously observed
EEG–EMG data. They proposed a combined use of tensor decomposition for dimensionality reduction and TimeVarying Graphical Lasso (TVGL) with Gaussian graphicalmodel (GGM) for analyzing multimodal time-series data
(Fig. 1). This year, they confirmed the applicability of themodel by applying it to both artificial data [1] and sleep EEG(in preparation).
Moreover, in collaboration with the A03 group, they proposed a model of the Sense of Agency (SoA), and found thatthe adaptation process of the SOA model can be describedby the Bayes’ rule rather than the stochastic gradient descent(SGD) algorithm[2].
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.
We improved the simulator to explain the changes of
postural control in the dual task for the above purpose.
A computational simulation environment for gait was constructed in order to consider not only the standing posture but
also the transition from standing to walking. This is because
our hypothesis that "increasing muscle tone in elderly leads
to a decreasing in motor function" is involved in walking.
In the constructed simulation environment, it is possible tochange the muscle tone in standing and gait. As a result,muscle tone was required for closing legs rather than opening
legs in the standing posture, and the gait became irregular
due to increasing muscle tone. These results show the validityof the computer model. It became possible to estimate theeffect of hypertonia on postural and gait control.
In addition, as a preliminary experiment for the dual task,
we conducted the dual task for 25 elderly in the Okoppe areain Hokkaido. The posture control task was to maintain thestanding posture with open and closed legs, and the cognitive
task was to repeatedly subtract from random numbers. As
a result, we confirmed increasing of postural sway in the
dual task, and also confirmed the precautions peculiar to theelderly in the protocol and instruction. Specifically, it may
not be possible to maintain a standing posture with the closed

C. Motor learning experiments with VR/Robot technologies
enhancing hyper-adaptability

Fig. 3.

Model-based robotic intervention for adaptability.

Moreover, they proposed a Collective Almost Synchronization (CAS) model for predicting EEG time-series byusing a weakly-coupled network of dynamical neurons (e.g.,
Hindmarsh-Rose model or Kuramoto model) [4].
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. Moreover, we will continue to investigate the motor tasks that can induce ”reconstruction ofneural structure” with dis-inhibition in the brain of elderly
people under the frailty state.
R EFERENCES[1] Sakai, K., Yano, S., and Kondo, T., Extraction of graphical structure
embedded in human periodic motions, Proc. the 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology
Society (EMBS), Virtual Conference, Montreal (7/20-24, 2020).
[2] Yano, S., Hayashi, Y., Murata, Y., Imamizu, H., Maeda, T., Kondo,T., Statistical Learning Model of the Sense of Agency. Frontiers in
Psychology, 11:539957, 2020.
[3] Yoshikawa, Y., Hayashi, Y., Yano, S., and Kondo, T., How DoesIntervening Strategy in Cooperative Motor Task Affect Individual
Motor Adaptation?, Proc. the 42nd Annual International Conferenceof the IEEE Engineering in Medicine and Biology Society (EMBS),
Virtual Conference, Montreal (7/20-24, 2020).
[4] Nguyen, P.T.M., Hayashi, Y., Baptista, M.D.S., Kondo, T., Collective
almost synchronization-based model to extract and predict features of
EEG signals. Scientific Reports, 10, 1:16342, 2020.

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

Abstract—We obtained the following two results.
1. The muscle synergies before and after the muscle rearrangement experiment of group A02 were analyzed and veriﬁed by amathematical model. We also simulated exercise using an existing
human upper limb musculoskeletal model.
2. Muscle activity and brain waves during ﬁnger movement
were measured at the same time, and the direction of movement
was estimated using muscle synergies. In addition, the signal
source was calculated from EEG, and the brain region which isrelated to the identiﬁcation of the ﬁnger movement direction was
searched.

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. Model of synergy change after tendon transfer
In collaboration with group A02, we investigated the recovery process after tendon transferring using two macaqueFig. 1. Reaching motion of musculoskeletal modelmonkeys. Tendons of the extensor and ﬂexor ﬁnger muscles
were transferred, and muscle activities were measured fortwo months. Then, we analyzed muscle synergies. The resultsof the analysis showed that time series of muscle synergies
switched between the synergies for extensor and the synergies
for ﬂexor muscles after tendon transferring, and returned totheir original relationships after approx. one month. During
this duration, the structure of the muscle synergies hardly
changed. In order to investigate the mechanism underlying theswitching and returning of temporal synergies, we compared
the measured muscle activities with the muscle activities
explained by the muscle synergies. The results showed thatthe component explained by the existing synergies decreased
during the process of switching and the component returned
when the relationship of temporal synergies returned. Thissuggests that during the recovery process, strategies for muscle
activities other than the existing synergies were explored, andthat the original synergy structure was selected as the resultof the exploration.
To investigate the relationship between these changes inmuscle synergies and the recovery of movement, we areconstructing a dynamical model that can simulate movementfrom muscle activities. In order to construct a musculoskeletal
model that can realize the reaching movement task of the upper
limb performed in monkeys, we used an existing human upper
limb musculoskeletal model [1] on the musculoskeletal simulation software OpenSim (Fig. 1). We estimated the muscle
activities by inverse dynamics analysis from the measured human reaching motion, and simulated the reaching motion using
the estimated muscle activities as control inputs. By ﬁtting thephysical parameters of this human musculoskeletal model withthose of monkey skeleton, and adding necessary muscles, weconstruct a musculoskeletal model that can simulate the effects

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Elbow 90 (Bending)Fig. 2. Muscle synergy which related ﬁnger directonof muscle transferring in monkeys.
B. Elucidation of reconstruction rule process by brain information decoding
For the purpose of conﬁrming where muscle synergies are
represented in the brain, muscle synergies calculated fromelectromyograms and source signals estimated from EEG
using data obtained by simultaneously measuring movementelectromyogram-electroencephalograms during the ﬁnger motion task.
The surface EMG contains a mixture of superﬁcial and deep
muscle activity, but since ﬁnger movement is also related tothe muscles in the deep part of the forearm, each muscle
activity is analyzed separately. There is a need to. Independent
component analysis was used to isolate muscle activity, andthen nonnegative matrix factorization was used to determine
muscle synergies [2].
In addition, the IC components related to the discriminationof movement were obtained from EEG signals by independent
component analysis, and the region in the brain was calculated
from the values of the independent components. Analysis of
the weights of the decoder that identiﬁes the direction ofﬁnger movement revealed that the right lingual gyrus, the left
posterior cingulate, the left inferior temporal, and the rightprecuneus[3].
Medial Left
13 Paracentral

12 Cingulum Posterior

5 Supplementary Motor

3 Cingulum AnteriorFig. 3. Brain region of the ICs for identiﬁcation of ﬁnger direction

From these results, task-related muscle synergies can be estimated at the ﬁnger movement level from the muscle activity
obtained by the array electrodes, and the brain region related
to work identiﬁcation from the simultaneously measured EEG
signals. Since it has become possible to estimate the brain
region related to task identiﬁcation from the EMG and EEG
measured at the same time, it has become possible to analyze
the acquisition process of muscle synergies from the EMG and
EMG during tasks such as virtual surgery.
In addition, since the relationship between brain areas canbe analyzed using fMRI. In order to clarify how much the brain
region obtained this time is related during motor learning, it isnecessary to identify the brain region by acquiring fMRI data
during ﬁnger motor learning task.
IV. F UTURE PERSPECTIVE
This year, we are conducting analysis of monkey ﬁnger
movements in collaboration with group A02, as we did lastyear, and constructing a musculoskeletal model that realizes
the reaching movement task of the upper limbs. In addition,
we investigated which region of the brain is involved inﬁnger movement and investigated the relationship with muscle
synergies in humans.
From next ﬁscal year onwards, we plan to promote joint
research with other groups.
R EFERENCES[1] K. R. Saul, X. Hu, C. M. Goehler, M. E. Vidt, M. Daly, A. Velisar,and W. M. Murray, “Benchmarking of dynamic simulation predictions
in two software platforms using an upper limb musculoskeletal model.”Computer methods in biomechanics and biomedical engineering, vol. 18,
no. 13, pp. 1445–1458, 2015.
[2] Y. Kim, S. Stapornchaisit, M. Miyakoshi, N. Yoshimura, andY. Koike, “The effect of ica and non-negative matrix factorization
analysis for emg signals recorded from multi-channel emg sensors,”
Frontiers in Neuroscience, vol. 14, p. 1254, 2020. [Online]. Available:
https://www.frontiersin.org/article/10.3389/fnins.2020.600804[3] M. M. Tellache, H. Kambara, Y. Koike, M. Miyakoshi, and N. Yoshimura,“Investigating neural representation of ﬁnger-movement directions using
electroencephalography independent components,” Journal of Biomedical
Science and Engineering, vol. in Press, 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 a method for
motor rehabilitation focusing on body consciousness; 2) wedeveloped an evaluation model to examine hemiplegic patients’recovery; 3) we developed a miniature robotic manipulandum forrodent studies.

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. Development of VR-based rehabilitation system focusing
on sense of agency
First, Asama and Wen’s group (The University of Tokyo)
proposed a VR-based motor rehabilitation system for stroke
patients. Specifically, in the proposed task, participants wereasked to perform vertical movements, which are very
commonly used in motor rehabilitations (Fig. 2). The positionof the actual hand was presented in virtual reality (VR). In theintervened condition, the hand position in VR was slightly
modified to show better visual feedback (e.g., slightly higher
when participants intended to reach a high position). Thisvisual intervention significantly enhanced the sense of agency
when moving one’s arm [1], and also showed positive effect on
motor motivation that was measure by the reaction time ofmuscle activities. Fig. 3 shows the model of the effect of visual
modification on the sense of agency and motor intention.
Visual intervention, which was consisted with one’s motor
intention, reduced prediction errors and enhanced the sense ofagency, and through this, boosted motor intention.

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 configuration of the VR-based motor rehabilitation
systemFig. 1 Processes of intervening hyper-adaptation via body
consciousness and emotion, and quantitative measures.

We also collaborated with A03 group (Imamizu and Ohata)
to examine the relationship between perceptual sensitivity tocontrol change and the efficiency of motor learning. As a result,
we found that people who are sensitive to the increase ofcontrol in environment also adapted faster in a motor learning
task. On the other hand, the perceptual sensitivity to thedecrease in control did not significantly correlate with theefficiency of motor learning. The results indicate that theprocesses underlying the increase and decrease of sense of

agency probably are partially independent from each other.
Furthermore, those processes probably contribute differently to
motor learning.

Then, to study neural background of such a hyperadaptability, he developed the miniature robotic manipulandum
for rodent studies. To understand how the computational
process of motor recovery and motor learning was embeddedin the neural mechanisms in the brain, intervention on theneural activities and measurement of neural activities arenecessary. However, the empirical evidence of computational
mechanisms of motor learning has been produced from humanstudies that take advantage of robotic devices. In the case ofhuman studies, intervention to the neural processes andmeasurement of neural activities were technically challenging.
Thus, here we aim to build a robotic device that can fit rodent
studies where DNA techniques and neuroimaging techniques
are established.

Fig. 3 The model showing the effect of visual modification on
the sense of agency and motor intention

The developed manipulandum realizes 5mm-20mm reachmovements in arbitral force environments, including thevelocity and the position-dependent force fields and thechannel force environment. The force sensor mounted on theend-effector provides us clues of the memory update processduring the learning. Therefore, this device enables us to
examine computational models of motor learning using rodents.

B. Evaluation for Recovery Process of Hemiplegic Patients
In Qi An’s (Kyushu University) research group, weinvestigate the hyper-adaptation process of recovery inhemiplegic patients with impaired motor function. In ourprevious study, we found that the timing of muscle synergy
activity improved before and after intervention by physicaltherapists when hemiplegic patients performed a standing
movement. We also proposed an appropriate rehabilitation
method for each patient based on the differences in the timingof muscle synergy activity [2]. Thus, it has been found that thetiming of muscle synergy activity changes with the severity
and functional recovery in hemiplegic patients.
However, it is thought that not only the timing of muscle
activity but also the force exerted by the muscle changes during
recovery in hemiplegic patients. To investigate the muscle
activity of hemiplegic patients using surface electromyography,
it is not possible to simply compare muscle potentials because
the impedance of the skin and the point of application are
different on different measurement days. In this study, wedeveloped a method to estimate the muscle activity during sitto-stand motion by optimization calculation from the joint
torque calculated by inverse dynamics calculation. This method
enables us to compare not only the timing of muscle activity
but also the magnitude of force exerted by hemiplegic patientswith different measurement dates. This result was presented at
the International Conference on Neural Rehabilitation(ICNR2020) and received the Best Paper Award [3].
C. Development of the miniature robotic manipulandum forrodent studies on computational mechanisms of motor
learning
Izawa (University of Tsukuba) found the change of thelearning rate which is an essential component of the hyperadaptability [4] in the collaboration with B04 group.

IV. FUTURE PERSPECTIVE
In the past year, we examined the role of body
consciousness in motor rehabilitation. We also developed amethod to quantitatively examine the recovery of hemiplegicpatients. Further, we developed a miniature robotic
manipulandum for rodent studies. In the next year, we plan toevaluation our rehabilitation system for stroke patient, tofurther examine the muscle synergies during the recovery ofhemiplegic patients, and to apply the new miniature robotic
manipulandum in computational research of motor learning.
REFERENCES
[1]

[2]

[3]

[4]

Aoyagi, K., Wen, W., An, Q., Hamasaki, S., Yamakawa, H., Tamura,Y., Yamashita, A., & Asama, H.. “Modified sensory feedback enhances
the sense of agency during continuous body movements in virtual
reality”, Scientific Reports, (accepted).
Yang, N., An, Q., et al., “Temporal muscle synergy features estimate
effects of short-term rehabilitation in sit-to-stand of post-stroke
patients”, IEEE Robotics and Automation Letter, vol. 5, no. 2, pp.
1796-1802, 2020.
Wang, R., An, Q. et al., “Muscle tension analysis in stroke patient sitto-stand motion by joint torque-based normalization”, Proceedings of
the 5th International Conference on NeuroRehabilitation (ICNR2020),
2020/Oct (Best Paper Award).
Tanamachi, K., Izawa, J., Yamamoto, S., Ishii, D., Yozu, A., & Kohno,
Y., “Motor neuroscience experience of after-effect of memory update
reduces sensitivity to errors during sensory-motor adaptation task”,
Frontiers in Human Neuroscience (in print).

Annual report of research project B04
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 13 coinvestigators (Shirafuji, Kaminishi, Omura, Ishii, Hamada,Kohno, Kishimoto, Yuine, Ishibashi, Etoh, Miyata, Osaki,
Kanaya).

is responsible for postural control. We introduced control thatmimics the vestibulospinal tract into the neural posture control
model that we have developed, based on the following
findings: 1) It projects to the entire spinal cord to coordinate
muscle activations of the entire body; 2) It maintains the headin a stable vertical position by maintaining the body in a
vertical position; 3) As an input, it receives mainly vestibularsensation in the vestibular nucleus in the brainstem; 4) As an
output, it has an excitatory effect on extensors and an inhibitory
effect on flexors (Fig. 1) [1]. We used this neural controller
model to maintain the stance posture of a musculoskeletal
model with 19 degrees of freedom and 94 muscles, and verified
the validity of the neural controller model.

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

Fig. 2 shows the center of pressure velocity of the simulation
results. In almost all muscle tone conditions, the value for thecase with the vestibulospinal tract was smaller than the value
for the case without the vestibulospinal tract. This is in linewith the results of the experiment with patients with unilateral
vestibular dysfunction, and confirms the validity of thecontroller model.

II. RESEARCH RESULTS AND FUTURE PLANS
A. Introduction of vestibulospinal tract-mimicking control
into a model of nervous system postural control
Ota et al. build a mathematical model that considers therole 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 plays an important role in human posturalcontrol. The vestibulospinal tract, along with the reticularspinal tract, which regulates muscle tone throughout the body,Fig. 2. Muscle tone index ||uff|||2 and center of pressure velocity in theanterior-posterior direction. Black shows the results without vestibulospinal
tract and gray shows the results with vestibulospinal tract.

In the future, we will use this neural controller model torepresent diseases such as Parkinson's disease, in which thefunction of the descending tract is increased or decreased. In
addition, we aim to describe the relationship between
neurotransmitters, postural control mechanisms, and behavioral
manifestations in conjunction with the results of humanexperiments.
Through musculoskeletal simulation, we are attempting toinvestigate the role of arms in maintaining human standing
posture. Musculoskeletal models with/without arms were
controlled using a neural controller model, and forward
dynamics simulations were conducted to apply external forces
to the models. As a result, the center of mass sway became
smaller in the condition using the musculoskeletal model witharms, suggesting that the presence of arms contributes to themaintenance of standing posture [2].
B. Evaluation of the role of neurotransmitters in posturalcontrol with multitask
Yozu et al. evaluate the role of neurotransmitters in posturalcontrol with multitask. In this year a) we designed a multitask
protocol featuring a postural task and a cognitive task, b) wedeveloped a system to measure kinematic and physiologicalresponses during the tasks, and c) we measured patient with
Parkinson’s disease.
a) Designing of multitask featuring a postural task and acognitive task
A multitask which could be performed by Parkinson’s
disease was designed. For a postural task, 30 seconds of quiet
standing was adopted. For a cognitive task, an arithmetic task
was adopted [3]. The participants were asked to recite serial
subtractions by 7.

Fig.3 Fluctuation of the center of pressure in Parkinson’s disease

C. Development of a Unilateral spatial neglect mouse model
Unilateral spatial neglect (USN) is a disorder of higher
brain function that occurs after a brain injury, such as stroke,
because patients with this condition often have other symptoms
due to the presence of several brain lesions, it is difficult toevaluate the recovery mechanisms and effect of training on
unilateral spatial neglect [4]. Yozu et al. induced a focal
infarction in the right medial agranular cortex (AGm) of miceusing photothrombosis, and created a mouse model of USN.
After induction of cerebral infarction, USN was evaluated. The
left-side selection rate was calculated as the symptom of USN.
After the final evaluation of the USN (18 days after cerebral
infarction), the brain was removed and cryosectioned (100 μm).
The sections were stained with cresyl violet, and subsequently
observed under a microscope to examine the infarct volumes,
the extent of the lesions, and the centroid of the lesion area.
The findings suggest that recovery from ipsilesional spatial
bias requires neural plasticity within the anterior AGm. Thisconditional mouse model of ipsilesional spatial bias may be
used to develop effective treatments for unilateral spatial
neglect in humans.

b) Development of a system to measure kinematic andphysiological responses during the tasks
We developed a system to measure the posture, the centerof pressure, and the surface electromyogram of the subjects.
Center of pressure was measured using a stabilometer (Anima,
Tokyo, Japan) with a sampling rate of 20 Hz. The surface
electromyogram of 16 of the following muscles was measured
by a wireless EMG system (Delsys, Tokyo, Japan): cervicalparaspinal, sternocleidomastoid, lumbar paraspinal, gluteus
maximus, quadriceps femoris, semitendinosus, tibialis anterior,and soleus (all bilaterally).

Fig.4. Development of infarction by photothrombosis

REFERENCES[1]c) Measurements in patients with Parkinson’s disease
To analyze the effect of multitask loading, participants wereasked to perform i) standing task without arithmetic task, ii)
multitask of the standing task and arithmetic task, and iii) only
the standing task. Two Parkinson’s disease patients wasmeasured. They were measured before and after drug
administration to evaluate the effect of dopamine on themultitask. As a result, there were fluctuation during one day,and center of pressure and electromyography changed partiallyconcordantly. We are now submitting the result to aninternational journal.

[2]

[3]
[4]

Y. Omura, K. Kaminishi, R. Chiba, K. Takakusaki and J. Ota,
"Investigation of effects of vestibulospinal tract on muscle tone bymusculoskeletal simulation", in 2020 42nd Annual International
Conference of the IEEE Engineering in Medicine and Biology Society(EMBC), 2020.
K. Kaminishi, R. Chiba, K. Takakusaki, and J. Ota, “Musculoskeletal
simulation to understand the role of arms in maintaining standing
posture against external forces”, in The 38th Annual Conference of the
Robotics Society of Japan, RSJ2020AC1H3-04, pp. 1-2, 2020 (inJapanese).
Granacher U, Bridenbaugh SA, Muehlbauer T, Wehrle A, Kressig RW.
“Age-related effects on postural control under multi-task conditions,”
Gerontology. Vol. 57, pp. 247-255, 2011.
Bowen, A, Hazelton, C, Pollock, A. & Lincoln, N. B. Cognitive
rehabilitation for spatial neglect following stroke. Cochrane Database
Syst Rev, CD003586, 2013.

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

Abstract—In this study, we apply Deep Reinforcement Learning for handling full-dimensional 7 degrees of freedom arm
reaching, and demonstrate the relations among motion error,
energy, and synergy emergence during the learning process, toreveal the mechanism of employing motor synergy. Althoughsynergy information has never been encoded into the reward
function, the synergy effect naturally emerges, leading to a similar
situation as human motion learning. To the best of our knowledge,
this is a pioneer study verifying synergy development in DRL for
multi-directional reaching tasks.

I. Introduction
In humans, our body has more degrees of freedom (DOF)
than the number of dimensions of our task space performedin our daily life. This redundancy is advantageous because
it enables us to execute a motor task by finding easier
motor coordination considering our physical bio-mechanicalconditions while maintaining a certain motion accuracy. Thus,
humans can achieve highly coordinated and efficient movements in sophisticated ways with the complex musculoskeletal
system. Elucidating control strategies that can overcome thesecomplexities to reproduce neural control of human movement
is a central issue in the field of human motor control.
Over the past few decades, several indexes are proposed forhuman movement optimization, for instance, by defining thatthe system accomplishes a task in minimum X, where X can bejerk, torque changes, motor command, or energy expenditure.
In redundant manipulators, the model-based cost function iscommonly adopted in the process of optimization. However,they basically rely upon the good knowledge of a physicalinverse dynamics model or approximation-based model for
precise control.
Due to the complex and nonlinear dynamic relationships
among muscle activation, joint torques and joint motions, controlling human movements would appear to be a challenging
task for the central nervous system (CNS). Indeed, researchers
in the field of neuroscience have proven that the concept ofmotor synergy that organizes and simplifies upper-level tasks
among a set of elemental controls exists within the CNSs.
By applying deep learning techniques in reinforcement
learning (RL), i.e. deep reinforcement learning (DRL) offers apromising model-free approach for overcoming complex learning problems. It enables the robot to learn directly from theinteraction with the environment. DRL has received increasing
attention in robotics research and demonstrates its strength inobtaining the optimal policies for high dimensional humanoid
robots. However, it should be pointed out that the current DRL

algorithm does not account for synergetic motor behaviors,
which can tend to produce unnatural and awkward control
results.
In this study, we apply DRL for handling multi-directionalreaching with full-dimensional arm, and demonstrate the relations among motion error, energy, and synergy emergence during the learning process, to reveal the mechanism of employing
motor synergy [1]. We believe that a better understandingof how error-energy index is actually related to the synergy
concept within deep reinforcement learning framework for
multi-directional reaching would provide useful insights into
improving DRL algorithm on managing the high dimensionality of motor control problems.
II. MATERIALS AND METHODS
A. Deep Reinforcement Learning Algorithm
RL enables an agent to learn optimal behaviours by trialand-error interactions with its environment. The task in reinforcement learning can be described by the framework of
infinite-horizon Markov decision process (MDP), characterized by the tuple (S, A, p, r) , comprising a state space S ∈ Rn
and a set of actions A ∈ Rm available to the agent. The
unknown state transition probability p : S × A × S → [0, ∞)represents the probability density of the next state st+1 ∈ Sgiven the current state of an agent at time t: st ∈ S and actionat ∈ S, while the reward function r : S × A → R returns
the immediate reward r(at , st ) to the agent after taking action
at in state st . We use ρπ to denote the trajectory distribution
induced by a policy π(at |st ).
In this study, it is essential to choose a DRL algorithm thatis capable of exploring an unknown environment stably and efficiently to solve complex robotics tasks [2]. The general goalin RL is to find a policy π that maximizes the agent’s expected
return as shown in (1). In contrast, soft actor-critic(SAC) is analgorithm that optimizes the stochastic policy. In particular,
the objective of SAC is to find a policy by maximizing theagent’s expected return in parallel to the expected entropy ofthe policy, πS AC weighted by an entropy term α.
π = argmax

TX

E(st ,at )∼ρπ [r(st , at )]

(1)

t=0

πS AC = argmax

T
X
t=0

E(st ,at )∼ρπS AC [r(st , at ) + α · H(πS AC (·|st ))](2)

Fig. 1. Endpoint transition for multi-directional reaching. (A) with DRL algorithm, and (B) with DRL with feedback control.

Fig. 2. Schematic representation of multi-directional reaching task.

III. EMERGENCE OF MOTOR SYNERGY
A. Simulated Agents
Our study is conducted on simulated agents using a simulation engine, called MuJoCo which is widely utilized forstudying multi-joint mechanical system in the DRL community. We apply the proposed DRL control scheme on ananthropomorphic 7-DOF robotic arm to analyze the motion
behaviors during the learning as depicted in Fig.2.

with different colors, as illustrated in Fig.3. The light blue
curves correspond to the early phase of training, the purple
curves and red curves correspond to the ending phase of the
training. The R2 accuracy varies based on the number of joint
synergies used in the reconstruction and as the number ofsynergy components increases, the elevation of accuracy ofreconstruction can be observed. It can be noticed that thesynergy level of the curves generally rises towards the endof the training, as fewer synergy components are required
to reach higher R2 accuracy. This phenomenon is illustrated
by the green arrows. These results indicate the interesting
phenomenon that joint synergy is emerged in multi-directionalreaching by using DRL algorithm [3].

B. Motor Control Results
To evaluate the performance of the proposed method for the
redundant robotic arm, the tracking result is firstly compared
between DRL and DRL with feedback controller. Fig.1 illustrates the motor control result of endpoint transition (A) with
DRL algorithm, and (B) with DRL with feedback control. A
PiYG color map (ranging from purple to green) is used for
DRL and a jet color map is used for PDRL, to illustratesequential learning transition. It is interesting to see thatDRL with feedback has less random exploration and better
tracking performance. This phenomenon can be explained
as the feedback control component may act as a supervised
signal that guides the system to select an action toward thatsupervision. Hence, DRL with feedback can obtain feasible
solutions faster without unnecessary random explorations.
C. Joint Synergy Analysis on Learned result
PCA is applied against Joint angles to estimate the matrix
of joint synergies W and the matrix of activation signals C.
After the joint synergies W and the activation signals C aregathered, the original control signals can be reconstructed witha certain degree of accuracy indicated by the R2 metric.
Once the signals are collected from different training checkpoints of the policy, then the R2 curves can be plotted for thesecheckpoints on the same diagram and associate each curve

Fig. 3. Graph of R2 accuracy vs the number of synergy components in multidirectional reaching. (a) with DRL, (b) with DRL with feedback control.

IV. Conclusion
We found that the reconstruction accuracy through synergy
turns into high level during learning process. Future direction
would be to extend this framework to other motor tasks.
References[1] F. M. Ramos, A. d’Avella, and M. Hayashibe, Identification of TimeVarying and Time-Scalable Synergies From Continuous Electromyographic Patterns, IEEE Robotics and Automation Letters, vol.4, no.3,
pp.3053–3058, 2019.
[2] 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.
[3] J. Han, J. Chai and M. Hayashibe, Emergence of Motor Synergy in Multidirectional Reaching with Deep Reinforcement Learning, IEEE/SICE
International Symposium on System Integration, 2021.

B05-2 Adaptation ability of human postural control
system revealed by a closed-loop electrical muscle
stimulation system
Daichi Nozaki1, Shota Hagio2
1

2

Graduate School of Education, The University of Tokyo
Graduate School of Human and Environmental Studies, Kyoto University

Abstract—This study aims to elucidate the ability of the humanpostural control system to adapt to a wide variety of dynamical
environments. To this end, we propose a novel methodology toalter the dynamics of the body during quiet stance using closedloop electrical muscle stimulation. Our idea is that the dynamics
can be artificially altered if the ankle joint torque can be
modulated by the electrical muscle stimulation depending on theposition, velocity, and acceleration of the center of body mass. This
year, we developed the system to validate this idea and obtained
preliminary data showing how the human postural control system
adapts to the change in postural dynamics.

I. INTRODUCTION
Human has a remarkable ability to maintain unstable upright
posture under a wide variety of environment. It is believed that
this stability is achieved by the function of the postural control
system to transform sensory information, including visual,vestibular, and proprioceptive signals, into an appropriate motor
command [1]. However, little is known about how theadaptation process progresses, which might be partly because
the conventional studies have limited to the methodologies toinvestigate spontaneous sway or the response to a transient
perturbation to the vision and supporting surface.
In the study using the reaching experimental paradigm, themethod to manipulate the dynamics using a robotic
manipulandum has been widely used [2]. A considerable
amount of knowledge has been accumulated as to how theadaptation process progresses from the viewpoint of behavior,neural representation, and computational mechanisms. In thisproject, we try to apply this powerful methodology to the studyof the human postural control system.
II. AIM OF THE GROUP
The application of the system to alter the dynamics topostural control is not easy. For example, one can consider the
perturbation methods, including exoskeleton robot [3], andpulling the body using a wire. However, these methods
sometimes are quite expensive and the accurate manipulation of
perturbation with the subtle postural sway information isdifficult. Furthermore, the presence of the device could interferewith the postural dynamics itself. As a solution overcoming
these problems, here, we propose that the tibialis anterior (TA)Fig.1 The dynamics of body can be artificially altered if the ankle dorsiflexion torque is manipulated depending on the COM position, velocity,
and acceleration.

muscle can be utilized as a “natural” perturbation device. If we
set up the electrical muscle stimulation (EMS) system tomodulate the ankle joint torque with the COM position, velocity,
and acceleration, the body dynamics can be artificially altered(Fig.1). This research project aims to develop the system to
implement this idea and investigate how the postural control
system adapts to novel dynamics imposed by this system. This
year, we have developed the system and validated our idea. We
also performed a preliminary experiment to impose novel body
dynamics during quiet standing.
III. RESEARCH TOPICS
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 plateFig.2 The closed-loop EMS system to alter the postural dynamics.

A. Development of the closed-loop EMS system
We measured the anterior-posterior displacement of COM
using a laser displacement sensor (IL-2000, Keyence, Japan).
This information was used to modulate the intensity of EMS(Isolator, ULI-100C-SI, Unique Medical, Japan) (Fig.2).

B. Identification of the system to obtain the EMS pattern toproduce arbitrary ankle joint torque
The biphasic pulse (width 0.5msec) was used as EMS, andthe frequency was maintained at 20 Hz to minimize the effect of
fatigue [4]. First, the amplitude was sinusoidally modulated withvarious frequencies, and the resultant ankle joint (dorsiflexion)
torque was measured (Fig.3). From the gain and phase of therelationship, the transfer function from the EMS to the ankle
joint torque was identified. Then, the transfer function of theinverse system that transforms the ankle joint torque to the EMS
intensity was obtained.

Fig.4 We validated if the EMS pattern obtained using the transfer functionof the inverse system could produce the ideal ankle joint torque constructedfrom the COM data collected from a participants.

Fig.3 Identification of the transfer function from the EMS to the ankle joint
torque. We measured the ankle joint torque when the EMS whose
amplitude changed sinusoidally with various frequencies was applied toTA. The transfer function was identified from the gain and phase
information.

Then, we validated if the EMS based on this transfer function
can actually generate the ankle joint torque that changes with the
COM position and velocity (Fig.4). More specifically, weconstructed an idealistic ankle joint torque from the COM datacollected from a participant. The EMS pattern was calculatedusing the transfer function of the inverse system. We directly
measured the ankle joint torque when this EMS was applied toTA. The results indicate that the resultant ankle joint torquereasonably reproduced the ideal ankle joint torque pattern(Fig.4).
C. Adaptation to a novel dynamical environment created by
the closed-loop EMS system
We performed a preliminary experiment that examined how
the human postural control system adapted to novel dynamical
environments created by the closed-loop EMS system. We
imposed three different types of dynamics: the perturbation
torque was modulated with the COM position, velocity, and both.
During 7 minutes adaptation phase, we interleaved a forward
perturbation to the ankle joint every 30 sec to see how theadaptation changed the response pattern. We observed the
response to the forward perturbation changed depending on theimposed dynamics throughout the adaptation phase (Fig.5).

Fig.5 As the postural control system adapted to a novel dynamics, theresponse to the forward pertubation gradually changed and thechanging pattern was specific to the imposed dynamics.

IV. FUTURE PERSPECTIVE
This year, we developed the new closed-loop EMS system.
We plan to perform the adaptation experiment more
systematically the next year. Furthermore, we will extend thisclosed-loop stimulation into the galvanic stimulation toinvestigate how the postural control system utilizes thevestibular sensory information for the postural adaptation.
REFERENCES
[1]
[2]

[3]

[4]

“Balance, Gait, and Falls” Eds. Day B. L. and Lord S. R. Elsevier, 2018.
R. Shadmehr and F. A. Mussa-Ivaldi. “Adaptive representation of
dynamics during learning of a motor task”. J Neurosci 14, 3208–3224
1994.
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.
T. Moritani, M. Muro, and A. Kijima. “Electromechanical changes during
electricallyinducedandmaximalvoluntarycontractions:Electrophysiologic responses of different muscle fiber types during
stimulated contraction”. Exp. Neurol. 88, pp. 471-483, 1985.

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 during the course of the human evolution. The factthat the 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
Japanese macaques generally have the ability to walkbipedally. Our group has been investigating bipedal locomotion
in Japanese macaques as a way to achieve a better
understanding of the evolution of human bipedalism [1-4]. The
acquisition of bipedal locomotion in an inherently quadrupedal
primate could be regarded as a modern analogue for theevolution of bipedal walking, offering a living model forclarifying and reconstructing the evolution of bipedal
locomotion. However, bipedal locomotion of Japanesemacaques is different from that observed in humans. For
example, the hip and knee joints are more flexed throughout
the gait cycle, and macaques do not generally exhibit thecharacteristic double-peaked vertical ground reaction force
profile seen in humans. Such differences in kinematics andkinetics of bipedal walking between humans and nonhumanprimates exist because of structural differences in themusculoskeletal system. Among these differences, those of thefoot seem to be 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 actually possesses an ability to spontaneously
reorganize itself in such a way to adaptively make use of themorphological change in the foot structure to accomplish morestable, robust and efficient bipedal locomotion. If the neuronalmechanism underlying such “hyper-adaptability” of humanlocomotion to the change of the body structure can beelucidated, the findings will not only contribute to clarifying
the neural basis of the evolution of human bipedal locomotion,
but also provides implications for effective therapeutic or
rehabilitative interventions to restore walking ability in oldadults who suffer from decline of bodily and neurological
functions.
II. AIM OF THE GROUP
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]. Dimensions andinertial parameters of the limb segments were determinedbased on this 3D model. Here, we considered 10 principal
muscle groups classified according to muscle disposition. Eachmuscle was modeled as a string connecting the origin andinsertion points. The force generated by a muscle wascalculated as the sum of the force generated by the contractile
element due to the activation signal from the nervous systemand the passive element parallel to the contractile element. The
maximum forces of the muscles were assumed 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 ishypothesized 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 (Fig. 2). The RG layer was modeled by
two phase oscillators corresponding to the phase signals for theleft 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.

A

1
2

When the foot morphology was altered (the calcaneal
tuberosity was inferiorly translated), the vertical groundreaction force profile shifted from a single- to a double-peaked
profile as in human walking. As the calcaneal point wasinferiorly translated, the cost of transport gradually decreased
compared with that of the original foot condition. The changes
in the nervous system parameters due to the alteration of thefoot morphology is currently under investigation.

3
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.
PG

γ1, 2
γ1,1
a1
a2

σ1,1

σ1, 2

μ1,1

μ1, 2

RG
Phase
oscillator

a3

a10

・・・

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, such as
the single-peaked vertical ground reaction force profile with apeak occurring in the early stance phase, the breaking peakmagnitude being slightly larger than that for propelling, and thebreaking period being shorter than the propelling period.
Simulation framework successfully reproduced the basic
kinematic and dynamic features of bipedal locomotion in theJapanese macaque.

6
7

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.
We evaluated how virtual manipulation of foot morphology
affects the kinematics, dynamics, and energetics of bipedal
locomotion. Specifically, we investigated how the alteration of
the foot from digitigrade to plantigrade by the inferiortranslation of the calcaneal tuberosity, which allows heel strike
bipedal locomotion, affects the kinematics, dynamics, andenergetics of bipedal locomotion in the Japanese macaqueusing computer simulation.

B

Cutaneous input

Fig. 2. Nervous system model.

V. FUTURE PERSPECTIVE
We constructed a computer simulation of bipedal
locomotion in the Japanese macaque based on aneuromusculoskeletal model to evaluate how structural
alteration of the foot affects kinematics, kinetics and energetics
of bipedal locomotion. The present nervous model incorporated
only the mathematical model of the CPG, but this is not
sufficient to explain the mechanism of “hyper-adaptability”.
The reticulospinal tract that involves in postural control andlocomotion, and the vestibulospinal tract responsible forvestibulospinal reflex necessary to maintain postural balanceagainst external perturbations should be incorporated in thepresent nervous model towards understanding mechanisms of“hyper-adaptability” of bipedal locomotion.
REFERENCES
[1]

[2]

[3]

[4]

[5]

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.

Annual report of research project B05-4
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.” In this year, I tried to develop a method to
identify the low-dimensional state using combination of timevarying graphical lasso, calculating Kullback-Leibler
divergence, and multi-dimensional scaling. The method
worked for the simulated data with four dimensions. However,the distinct low dimensional representations for different tasks
were not found in the public dataset measured byelectroencephalogram, suggesting need for improving themethod.

I. INTRODUCTION
Human movements vary across trials because of neuralnoises [1]．Recent studies suggest that such the variability of
movements is derived from not only noises but also position orstate in the low dimensional neural subspace composed from
population of the neural activities (i.e., Neural manifold) in thephase of movement planning [2]. For example, in theexperiment for the non-human primates, it has been suggested
that the reaction time is determined depending on the state of
the neural population of premotor cortex in the planning phase.
From these results, it is hypothesized that movements are
generated using dynamical systems whose inputs or initial
position are state of the neural population in the planning [3]．
Above mentioned neurophysiological studies (e.g. [2, 3])
showed the importance of identifying low dimensional
subspaces. On the other hand, human neuroimaging studies
have been reported that a relationship between several brain
regions other than the motor cortex and errors or movement
accuracy [4, 5]. However, it is unclear how these regions andconnectivity between the regions are related to the movement
variability and the errors. Also, it has been reported a
possibility that human perception and behavioral performances
are influenced by pre-stimulus activity or resting-state activity[6, 7]. While it remains unclear how these pre-stimulus
activities influence perceptual and behavioral performances, it
is hypothesized that low dimensional neural subspace in theplanning is influence by the pre-stimulus activity. In addition, I
think that such the different neural subspace representations arebase of “hyper-adaptability.” Therefore, in this study, I try to
identify low dimensional neural subspace related to the
movements in the whole-brain levels as state-space dynamics.
Then, I would like to understand the mechanism of hyperadaptability using non-invasive brain stimulation.

II. AIM OF THE GROUP
The purpose of this research is to examine neuralrepresentation (manifold) related to movement variability inhumans. 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. Furthermore, we are going to try to
manipulate such a representation using a brain stimulation
technique.
III. RESEARCH TOPICS
A. Development of the method to identifying low dimensional
subspace for EEG data
First, this study examined the method to identifying 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
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 first tried to identify the
low dimensional subspace for EEG data using a method basedon TVGL. Using TVGL, we obtained covariance matrices andprecision matrices. Next, we calculated a distance matrix fromthe covariance matrices using Kullback-Leibler divergences
and obtained distance (i.e., similarity) among the components.

verified. Therefore, further investigation into this issue isrequired. In the next fiscal year, we are planning to conduct anexperiment with a task that generates variability, andmanipulation of the state using brain stimulation. Also,
collaboration with other groups is expected to deeply
understand the low dimensional neural subspace and hyperadaptability．

Fig. 1. The results of simulation.
(a) Simulation data and its structure. (b) A low-dimensional map for different
network states.

Then, we used multidimensional scaling to visualize low
dimensional subspace of the brain networks. Currently, we
mapped the distance matrix into 2-D subspaces.
To evaluate this method, we first generated artificial data.
The data had four variables that followed multi-variate
gaussian distribution. Further, connectivity (or networks) ofeach variable changed depending on the time (Fig. 1A). We
applied the above-mentioned method to identify low
dimensional subspaces. The result showed that we could
visualize the relationship or structure of the data that changes
in every 100 epochs in the two-dimensional subspace (Fig. 1B).
Next, we applied this method to the real EEG data. In thepresent study, we used a public dataset for the brain-computerinterfaces (BCI competition 2008 Graz A [10]). The task wasfour types of motor imagery (left-hand, right-hand, foot,tongue). We examined whether the proposed method detects
changes in the data structures associated with tasks. The result
showed that all the data seemed to be mixed and no distinct
state was observed.
Taken together these results, a method that we evaluated
was able to identify low dimensional subspace for the artificial
data but not the real EEG data. This could be due to thecomplexity of the data or noises contained in the data, which
may influence the precise estimation of the covariance matrix.
Therefore, it is necessary to further develop a method to
identifying low dimensional subspace precisely.
IV. FUTURE PERSPECTIVE
In this study, we tried to develop a method to identify the low
dimensional subspace for EEGs in humans. We proposed amethod by extending TVGL and applied it to artificial and real
EEG data (BCI competition data) in this fascial year. The
effectiveness of the proposed method for real EEG data is not

Fig. 2. The results of real EEG data. Each axis indcates dimension identified
by multi-dimensional scaling. Each color indicates task-state, respectively.

REFERENCES[1]

A. A. Faisal, L. P. J. Selen and D. M. Wolpert, "Noise in the nervous
system", Nature Rev. Neurosci., vol. 9, no. 4, pp. 292-303, 2008.J. Clerk
Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2.
Oxford: Clarendon, 1892, pp.68-73.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] H. van Dijk, J. Schoffelen, R. Oostenveld and O. Jensen, "Prestimulus
oscillatory activity in the alpha band predicts visual discrimination
ability", J. Neurosci., vol. 28, no. 8, pp. 1816-1823, 2008.
[7] A. Kucyi, M. J. Hove, M. Esterman, R. M. Hutchison and E. M. Valera,"Dynamic Brain Network Correlates of Spontaneous Fluctuations in
Attention", Cerebral Cortex (New York N.Y.: 1991), vol. 27, no. 3, pp.
1831-1840, Mar. 2017.
[8] 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.
[9] 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.
[10] M. Tangermann, "Review of the BCI competition IV", Frontiers
Neuroscience, vol. 6, no. 55, pp. 1-31, 2012

B05-5 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 study, we developed a motor learning model
based on an estimation of partial dynamics of the target motor
control system and a transformation estimation model between
the partial dynamics. We show that it is possible to reconstruct
the controller using the information of the other arm’s controller
when the controller of one arm is partially defective.

ଶ

ଶ

ଷ
ଵ

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 was shown thatwhen one hand is paralyzed, that hand can be controlled by adifferent neural circuit from the ordinary one [1]. There havebeen many studies on human motor learning models, suchas the integration of multiple dynamics estimation models.
However, the process of reuse and substitution of neuralcircuits has not been modeled yet. If we can develop sucha model, we can expect to deepen our understanding of thehuman adaptation process and apply it to rehabilitation.
In this study, we aim to construct a motor learning modelthat can explain the process of reusing a part of a onceacquired motor control model according to the situation. We
propose a model that automatically generates a controller by
estimating the dependency among various sensor signals inmotion control based on our past achievement [2], and develop
a motion learning model that explains the process of reusing
a part of dynamics in a previously acquired controller byintroducing a new mechanism called transformation estimation
among mappings to the model.
II. ACHIEVEMENTS
There are mainly two achievements summarized as follows.
A. Construction of motor learning model based on estimationof partial dynamics
As a precondition for the proposed model, which is based on
the estimation of transformations between mappings, we constructed and veriﬁed an algorithm that automatically generates
a motion controller by estimating the relationships between
observable sensor variables (mappings) as partial dynamics,
unlike conventional motion learning models that focus only
on the ﬁnal input and output of the target system.
The structure of the musculoskeletal arm system targeted inthis study is shown in Fig.1. The arm robot consists of three, 

ସ

ଵ



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

Fig. 1.
modelଵMusculoskeletal arm Fig. 2. Variables of manipulator
TABLE IS ENSOR VARIABLESθ̇ [rad/s]

d1 ∈ R2

τ [N/m]d7 ∈ R2ẋ [m/s]d2∈ R2

L̇ [m/s]

d8 ∈ R4

d3

∈ R2

L [m]

d9 ∈ R4

d4∈ R2

L̈ [m/s2 ]d10 ∈ R4

d5

∈ R2

F [N]

d11 ∈ R4

d6

∈ R2

T [◦ C]d12 ∈ R4

θ [rad]x [m]θ̈ [rad/s2 ]ẍ [m/s2 ]links and two joints, and the base link is ﬁxed. Each joint isdriven by two artiﬁcial muscles. In this study, the control input
to the robot is the temperature of the muscles.
The sensor variables are shown in TABLE I. x,θ, L, τ ,F , T denote the position of the end-effector, the angle ofeach joint, the length of the muscle, the joint torque, themuscle force, and the muscle temperature, respectively. The
correspondence between each variable and the components of
the robot is shown in Fig.2. To express the unknown structureof the robot, we assume that the physical meaning of eachsensor variable is unknown. Therefore, each sensor variable
such as x, θ, etc. is treated as d1 , d2 . However, the relationshipbetween the time derivatives of the variables θ, θ̇, θ̈, etc. andthe control input µ = d12 = T are known. The goal of the arm
robot is to generate a control law that leads the end-effector
position dtarget = d4 = x, to the target value ddes .
We use mutual information to evaluate the relationship between sensor variables, deﬁne the weakness of the relationshipas the cost, and search for the connection of variables withthe minimum cost from the control target to the control input
using the Dijkstra method. The connection of the variables isdeﬁned as a chain, and the feedback control is realized by theordinarycontrol$%'(,')
1.4

%'(,&

1.2

%&,')
%#,&
!"

partial converted Jacobian
disorder used

1
0.8

%#,#

0.6

%&,#

%&,&

0.4

!#

0.2

reference
position_x0Fig. 3. A partial visualization of Jacobian in the state space with selforganizing elastic grid model.

control measure generation algorithm based on the chain. To
generate the control law, we use locally linear approximation
model. This local linear approximation model plays the roleof the ‘estimated partial dynamics’ in this study.
B. Estimation of transformation between partial dynamics

Ri,j ≈ T (L Ri,j ).

-0.4
0

1000

2000

3000

4000

5000

6000

7000

Fig. 4. Partial disorder and its recovery process based on conversion of
Jacobian (x position).
ordinary
controlpartial converted Jacobian
disorder used

3

Based on the above-motioned learning model, we proposedand constructed a model to estimate the transformation of thepartial dynamics between the controllers of the two arms. The
partial dynamics of the left and right arms are denoted as
LRi,j (controller L) and R bmathRi,j (controller R). Transformation between the partial dynamics is represented by
R

-0.2

2.5

2

reference
position_y
1.5

(1)

Fig.3 shows a visualization of a self-organizing elastic gridfor estimating the transformation T between the left and rightpartial dynamics. This transformation estimation is represented
by a group of nodes distributed in a grid deﬁned in the variable
space where the partial dynamics R are deﬁned. Each node
has a position in the space and holds a scale transformation
coefﬁcient between the left and right R at that position. If thistransformation coefﬁcient takes an appropriate value, the left
and right R satisfy the relation (1). This matching is expressed
as a cost function, and the matching problem is solved byminimizing the cost function using the gradient method.
Examples of numerical experiments assuming the disappearance of partial dynamics in the controller R are shown in Fig.4
and Fig.5. Assuming a case where R Ri,j is not available, using
L
Ri,j and estimated T , R Ri,j is substituted by T (L Ri,j ).
In the section labeled “ordinary control” in the ﬁgure, thereference trajectory of the hand position is followed by theacquired motion controller. In contrast, in the section labeled“partial disorder,” due to the lack of proper control, whenever ahand position goes out of the speciﬁed area, the hand position
is repositioned (reset). Therefore, we can see that resets occur
frequently. It was conﬁrmed that by using the controller withthe partial dynamics substituted by T (L Ri,j ), the control that
follows the reference trajectory was recovered again.
III. S UMMARY AND FUTURE PLAN
The following results were obtained in this ﬁscal year:• For two-arm systems with varying scales of link lengths,
it was conﬁrmed that an automatic control law generation

0

1000

2000

3000

4000

5000

6000

7000

Fig. 5. Partial disorder and its recovery process based on conversion of
Jacobian (y position).

algorithm based on the estimation of partial dynamics cangenerate a hand position controller for each arm.
• It was conﬁrmed that appropriate motion control could
be achieved by transforming the partial dynamics of thecontroller of the other arm when the information of thecontroller of one arm was lost by the transformation
estimation between partial dynamics that could absorb
the above-mentioned scale change.
In the future, the transformation estimation method implemented this year will be extended to be applicable to varioustransformations by referring to motion learning methods suchas [4], and experiments on human motion learning will be
conducted to verify the explainability of the proposed motion
learning model.
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] C. G. Atkeson, A. W. Moore, and S. Schaal, Locally weighted learningfor control, Artiﬁcial Intelligence Review, 11(1), pp.75-113, 1997.
[4] F. Arjonilla and Y. Kobayashi, Supervised learning of mapping fromsensor space to chained form for unknown non-holonomic driftlesssystems Industrial Robot, in press.

Annual report of research project B05-6
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 embodiment of a robot through robot operation, whichis under 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 this year,
we constructed an experimental environment to evaluate effects
of intervention of the robot controller with AI on the operator’s
sense of agency 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 compete a target task if necessary.
Through experiments, we confirmed that the operator’s sense ofagency tended to decrease when the robot controller intervened
operator’s manipulation. On the contrary, other results suggestthat the sense of agency increases if the intervention from the
AI robot controller is not conscious for the operator.

Fig. 1. Configuration of the teleoperation system with HSR and Oculus Rift
/Touch[2]

I. I NTRODUCTION
Shared control is a control approach in which a human anda machine work cooperatively, and each of them performs fulloperation process from perception to action in a consistentmanner. It is expected to reflect the operator’s intentions andachieve accurate operation, and has been applied to driving
assistance for automobiles and rehabilitation robots.
However, if the operational intervention by the controller isdifferent from the operator’s intention, discomfort or sickness
may occur. For example, it has been confirmed that sickness
is induced by the operational intervention to avoid obstacles
during the teleoperation of a robot with an automatic collision
avoidance function. It is assumed that one of the reasons forthis phenomenon is that the operator’s sense of agency (thesense of recognizing that the motion of the observed object iscaused by himself or herself[1]) is decreased when the manipulated object behaves differently from the operator’s intention.
Therefore, interventions which do not degrade operator’s senseof agency are required.

Fig. 2. Image of operational intervention: When the robot arm approaches
near the target position, it moves there regardless of the operator’s controller
operation.

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
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.

Fig. 3. The experiment to evaluate the Sense of Agency

III. ACHIEVEMENTS
A. Manipulation intervention using the intuitive teleoperation
systemTo investigate the effect of manipulation intervention bya robot controller on robot teleoperation, we used the intuitive teleoperation system that has been developed in ourlaboratory[2].
The intuitive teleoperation system is a system for remotely
controlling TOYOTA HSR (Human Support Robot) using VR

devices (head-mounted display and controller)(Fig. 1). In thissystem, the position and posture of the operator’s left handcorresponds to the position and posture of the arm tip of
HSR, and the position and posture of the operator’s headcorresponds to the position and posture of the head cameraof the HSR, which are controlled in real time. In addition, theimages from the binocular camera mounted on the robot’s headare displayed on the head-mounted display, and the operator
is presented with parallax images. This system allows theoperator to teleoperate the robot with a sense of immersion.
In this year, we introduced a function to support operations
such as grasping an object by intervening to correct the arm tip
position of HSR. When the distance between the arm tip andthe target position becomes less than a certain value, the arm
tip moves to the target position regardless of the operator’s
controller operation (Fig. 2). This correction is expected to
improve the positioning accuracy of the arm, however, at thesame time, it is also expected to reduce the operator’s senseof agency. In order to achieve accurate manipulation without
degrading the operator’s sense of agency, it is necessary to
clarify the relationship between the intervention in manipulation and the sense of agency.
B. Evaluation of the effect of manipulation intervention on thesense of agency
We conducted evaluation experiments to examine the effectof manipulation intervention on the operator’s sense of agency
in robotic teleoperation. The 3D camera on HSR’s head
recognized an AR marker on the wall, and placed a tennis ballat the target position based on the marker(Fig. 3). We set atask to move the robot arm to a position where it can grasp theball, and randomly changed the conditions with and without
correction. Two sets of 10 trials (5 trials for each condition)
were performed per person.
The evaluation indices were the sense of agency toward thearm manipulation, the time required to accomplish the task,
and the time required to feel correction (discomfort), and wecompared the results with and without correction. In oder toevaluate the sense of agency, we conducted a questionnaire
survey after each trial and asked the participants to rate on
a 5-point scale from 1 to 5 whether their own manipulations
were reflected in the movements of the robot arm. In order tomeasure the time required to feel the correction (discomfort),
the subjects were instructed to pull the trigger of the controller
at the moment they felt the correction (discomfort).
As a comparison condition, we conducted an evaluation
experiment by changing the intensity of the correction. The
results of the comparison of subject A’s sense of agency
and task completion time in the conditions with and without
correction are shown in Fig. 4. Although there are individual
differences in the results, the correction tended to shorten
the task completion time and decrease the sense of agency.
However, in the case of subject B, when the rate of noticing
the correction was low, the sense of agency was improved by
the correction (Fig. 5).

(a) Score of questionnaire(b) Task completin time

Fig. 4. Experimental results of the subject A: With/without correction atgain K = 0.015, which determines the intensity of the correction. (Figures inparentheses indicate the percentage of times the trigger was pulled. Averagevalue is calculated excluding outliers of task completion time.)(a) Score of questionnaire(b) Task completin time

Fig. 5. Experimental results of the subject B: With/without correction atgain K = 0.015, which determines the intensity of the correction. (Figures inparentheses indicate the percentage of times the trigger was pulled. Averagevalue is calculated excluding outliers of task completion time.)

IV. F UTURE PERSPECTIVE
In this year’s study, we constructed an environment in which
the operator can intervene in the teleoperation of the robot
arm, and conducted an experiment to evaluate the effect of
the intervention on the operator’s sense of agency.
We found that the operator’s sense of agency tended todecrease when the teleoperated robot arm underwent position
correction. However, there were cases in which the senseof agency was improved by the correction when the rate ofnoticing the correction was low. This suggests that the positive
task performance influenced the operator’s sense of agency[3].
In the future, we plan to add more subjects, and to examineand evaluate intervention methods that take into accountfactors such as manipulation speed and trajectory.
R EFERENCES[1] S. Gallagher, Philosophical Conceptions of the Self: Implications for
Cognitive Science,” Trends Cogn. Sci., 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. 12391253, 2020.
[3] W. Wen, A. Yamashita, H. Asama, The Sense of Agency during Continuous Action: Performance Is More Important than Action-Feedback
Association, PLOS ONE, vol. 10, no. 4, p. e0125226, doi: 10.1371/journal.pone.0125226 April 20, 2015.

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— The intermittent control is a novel hypotheticalneural strategy for stabilizing upright posture during quiet
stance. Although intermittency in the action of nonlinear posturalcontrollers have been well established recently in the literature,and some studies are beginning to associate intermittency with ahealthy control strategy and loss of intermittency withdeterioration in postural function, it is still controversial whether
such time-discontinuous, nonlinear controllers are more
physiologically plausible compared to the traditional timecontinuous linear controllers. Here, we are conducting a research
to elucidate information processing in the central nervous system(CNS) as a neural basis of the intermittent control during quiet
stance for providing supporting evidence for the intermittent
control hypothesis.

I. INTRODUCTION
It has been considered that the human quiet posture isstabilized by the tonic, i.e., continuous tension of theantigravity calf muscles and the associated high stiffness of theankle joint with a help of spinal stretch reflex (continuous
control model). Ten years ago, we proposed an intermittent
control hypothesis that contradicts this traditional hypothesis[1]. In recent years, several research groups have announced
the results of data assimilation based on our new hypotheticalmodel with postural fluctuation data, showing that the newmodel can fit the data more accurately than the traditionalmodel. In this study, bio-systems theory, measurement of
postural dynamics in healthy people and patients with
Parkinson's disease (PD), associated electroencephalograms(EEG) and electromyograms (EMG), data assimilation basedon Bayesian inference, evaluation of cortico-spinal excitability
by transcranial magnetic stimulations (TMS), andneuromodulation by repetitive transcranial magnetic
stimulations (rTMS) are utilized. By integrating outcomes fromthose multi-modal information, we aim to elucidate the brain
mechanism of the intermittent control during human uprightstanding.
II. AIM OF THE GROUP
According to the intermittent control hypothesis, the CNS
inactivates (switches OFF) and activates (switches ON) the
active contraction of medial gastrocnemius at a sequence ofappropriate 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 the supraspinal
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 theintermittent control model, a timing of switch OFF theactivation of gastrocnemius plays a key role for the postural
stabilization. This seemingly paradoxical property allows themodel 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 stateof upright posture. The cortico-basal ganglia loop is a coremechanism for such information processing, regulating
activity of direct (corresponding to Go signal) and indirect(corresponding to NoGo signal) pathways.
The first goal of year 2020 was to perform a data assimilation
analysis using the intermittent control model and a number of
postural sway data acquired from healthy young and elderly
people and PD patients to demonstrate that the healthy
strategy for postural stabilization is the intermittent control,
and the intermittency is lost, leading to the continuous control,in PD patients.
The second goal of 2020 was to characterize a long latency
and long-lasting event-related synchronization (ERS) at the
beta
band
and
accompanied
anevent-related
desynchronization (ERD) at the theta band that appear in
response to a support surface perturbation during quiet stance.
Those novel EEG responses were found recently in our
preliminary study for postural dynamics during standing.
Similar beta ERS has been known for motor control of upper
extremities, referred to as beta rebound, because it appears
after motor execution or suspension of motor execution(NoGo response in Go/NoGo tasks). Here, we hypothesizethat the beta ERS for the postural recovery reflects a neuralprocess for selecting either activation (Go) or inactivation(NoGo) of medial gastrocnemius along with active monitoringof the latest phase of the postural recovery, which might
correspond to a processing of reafferent sensory motor
information. In this year, we tried to establish a basis that are
required to validate the hypothesis in 2021, using TMS andrTMS for evaluating and modulating cortical activity.

III. RESEARCH TOPICS
A. Data assimilation for postural sway data
The intermittent control model, which can also represent thetraditional continuous control model for a specific choice of its
parameter value, was assimilated into postural sway data
during quiet stance acquired from hundreds of healthy people(young and elderly) as well as PD patients, using a method ofBayesian parameter inference [2].
Fig. 1 shows a set of posterior distributions of inferredparameter values for healthy people (upper panels) and PD
patients (lower panels). The 6 graphs aligned at the bottom ofeach of upper and lower set of panels are the posterior
distributions, in which the 3rd from the left is the one for theparameter  associated with the degree of intermittency in theactive control. As can be confirmed in Fig. 1, -values
distributed around 0.5 for the healthy people, and they wereclose to 1.0 for the PD patients, implying that the upright
posture of healthy people is stabilized by the intermittent
control, whereas it is achieved rigidly by the continuous control[2].

B. Beta ERD and ERS during postural recovery
A high-beta ERD followed by a high-beta ERS, as well as atheta ERD were characterized. Unlike in motor tasks of upper
extremities, the beta rebound in this case was initiated before
the postural recovery was completed, and sustained for as longas three seconds with small EMG responses for the first half
period and then with no excessive EMG activities for thesecond half period of the beta rebound. We speculate that thosenovel characteristics of the beta rebound might be caused byslow postural dynamics along a stable manifold of the unstablesaddle-type upright equilibrium for the postural control system
without active feedback control, but with active monitoring of
the postural state, in the framework of the intermittent control.
Although the intermittent feedback is the involuntary
control for automatic postural stabilization, selecting on or offof the switch is conceptually similar to the decision-making
processes, which might generate the high-beta ERD and ERS
as in the decision-making processes for selecting Go or NoGo
actions in Go/NoGo tasks. Because selecting (or switching
between) off and on requires a reliable estimate of the current
state, active and continuous monitoring of the postural stateplays an important role for achieving the intermittent posturalcontrol. Such active monitoring could also be a cause of thehigh-beta ERS, as suggested by previous studies such that thebeta ERS represents a processing of afferent sensory
information.
IV. FUTURE PERSPECTIVE
Causal relationship for the correlation between beta ERS andslow dynamics of the postural state along the stable manifoldof the upright posture with switched-off feedback controller isnecessary for validating the hypothesis. We are planning toutilize TMS and rTMS for evaluating and modulating corticalactivity during the period of beta ERS.
REFERENCES
[1]

[2]

[3]

Figure 1.
Upper: Posterior distributions of inferredparameters for healthy people. Lower: Posterior
distributions of inferred parameters for PD patients. For theparameter , =1.0 represents the continuous control model,
whereas ~0.5 represents a typical intermittent control
model.

Y. Asai, Y. Tasaka, K. Nomura, T. Nomura, M. Casadio and P. Morasso,“A model of postural control in quiet standing: robust compensation ofdelay-induced instability using intermittent activation of feedback
control,” PLoS ONE, vol. 4(7), e6169, 2009.
Y. Suzuki, A. Nakamura, M. Milosevic, K. Nomura, T. Tanahashi, T.
Endo, S. Sakoda, P. Morasso and T. Nomura, “Postural instability via aloss of intermittent control in elderly and patients with Parkinson’s
disease: A model-based and data-driven approach,”, Chaos: An
Interdisciplinary Journal of Nonlinear Science, vol. 30 (11), 113140,
2020.
A. Nakamura, Y. Suzuki, M. Milosevic and T. Nomura, “Long-lasting
event-related beta synchronizations of electroencephalographic activity
in response to support-surface perturbations during upright stance,”
submitted.

Annual report of research project B05-8
Kazuhiro Sakamoto
Faculty of Medicine Tohoku Medial and Pharmaceutical University, Tohoku University Graduate School of Medicine
Abstract—In this research, we studied the learning mechanism
for adapting to an indefinite environment, where the possible
states of the environment are not even determined, through the
exploration-exploitation trade-off problem in reinforcement
learning. In this year, we developed a prototype neural network
reinforcement learning model that performs a target search task
in which the exploration and exploitation trial periods appear
alternately. In addition, through a meta-analysis of cognitive andbehavioral tasks in mice with leptin-related gene mutations, we
discussed the relationship between learning and memory,
depression, and anxiety, and obtained a foothold for adapting theidea of “hyper-adaptability” to mental activity.

I. INTRODUCTION
There are two kinds of uncertainty. One is like dice, where thestate space is fixed, but which state is taken is probabilistic. The
other is one in which even the state space is not fixed. The
environment that contains the latter is called the indefinite
environment. Living systems seem to have a much higher ability
to adapt to an indefinite environment than existing machines.
Reinforcement learning brings a theoretical framework foracquiring the ability to adapt to the environment from only
correct and incorrect information. One of the major problems inreinforcement learning is the exploration-exploitation trade-off
problem (Sutton and Barto, 1998). In other words, the questionis how to use the seemingly contradictory strategies of using
previous experience (exploitation) or searching for new
effective behaviors (exploration) in an ever-changing
environment. Through this exploration-exploitation trade-off
problem, we explore a framework for learning with highadaptability to an indefinite environment.
II. AIM OF THIS RESEARCH
The specific purpose of this research item is to elucidate theneural mechanisms underlying the execution of a target searchtask (Kawaguchi et al., 2015) that we have previously used inphysiological experiments in primates. In the task (one-target
search task, Fig. 1), subjects are presented with four points
while fixating on a fixed viewpoint, and are rewarded if theysee one of the hidden targets. If the subject answers correctly
for a specified number of trials in a row, another point becomes
the hidden target without instructions. In this case, the subject
searches for a new target through trial and error and correct andincorrect answers. In other words, the trial period of
exploitation, in which the subject repeatedly looks at the foundtarget to obtain the correct answer, and the trial period of
exploration, in which the subject has to search for a new target,
come alternately. Therefore, this task is suitable for dealing
with the exploration-exploitation trade-off problem.

1st state

2nd

3rd

4th

Correct

Trial start(500 ms)

Fixationspot on(500 ms)

Peripheral
spots on(500 ms)Go &gaze shift(500 ms)

Error

RewardNo reward

Fig. 1. The event sequence of the target search task.

III. RESEARCH TOPICS
A. Prototype of a Newelnet Reinforcement Learning Modelfor Learning One-Target Search Tasks
In this year, we have developed a prototype neural network
reinforcement learning model that learns the one-target searchtask [1]. The neural network part consists of an input layer I andan output layer O (Fig.2A), and the softmax function determines
the action based on the activity of the output layer.

A
Input(spots)

LU U RU

B

L

Qij(t) = Wij(t)Outi(t)Inj(t)CR

LD D RD

Self-excitation

Input
layer IU … RD

LU

Output(gaze)

Mean correct rate 59.8%
Target switch 39 times

Vj(t) = Wj(t)Inj(t)Weights E
OutputlayerO LU

Actionselection

Ideal value

U … RD
LU U RU
L

C

R

0Trial No 1,000

LD D RD

Fig. 2. Schematic view of the input–output part of the proposed model (A)and its performance (B).

The connection weights E between the input and output layers ischanged based on the reward prediction error δ. However, bycalculating the action value function Q required to calculate δfor the combination of input In and output Out, only a limitedpart of the connection weights is changed, and as a result, newtargets can be found in a short time even if the target is changed
repeatedly. (Fig. 2B up: t is the time step of the calculation, WAnalysis revealed that these mutant mice showed significant
abnormalities in performance compared to controls in memoryrelated tasks such as the Morris water maze task (Fig. 3A) andtasks used to assess depression-like behavior such as the tailsuspension test. However, no significant changes were observedin tasks used to determine anxiety-like behavior, such as theelevated plus maze test (Fig. 3B).
Addiction is said to be related to anxiety, and the idea that it
can be modeled a bias D in the reward prediction error δ
calculation, as in

δ(t) = max{ r(t) + γQ(t + 1) – Q(t) + D, D},
where t is the time step in the calculation, r is the reward, γ is thediscount rate, and Q is the behavioral value function (Redish,2004). On the other hand, depression has recently beensuggested to be associated with a decrease in the discount rate
(Yoshida et al., 2019). In other words, in the above equation, δrelated to the amount of neural network modulation associatedwith learning and memory, γ related to depression, and D related
to anxiety are integrated as different contributions in one
equation. Although some psychotropic drugs exhibit bothanxiolytic and antidepressant effects, the discrepancy between
depressive-like and anxiety-like behaviors in the geneticallymutant mice dealt in this study suggests that these are closely
related but different factors, and does not negate the above
formulation. The results of this study provide a great suggestion
for the application of the concept of “hyper-adaptability” in thisarea to higher brain functions and mental activities.

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Combined
-5

8

db/db
ob/ob

0

Standardized mean differencep = 0.05

2

Y-maze (spatial novelty recognition)
Forced swim
Novel object recognition
Morris water mazeOpen field (% time spent in periphery)
Elevated plus mazeOpen field (total distance)Y-maze (spontaneous alternation)

B

Morris water maze test(% time spent in target quadrant)

absolute value of Z score

B. A Meta-Analysis of Cognitive Behavioral Tasks in Leptinand Leptin Receptor Deficient Mice
The reward prediction error δ described above is thought to be
carried by the activity of dopamine cells in the substantia nigraof the midbrain (Schultz et al., 1997). In addictions such as
bulimia, the activity of these dopamine cells and the calculationof reward prediction error δ may be altered. Therefore, this year,
we conducted a meta-analysis of 34 papers that met the criteria
out of 54 papers obtained for cognitive and behavioral tasks inleptin-deficient (ob/ob) and leptin receptor-deficient (db/db)
mice, which are known to be adipocyte-derived feeding activity
inhibitory hormones [2].

A

Light-dark box
Tail suspension

is the weight for Q or V calculation). However, when the valuefunction is the state value function V, which is calculated only
for the input, the modulated E is not limited, and as a result, thetask is not learned correctly (Fig. 2B bottom).

Fig. 3. Integrated behavioral measures obtained from behavioral tests
analyzed. (A) The Morris water maze test. (B) Z scores in all tests analyzed.

C. The time-frequency pattern of local field potentials during
action planning differs between dorsal and ventral lateralprefrontal cortex
In addition to the above, we conducted a time-frequency
analysis of the local field potential (LFP) recorded from themonkey's prefrontal cortex during an action planning task
(shape manipulation task). We found that in the ventral part ofthe brain, θ waves transiently intensified immediately after thestart of the task and just before the presentation of the first shape,whereas in the dorsal part, θ waves transiently intensified justbefore the presentation of the second shape, when action
planning became possible [3,4].
IV. FUTURE PERSPECTIVE
Based on this year's model that can perform the one-target
search task, we will build a model that can perform a two-target
search task (a task in which two of the four light points aretargeted alternately) in the next year. At the same time, we will
actively engage in intra-disciplinary exchange and joint
research, which did not progress as expected in the coronadisaster.
REFERENCES
[1]

[2]
[3]

[4]

N. Sato, H. Okuzaki, H. Mushiake, K. Sakamoto, "A neural-network
reinforcement learning model that learns a target search Task stepwisely,"
The 30th Annual Conference of the Japanese Neural Network Society,P1-19, December, 2020.
T. Watanabe, K. Sakamoto, "Meta-analysis of cognitive and behavioral
tests in leptin- and leptin receptor-deficient mice," Neurosci. Res., in press.
K. Sakamoto, N. Kawaguchi, H. Mushiake, "Differences in task-phasedependent time-frequency patterns of local field potentials in the dorsal
and ventral regions of the monkey lateral prefrontal cortex," Neurosci.
Res., Vol. 156, pp. 41-49, July 2020.
IK. Sakamoto, N. Kawaguchi, H. Mushiake, "Theta and gamma
oscillations characterize subregional difference within the lateralprefrontal cortex of monkeys," The 43rd Annual Meeting of the Japan
Neuroscience Society, 2P-171, July, 2020.

Annual report of research project B05-9
Takeshi Sakurada
College of Science and Engineering, Ritsumeikan University

Abstract— The research project B05-9 aims to establish a tailormade EEG-based neurofeedback (NF) training system for theelderly to improve motor and cognitive functions by quantifying
an individual’s ability to control attention using brain activity.
We try to demonstrate that the training considering individual
differences can promote the functional network’s reconstruction
to control attention during motor tasks (i.e., hyper-adaptability)
and that the reconstructed network leads to better motor
performance. The achievements in this year are as follows: 1) We
have built a noiseless sensory stimulator to induce EEG (steadystate somatosensory evoked potentials [SSSEP] and steady-statevisual evoked potentials [SSVEP]) used for neurofeedback
training. 2) We have developed the EEG processing algorithms
for quantifying attentional states in real-time using the sensory
evoked potential. 3) We have attempted to modulate (amplify) the
corresponding EEG using this NF training system.
Fig. 1 EEG-based neurofeedback system

I. INTRODUCTION
In this research project, we aim to demonstrate that a tailormade NF training that matches each individual's attentional
control ability can facilitate the reconstruction of neural circuitand to propose a prediction model for the training effect.
Because previous training protocols for NF set consistent goals
without considering differences in individual brain function,
the conventional protocols cause large individual differences inthe effect of training [1]. To overcome this issue, based on theindividual differences in the optimal attentional strategy during
motor tasks (i.e., Internal focus or External focus) and thesomatosensory and visual cortexes’ characteristics reflecting
the individual optimal attentional strategy [4], we propose a
new NF training protocol that appropriately sets the goals foreach 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 through atailor-made NF training that shows the individual's estimatedattention state in real-time (Fig. 1). Especially, we expect
improvements in adaptation and motor learning abilities in theelderly after the NF training. To estimate the attention state,
this NF training system focuses on the activities in the sensory
areas instead of those in the prefrontal cortex and parietal
association areas, which control attention. We also try to
propose a prediction model of NF training effect based on
individual differences in brain functions and to maximize
improvement of brain functions under appropriate training
protocols for individuals.

III. RESEARCH TOPICS
A. Development of noiseless stimulators
Steady-state somatosensory evoked potentials (SSSEP) andSteady-state visual evoked potentials (SSVEP) observed fromthe somatosensory and visual cortices, respectively, are used in
the NF system in this study. For eliciting the SSSEP, we had
developed a device capable of presenting any vibration
frequency using a brushless direct current (DC) motor (Fig. 2).
This vibrotactile stimulator has a mechanism converting therotational motion of a DC-motor into a linear motion, and we
succeeded in reducing the influence of electrical noise on the
EEG signal. Meanwhile, for a visual stimulus to elicit SSVEP,
we have also developed a device to control LEDs’ flickering
frequency from a computer.

Fig. 2 Vibrotactile stimulator using a brushless DC motor

B. Implementation of algorithms to estimate the attention
state
Top-down attention to sensory stimuli can increase theresponse of SSSEP and SSVEP. In other words, these evoked
potentials reflect the cognitive processes for attention control.
Therefore, in this NF training system, we applied the left andright somatosensory cortices for SSSEP recording and the left
and right visual cortices for SSVEP recording, respectively.
Regarding real-time analyses, EEG data from these low
sensory cortices was buffered for several seconds and weapplied FFT to detect SSSEPs or SSVEPs. Moreover, we
implemented an algorithm to estimate the attention state basedon the modulation of SSSEP or SSVEP intensity when
individuals direct the attention to a sensory stimulus. Finally,
the trainees themselves could recognize their own attention
state by presenting the degree of modulation of the calculated
SSSEP or SSVEP as auditory feedback.

At present, participants performed the training under thecondition that they monitored their own EEG (Real group). In
the next step, to demonstrate the effectiveness of this NF
training system, we prepare the control group (Sham group)that provides pseudo-feedback of other people’s brain activity
recorded in advance.

Previous studies often used visual information for feedback
modalities on attentional states. However, when using visual
modality, it is difficult for the trainee to properly focus on
sensory stimuli because attentional resources are devoted tofeedback information. Thus, as a feedback modality suitable
for this NF training system, we examine whether visual orauditory feedback modalities promote the effect of training.
C. The modulation of brain activity using the NF system(SSSEP)
Based on the NF training system with the algorithm
estimating the attention state, we conducted a preliminary
experiment to confirm the modulation of SSSEP responses.
The NF training consisted of three days and SSSEP responses
were evaluated before and after the training. EEG electrodes
were placed on the left and right somatosensory cortices andvibrotactile stimuli with 22 Hz and 25 Hz were presented to
the left and right fingers.
Figure 3 shows a typical result of the SSSEP response after
the NF training. When calculating spectral power, a peak at 25
Hz in the left somatosensory cortex elicited by the stimulationon the right hand and a peak at 22 Hz in the rightsomatosensory cortex elicited by the stimulation on the left
hand were observed. It was confirmed that these peaks were
SSSEPs, and the corresponding EEGs were properly inducedin this NF training system. Although an example in Figure 3
shows responses relatively expectedly in the preliminary study,some cases were observed no significant modulation in
response strength after training during three days. Therefore,
we examine whether individuals who failed to modulate brain
activity by using SSSEP can be successfully trained using
SSVEP. In addition, we focus on the relationship between
individuals’ ability to attention control and the NF training
effect.

Fig. 3 SSSEP responses after NF training

IV. FUTURE PERSPECTIVE
In B05-9, we have developed a noiseless sensory
stimulator for NF training and implemented a method toquantify the attention state in real-time. We also performed apreliminary experiment on the training for amplifying the
response of SSSEP and SSVEP. In the next fiscal year, we try
to evaluate whether motor learning can be promoted bymodulating brain activity. Moreover, we aim to evaluate thetraining effect in both young and elderly populations and to
propose a model for predicting the training effects tomaximize individual brain functions’ improvement.
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, “Individual optimal attentional
strategy during implict motor learning boosts frontoparietal neuralprocessing efficiency: A functional near-infrared spectroscopy study,”
Brain Behav., vol. 9(1), e01183, 2019.
T. Sakurada, A. Goto, M. Tetsuka, T. Nakajima, M. Morita, S.
Yamamoto, M. Hirai, and K. Kawai, “Prefrontal activity predicts
individual differences in optimal attentional strategy for preventing
motor performance delicne: a functional near-infrared spectroscopy
study,” Neurophotonics, vol. 6(2), 025012, 2019.
櫻田 武, 吉田 将貴, 永井 清, 運動学習における注意適正個人差を反映する体性感覚・視覚野の応答特性, 第 41 回 バイオメカニズム学
術講演会, オンライン開催, 2020.

B05-10 Modeling of the motor recovery process andoptimization of rehabilitation strategy using VR
Tetsunari Inamura
National Institute of Informatics / The Graduate University for Advanced Studies
I. INTRODUCTION
Currently, rehabilitation for motor dysfunction has been asubjective and empirical decision-making process in which thepatient works on a rehabilitation program according to apolicy set by a physical therapist. The physical therapist
observes the process and formulates a rehabilitation policy by
estimating the recovery status of physical functions. This
study aims to realize a system that optimizes the process ofinteraction between a physical therapist and patient andprovides an optimal rehabilitation program according to theindividual patient's condition. Suppose we can model thefunction f of the rehabilitation process Y=f(X), which is torestore the motor function to state Y by applying therehabilitation strategy f to the current motor function state X
with motor disability. In that case, it will pave the way for
optimizing a highly effective rehabilitation strategy. The
purpose of this study is to clarify the methodology ofmodeling this rehabilitation strategy f.
II. RESEARCH METHOD
When the rehabilitation process is expressed as Y=f(X), f
cannot be obtained simply by mapping the relationshipbetween X and Y using machine learning. Because f is theprocess of mid-to-long-term interaction between the physicaltherapist and the patient over weeks or months, and thephysical therapist intends to transition X to the state of Y
through trial and error.
The key idea to realize modeling is to record and create a
database of the interaction process between physical therapists
and patients. In conventional clinical practice, there is no timeseries data in a form that can be subject to informatics analysis.
However, rehabilitation histories in the form of subjective
verbal expressions written in medical records remain. In thisstudy, we collect data to model the rehabilitation process
informatically by recording rehabilitation tasks (VR images
and movement patterns) presented by physical therapists,patients' reactions to these tasks (body response movements
and EMG signals), and evaluations by physical therapists overa long period. We will also test whether it is possible tooptimize rehabilitation strategies based on the data.
Next, we aim to improve rehabilitation efficiency byconsidering the patient's cognitive and internal states. We
introduce two intermediate parameters. The first parameter isthe posture distribution θ1, which increases the effectiveness of
motor intervention rehabilitation. In other words, x is theaccuracy of the sense of body ownership, which varies

according to the different arm posture[1]. The second parameterθ2 is an extension coefficient used in the VR movement
induction function in KINVIS system[2] by Kaneko et al. Using
these intermediate parameters, we model the rehabilitation
strategy f in the form Y=f(X; θ1, θ2) to establish an optimization
method.
The research questions to be clarified are the following two
points.
Q1) Is it possible to construct a rehabilitation strategy
model that achieves effective motor function recovery byrecording the rehabilitation process?
We hypothesize that the modeling of the relationship Y=f(X)described above is possible only when the rehabilitation
process is recorded over a med-to-long-term period. We will
test this hypothesis by identifying f using machine learning
under two conditions: one in which the patient's physical motor
abilities and the stimulus pattern presented to the patient arerecorded over a med-to-long-term period. Another condition
does not use the patient's physical motor abilities and thestimulus pattern presented to the patient. We analyze thedifference in accuracy between the two conditions.
Q2) Can the accuracy of modeling the rehabilitation
process be improved by using intermediate parameters such as
the accuracy of the sense of body ownership θ1 and the
expansion coefficient θ2 in the VR movement induction
function?
Even if the history of movement X and Y over the med-tolong-term period is used, it would not be easy to find an
effective rehabilitation strategy. One factor is the lack of datathat reflects the patient's cognitive internal state. Therefore, we
introduce parameters related to the patient's cognitive state,such as the sense of body ownership and the augmentation
coefficient, which generates visual stimuli by augmenting theactual motion in VR, and examine whether these parameters
improve the accuracy of identifying f.
III. MODEL OF THE REHABILITATION PROCESSIn modeling the rehabilitation process, we consider the
relationship between the movement X and the intermediateparameters θ1 and θ2 for the rehabilitation task from two
perspectives: the prediction problem to find X from θ1 and θ2,
and the control problem to find θ2 to optimize X. We propose amodel for the prediction problem based on a probabilistic
generative model framework as Fig.1. Because X is timeseries data and Y has a similar characteristic of the probability
distribution function.

At present, we have only completed the investigation of theprobabilistic generative model and the development of theexperimental system using VR. We have not yet started thedata collection and analysis of the subject experiments. We
will continue to evaluate the model structure and the model
learning method by conducting preliminary experiments on
healthy subjects, since it is expected that the experiments on
subjects in rehabilitation facilities will not proceed as planned
due to the COVID-19 situation.
V. CONCLUSION AND FUTURE WORKS

Fig. 1. A prediction model of X from hyper parameterof θ1andθ2

Here, θ1 is not easy to measure by sensors, and it requires
several tens of minutes of subject experiments to obtain the
distribution. Therefore, the distribution at time t should be
used continuously, and the parameter should be updated
immediately after the subject experiment is conducted.

Fig. 2. An optimization model of θ from X

As shown in Fig. 2, we also prepare a model with different
transition directions in the graph to infer θ1θ2 from the
movement X. For the model learning, we use a probabilistic
generative model[3][4] to predict time series data using
probability distributions.

Since the COVID-19 problem made it difficult to conduct
experiments on subjects in rehabilitation facilities and hospitals
this year, we developed an infrastructure system for conducting
experiments using VR devices remotely. We have developed acloud-based VR platform based on AWS i extending ourprevious research system on the sense of agency and sense ofbody ownership using VR[5]. This system enables subjects towear HMD at home and gather with a physical therapist at aremote location in a VR space rehabilitation facility. Currently,
we are operating this system as a robotics competition systemii.
Although the system is used in a different domain from this
hyper adapt project, the effectiveness of the system has been
confirmed[6]. We also plan to use the cloud-type VR platform
to cope with the COVID-19 situation.
We will soon start experiments on subjects using the above
system to verify the accuracy of the prediction of movement X
by the probabilistic generative model and the effectiveness ofthe optimization of the rehabilitation strategy.
REFERENCES
[1]

[2]

IV. EVALUATION OF THE PROPOSED MODEL
One of the problems in using this model is determining thetime range and sampling rate of the target time-series data.
These settings vary depending on the focus target that captures
short-term movement changes due to rehabilitation or
rehabilitation effects over the med-to-long-term. The first way
to evaluate the model is to automatically adjust the range oftime series data to be trained from the viewpoint of slow
dynamics and fast dynamics and consider the time range inwhich the model training is practical.
For the Research Question (Q2), we evaluate how theestimation accuracy varies depending on whether θ1, aparameter with high measurement cost, is included in themodel or not, and examine the contribution of θ1 and the costeffectiveness of how often it should be measured.

i
ii

[3]

[4]

[5]

[6]

T. Kondo et al., Embodied-Brain Systems Science and Rehabilitation
Medicine Vol.2, Chapter 4, “Mathematical Model of Body
Consciousnessense”, University of Tokyo Press, 2018.
M. Okawada, F. Kaneko et al., “Kinesthetic illusion induced by visual
stimulation influences sensorimotor event-related desynchronization in
stroke patients with severe upper-limb paralysis: A pilot study,” Restor.
Neurol. Neurosci., vol. 38, no. 6, pp. 455–465, Jan. 2021.
S. Gigante, D. van Dijk, K. R. Moon, A. Strzalkowski, G. Wolf, and S.
Krishnaswamy, “Modeling Global Dynamics from Local Snapshots with
Deep Generative Neural Networks,” in 2019 13th International
conference on Sampling Theory and Applications, 2019, pp. 1–4.
J. Yoon, D. Jarrett, and M. van der Schaar, “Time-series Generative
Adversarial Networks,” in Advances in Neural Information Processing
Systems 32 (NeurIPS 2019), 2019.
T. Inamura, S. Unenaka, S. Shibuya, Y. Ohki, Y. Oouchida, and S.
Izumi,“DevelopmentofVR
platformforcloud-basedneurorehabilitation and its application to research on sense of agency
and ownership,” Advanced. Robotics, vol.31, no.1–2, pp. 97–106, 2017.
Y. Mizuchi, H. Yamada and T. Inamura, “VR Platform that Realizes
Crowdsourcing of Embodied Interaction Experiments with Robots,”
Annual Conference on Robotics Society of Japan, p.2E2-06, 2020.

Amazon Web Service: https://aws.amazon.com/jp/
RoboCup@Home Simulation: https://sites.google.com/view/robocup-athome-sim/home

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 rapid 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 quantifiably estimatedsensorimotor information of a human infant and constructed a
musculoskeletal dynamic simulation to understand themechanisms underlying early sensorimotor development with
rapid physical growth.

In this project, we investigate the developmental
mechanism by combining measurements from experimentswith actual infants and from physical dynamic simulations (Fig.
1).

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 FY2020, we estimated the sensorimotor information
structure in human neonates and infants during spontaneous
movements. In addition, we constructed and modified theinfant musculoskeletal model for the implementation ofdevelopmental 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 by suchkinds of spontaneous movements have an essential role formaturation of sensorimotor modules. Although motor output
patterns have been well characterized, whether and how ahuman infant acquires and augments these types of motor
modules remains unclear. In this study, we collected detailed
whole-body motion data from neonates and infants, andquantifiably estimatedsensorimotor modules using
musculoskeletal dynamic simulation.

Fig. 1. Research overview

III. RESEARCH TOPICS
A. Infantile sensorimotor information structure induced byspontaneous movements
We first conducted a full-body motion capture of the
spontaneous movements of 10 neonates (within seven days
after birth) and 10 infants (three-months-old). A total of 144
muscle activities and proprioceptive sensory feedbacks were
estimated by a musculoskeletal inverse dynamic simulation.
Next, we quantified the sensorimotor interaction by calculating
the information flow between all muscle pairs of motor
activities and proprioceptive sensory inputs (Fig. 2, left).
Finally, we identified sensorimotor modules and state transition
among muscle activities and proprioceptive sensory inputs
based on an Infinite Relational Model (Fig. 2, right). The
results show that the infants have a larger number of modules
than the neonates. Our results demonstrated a developmental
increase in the sensorimotor modules, which is consistent withprevious research that suggests the argumentation of motor
modules in the early developmental phase [1, 2].

Fig. 3. Newborn and infant simulation on MuJoCo
Fig. 2. sensorimotor interactions and state transition

B．Infantile musculoskeletal model
Although the above results suggest that sensorimotor
information structure changes along with integration /separation during early infancy, mechanisms underlying thesedevelopmental changes remains unclear. To investigate thedevelopmental mechanisms, we have developed fetus /infantile simulations using musculoskeletal models [3]. Here,we constructed a new infant musculoskeletal model based on
an upper arm model (MoBL-ARMS Dynamic Upper Limb)
and lower limb model (LaiArnold2017 model), adding interjoint coordination and muscle constraint models. Furthermore,
the model was built to be able to be used in the MuJoCoenvironment, enabling contact force calculations with multijoints models. By introducing a neural oscillator model and aspinal reflex circuit to generate muscle activities, this infantile
musculoskeletal model showed spontaneous movements(unpublished).

IV. FUTURE PERSPECTIVEIn FY2020, we quantitatively analyzed sensorimotor
information structure during spontaneous movements inhuman neonates and infants. In addition, we constructed an
infant musculoskeletal model with inter-joint coordination andmuscle constraint models. In the next year, we aim toinvestigate a learning model to explain the developmental
changes of the sensorimotor information structure.
REFERENCES
[1]

[2]

[3]

H Kanazawa, Y Yamada, K Tanaka, M Kawai and Y Kuniyoshi,
Developmental change of sensorimotor modules during early infancy,
International Congress of Infant Studies, 2020
Hoshinori Kanazawa、Infantile spontaneous movements and maturationof neuronal circuit in early infancy, The Japanese Society of Baby
Science 20th research conference, 2020
Y Yamada, H Kanazawa, and Y Kuniyoshi. Embodied Brain Model for
Understanding Functional Neural Development of Foetuses and Infants
Chapter in J. Lockman & C. Tamis-Lemonda (Eds.), Cambridge
handbook of infant development.

List of Publications (2020)

Journal Papers
1.

Suzuki M, Onoe K, Sawada M, Takahashi N, Higo N, Murata Y, Tsukada YH, Isa T, Onoe H,
Nishimura Y, The ventral striatum is a key node of cortical reorganization required for functional
recovery of finger dexterity after spinal cord injury in monkeys, Cerebral Cortex, 30, 3259-3270, 2020

2.

K. Kaminishi, R. Chiba, K. Takakusaki, and J. Ota, Investigation of the effect of tonus on the change
in postural control strategy using musculoskeletal simulation, Gait & Posture, 76, 298-304, 2020

3.

W. Wen, and P. Haggard, Prediction error and regularity detection underlie two dissociablemechanisms for computing the sense of agency, Cognition, 195, 104074, 2020

4.

M. Toeda, S. Aoi, S. Fujiki, T. Funato, K. Tsuchiya, D. Yanagihara, Gait Generation and Its Energy
Efficiency Based on Rat Neuromusculoskeletal Model, Frontiers in Neuroscience, 13, 1337, 2020

5.

N. Yang, Q. An, H. Kogami, K. Yoshida, H. Yamakawa, Y. Tamura, S. Shimoda, H. Yamasaki, M. Sonoo,
M. Itkonen, F. Alnajjar, N. Hattori, M. Kinomoto, K. Takahashi, T. Fujii, H. Otomune, I. Miyai, A.
Yamashita, H. Asama, Temporal muscle synergy features estimate effects of short-term rehabilitationin sit-to-stand of post-stroke patients, IEEE Robotics and Automation Letters, 2020

6.

R. Ohata, T. Asai, H. Kadota, H. Shigemasu, K. Ogawa, and H. Imamizu, Sense of agency beyond
sensorimotor process: Decoding self-other action attribution in the human brain, Cerebral Cortex, 30,
7, 4076-4091, 2020

7.

Y. Koike, T. Kawase, H. Kambara, N. Schwighofery, N. Yoshimura, Muscle Synergy Analysis for
Rehabilitaion, Examines in Physical Medicine and Rehabilitation, 2, 5, 1-2, 2020

8.

D. Tamura, S. Aoi, T. Funato, S. Fujiki, K. Senda, K. Tsuchiya, Contribution of Phase Resetting to
Adaptive Rhythm Control in Human Walking Based on the Phase Response Curves of a
Neuromusculoskeletal Model, Frontiers in Neuroscience, 14, 17, 2020

9.

W. Wen, H. Shibata, R. Ohata, A. Yamashita, H. Asama, and H. Imamizu, The Active Sensing ofControl Difference, iScience, 23, 101112, 2020

10. Takeru Honda, Hiroshi Mitoma, Hirotaka Yoshida, Kyota Bando, Hiroo Terashi, Takeshi Taguchi,
Yohane Miyata, Satoko Kumada, Takashi Hanakawa, Hitoshi Aizawa, Shiro Yano, Toshiyuki Kondo,
Hidehiro Mizusawa, Mario U Manto, Shinji Kakei, Assessment and rating of motor cerebellar ataxias
with the Kinect v2 depth sensor: extending our appraisal, Frontiers in Neurology, 11, 179, 2020

11. Xinzhe Li, Bruno Mota, Toshiyuki Kondo, Slawomir Nasuto, and Yoshikatsu Hayashi, EEG Dynamical
Network Analysis Method Reveals the Neural Signature of Visual-Motor Coordination, PLoS ONE,15, 5, e0231767, 2020
12. Y. Nagumo, Y. Ueta+, H. Nakayama+, H. Osaki, Y. Takeuchi, N. Uesaka, M. Kano, M. Miyata*, Tonic
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thalamus and ectopic sensations, Cell Reports, 31, 12, e107797, 2020
13. Aizawa H, Sun W, Sugiyama K, Itou Y, Aida T, Cui W, Toyoda S, Terai H, Yanagisawa M, Tanaka K,
Glial glutamate transporter GLT-1 determines susceptibility to spreading depression in the mouse
cerebral cortex, GLIA, 2020
14. Chiyohara S, Furukawa J, Noda T, Morimoto J, and Imamizu H, Passive training with upper extremity
exoskeleton robot affects proprioceptive acuity and performance of motor learning, Scientific Reports,
10, e11820, 2020
15. Vancraeyenest P, Arsenault JT, Li X, Zhu Q, Kobayashi K, Isa K, Isa T, Vanduffel W, Selective
mesoaccumbal pathway inactivation affects motivation but not reinforcement-based learning in
macaques, Neuron, 108, 568-581, 2020
16. Cui W, Aida T, Ito H, Kobayashi K, Wada Y, Kato S, Nakano T, Zhu M, Isa K, Kobayashi K, Isa T,
Tanaka K, Aizawa H, Dopaminergic signaling in the nucleus accumbens modulates stress-coping
strategies during inescapable stress, J Neurosci, in press, 2020
17. Suzuki K, Elegheert J, Song I, Sasakura H, Senkov O, Matsuda K, Kakegawa W, Clayton AJ, Chang VT,
Ferrer-Ferrer M, Miura E, Kaushik R, Ikeno M, Morioka Y, Takeuchi Y, Shimada T, Otsuka S,
Stoyanov S, Watanabe M, Takeuchi K, Dityatev A, Aricescu AR, Yuzaki M, A synthetic synapticorganizer protein restores glutamatergic neuronal circuits, Science, 369, 6507, eabb4853, 2020
18. Ito H, Nozaki K, Sakimura K, Abe M, Yamawaki S, & Aizawa H, Activation of proprotein convertase in
the mouse habenula causes depressive-like behaviors through remodeling of extracellular matrix,
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19. Nguyen P.T.M, Hayashi Y, Baptista M.D.S, and Kondo T, Collective almost synchronization-basedmodel to extract and predict features of EEG signals, Scientific Reports, 10, 1, 16342, 2020
20. Kikutani K, Giga H, Hosokawa K, Shime N, Aizawa H, Microglial translocator protein and stressorrelated disorder, Neurochem Int, in press, 2020
21. Wen W, Shimazaki N, Ohata R, Yamashita A, Asama H, & Imamizu H, Categorical perception ofcontrol, eNeuro, 7, 5, ENEURO.0258-20.2020, 2020
22. Okamoto S, Sohn J,Tanaka T, Takahashi M, Ishida Y, Yamauchi K, Koike M, Fujiyama F, Hioki H,
Overlapping Projections of Neighboring Direct and Indirect Pathway Neostriatal Neurons to Globus

Pallidus External Segment, iScience, 23, 9, 2020
23. Giga H, Ji B, Kikutani K, Fukuda S, Kitajima T, Katsumata S, Matsumata M, Suhara T, Yamawaki S,
Shime N, Hosokawa K, Aizawa H, Pharmacological and genetic inhibition of translocator protein 18
kDa ameliorated neuroinflammation in murine endotoxemia model, SHOCK, in press, 2020
24. Yaron A, Kowalski D, Yaguchi H Takei T, Seki K, Forelimb force direction and magnitude
independently controlled by spinal modules in the macaque, PNAS, 117, 44, 27655-27666, 2020
25. Yamashita A, Sakai Y, Yamada T, Yahata N, Kunimatsu A, Okada N, Itahashi T, Hashimoto R, Mizuta
H, Ichikawa N, Takamura M, Okada G, Yamagata H, Harada K, Matsuo K, Tanaka SC, Kawato M,
Kasai K, Kato N, Takahashi H, Okamoto Y, Yamashita O, Imamizu H, Generalizable brain network
markers of major depressive disorder across multiple imaging sites, PLos Biology, 18, 12, e3000966,
2020
26. Watanabe T, Sakamoto K, Meta-analysis of cognitive and behavioral tests in leptin- and leptin
receptor-deficient mice, Neuroscience Research, in press, 2020
27. Oya T, Takei T, Seki K, Distinct sensorimotor feedback loops for dynamic and static control of primate
precision grip, Communications Biology, 3, 156, 2020
28. Piovanelli Enrico, Piovesan Davide, Shirafuji Shouhei, Su Becky, Yoshimura Natsue, Ogata Yousuke,& Ota Jun, Towards a simplified estimation of muscle activation pattern from MRI and EMG using
electrical network and graph theory, Sensors, 20, 3, 724, 1-20, 2020
29. Yano S, Hayashi Y, Murata Y, Imamizu H, Maeda T, and Kondo T, Statistical Learning model of theSense of Agency, Frontiers in Psychology, 11, 539957, 2020
30. Inada R, Miyamoto K, Tanaka N, Moriguchi K, Kadomatsu K, Takeuchi K, Kusunoki S, Chondroitin
sulfate N-acetylgalactosyltransferase-1 knockout shows

milder phenotype

in experimental

autoimmune encephalomyelitis than in wild type, Glycobiology, in press, 2020
31. Suzuki Y, Nakamura A, Milosevic M, Nomura K, Tanahashi T, Endo T, Sakoda S, Morasso P, Nomura
T, Postural instability via a loss of intermittent control in elderly and patients with Parkinson's disease:
A model-based and data-driven approach, Chaos, 30, 11, 113140, 2020
32. Sakamoto K, Kawaguchi N, Mushiake H, Differences in task-phase-dependent time-frequency
patterns of local field potentials in the dorsal and ventral regions of the monkey lateral prefrontal cortex,
Neuroscince Research, 156, 41-49, 2020
33. Kato J, Yamada T, Kawaguchi H, Matsuda K, Higo N, Functional near-infrared-spectroscopy-based
measurement of changes in cortical activity in macaques during post-infarct recovery of manual
dexterity, Scientific Reports, 10, 1, 1-12, 2020

34. Amemiya K, Morita T, Hirose S, Ikegami T, Hirashima M, Naito E, Neurological and behavioralfeatures of locomotor imagery in the blind, Brain Imaging and Behavior, 2020
35. Naito E, Morita T, Asada M, Importance of the primary motor cortex in development of human
hand/finger dexterity, Cerebral Cortex Communications, 2020
36. Reinhard Blickhan, Emanuel Andrada, Eishi Hirasaki, Naomichi Ogihara, Trunk and leg kinematics ofgrounded and aerial running in bipedal macaques, Journal of Experimental Biology, jeb.225532, 2020
37. Isa K, Sooksawate T, Kobayashi K, Kobayashi K, Redgrave P, Isa T, Dissecting the tectal output
channels for orienting and defense responses, eNeuro, 7, ENEURO.0271-20.2020, 2020
38. Tokuoka K, Kasai M, Kobayashi K, Isa T, Anatomical and electrophysiological analysis of cholinergic
projections from the parabigeminal nucleus to the superficial superior colliculus, J Neurophysiol, 124,
1968-1985, 2020
39. Matsumoto M, Sakurada T, Yamamoto SI, Distinct bilateral prefrontal activity patterns associated with
the qualitative aspect of working memory characterized by individual sensory modality dominance,
PLoS ONE, 15, 8, e023835, 2020
40. Schwerdt HN, Amemori K, Gibson DJ, Stanwicks LL, Yoshida T, Bichot NP, Amemori S, Desimone
R, Langer R, Cima MJ, Graybiel AM, Dopamine and beta-band oscillations differentially link to striatal
value and motor control, Sci Adv, 25,6(39), eabb9226, 2020
41. J. Chai, M. Hayashibe, Motor Synergy Development in High-performing Deep Reinforcement
Learning algorithms, IEEE Robotics and Automation Letters, 5(2), 1271-1278, 2020
42. D. Achanccaray, M. Hayashibe, Decoding Hand Motor Imagery Tasks within the Same Limb fromEEG Signals Using Deep Learning, IEEE Transactions on Medical Robotics and Bionics, 2(4), 692699, 2020
43. Inoue T, Kobayashi K, Matsumoto R (CA), Inouchi M, Togo M, Togawa J, Usami K, Shimotake A,
Matsuhashi M, Kikuchi T, Yoshida K, Kawawaki H, Sawamoto N, Kunieda T, Miyamoto S, Takahashi
R, Ikeda A, Engagement of cortico-cortical and cortico-subcortical networks in a patient with epileptic
spasms: An integrated neurophysiological study, Clin Neurophysiol, 131, 2255-2264, 2020
44. Nakatani M, Matsumoto R (CA), Kobayashi K, Hitomi T, Inouchi M, Matsuhashi M, Kinoshita M,
Kikuchi T, Yoshida K, Kunieda T, Miyamoto S, Takahashi R, Hattori N, Ikeda A, Electrical Cortical
Stimulations Modulate Spike and Post-Spike Slow-Related High-Frequency Activities in Human
Epileptic Foci, Clin Neurophysiol, 131, 1741-1754, 2020
45. Nakae T, Matsumoto R (CA), Kunieda T, Arakawa Y, Kobayashi K, Shimotake A, Yamao Y, Kikuchi
T, Aso T, Matsuhashi M, Yoshida K, Ikeda A, Takahashi R, Lambon Ralph MA, Miyamoto S,
Connectivity Gradient in the Human Left Inferior Frontal Gyrus: Intraoperative Cortico-CorticalEvoked Potential Study, Cerebral Cortex 30, 4633-4650, 2020
46. Shibata S, Yamao Y, Kunieda T, Inano R, Nakae T, Nishida S, Inada T, Takahashi Y, Kikuchi T,
Arakawa Y, Yoshida K, Matsumoto R, Ikeda A, Mima T, Miyamoto S, Intraoperativeelectrophysiological mapping of medial frontal motor areas and functional outcomes, World
Neurosurgery, 138, e389-e404, 2020
47. Oga K, Yozu A, Kume Y, Seki H, Tsuchiya N, Nakai K, Matsushita A, Mutsuzaki H, Kohno Y, Robotic
rehabilitation of the paralyzed upper limb for a stroke patient using the single-joint hybrid assistive
limb: a case study assessed by accelerometer on the wrist, Journal of Physical Therapy Science, 32, 2,
192-196, 2020
48. Koseki K, Yozu A, Takano H, Abe A, Yoshikawa K, Maezawa T, Kohno Y, Mutsuzaki H, Gait training
using the Honda Walking Assist Device® for individuals with transfemoral amputation: A report of twocases, J Back Musculoskelet Rehabil, 33, 2, 339-344, 2020
49. Hiroshi Kishimoto, Arito Yozu, Yutaka Kohno, Hirotaka Oose, Nutritional improvement is associatedwith better functional outcome in stroke rehabilitation: A cross-sectional study using controlling
nutritional status, Journal of Rehabilitation Medicine, 52, 3, jrm00028, 2020
50. Okamoto S, Yamauchi Y, Sohn J, Takahashi M, Ishida Y, Furuta T, Koike M, Fujiyama F, Hioki H,
Exclusive Labeling of Direct and Indirect Pathway Neurons in the Mouse Neostriatum by an AdenoAssociated Virus Vector with Cre/lox System, Star Protocols, 19, 2021
51. Ishii D, Osaki H, Yozu A, Ishibashi K, Kawamura K, Yamamoto S, Miyata M, Kohno Y, Ipsilesional
spatial bias after a focal cerebral infarction in the medial agranular cortex: A mouse model of unilateral
spatial neglect, Behav Brain Res, in press, 2021
52. Kato J, Y Murata Y, Takashima I, Higo N, Time- and area-dependent macrophage/microglial
responses after focal infarction of the macaque internal capsule, Neuroscince Research, in press, 2021
53. Kei Aoyagi, Wen Wen, Qi An, Shunsuke Hamasaki, Hiroshi Yamakawa, Yusuke Tamura, Atsushi
Yamashita and Hajime Asama, Modified sensory feedback enhances the sense of agency duringcontinuous body movements in virtual reality, Scientific Reports, 11, 2021
54. Morita T, Naito E, Asada M, Examination of the development and aging of brain deactivation using a
unimanual motor task, Advanced Robotics, in press, 2021
55. Kato R, Hayashi T, Onoe K, Yoshida M, Tsukada H, Onoe H, Isa T, Ikeda T, The posterior parietal
cortex contributes to visuomotor processing for saccades in blindsight macaques, Communications
Biology, in press, 2021
56. Chen C-Y, Matrov D, Veale R, Onoe H, Yoshida M, Miura K, Isa T, Properties of visually-guided
saccadic behavior and bottom-up attention in marmoset, macaque, and human, J Neurophysiol, 125,

437-457, 2021
57. Takakuwa N, Isa K, Onoe H, Takahashi J, Isa T, Contribution of the pulvinar and lateral geniculate
nucleus to the control of visually guided saccades in blindsight monkeys, J Neurosci, in press, 2021
58. Zubair M, Murris S, Isa K, Onoe H, Koshimizu Y, Kobayashi K, Vanduffel W, Isa T, Divergent wholebrain projections from the ventral midbrain in macaque monkeys, Cerebral Cortex, in press, 2021
59. Koshimizu Y, Isa K, Kobayashi K, Isa T, Double viral vector technology for selective manipulation ofneural pathways with higher level of efficiency and safety, Gene Therapy, in press, 2021
60. J. Han, J. Chai, M. Hayashibe, Synergy Emergence in Deep Reinforcement Learning for Fulldimensional Arm Manipulation, IEEE Transactions on Medical Robotics and Bionics, in press, 2021
61. Arjonilla FJ, Kobayashi Y, Supervised learning of mapping from sensor space to chained form for
unknown non-holonomic driftless systems, Industrial Robot, in press, 2021
62. Ken-ichi Okada, Kenichiro Miura, Michiko Fujimoto, Kentaro Morita, Masatoshi Yoshida, Hidenaga
Yamamori, Yuka Yasuda, Masao Iwase, Mikio Inagaki, Takashi Shinozaki, Ichiro Fujita, Ryota
Hashimoto, Impaired inhibition of return during free-viewing behaviour in patients with schizophrenia,
Scientific Reports, 11, 3237, 2021

International Conference
1.

Takai A, Rivela D, Lisi G, Noda T, Teramae T, Imamizu H, and Morimoto J, Neural investigation
towards motor skill improvements through brain-computer interface-based training, The 6thInternational Conference Brain-Computer Interface: Science and Practice (BCI Samara 2020), Online,
2020

2.

Takeuchi K, Suzuki K, Yuzaki M, A synthetic synapse organizer restores the functions from spional
cord injuries, Federation of Europian Neuroscience Societies 2020, Online, UK, 2020

3.

Takakusaki K, Takahashi M, Fukuyama S, Noguchi T, Chiba R, Possible pathophysiologicalmechanisms of postural disturbance in Parkinson's disease, EMBC 2020, Online, Canada, 2020

4.

Takakusaki K, Takahashi M, Noguchi T, Chiba R, Postural control during movements by an interaction
between the reticulospinal tract and flexion reflex afferents, Online, Japan, 2020

5.

Ruoxi Wang, Qi An, Ningjia Yang, Hiroki Kogami, Kazunori Yoshida, Hiroyuki Hamada, Shingo
Shimoda, Hiroshi Yamasaki, Moeka Sonoo, Fady Alnajjar, Noriaki Hattori, Kouji Takahashi, Takanori
Fujii, Hironori Otomune, Ichiro Miyai, Atsushi Yamashita and Hajime Asama, Muscle Tension
Analysis in Stroke Patient Sit-to-Stand Motion by Joint Torque-Based Normalization, 5thInternational Conference on NeuroRehabilitation (ICNR2020), Online, Spain, 2020

6.

H Kanazawa, Y Yamada, K Tanaka, M Kawai and Y Kuniyoshi, Developmental change of sensorimotor
modules during early infancy, International Congress of Infant Studies, Online, UK, 2020

7.

Isa T, Systems neuroscience of functional recovery after brain and spinal cord injury, Plenary lecturein the 1st Taiwan Society for Neuroscience Meeting, online, Twaiwan, 2020

8.

Isa T, How does the non-conscious visuo-motor function emerge after damage to the primary visualcortex?, 1st CU-KU Symposium "Advances in Neuroscience Research", Bangkok, Thailand, 2020

9.

Isa T, Neurobiology of recovery after brain and spinal cord injury in macaque models, NIH
Neuroscience Seminar Series, Bethesda, USA, 2020

10. Ken-ichi Amemori, Identifying the function of primate striosome-related circuitry on pessimistic
valuation and anxiety, The 43rd Annual Meeting of the Japan Neuroscience Society, Online, Japan,
2020
11. Ken-ichi Amemori, Physiological approach to identify primate cortico-basal ganglia circuits thatgenerate anxiety, The 43rd Annual Meeting of the Japan Neuroscience Society, Online, Japan, 2020
12. J. Han, J. Chai, M. Hayashibe, Emergence of Motor Synergy in Multi-directional Reaching with Deep
Reinforcement Learning, IEEE/SICE International Symposium on System Integration, Online, Japan,

2020
13. Matsumoto R, Cortico-Cortical Evoked Potential, Online Conference from the Intraoperative
Monitoring Spanish Association 2020 (AMINE 2020), Online, Spain, 2020
14. Matsumoto R, Probing connectivity & epileptogenicity using cortico-cortical evokedpotential, 2nd
Xuanwu Epilepsy & Brain Science International Forum, Online, China, 2020

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 (Research Manager, NICT)

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 andchallenges for prevention of decline in latent adaptive capacity
Principal investigator

Tadashi Isa (Professor, Kyoto University)

Funded co-investigator Eiichi Naito (Research Manager, NICT)
Funded co-investigator Hidenori Aizawa (Professor, Hiroshima University)
Funded co-investigator Minoru Asada (Specially Appointed Professor, Osaka University)
Co-investigator

Onoe Hirotaka (Project Professor, Kyoto University)

Co-investigator

Tomohiko Takei (Project Associate Professor, Kyoto University)

Co-investigator

Reona Yamaguchi (Project Assistant Professor, Kyoto University)

Co-investigator

Yusuke Yamamoto (PhD Student, Kyoto University)

Co-investigator

Toshinari Kawasaki (PhD Student, Kyoto University)

Co-investigator

Satoko Ueno (PhD Student, Kyoto University)

Co-investigator

Masahiro Mitsuhashi (PhD Student, Kyoto University)

Co-investigator

Tomoyo Morita (Specially Appointed Associate Professor, Osaka University)

Co-investigator

Satoshi Hirose (Researcher, NICT)

Co-investigator

Nodoka Kimura (Researcher, NICT)

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)

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

Tatsuya Umeda (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

Yuki Hara (Lecturer, University of Tsukuba)

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, The University of Tokyo)

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)

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

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 A05-1: Elucidation of the hyper-adaptation mechanism of upper limb recovery in stroke
patients
Principal investigator

Shinichi Izumi (Professor, 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)

Research Project A05-2: Synchronization of neural oscillation among primate limbic structures and thestriatum during recovery from anxiety like state.
Principal investigator

Kenichi Amemori (Associate Professor, Kyoto University)

Co-investigator

Oh Jungmin (Ph.D Student, Kyoto University)

Co-investigator

Satoko Amemori (Assistant Technical Staff, Kyoto University)

Research Project A05-3: Mechanism of Hyper-Adaptivity of the human premotor area: electrophysiologicalconnectome analysis with electrocorticogram
Principal investigator

Riki Matsumoto (Professor, Kobe University)

Co-investigator

Akihiro Shimotoake (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, Kyoto University)

Research Project A05-4: Neural circuit rearrangement mechanisms underlying the recovery from learning
deficits in Parkinson’s disease model animals
Principal investigator

Kazuto Kobayashi (Professor, Fukushima Medical University)

Research Project A05-5: 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 (Section Chief, National Center of Neurology and Psychiatry)Co-investigator

Kazumasa Uehara (Assistant Professor,
National Institute for Physiological Sciences)

Research Project A05-6: 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,
National Institute for Physiological Sciences)

Research Project A05-7: 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 A05-8: 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, National Center of Neurology and Psychiatry)Co-investigator

Tsukasa Okimura (School of Medicine, Keio University)

Co-investigator

Hiroki Oi (School of Medicine, Keio University)

Research Project A05-9: 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 (Assistant Professor, 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 (Assistant Professor, Aichi Medical University)

Co-investigator

Yuki Morioka (Research Technician, Aichi Medical University)

Research Project A05-13: Reconstruction of Basal Ganglia by Aging and Neurodegenerative Disease
Principal 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 A05-14: Development of techniques to improve brain functions by using hyper-adaptability
Principal investigator

Yoshito Masamizu (Deputy Team Leader, RIKEN)

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 Tetsuro Funato (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

Ningjia Yang (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

Yuichiro Omura (Master's Student, The University of Tokyo)

Co-investigator

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

Co-investigator

Hiroyuki Hamada (Assistant, Bunkyo Gakuin University)

Co-investigator

Yutaka Kohno (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

Hitohiro Etoh (Undergraduate Student, The University of Tokyo)

Co-investigator

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

Co-investigator

Hironobu Osaki (Assistant Professor, Tokyo Women’s Medical University)

Co-investigator

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

Research Project B05-1: 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)

Research Project B05-2: 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 B05-3: 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 B05-4: 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,
National Institute for Physiological Sciences)

Research Project B05-5: 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)

Research Project B05-6: Shared-control of teleoperated robot maintaining operator’s embodiment underintervention of AI
Principal investigator

Yasuhisa Hasegawa (Professor, Nagoya University)

Co-investigator

Tomoya Morita (Nagoya University)

Research Project B05-7: Systems engineering approach for understanding supraspinal mechanisms of theintermittent feedback control during human upright stancePrincipal investigator

Taishin Nomura (Professor, Osaka University)

Co-investigator

Yasuyuki Suzuki (Lecturer, Osaka University)

Co-investigator

Akihiro Nakamura (PH.D Student, Osaka University)

Co-investigator

Matija Milosevic (Assistant Professor, Osaka University)

Co-investigator

Kimitaka Nakazawa (Professor, The University of Tokyo)

Co-investigator

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

Takuyuki Endo (Doctor,
National Hospital Organization Osaka Toneyama Medical Center)Research Project B05-8: A reinforcement learning model with dynamic state space that enables adaptation toindefinite 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 B05-9: 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

Dongmin Kim (Graduate Student, The University of Tokyo)

Co-investigator

Masahiko Kawai (Project Associate Professor, Kyoto University)