2019 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)
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)
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 PROJECTWith coming of a super-aging society in Japan, we arefacing the urgent problems of sensory-motor impairments,
declining higher-order brain functions, cognitive impairment,
loss of motivation, and mood disorders caused by aging, and inturn extreme decline of bodily and neurological functions. Allof these problems have a common source: inability to adapt
appropriately to a brain-body system changed with aging andimpairments.
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).

the “bottom-up” approach relying on experiments andanalyses, it would be difficult to clarify hyper-adaptability thatis manifested by systematic behavior of a neural network.
Therefore, we apply an interdisciplinary approach that
integrates the mathematical modeling technology of systems
engineering with neuroscience (Fig. 1). We adopt two new
analytical approaches: (a) Robotic-interventional neuroscience,
i.e. combinatory use of well-controlled robotic technologies
and biological approaches of viral vector, optogenetics,
chemogenetics and brain stimulation. This allows verificationof cause-effect relationship of neural activity and its generatedfunctions and behaviors. (b) Function-oriented neural
encoding, which constitutes a model that may incorporate any
knowledge of brain functions into gray-box modeling or
hypothesizes the structure of a model based on statisticalmethods.

In light of such facts, we believe that clarifying the brain’s“hyper-adaptability” may resolve the abovementioned issues.
The goal of our research project is to elucidate the neuraland computational principles of hyper-adaptability in which thebrain manages impairment of brain functions by linking
neuroscience with systems engineering in order tocomprehensively understand acute/chronic impairments anddisorders, and the principle of frailty.
II. CONTENT OF THE RESEARCH PROJECT
When a person experiences acute/chronic impairment or
disorder due to aging, the brain reorganizes neural networks
by disinhibiting pre-existing neural network that is normallysuppressed and searching for latent but available network thathas long been unutilized through course of evolution anddevelopment. We call this process of functional compensation
as “reconstruction of neural structure”, i.e. a neural entity that
achieves hyper-adaptability. In order to implement practical
functions to this reconstituted neural network, the network
should acquire a new control policy of motor effectors basedon precise recognition of the present states of the brain and the
body. Here, the brain has to activate the new network by
repeatedly performing neural computations and updates the
network based on prediction error. We call this learning cycle
in a new control space as “reconstitution of sensorimotor
control rules”, i.e. neural computation principle that enables
hyper-adaptability.
In order to verify the hypotheses described above,
knowledge of neuroscience is essential. However, with only

Fig. 1 Whole constitution of the project

Expected Research Achievements and Scientific SignificanceThe following research achievements and scientificsignificance can be expected in the research project:
1.
2.

3.

Systematization of “science of hyper-adaptability” byelucidating its underlying neural mechanisms andthrough its computational modelings
Construction of mathematical modeling (gray-box
model), which can describe brain functions by
integrating multi-modal experimental data such as
electrophysiology, brain imaging, and behavior.
Construction of a comprehensive theory that can explain
adaptation principle from its neural entity to its neuralcomputation principle.

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, Yano, Wen, An, and Yozu).
IV. ACTIVITIESFollowing events were held by management group.
A. Activities organized by the project- 1st management meeting
Date: July 17th, 2019. 17:00-19:00
Place: The university of Tokyo
Contents: members of the management group discussed about
the operation method, persons in charge of the project
symposiums, publications, supports for the activities, etc.
- 2nd management meeting
Date: September 20th, 2019. 12:00-13:00
Place: The university of Tokyo
Contents: members of the management group discussed about
the operation method, management of 1st plenary conference,organization of young researches group, etc.
- Kickoff symposium
Date: September 20th, 2019. 13:30-16:00
Place: Ito hall, the university of Tokyo
Consents: kickoff symposium of the project. Project
representative (Ota), group A representative (Isa), and group B
representative (Kondo) introduced the overview of the area
and research groups. Project representative (Ota) explained
about public offering. More than 100 people participated in
the symposium and shared various opinions.
- 1st plenary conference
Date: March 4th - 6th, 2020
Place: Iwate
Contents: summary of the researches in the year was presented
by project representative and researchers in the project.
- 3rd management meeting
Date: Marge 6th, 2020. 11:30-12:30
Place: Iwate
Contents: members of the management group discussed about
the operation method, management of 2nd plenary conference,etc.
B. Activities in academic societies- Next generation brain project winter Symposium
Date: December 18th -20th, 2019.
Place: Hitotsubashi hall, Hitotsubashi University
Contents: project representative (Ota) presented about theoverview of the project. Information about the project wasalso published with poster.
- 32th distributed autonomous system symposium OS
Date: January 25th - 26th, 2020.

Place: Shibaura institute of technology
Contents: Project organized an OS named "hyper-adaptability"
in distributed autonomous system symposium organized by
SICE. Nine researches presented in the OS.
C. Other activities- Construction of the project homepage
The management group constructed a project homepage forpublication and for sharing the project information.
URL: https://www.hyper-adapt.org/- Organization of young researchers' group
We organized a young researchers' group for the sake of thecommunication among young researchers in the project andfor their supports. Details are described below.
- Establishment of international collaboration program
In order to support the international collaboration of project
researchers, the management group established a financial
support program for international collaboration.
V. ACTIVITES BY YOUNG RESEARCHERS
This program promotes activities of young researchers
chaired by Dr. Qi An (The University of Tokyo). The
followings are the list of them:A. Kick-off meeting for young researchers
On November 21, 2019, a kick-off meeting for young
researchers was held at the Hongo Campus of the Universityof Tokyo. In this meeting, the activities of the future young
researcher associations will be confirmed to have researchexchange meetings, literature study sessions, lecturers inviting
senior researchers, and gathering important literature in therelated field to be disseminated throughout all the member inthis research area.
B. 1st research exchange meeting
A research exchange meeting was held on January 26,
2020, in conjunction with the 32nd Autonomous
Decentralized System Symposium. Ten researchers
participated in this meeting. The presentations briefly
introduce their own research first and Dr. Qi An gave a talkabout effects of cognition and emotion on the rehabilitation ofhemiplegic patients.
C. First literature study session (planned)
On March 26-27, 2020, a study session on“Computational Neuroscience” by Hirokazu Tanaka was heldat Hongo Campus of the University of Tokyo. Ten researchers
from inside and outside the field participated and learned
about a systematic approach to neuroscience.
VI. FUTURE PERSPECTIVE
As main activities of the next fiscal year, the management
group plans a 2nd plenary conference on May 2020 in Tokyo,
a public symposium on July 2020 and 3rd plenary conference
on March 2021.

Group A. Research Report FY2019
Principal Investigator: Tadashi Isa

Kyoto University, Graduate School of Medicine, WPI-ASHBi

I. AIM OF THE GROUP
Group A aims at clarifying how the process of“reconstruction of biostructures” and “restructuring of action
principles” are involved in the hyper-adaptation process
following the neural injuries, aging, reformation of the bodystructure, painful learning process or malfunction ofneurotransmitter systems through experimental studies on
animals or humans.
Group A01 will examine the hypothesis that disinhibition
across the large-scaled network of the brain is the basis of
hyper-adaptation by “reconstruction of biostructures” byexperimental studies on rodents, nonhuman primates andhumans.
Group A02 will investigate the adaptation of neural systems to
the change in skeleton-muscular structures and its underlying
principles.
Group A03 will study how the body recognition and emotion
systems promote the hyper-adaptation, that is, the motor
learning under difficult condition such as change in the body
frame.
Group A04 will test the working hypothesis that “restructuringof action principles” against the change in the brain dynamics
accompanying depletion of neurotransmitters such as dopamine
and acetylcholine in aging or neurodegenerative disorders will
induce hyper-adaptation.
II. MEMBERS OF THE GROUP
A01: “Elucidation of the hyper-adaptation mechanism byreconstruction of biostructures and challenges for preventionof decline in latent adaptive capacity”
PI: Tadashi Isa (Kyoto Univ)
Collaborator 1: Eichi Naito (CiNet)
Collaborator 2: Hidenori Aizawa (Hiroshima Univ)
A02: “Elucidating the neural mechanism of hyper-adaptation
to the change in skeleton-muscular structures”
PI: Kazuhiko Seki (NCNP)
A03: “Mechanisms of body cognition and emotion inducing
hyper-adaptability”
PI: Hiroshi Imamizu (Tokyo Univ)
Collaborator: Ken-Ichiro Tsutsui (Tohoku Univ)
A04: “Alteration of brain dynamics as underlying mechanismsof hyper-adaptability in neurotransmitter disorders”
PI: Kaoru Takakusaki (Asahikawa Medical univ)
Collaborator: Takashi Hanakawa (Kyoto Univ/NCNP)
III. RESEARCH ACTIVITY[Group A01] (1) Date: 2019/09/06, Place: Kyoto Univ,
Discussion on measurement of monoamine release in the brain

with voltammetry. Participants: Isa, Onoe, Yamaguchi and
Aizawa.
(2) Date: 2019/11/09, Place: Science Council of Japan(Tokyo), Discussion on the hyper-adaptation in para-athletes.
Participants: Isa, Naito.
[Group A02] (1) Date: 2019/12/18, Place: Univ of ElectroCommunications. Discussion on analysis method on EMG
data obtained from cross-union experiments in monkeys andthe current results, etc. Participants: Seki, Funato, Uchida,
Kondo, Yano, Ishitsubo, Naganuma, Tsuchiya.
[GroupA03] (1) Date: 2019/08/02, Place: Tohoku Univ,
Discussion on how to measure the subjective sense and will inanimal experiments. Participants: Tsutsui, Nakamura, Izawa,
Ohata, Imamizu.
(2) Date: 2019/10/21, Web meeting. Discussion on how tounderstand the subjective sense and will in the hierarchicalorder as recognition of controllability and possibility ofinvestigating their influence on ordinary motor learning anddifficult motor learning by adding perturbation at various
levels, etc. Participants: Tsutsui, Nakamura, Izawa, Ohata,
Tanaka, Kikuchi, Imamizu.
(3) Date: 2019/12/24, Web meeting. Discussion on how toconstruct the experimental paradigm to understand thestructural learning about the controllability of the systems
from the viewpoint of will and subjective sense. Participants:
Tsutsui, Nakamura, Izawa, Ohata, Tanaka, Imamizu.
(4) Date: 2020/1/17, Web meeting. Discussion on how toinvestigate how recognition of controllability is generalized
between decision making and motor learning. Participants:
Izawa, Ohata, Imamizu.
[GroupA04] (1) Date: 2019/07/24, Place: Tokyo Univ, Joint
meeting of A04 and B04. Discussion on mathematicalmodeling of postural control during standing. Participants:
Takakusaki, Ota, Asama, Uenishi.
(2) Date: 2019/08/09, Place: Tokyo Univ. Joint meeting of
A04 and B04. Discussion on the mathematical modeling ofcentral mechanism of postural control. Participants:
Takakusaki, Ota, Uenishi.
(3) Date: 2019/09/10, Place: Asahikawa Medical Univ. Jpint
meeting of A04 and B01. Central mechanism of postural
disturbance of Parkinson disease patients.
(4) Date: 2019/11/21, Place: Tokyo Univ. Joint meeting of
A04 and B04. Presentation and general discussion on the
research plan during the next 5 years. Participants: Takakusaki,
Hanakawa, Ota, Yotsu, Nakajima, Takahashi, Yoshinaga,
Higashiguchi, Shirafuji, Omura, Uenishi, Ishii.

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
Abstract—The Group A01 will try to test the hypothesis thatdisinhibition across wide brain areas underlies the hyperadaptation process against the neural injuries or frailty. For thispurpose, Isa Group works on the recovery after the spinal cord
injury in the macaque monkeys, Naito Group works on theneuroimaging during performance of motor tasks in aged people,
and Aizawa Group works on the diffuse projection systems suchas dopaminergic and serotonergic systems as the possible neuralsubstrate of the global disinhibition in mice.

I. INTRODUCTION
The A01 Group will examine the hypothesis that disinhibition
across the large-scaled network of the brain is the basis of
hyper-adaptation by “reconstruction of biostructures” by fusionand further development of our current researches. Our
previous researches have shown that the brain is equipped withthe global disinhibition mechanisms and in case of spinal cord
injury, the mechanism is triggered to recruit the latent circuit
for functional recovery, and that the diffuse projection systems
such as monoaminergic neurons would be involved in theprocess. However, details of the underlying neural mechanism
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 with demonstration of the frailty
in the aged people. Isa Group will record the movementrelated activity in the sensorimotor cortices withelectrocorticography (ECoG) and the electromyogram (EMG),
and the cortical and muscle responses to electrical stimulation
through the ECoG electrodes longitudinally before after the
spinal cord injury in monkey to clarify the mechanism of
disinhibition across the large-scaled brain network. In rodents,
Aizawa Group will perform the activity measurement andoptogenetical stimulation of monoaminergic 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. METHODSIn Isa’s Group study, A male macaque monkey (Macaca
fuscata) was trained to reach, grasp and retrieve a small pieceof sweet potato through a narrow vertical slit using both theindex finger and thumb. Then, a pair of 18-channel ECoG
electrodes were chronically implanted on bilateral PM/M1/S1.
We made sub-hemisection at C4/C5 and longitudinallymonitored the cortical activity during a reach and grasp task,
and applied direct electrical stimulation through the ECoG
electrodes to probe the connectivity between motor-relatedareas and muscles before and after the sub-hemisection (up to
5 months postoperatively).
Naito’s Group conducted fMRI experiments and measured
brain activity when right-handed healthy old adults (n = 50;
aged 65-78 years) and right-handed healthy young adults (n =30; aged 20-27 years) experienced illusory movement of righthand. They also evaluated the right hand dexterity of patientsusing a standard peg test.
Aizawa Group developed the methods to record theextracellular dynamics of dopamine and serotonin, andexamined how their experimental manipulation affects thebrain activity and behavior of the animals to investigate
whether dopamine and/or serotonin underlies the global
disinhibition for hyper-adaptation. They conducted 1)
development of electro-chemical technique to record theextracellular release of dopamine and serotonin with highsensitivity and speed, 2) investigation of the effect ofmanipulating the dopaminergic system on the brain activity
and behavior of mice, and 3) development of local infarction
model animals using photo-active dyes in the head-fixed miceand evaluation of feasibility of the animal model by
combining the photo-imaging of neural activity.
IV. Research Topics
A. Global dinsinhibition after spinal injury in monkeys
Preoperatively, success ratio of precision grips was nearly
100 %. On day 32 post-injury, success ratio of the grasping
guided by the slit edge reached 41.5 % and then saturated

around 90 % from day 40 post-injury. On the other hand,
precision grip did not completely recover and success ratio of
the precision grip had been 0 % from the lesion. These results
indicate that the dexterous finger movements did not fully
recover after the sub-hemisection. We used direct electricalstimulation with ECoG electrodes to investigate theconnectivity between motor-related areas and muscles for theaffected side before and after the sub-hemisection. Fig.1
shows the induced muscle twitches in the affected forelimb by
the electrical stimulation of contralesional PM. Muscle
twitches started being induced in proximal muscles from thecontralesional 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.
These results suggested that disinhibition spread incontralesional cortical network along with the recoverFig.1 Muscle twiches in various body parts by contralesional PM stimulation.

B. fMRI investigation of lacking of ipsilateral motor
deactivation in the healthy elderly and its relation withaging-related decline in hand dexterity
Naito Group found significant deactivation in the hand sectionof the ipsilateral (right) motor cortex (M1) during motor
illusion in young group. In contrast, such ipsilateraldeactivation generally reduced in old adults and some of themshowed ipsilateral activation rather than deactivation. When
we examined brain regions wherein activity is correlated withthe peg test performance, we found that activity in the hand
section of the ipsilateral M1 was significantly correlated withthe performance, and that the M1 was the only region showing
such correlation in the entire brain (see figure below). Namely,
older adults with higher right-hand dexterity (shorter timerequired to complete peg test) showed ipsilateral M1
deactivation, whereas those with lower right-hand dexterity

Fig.2 Relation between ipsilateral M1 activity and score of Peg-test.

showed ipsilateral activation rather than deactivation (Fig.2).
These results suggested that lack of ipsilateral M1 deactivationin the elderly may disturb and deteriorate their digital dexterity.
C. Sensitive quantification of extracellular monoamines inrodents
Aizawa group addressed the involvement of monoaminergic
systems as a basis of global disinhibition for hyper-adaptation
by analyzing dynamics of extracellular monoamines in therodent brain. Carbon fiber microelectrode was used to record
the oxidative peak of current intensity peculiar to themonoamines following the adsorption of positively charged
monoamines by application of negative ramping potential
through the electrodes placed in the multiple brain regions(Fast scan controlled adsorption voltammetry, FSCAV).
Electrodes, calibrated sensitivity in vitro, were able tomonitor the ambient level of extracellular dopamine in theregions such as somatosensory cortex, dorsal and ventral
striatum and olfactory tubercle as shown below. Furthermore,
FSCAV revealed that the dopamine reuptake inhibitor(nomifensine) consistently increased extracellular level of
dopamine in those brain regions, providing thepharmacological evidence supporting the advantage of thecurrent technology. Interestingly, behavioral analyses
unraveled the mice with excessive extracellular dopamine withnomifensine exhibited the increased locomotor activity,
anxious and hedonic behaviors suggesting the modulation of
global cerebral activity.

Fig.3 Highly sensitive measurement ofdopamine release in multiple regions.

IV. FUTURE PERSPECTIVE
In this fiscal year, Isa Group found that disinhibition occurredin the motor cortices in the recovery process, which would bea key mechanism for novel brain areas to be recruited tocompensate for the impaired function. In next fiscal year, they
will conduct the same experiment on the 2nd monkey andinvestigate the inter-individual difference and change in thebrain activity and connectivity over time. Naito Group willsubdivide the older adults into two groups, and to train eachgroup with different type of motor training for several months.
They then test if each training can effectively restore
ipsilateral M1 deactivation and further reveal causal
relationship between restoring of ipsilateral M1 deactivationand possible improvement of hand dexterity. Aizawa Groupwill analyze the dynamics of monoaminergic activation duringhyper-adaptation to the neural circuit injury by applying themethods for measurement of the monoaminergic activation to
the rodent model of strokeAnnual report of research project A02
Kazuhiko SEKI
National Institute of Neuroscience, NCNP

Abstract—In the FY2019, we established 1) the method tomanipulate selectively the activity of somatosensory primaryafferent by optogenetics, 2) the method to evaluate thesensorimotor function in freely behaving monkeys, and 3) the
method to assess the activity of spinomuscular- andcorticomuscular closed loops in monkeys performing motor
task.The abstract goes here.

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. Optogenetic manipulation of spinal reflex pathways fromsomatosensory primary afferents in rats.
Previously, fundamental structures and their mode of actionin the spinal reflex circuit were determined by confirming their
input-output relationship using electrophysiological techniques.
In those experiments, the electrical stimulation of afferent fibres
was used as a core element to identify different types of reflex
pathways; however, a major disadvantage of this technique is its
non-selectivity. In this study, we investigated the selective
activation of large-diameter afferent by optogenetics combinedwith a virus vector transduction technique (injection via theFig.1. Part of yellow fluorescent protein (YFP) in the dorsal root
ganglion (DRG) of the rat. Selective gene transduction(Channelrhodopsin 2, YFP) with a preference for medium-to-large-sizedcells was achieved using the AAV 9.

sciatic nerve) in non-transgenic male Jcl:Wistar rats. We foundthat green fluorescent protein (GFP) gene transduction of rat
dorsal root ganglion (DRG) neurons with a preference formedium-to-large-sized cells was achieved using adenoassociated virus 9 (AAV9) vector, compared with AAV6 vector(p=0.021). Furthermore, the optical stimulation of
Channelrhodopsin 2 (ChR2) expressing DRG neurons(transduced by AAV9-expressing the ChR2 gene) produced
compound action potentials in afferent nerves originating fromfast-conducting nerve fibres. We also confirmed thatphysiological responses to different amplitudes werecomparable between optogenetic and electrophysiologicalactivation. However, the optically elicited responses had lower
sensitivity with stimulus frequency, unlike electrically elicited
responses. Finally, we showed that afferent volleys evoked by
optical stimulation were sufficient to activate postsynaptic
neurons in the spinal reflex arc. These results provide new
insights for understanding the role of sensory afferent input tothe central nervous system regarding behavioural control, andapplicable to analyze the phenotype of the muscle relocation
primate models. (Kubota et al. J. Physiol. 2019).
B. Decoding of muscle activity from the sensorimotor cortex
in freely behaving monkeys
In this study, we demonstrate accurate decoding of muscle
activity from electrocorticogram (ECoG) signals in unrestrained,
freely behaving monkeys. We recorded ECoG signals from thesensorimotor cortex as well as electromyogram signals frommultiple muscles in the upper arm while monkeys performed
two types of behaviors with no physical restraints, as follows:forced forelimb movement (lever pulling task) and natural
whole-body movement (free movement within the cage). As inprevious reports using restrained monkeys, we confirmed thatmuscle activity during forced forelimb movement was
accurately predicted from simultaneously recorded ECoG data.
More importantly, we successfully demonstrated that accurate
prediction of muscle activity from ECoG data was possible inmonkeys performing natural whole-body movement. We foundthat high-gamma activity in the primary motor cortex primarily
contributed to the prediction of muscle activity during natural
whole-body movement as well as forced forelimb movement. In
contrast, the contribution of high-gamma activity in the
premotor and primary somatosensory cortices was significantly
larger during natural whole-body movement, which suggeststhat activity in a larger area of the sensorimotor cortex wasneeded to predict muscle activity during natural whole-bodymovement. Based on this result, we now developing a method
to assess the mechanism of sensory gain modulation of tendon
relocation model in an free behaving setting. (Umeda et al.
Neuroimage, 2019).

Fig.2. Reconstruction of shoulder (Del) and hand (FDS) muscle
activities using ECoG signals.

C. Distinct sensorimotor feedback loops for dynamic andstatic control of movement mediated by spinal and motor
cortical neurons.
Volitional limb motor control involves dynamic and static
muscle actions. It remains elusive how such distinct actions arecontrolled through separated or shared neural circuits. In thisstudy, we explored the potential separation for dynamic andstatic controls in the primate hand actions, by investigating theneuronal correlated activity between local field potentials(LFPs) of the spinal cord and the forelimb electromyographic
activity (EMGs), and LFPs of the motor cortex and the EMGs
during the performance of a precision grip in macaque monkeys.
We analyzed neural coherence and information flows between
LFPs from the spinal cord and the motor cortex, and EMG
activity of the forearm, while the macaque monkey performed a
precision grip task that involved the control of both dynamic
grip and static hold. We found the emergence of significant
spinomuscular and corticomuscular coherence as distinct timefrequency patterns relevant to the dynamic and static grip phases.
Furthermore, directional information analyses indicated thatFig.3. Power spectra of neural LFPs (top), EMGs (middle), andand their coherence between spinal β band (NB) LFP and AbPL (e),and cortical NB LFP and AbDM (f ).

spinal and cortical beta-range coherence comprised a reciprocalinteraction with the muscles, with corresponding time lags forbeta oscillations. Furthermore we showed that these two
feedback loops (i.e., spinal local feedback vs. cortical divergent
feedback loops) differ in the muscles involved. These results
indicate that distinct sensorimotor feedback loops are engaged
in the dynamic and static control of precision grip of primates.
Therefore, a comparable behavioral task could be applied in themuscle relocation model to document the way their sensorimotor
loop could change in the process of re-learning motor skill using
de novo sensory prediction error. (Oya, Takei et.al.
Communication Biology, 2020).

IV. FUTURE PERSPECTIVE
Along with the research described in above, we have improved
the surgical technique for making the tendon relocation modelmonkeys and activity of more than ten muscles are recorded inongoing experiments. We will also focus on the assessment ofneuronal adaptation starting from FY2020.
REFERENCES
[1]

[2]
[3]

Kubota S, Sidikejiang W, Kudo M, Inoue KI, Umeda T, Takada M, Seki
K. Optogenetic recruitment of spinal reflex pathways from large-diameter
primary afferents in non-transgenic rats transduced with
AAV9/Channelrhodopsin 2. J Physiol.597:5020-5040, 2019.
Umeda T, Koizumi M, Katakai Y, Saito R, Seki K. Decoding of muscle
activity from the sensorimotor cortex in freely behaving monkeys.
NeuroImage 197: 512-526, 2019.
Oya T, Takei T, Seki K. Umeda T, Koizumi M, Katakai Y, Saito R, Seki
K: Distinct sensorimotor feedback loops for dynamic and static control ofprimate precision grip. Commication Biol. in press.

Annual report of research project A03
Hiroshi Imamizu

Graduate school of Humanities and Sociology, The University of Tokyo

Abstract— Our research project aims to reveal neuralmechanisms in which body cognition and positive emotion, suchas motivation, facilitate motor learning in challenging situation(“hyper-adaptation”). Our main achievements in this fiscal year
are 1) finding a behavioral evidence that sense of agency (a sensethat “I am the one causing an action,” which is an aspect of bodycognition) facilitates motor learning, 2) identification of neuralcorrelates for a sense of control, which supports the sense ofagency, by using fMRI, 3) establishing a behavioral paradigm formonkeys to study the motor learning process with the motivation
factor monitored or controlled.

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 haveinterests 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 neurophysiologicalmechanisms in which motivation and body cognition facilitate
motor learning. We expect that investigation of suchmechanisms leads to the development of methods for inducing
efficient motor learning even in challenging situations.
II. AIM OF THE GROUP
Our aims are 1) understanding mechanisms in which
motivation and body cognition facilitate motor learning inchallenging situations, and 2) development of methods forfacilitating motor learning through artificial control ofmotivation and body cognition. Our research activity willcontribute to understanding mechanisms of the “hyperadaptability” and future development of the methods for
inducing and facilitating it.
III. RESEARCH TOPICS
A. Apprach to sense of agency and motor learning frombehavioral experiments
1) Effect of sense of agency on motor learning: A group of theprincipal investigator (Masaru Tanaka and Hiroshi Imamizu)
and Jun Izawa of the B03-group conducted a behavioral
experiment. This experiment demonstrated that attribution of
the observed movement to self, which is a fundamental

process in the establishment of a sense of agency, facilitatesmotor learning. Specifically, participants moved a stylus on a
pen-tablet to control a cursor on a screen placed over the tablet.
Participants moved the cursor from the starting position to thetarget position located 15-cm to the right (Fig. 1). The cursor
was presented as a feedback of the movement when it moved
15 cm in the right direction. The cursor position was
artificially shifted by -2, 0 or +2 cm in the perpendicular
direction to the start-target line (Fig. 1A). After the movement,
participants reported whether the feedback reflected their
movement (self-attribution of the movement) or not (non-selfattribution). As a result, we found that the participants biased
their pen movements in the next trial toward the opposite
direction to the shifted direction (Fig. 1B). Moreover, the bias
was more significant after the self-attribution than the nonself-attribution. The bias reflects a learning rate between thetrials. Thus, our results indicate that the attribution of the
movement affects motor learning [1].

Fig. 1 Artificial shift of movement feedback and bias in thenext trial
2) Mechanisms linking action to its consequence: A subjective
compression of an interval between the intentional action andits consequence (“intentional binding”) is often used as ameasure of sense of agency. We found that subjective time
globally shortens but locally elongates when the action and its
consequences repeated several times, suggesting multiple
timescales in the binding [2].
B. Neural basis for sense of agency (attributiono of movment
to sef or other)
A group of the principal investigator (Ryu Ohata, Tomohisa
Asai, and Hiroshi Imamizu) developed a decoding method toattribution of movement to self or other, which constitutes thefundamental technology for our project. This work was
accepted as a research article [3].
C. Neural basis for sense of agency (controllability cognition)
A group of the principal investigator (Ryu Ohata and Hiroshi
Imamizu), Wen Wen, and Hajime Asama of the B03-groupmeasured brain response to change in the controllability of amanipulated object (i.e., a fundamental aspect of the sense ofagency). Participants manipulated a joystick to move a cursor
on a screen. While a participant was moving the cursor, wechanged the cursor controllability by mixing the movement
made by another participant with the participant’s movement.
We measured brain activity when the controllability increased
or decreased. As a result, activity increased in the dorsolateralprefrontal cortex and the lateral cerebellum when thecontrollability increased (Fig. 2A). Activity decreased in theinsula when the controllability decreased (Fig. 2B). Thechange in activity correlated with the rate of change in thecontrollability. Our result suggested different mechanisms
underlying cognition of increase or decrease in controllability.
This result indicates that we must change regions for brain
stimulation according to an increase or decrease in the senseof agency to manipulate the sense of agency in our futurestudies.

Fig. 2 Brain activity related to increase (A) or decrease (B) in
controllability
D. Establishment of a behavioral paradigm for studying the
motor learnng process with the motivation factor
monitored or controlled
A group of the co-investigator (Ken-Ichiro Tsutsui and hiscolleagues) aimed to establish a behavioral paradigm to study
the motor learning process with the motivation factor
monitored or controlled. They modified the Brinkman board
test (Fig. 3A) to have different levels of difficulty: wide slots
for “easy” task (Fig. 3B) and narrow slots for “difficult” task
(Fig. 3C).

specific satiety. In the “hungry” condition, the monkeys
performed around 15-20 sessions of the “easy” task andaround 10 sessions of the “difficult” task, until they stopped
performing. (A completion of a board was counted as one
session.) In the “satiated” conditions, we gave sweet potatoes
to the monkeys before the task performance, in order to givesensory specific satiety. In the “moderately satiated” condition,
the monkeys performed around 15-20 sessions of the “easy”
task, but less than 5 sessions of the “difficult” task. In the“highly satiated” condition, the monkeys did not perform boththe “easy” and “difficult” task. The time needed to complete asession for either “easy” or “difficult” task did not differ
between the “hungry” and “moderately satiated” conditions.
These results indicate that we can dissociate the motivationand motor factors by using the modified Brinkman test, inwhich the total number of session performed and the timespent to complete a session can be regarded as indices ofmotivation and motor factors, respectively. Furthermore, it
was suggested that the motivation and the effort cost are in a
non-linear relation in these monkeys, as the number ofsessions performed was the same between “hungry” and“moderately satiated” conditions for the “easy” task, but
smaller in “moderately satiated” condition for the “difficult”
task.
IV. FUTURE PERSPECTIVE
We made advances in developing a behavioral paradigm toinvestigate the relationship between the sense of agency andmotor learning, finding neural correlated for the cognition ofcontrollability (as an aspect of the sense of agency) in humans,
and a behavioral paradigm to study the motor learning processwith the motivation factor monitored or controlled in monkeys.
Besides these results, we continued intensive discussions forthe paradigm shared with human and monkey experiments toinvestigate the effects of controllability cognition on motor
learning. In the next fiscal year, we will establish an
experimental paradigm with humans and monkeys.
REFERENCES
[1]

[2]

Fig. 3 Modified Brinkman board test
The original Brinkman board test is used for testing themanual dexterity. In this study, we aimed to modify this task
so that it can evaluate the motor function while the motivation
factor is being monitored or manipulated. We used pieces ofsweet potato and tested the task performance of two Japanesemonkeys (Macaca fuscata) under different levels of sensory

[3]
[4]

田中 大，井澤 淳，今水 寛 (2019) 誤差の原因帰属の主観的判断が運動学習に与える影響. 第 13 回 Motor Control 研究会, 東京大学農学部弥生講堂（東京都文京区弥生）, 第 13 回 Motor Control 研究会
抄録集, p. 63
Imaizumi, S., Tanno, Y., and Imamizu, H. (2019) Harmonization ofresting-state functional MRI data across multiple imaging sites via theseparation of site differences into sampling bias and measurement bias.
Consciousness and Cognition, 73, e102768J. Clerk Maxwell, A Treatise
on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892,
pp.68-73.
Ohata, R., Asai, T., Kadota, H., Shigemasu, H., Ogawa, K., andImamizu H. Sense of agency beyond sensorimotor process: Decoding
self-other action attribution in the human brain. Cerebral Cortex, in press.
Imamizu, H., Wen, W., Ohata, R., Tanaka, M., Yamashita, A., andAsama, H. (2019) Response to gaining and losing control in humanbrain. The 83rd Annual Convention of the Japanese Psychological
AssociatioI.S. Jacobs and C.P. Bean, “Fine particles, thin films andexchange anisotropy,” in Magnetism, vol. III, G.T. Rado and H. Suhl,
Eds. New York: Academic, 1963, pp. 271-350

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 person. In this firstyear, we established methods of both studies. Takakusaki andcolleagues established methods for evaluating optimal posturalcontrol in multitask voluntary movements (from gaze fixation inquite standing to postural preparation and forelimb reaching) incats. This experimental system will be also applied to the model
animals with damages in DA and ACh so that roles of theseneurotransmitters in the alteration of optimal postural control
during voluntary movements can be elucidated. Hanakawa andcolleagues used a simultaneous EEG-fMRI for evaluating a
dynamic profile of functional connectivity between distinct
neural networks. They also started to examine neuromelanin
MRI and DA transporter (DAT) SPECT for evaluation of
dopamine systems. These studies will clarify relationship acrosscognitive functions, neural network dynamics and neurotransmitters underlying the generation of hyper-adaptation.

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. Single photon emission computed tomography(SPECT) with a DA transporter tracer (DAT) shows that DA
nerve terminals are reduced 1-2% a year. Decreases in DA
result in dysfunctions of multiple and parallel basal gangliacortical circuits, thereby affecting both motor and cognitive
functions [1]. Furthermore, amyloid beta (Aβ), a key moleculeof AD pathophysiology, accumulates over 10-20 years before
manifestation of cognitive decline. A positron emission tomography (PET) with Aβ tracers indeed detects positive Aβ
accumulation in the brain in 30% of cognitively normal elderly
population [2]. Many factors may affect cognitive functions inelderly persons, influential factors such as DA and Aβ should
ideally be measured for the assessment of cognitive functions.
Based on the above considerations, the goal of this research
project (A04) is elucidating brain-connectivity dynamics in thecortical and subcortical neural structures that underly thegeneration of hyperadaptation in elder person due to decline of
the above-mentioned neurotransmitters and molecule.

II. AIM OF THE GROUP
To achieve the above goals, basic experiments in animals
and clinical researches in humans are performed.
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. Specifically,
they examine postural control of the cat that has learned the"multitask voluntary movements" from quiet standing with
fixed gaze position to forelimb reaching to the target in various
conditions. Either DA or ACh system is, then, manipulated bypharmacological and molecular-optogenetic techniques to
examine the modification of postural control. Attempts arefurther made to determine the role of cortical (frontoparietal
networks) and subcortical (cortico-brainstem-spinal pathways)
mechanisms involved in these postural control processes [3-4]using electrophysiological and neuroanatomical techniques.
Hanakawa (a research collaborator) and his colleagues in
Kyoto University and NCNP perform 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 (https://padni.
org/), which is a longitudinal cohort study involving healthyelderly people as well as patients with PD and AD. In this year
they developed a method to examine relationship between
cognitive functions and dynamic profiles of functional
connectivity among distinct neural networks (simultaneousEEG-fMRI) of elder subjects in a comprehensive manner sothat they provide necessary data for modelling to engineeringresearch groups. The Hanakawa lab also started a basic
research on neuromelanin MRI to assess DA production in thesubstantia nigra (SN) [5] in addition to DA transporter (DAT)SPECT for evaluating the activity of the DA system.
III. RESEARCH TOPICS
The major results of this year are following three points.
A. Multitasking experiment system for model animals
Takakusaki’s group constructed experimental system that
requires cognitive-motor coordination of the cat during its
execution of multitask voluntary movements. (Fig.1A). The cat
has to keep standing posture on the narrow space beneath its
feet. The cat gazes the target (attention) in front, then prepares
its appropriate posture for reaching the target by either forelimb.

Fig.2. Dynamic relationship offunctinal connectivity between
DMN and FPN. (left).

Fig.3. An example ofneuromelanin MRI from apatient with PD
Fig. 1. Forelimb reaching task in the cat

The center of vertical pressure (CVP), which was calculated
from ground reactive force exerted on each limb, was used as aparameter for posture control reflecting the center of gravity.
When the cat gazed the target, CVP position was fixed at(□) in Fig.1B. Until lifting up the left forelimb, CVP moved
from □ to 〇. During forelimb reaching, CVP further moved
from 〇 to ●, but the two CVP positions were very close. In 6
animals, there was no significant difference in coordinates of
CVP at lifting (〇) and reaching (●), indicating that the optimal
CVP position was prepared in advance so that it maintained
cat’s posture during forelimb movements. It follows that the
distance between the two CVP positions can be considered as“postural prediction error”. When the distance of the cat andtarget was increased (Fig.1C), the prediction error ( ○ - ●distance) increased, indicating that voluntary movement isprecedingly supported by postural adjustment that is optimal to
the goal-directed action depending on the interaction between
the cat and circumstance. Because injections of small amountof muscimol into the motor-related (4 and 6 areas) and parietal(5 and 7 areas) cortices altered the series of postural control
during the task, such an optimal postural adjustment can beachieved by the operation of frontoparietal network [5].
B. Non-invasice multimodal measurement of dynamicchanges of brain activity and connectivity in humans
Second, Hanakawa lab has worked on developing
simultaneous EEG-fMRI recording to non-invasively measuredynamic changes of brain activity and connectivity in humans.
By applying in-dependent component analysis (ICA) to fMRI
data, the default mode network (DMN) and fronto-parietal
network (FPN) were retrieved. Inter-network correlation offMRI time-series (functional connectivity, FC) was assessed
for each time window, resulting in the observation of dynamicchanges of FC (Fig.2). Hanakawa lab is currently working on
developing a method to integrate dynamics information fromfMRI and EEG.
C. Development and validation of DA imaging techniques
Third, the Hanakawa lab has started to include neuromelanin
MRI for the potential assessment of DA synthesis in the SN, in
addition to DAT SPECT for the potential assessment of presynaptic DA terminals in the striatum (Fig.3). However, since

how much of the neuromelanin MRI contrast really reflect DA
synthesis remains controversial, this group is planning to combine the MRI method with the histochemical assessment inspecimen. This year, necessary arrangement was made to makesuch an experiment possible.
IV. FUTURE PERSPECTIVE
In this year, Takakusaki’s group established an experimental
system for measuring and evaluating optimal posture control
during multitasking. Several animals were trained to stably
perform the task. In future, they plan to identify cortical andsub-cortical mechanisms of optimal postural control using
pharmacological and molecular-optogenetical methods combined with electrophysiological techniques, so that role of theDA-ACh systems in the generation of hyper-adaptable posturalcontrol mechanisms can be understood.
This year, Hanakawa’s group developed simultaneous EEGfMRI measurements for evaluating a dynamic profile offunctional connectivity. They plan to apply this method to the
PADNI cohort study, while repeating basic studies on thistechnique and DA imaging. In the PADNI cohort, DATSPECT will also be employed to detect a decrease in striatal
DA. In future, neuromelanin MRI measurement, which isthought to reflect DA production in the substantia nigra, will befurther employed, assuming new subjects will be recruited.
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]

T. Hanakawa, A.M. Goldfine, M. Hallett. “A common function of basal
ganglia-cortical circuits subserving speed in both motor and cognitive
domains” eNeuro 4(6), 2017, e0200-17.
Jansen WJ et al. “Prevalence of cerebral amyloid pathology in persons
without dementia: a meta-analysis” JAMA 313(19), 2015, 1924-3.
Takakusaki K. “Functional neuroatatomy for posture and gait control”.
J Mov Disord 10 (1), 2017, 1-17.
Takakusaki K, Takahashi M, Obara K, Chiba R. “Neural substrates
involved in the control of posture”. Adv Robot 2017, 31 (1-2), 2-23.
Sulzer D et al. “Neuromelanin detection by magnetic resonance imaging(MRI) and its promise as a biomarker for Parkinson's disease” NPJ
Parkinsons Dis 2018, 4:11
Takahashi M, Nakajima T, Miyagishi S, Chiba R, Obara K, Takakusaki
K. “Roles of the parietal cortex in postural adjustments that accompany
forelimb reaching in the cat”. Abstract of 42th Annual Meeting of theJapan Neuroscience Socity, pp 258.

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.

モデル
モデル
モデル

データ

理解

モデル

介入技術
・戦略

実験
実験
実験

仮説

新たな
仮説

Fig. 1. Roles of systems engineering group.

II. M EMBERS
To achieve the above mentioned research objective, weorganized the following four 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), Shiro Yano (TUAT)
Co-investigators: 2
Research Outline: This research group aims to realize
systems modeling of hyper-adaptability mechanism with functional disinhibition 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.
B02 Modeling of ultra-adaptive to body change
Principal investigator: Yasuharu Koike (Tokyo Tech)
Funded co-investigator: Tetsuro Funato (UEC)
Co-investigators: 5
Research Outline: This research group aims at the modelingof the mechanism underlying the recovery of the motor
function after the alteration of the body, especially from theviewpoint of the reconstruction of a neural structure in hyperadaptability.
B03 Systematic understanding and realization of hyperadaptive phenomena focusing on cognition and emotion
Principal investigator: Hajime Asama (U Tokyo)
Funded co-investigator: Jun Izawa (U Tsukuba), Wen Wen(U Tokyo), An Qi (U Tokyo)
Research Outline: This research group focuses on rehabilitation, the mechanism of hyper-adaptability is investigated froma systematical approach, and new rehabilitation methods aredeveloped based on the investigation of effect of cognition andemotion on behavior adaptation and motor learning.
B04 Modeling of hyper adaptability in human postural control
considering the role of neurotransmitters
Principal investigator: Jun Ota (U Tokyo)
Funded co-investigator: Arito Yozu (Ibaraki PUHS)
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.

III. ACTIVITIES
Activities mainly organized by the members in Group B are
described as follows:• September 13th, 2019, B01-B02-A02 meeting (NCNP)• September 27th, 2019, B03-B04 meeting (Ibaraki PUHS)• Nobember 21st, 2019, B04-A04 meeting (U Tokyo)• December 18th, 2019, B01-B02-A02 meeting (UEC)• January 25-26th, 2020, Organized session with SICE
Technical Committee on Decentralized Autonomous Systems (SIT), 9 oral presentations.
Please refer the report of each research project for theirconcrete research outcomes.
IV. F UTURE PLAN
In the next fiscal year, subscription research projects will
join. Sooner we will have Group B meeting for deepening
mutual understanding, will start 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 Brugere 2018]

脳波データ

グラフィカル・モデル/
ネットワーク構造推定

長期的筋電データ

行列/テンソル分解

脱抑制ネットワーク構造

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.

運動パターンの日次変化[Ishitsubo, Yano et al., 2018]

Fig. 1.

Statistical modeling approach.

Fig. 2.

Constructive modeling approach.

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

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.
III. R ESEARCH OUTCOMES
A. Probabilistic latent variable model for analyzing longterm multi-modal data
Prof. Yano (Tokyo University of Agriculture and Technology) and his colleagues develop a statistical tool to quantify
the time varying structural shift behind the hyper-adaptability
based on the simultaneously observed EMG-EEG data.
They proposed two novel analysis methods for EMG data
which are the extended methods of the well-known synergy
analysis method [1], [2]. The proposed method [1] analyses
the long-term EMG data, which ranges from weeks to themonths. The data consists of EMG time series data obtainedby performing one training task multiple times. The method
estimates the long-term adaptation effects on the muscle
activity caused by the training. The second one [2] is also
the extended method of the synergy analysis method. They
combined Gaussian graphical models with standard synergy
analysis and formulated network structure inference problem. It appears that different motion data contains different
network structure, i.e. cooperative activity behind EEG data.
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 construct the experimental system and improve thesimulator to explain the change of postural control in the
dual task. The standing task and the cognitive task should
be designed to show the effects of the dual task. With apreliminary experiment, we adopt a closed reg standing task
which shows large sway. Also, we adopt a cognitive task inwhich subjects calculate by subtracting 7 from the given 3digit integer. The postural control simulator is improved to
be able to express the closed leg posture. By applying thenervous system control model, which includes the control
of muscular tonus with musculoskeletal body model [3], we
can investigate the effects of dual tasks on "muscular tonus".
We made an experiment on young healthy subjects incollaboration with the B04 research group. As the results,
the sway in the open leg standing during dual tasks was

reduced. We estimated the change of parameters using the
proposed simulator. The results showed that the decrease inmuscular tonus could lead to the increase of postural swayin open leg posture, and we showed the possibility that themuscular tonus cannot be reduced in the closed leg posture.
C. Motor learning experiments with VR/Robot technologies
enhancing hyper-adaptability
Prof. Kondo (Tokyo University of Agriculture and Technology) and his colleagues investigated cooperative motor
learning tasks that can enhance their adaptability after the
training using VR and/or haptic robot technologies. They
found that a skill-level matching algorithm for adjusting thedegrees of robotic motion intervention would be promisingfor enhancing learners adaptability (in preparation). Moreover, they developed a cooperative targets tracking task [4],and found that pairs of participants exchanged their motor
intention using a kind of proto-language.
We also investigated the brain activity data obtained undera target tracking task [5]. We can discriminate the brain dynamics using an graph theory-based model. Different features
were identified under three different conditions (movement
only, visual observation only, visual pursuit movement).
IV. F UTURE PERSPECTIVE
From both statistical and constructive modeling standpoints, we have just started the discussion on the methodology for the modeling of hyper-adaptability. Moreover,
we started searching for motor tasks that enhance learners
adaptability.
In the next fiscal year, we will continue to deepen themodeling methodology, and furthermore we try to modelneurophysiological data such as ECoG and EMG fromneuroscience group. Moreover, we will continue to search
for the motor tasks that induce ”reconstruction of neural
structure” with dis-inhibition in the brain of elderly people
under the frailty state.
R EFERENCES[1] S. Yano, A novel matrix decomposition method for analyzing Daily
motor learning process of Macaque monkey, Workshop on NeuroRobotics 2019.
[2] K. Sakai, S. Yano, T. Kondo, Network structure inference and dimensionality reduction for periodical bodily motions, The 37th annual
Conference of the Robotics Society of Japan, 2019.
[3] 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.
[4] N. Thorne, J. J. Honisch, T. Kondo, S. Nasuto and Y. Hayashi,
Temporal Structure in Haptic Signaling Under a Cooperative Task,
Frontiers in Human Neuroscience, Volume 13, 372, 2019.
[5] N. T. M. Phuong, X. Li, Y. Hayashi, S. Yano and T. Kondo, Estimationof brain dynamics under visuomotor task using functional connectivity
analysis based on graph theory, Proc. the 19th annual IEEE International Conference on Bioinformatics and Bioengineering (BIBE 2019),
Athens, Greece, 2019.

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

Abstract—We obtained the following three results. 1. In order
to measure EMG signal efficiently, array EMG measuring device
was developed and the relationship between the measured forces,
movements and EMG signals was analyzed using a musculoskeletal model. 2. We analyzed the muscle synergy before and after
tendon transfer using a mathematical model. Results suggested
that slow changes in muscle synergy and fast adaptation of temporal patterns caused the change in temporal patterns obsearved
in the experiments. 3. We analyzed the reconstruction rule using
EEG, and found that the characteristics of the movement can be
reproduced in the movement planning stage immediately before
the movement by EEG, and that the information was the direction
and distance of the movement, not the target position itself.

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. Realization of human body transformation experiment by“virtual surgery”“Virtual surgery” environment was conducted in which therelationship between human muscle activity and hand force
was modeled, and changing the direction of the force generated
by the experiment participants and the direction of the force
realized on the screen. For this purpose, it is necessary toappropriately measure the muscle activity generated by theexperiment participants. If electrodes can be quickly installed
without using information on muscle anatomical locations,
it will be easier to apply the same technique to patients infuture rehabilitation. For this reason, using an aray electrodefor EMG measuring, the relationship between forces andmovements measured simultaneously with muscle activity
obtained from 96-channel electrodes was calculated using a
musculoskeletal model. As a result, when the electrodes arearranged in an array, not only surface muscle activity but also
deep muscle activity can be measured, and the accuracy of
torque and position estimation has been improved[1], [2].
B. Model of synergy change after tendon transfer
A02 group investigated the neural activities of monkeys inlever operating task, before and after transferring the flexor(EDC: extensor digitorum) and extensor (FDS: flexor digitorum superficialis) muscles of fingers. We collaborated with
A02 group, analyzed the synergies of the measured muscles
and calculated the correlation coefficient of the muscle synergies for over the four months of the experiment (Fig. 1AB,
Synergy 1: mainly flexor muscles activities and Synergy 3:
mainly extensor muscles activities). As a result, we found “thetemporal coefficient of muscle synergies soon after the tendon
transfer were different from those before the transfer, and they
gradually returned” (see the report of A02 group). We assumed
that the difference in the changing speed between muscle
synergy and temporal coefficient caused this phenomenon, andanalyzed the mechanism using a mathematical model.
Muscle synergy slowly changed with approximately twomonths after tendon transfer, and we assumed that the temporalcoefficient adaptably generated for the achievement of the motion. In other words, muscle synergy slowly changes and thetemporal coefficient can rapidly change for adaptation. Basedon this assumption, we investigated the generated temporal
patterns for the muscle synergies of each stage.
Now we consider the mathematical model of the above
phenomenon. For the achievement of task, motion itself

···

mn =

W H,

(2)

here, W is muscle synergy and H is temporal coefficient .
We model the tendon transfer as a change of contribution
ratio of muscle activity to the joint torque. Namely, tendon
transfer of first and second muscle is represented as follow.

1.6

Synergy 1

1.6

3

1.2
0.8

0.4

0.4

0

0

0
3

2

2
1.6

1.2

1.2

0.8

0.8

0.4

0.4

0

0

0
3

J=

∑(

a W H − aW H
+

+

+

)2

(4)

This optimization determines one optimal temporal pattern
H + for each value of muscle synergies W + . We investigatedthe change of temporal pattern H + with changing the valueof muscle synergies W + .
In the experiment, EMGs related to the finger movement
were measured for 13 points, and the EDC and FDS werecross-transfered. Thus, the number of muscles for the modeln = 13, and the contribution ratio aj of EDC and FDS were
exchanged. For simplicity, the number of joint was set 1 andthe contribution ratio of each muscle aj were set ECR=0.5,
ECU=1, EDC=1, ED23=1, and 0 for the remaining muscles.
In order to calculate the optimal temporal patterns for themuscle-set after tendon transfer, the contribution ratio a were
exchanged, and the activity level of EDC and FDS in muscle
synergies were gradually exchanged with 60 steps. For eachexchange level, the temporal coefficients were obtained as Fig.
1C. Fig. 1D shows the correlation coefficient of the temporalcoefficient between before and after transfer. Temporal patterns of the initial phase (after exchange of a, no change inmuscle synergies) were different from those of before transfer(low correlation coefficient), and the correlation coefficient
gradually increased with the change in muscle synergies.
Our current model was rather simple, but the obtained
phenomenon about the temporal coefficient were similar to thatof the experiment. This result supported our assumption thatthe slow change in muscle synergy and fast change in temporalcoefficient generated the change in the temporal patterns of themuscle synergies.

1

2

2
1.6

1.2

1.2

0.8

0.8

0.4

0.4

0

0

0
3

2

2

1.6

1.6

1.2

1.2

0.8

0.8

0.4

0
-20

0

20

40

60

80

100

1

2

1

0
-20

0

20

40

60

2017/6/30

B

100

0

2017/9/20

1

80

D

Experiment

0.5

50

100

150

60

Model Simulation

Correlation between synergy 1 of simulationand synergy 1, 3 of pre transfern
1
0.5
0

-0.5
-1

2

0.4

0

0

Temporal coefficient H is determined for matching the torquebefore and after tendon transfer, i. e., optimization that reducing the following evaluation function.

2

1.6

Correlation between synergy 1 of post transfer
and synergy 1, 3 of pre transfer

(3)

1

1.6

1

j+
j
j
aj+
2 = a1 , a1 = a2

2

1.2
0.8

Synergy 2

Synergy 1
Synergy 3

m2]

Synergy 4[
m1

Model Simulation

Post Transfer
2

Muscle activities are composed of muscle synergies.
∑

C

Experiment
Pre Transfer
2

Synergy 3(1)

A

Synergy 4

τj = aj1 mj1 + aj2 mj2 + · · · + ajn mjn

Fig. 1. Temporal coefficient of synergies in tendon transfer experimentand in simulation. A,C: temporal coefficients of synergies in pre and post
tendon transfer(A) and those of simulation(C). B,D: correlation coefficient ofsynergies in the experiment (B) and simulation (D)Synergy 2

may change. But for the simplicity of model, the motion
is supposed to be the same between before and after thetendon transfer. Then, we consider the temporal coefficientis determined so that the torque of before transfer is achieved
with muscle condition of after transfer. jth joint torque τj canbe represented as weighted sum of ith muscle activity mji with
contribution ratio to the torque aji as follow.

post transfer
0

10

20

30 40 50 60
day from (2017/6/30)

synergy 1
synergy 3
7080-0.5 post transfer-10synergy 1
synergy 3

10 20 30 40 50 60
simulation trial

C. Elucidation of reconstruction rule process by brain information decoding
Focusing on muscle synergy, which is a cooperative structure between multiple muscle activities, among the characteristics of biological information, and examining the process ofchanges in the structure of muscle synergy, we clarify the restructuring rules of the biological structure. In order to confirm
where and when this information is calculated in the brain,
confirm whether the direction, distance, and target positionof the movement can be estimated from the brain activity
during exercise planning when reaching multiple targets did.
As a result, the direction and distance of movement could
not be estimated with high accuracy from all experimental
participants[3].
IV. F UTURE PERSPECTIVE
This year, in cooperation with the A02 team, we started
building a model that explains the data actually measured
by the A02 team. Although it was a simple model, theresults reproduced animal experiments. From next fiscal year
onwards, we will improve the accuracy and promote modelingof super adaptation to body transformation.
R EFERENCES[1] S. Stapornchaisit, Y. Kim, A. Takagi, N. Yoshimura, and Y. Koike, “Finger
angle estimation from array EMG system using linear regression modelwith independent component analysis,” Frontiers in Neurorobotics, 2019.
[2] Y. Koike, T. Kawase, H. Kambara, N. Schweighofer, and N. Yoshimura,“Muscle Synergy Analysis for Rehabilitation,” Examines in Physical
Medicine and Rehabilitation, vol. 2, no. 5, pp. 1–2, 2020.
[3] H. Kim, N. Yoshimura, and Y. Koike, “Characteristics of Kinematic
Parameters in Decoding Intended Reaching Movements Using Electroencephalography (EEG),” Frontiers in Neuroscience, vol. 13, no. November,
pp. 1–13, 2019.

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 found out different
mechanisms and neural basis for the perception of gaining andlosing control; 2) we investigated the hyper-adaptation processes
in post-stroke patients; 3) we examined the influence of body
consciousness on motor learning.

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 and cerebral palsy patients, and tomodel their motor recovery, and to examine the effect ofmodel-based methods.

III. RESEARCH TOPICS
B03 accomplished the following three studies in the pastyear.
A. Clarification of the mechanisms and neural basis of thesense of agency
First, B03 group (Asama and Wen) examined themechanism for the sense of agency, proposing a novel
mechanism of regularity detection, besides the traditional
comparator mechanism [1]. Figure 2 shows the model
considering the new regularity detection mechanism.
Specifically, we designed a novel control detection task, inwhich the participants detected one target dot over which they
had the sense of agency, among three moving dots. The actual
control over the target dot was either nonlinearly disturbed bymixing other’s motion, or linearly disturbed by applying anangular bias. The results from the motor control task showedthat 60% of other’s motion resulted in comparable amount ofprediction errors with 90 degree angular bias. However, control
detection accuracy was significantly lower in the former(nonlinearly disturbed) condition than the latter (linear
disturbed) condition. This was because nonlinear disturbance
produced prediction errors and impaired regularity at the sametime, while linear disturbance only produced prediction errors
but left regularity intact. In conclusion, we suggest that
regularity detection is an important process that contributes tothe sense of agency besides the traditional comparator
mechanism.

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 model of two separate contributions to the sense ofagencyFig. 1 Processes of intervening hyper-adaptation via body
consciousness and emotion, and quantitative measures.

Second, we examined the hypothesis that there are distinct
neural basis for perception of gaining and losing control, bycollaborating with A03 group (Imamizu and Ohata). We foundthat the activities in the left cerebellum and the rightdorsolateral prefrontal cortex positively correlated with thedetection of an increase in control. In addition, we also foundthat the activity in the left anterior insula was negatively

correlated with the detection of a decrease in control, while thatin the bilateral frontoparietal regions was positively correlated.
In short, our results clearly showed that the detection of gaining
and losing control is linked to different brain mechanisms [2].
B. Evaluate Motor Impairment of Stroke Patients
In Qi An’s (Co-PI) research group, we investigate
adaptation process while post-stroke patients recover theirmotor function after stroke onset [3]. In particular, we focus onhuman sit-to-stand motion, which is important daily activity,
and we clarify how muscle synergy structure is varied among
patients who have different motor severity. We firstly
measured motion and sEMG data from 33 post-stroke patients
who stayed in Morinomiya hospital. These patients are divided
into two groups based on Fugl-meyer assessment (FMA) scoreof lower limb; a severe group is defined as patients whose
FMA score is less than 20 and a mild groups is defined as
patients whose FMA score is not less than 20. Firstly, fourmuscle synergies were extracted from muscle activity of eachtrial of sit-to-stand motion. Next random forest model is used
to develop motor impairment classifier. The model uses
temporal features of muscle synergies to classify whether a trialof stroke patients sit-to-stand is evaluated as severely impairedor mildly impaired. As a result, the developed model could
successfully classified motor impairment difference with
84.5%. Especially, we found that activation start, peak and end
timing becomes earlier in mildly impaired group rather thanseverely impaired group. From this finding, it is indicated that
activation timing of muscle synergy could be evaluation index
of motor recovery and adaptation process in sit-to-stand motionof post-stroke patients. In our future study, we investigate how
cognitive and emotion affect motor recovery of rehabilitation.
C. Experience of after-effect of memory update reduces
sensitivity to errors during sensory-motor adaptation task
Izawa’s group together with Dr. Yozu investigated theeffect of recognition of aftereffect of learning in human sensory
motor learning mechanism in order to study a theoreticalbackground of hyper-adaptability. This is important fordeveloping a theory that can be used to design and develop
efficient neurorehabilitation strategy.
Motor learning is the updating of motor commands in
response to a trajectory error induced by perturbations on the
body or vision. The brain has a great capability to acceleratelearning by increasing the sensitivity of this updating process tothe perception of errors. Conventional theory suggests that thestatistics of the perturbation and of the corresponding errors
determine the sensitivity to errors. However, the potential
effect of another type of error perception, a self-generated error
as a result of motor command updates (i.e. an after-effect), on
error sensitivity has not been considered yet. In this study, we
dissociated the two kinds of errors by controlling theperception of the after-effect using a channel-force
environment. One group experienced errors due to the aftereffect of the learning process, while the other did not. We
found that the participants who perceived the after-effect of thememory updates exhibited a significant decrease in error

sensitivity, whereas the participants who did not perceive theafter-effect did not show an increase or decrease in error
sensitivity(Figure3).

Fig. 3 Error sensitivity in each condition
This suggests that the perception of the after-effect of learning
prevented participants from updating their motor commands
and that both self-generated and externally induced errors
modulate learning speed.

IV. FUTURE PERSPECTIVE
In the past year, we examined the mechanisms and neuralbasis of the sense of agency, attempted to quantitatively
measure post-stroke patients’ motor functions, and examined
the influence of learning process on motor error detection. In
the next year, we aim to further propose a model to explain theinfluence of emotion on body consciousness, and via it, on
motor learning.
REFERENCES[1]

W. Wen and P. Haggard, “Prediction error and regularity detection
underlie two dissociable mechanisms for computing the sense ofagency,” Cognition, vol. 195, p. 104074, 2020.

[2]

W. Wen, R. Ohata, M. Tanaka, A. Yamashita, H. Asama, and H.
Imamizu, “Two dissociable processes for detecting gaining andlosing control in human brain,” in The 23rd Annual Meeting of
Association for the Scientific Study of Consciousness (ASSC23),
2019.

[3]

N. Yang et al., “Temporal features of muscle synergies in sit-tostand motion reflect the motor impairment of post-stroke patients,”
in IEEE Transactions on Neural Systems and Rehabilitation
Engineering, 2019, vol. 27, no. 10, pp. 2118–2127.

Annual report of research project B04
Jun OTA (Research into Artifacts, Center for Engineering (RACE), School of Engineering, The University of Tokyo)
Arito YOZU (Center for Medical Sciences, Ibaraki Prefectural University of Health Sciences)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 fiveco-investigators (Shirafuji, Kaminishi, Omura, Ishii, andHamada).
II. RESEARCH RESULTS AND FUTURE PLANS

postures in the neck, such as antecollis. To express this posture,
a musculoskeletal model with 19 joint degrees of freedom and
94 muscles was constructed by adding the joint degrees of
freedom and muscles of the neck to an existing
musculoskeletal model that focused on lower limbs. It is
known that Parkinson’s disease patients have excessively
increased muscle tone. Therefore, the constructedmusculoskeletal model was controlled by using a neuralcontroller model that models the reticulospinal tract that has thefunction to regulate muscle tone. A normal upright posture andan abnormal posture of a Parkinson's disease patient were
given as target postures for the neural controller model, andcontrol parameters were calculated by optimizations for eachposture condition. As a result, the musculoskeletal model was
successfully maintained his upright posture in both the upright
posture and the abnormal posture conditions. The objective toreproduce the abnormal posture of Parkinson’s disease patientson a mathematical model was achieved [1]. Comparing theresults of a comprehensive search for muscle tone that enables
both an upright posture and an abnormal posture, we found that
abnormal muscle posture requires greater muscle tone (Fig. 1).
Because Parkinson's disease patients show increased muscle
tone, so it is appropriate that increased muscle tone is simulated
in abnormal postures. Furthermore, through calculations ofmuscle tone for each body part, increased muscle tone wasobserved especially at an ankle (Fig. 2). It has been reported
that a healthy person controls his posture by the coordination of
feedback gains and ankle stiffness for disturbances. We
consider that Parkinson's disease patients take an abnormal
posture to increase their ankle stiffness involuntarily andcannot coordinate their ankle stiffness and feedback gains, thatcause postural control disorders. In the future, we introduce thevestibular spinal tract, which has the function of maintaining
our body vertical, in the neural controller model, and aim toclarify the relationship between abnormal posture andabnormal postural control. In addition, we perform experimentswith subjects to verify the built mathematical model.
A

B

A. Reproduction of Abnormal Posture on a MathematicalModel
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 fiscal year, we aimed to reproduce the abnormal
posture peculiar to Parkinson's disease patients, on amathematical model. Characteristics of Parkinson’s disease
patients are abnormal posture and increased muscle tone. We
reproduce the abnormalities with a neural controller model thatconsiders muscle tone to obtain clues to elucidate themechanism of posture control of Parkinson’s disease patients.
Parkinson's disease patients show characteristic abnormal

Fig. 1. Muscle tone that can maintain upright postures. (A) Normal posture.
(B) Abnormal posture. The higher the values of the vertical axis, the larger
muscle tone is.

1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0

measured using a stabilometer (Anima, Tokyo, Japan) with asampling rate of 20 Hz. The surface electromyogram of 16 of
the following muscles was measured by a wireless EMG
system (Delsys, Tokyo, Japan): cervical paraspinal,
sternocleidomastoid, lumbar paraspinal, gluteus maximus,
quadriceps femoris, semitendinosus, tibialis anterior, andsoleus (all bilaterally).

Upright

Neck

Biarticular

Lumbar

Ankle

Knee

Hip

||uff||2

AbnormalFig. 2. Simulated muscle tone of each muscle group. The higher the values ofthe vertical axis, the larger muscle tone is. Black is for normal upright postureand gray is for abnormal upright posture.

Through musculoskeletal simulations and experiments withsubjects, we investigated how muscle tone affects the response
to external forces. Simulations suggest that increased muscle
tone promotes the use of an ankle strategy, maintaining astanding posture based on the movement of ankle joints [2].
The simulation results were consistent with the experimental
results [3], indicating the importance of muscle tone when we
maintain an upright posture.

c) Measurements in healthy subjects and a patient with
Parkinson’s disease
To analyze the effect of multitask loading, participants
were asked to perform i) standing task without arithmetic task,ii) multitask of the standing task and arithmetic task, and iii)
only the standing task. The reason we measured iii) only thestanding task again was to evaluate the leaning effect of
repeated measurement. We confirmed that measurements withhealthy young adults and a Parkinson’s disease patient werestable in our experimental environment. Parkinson’s disease
patient was measured before and after drug administration toevaluate the effect of dopamine on the multitask. In the nextyear, we are planning to measure large number of healthy
adults and Parkinson’s disease patients. We are going toanalyze the effect of dopamine with these data next year.

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 first year of the project, a) we
designed a multitask protocol featuring a postural task and acognitive task, b) we developed a system to measure kinematicand physiological responses during the tasks, and c) we statedmeasurements in healthy subjects and a patient with
Parkinson’s disease.
a) Designing of multitask featuring a postural task and acognitive task
A multitask which could be performed by aged subjects or
patients with Parkinson’s disease was designed. For a posturaltask, 30 seconds of quiet standing was adopted. For a cognitivetask, an arithmetic task was adopted [4, 5]. The participants
were asked to recite serial subtractions by 7, starting from arandomly selected number between 300 and 900. The
calculated result was reported at the end of the task to avoid theinfluence of articulation on postural sway [6].
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.
Posture was recorded both anteriorly and laterally using video
cameras. We applied markers to the head (Cz, according toInternational 10–20 System), external occipital protuberance,spinous process (C7), acromion (bilaterally), xiphoid process,anterior superior iliac spine (bilaterally), greater trochanter(bilaterally), knee (bilaterally), lateral malleolus (bilaterally),
tuberosityofcalcaneus(bilaterally),
and
fifth
metatarsophalangeal joint (bilaterally). Center of pressure wasFig. 3 Example data of center of pressure in healthy subject

REFERENCES
[1]

[2]
[3]

[4]
[5]

[6]

Omura,Y., Kaminishi,K., Chiba,R., Takakusaki,K. and Ota,J.,
Representation of abnormal posture in computational model for
constructing posture control model of Parkinson’s disease，32nd Symp.
on Autonomous Decentralized Systems, HA7, 2020, in Japanese.
Kaminishi,K., Chiba,R., Takakusaki,K., and Ota,J., Investigation of theeffect of tonus on the change in postural control strategy using
musculoskeletal simulation. Gait & Posture, 76, 298-304, 2020.
Kaminishi,K.,Chiba,R., Takakusaki,K., and Ota,J., Effect of muscle toneon postural control strategyfor a horizontal support surface perturbation，
32nd Symp. on Autonomous Decentralized Systems, HA6, 2020, inJapanese.
Granacher, U, Bridenbaugh, S.A., Muehlbauer, T., Wehrle, A., Kressig,
R.W., Age-related effects on postural control under multi-task
conditions, Gerontology, 57, 247-255, 2011.
Wild, L.B., de Lima, D.B., Balardin, J.B., Rizzi, L., Giacobbo B.L.,
Oliveira H.B., de Lima Argimon II, Peyré-Tartaruga, L.A., Rieder, C.R.,
and Bromberg, E., Characterization of cognitive and motor performance
during dual-tasking in healthy older adults and patients with Parkinson's
disease, J Neurol. 260, 580-589, 2013.
Dault, M.C., Yardley, L., Frank, J.S., Does articulation contribute tomodifications of postural control during dual-task paradigms?, Brain Res
Cogn Brain Res.,16, pp. 434-440, 2003.

List of Publications (2019)

Journal Papers
1.

H. Oshima, S. Aoi, T. Funato, N. Tsujiuchi, and K. Tsuchiya, Variant and Invariant Spatiotemporal
Structures in Kinematic Coordination to Regulate Speed During Walking and Running, Frontiers in
Computational Neuroscience, 13, 63, 20192.

B. N. Tran, Y. Shiro, and T. Kondo, Coordination of human movements resulting in motor strategies
exploited by skilled players during a throwing task, PLoS ONE, 14, 10, e0223837, 20193.

N. Thorne, J. J. Honisch, T. Kondo, S. Nasuto and Y. Hayashi, Temporal Structure in Haptic Signaling
Under a Cooperative Task, Frontiers in Human Neuroscience, 13, 372, 20194.

T. Morita, M. Asada, and E. Naito, Developmental changes in task-induced brain deactivation inhumans revealed by a motor task, Developmental Neurobiology, 79, 6, 536-558, 20195.

S. Uehara, N. Mizuguchi, S. Hirose, S. Yamamoto, and E. Naito, Involvement of human left
frontoparietal cortices in task-switching between two sequences of skilled finger movements, Brain
Research, 1722, 146365, 2019

6.

T. Morita, M. Asada, and E. Naito, Right-hemispheric dominance in self-body recognition is altered inleft-handed individuals, Neuroscience, 425, 68-89, 20197.

Nozaki K, Ito H, Ohgidani M, Yamawaki Y, Sahin EH, Kitajima T, Katsumata S, Yamawaki S, Kato TA,
Aizawa H., Antidepressant effect of the translocator protein antagonist ONO-2952 on mouse
behaviors under chronic social defeat stress., Neuropharmacology, 162, 107835, 20198.

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, pii: bhz307, 20199.

Ishida A, Kobayashi K, Ueda Y, Shimizu T, Taijiri N, Isa T, Hida H, Dynamic interaction between
conrtico-brainstem pathways during training-induced recovery in stroke model rats., Journal of
Neuroscience, 39, 37, 7306-7320, 201910. Umeda T, Isa T, Nishimura Y, Somatosensory cortex receives information about motor output.,
Science Advances, 5, 7, eaaw5388, 2019
11. Isa T, Dexterous hand movements and their recovery after central nervous system injury., Annual
Review of Neuroscience, 42, 315, 2019

12. Isa T, Mitsuhashi M, Yamaguchi R, Alternative routes for recovery of hand functions after corticospinal
tract injury in primates and rodents., Current Opinion in Neurology, 32, 6, 836, 2019
13. 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
14. W. Wen, Does delay in feedback diminish sense of agency? A review, Consciousness and Cognition,
73, 102759, 2019
15. W. Wen, and P. Haggard, Prediction error and regularity detection underlie two dissociablemechanisms for computing the sense of agency, Cognition, 195, 104074, 2020
16. 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
17. N. Yang Q. An, H. Kogami, H. Yamakawa, Y. Tamura, K. Takahashi, M. Kinomoto, H. Yamasaki, M.
Itkonen, F. Aanajjar, S. Shimoda, N. Hattori, T. Fujii, H. Otomune, I. Miyai, A. Yamashita, H. Asama,Temporal Features of Muscle Synergies in Sit-to-stand Motion Reflect the Motor Impairment of PostStroke Patients, IEEE Transaction on Neural System and Rehabilitation Engineering, 27, 10, 21182127, 201918. 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
19. 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, , inpress
20. 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
21. 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
22. Nozu T, Miyagishi S, Nozu R, Takakusaki K, Okumura T, Butyrate inhibits visceral allodynia andcolonic hyperpermeability in rat models of irritable bowel syndrome. Scientific Reports, 19, 1, 19603,
2019International Conference
1.

Shouhei Shirafuji, Kaito Tsunetomo, and Jun Ota, Correlation Analysis between Functional Rocker
Transition and Walking Gait, 1st IFAC Workshop on Robot Control (IFAC WROCO 2019), Daejeon,
Korea,20192.

Nguyen Thi Mai Phuong, Xinzhe Li, Yoshikatsu Hayashi, Shiro Yano and Toshiyuki Kondo, Estimationof brain dynamics under visuomotor task using functional connectivity analysis based on graph theory,
The 19th annual IEEE International Conference on Bioinformatics and Bioengineering (BIBE 2019),
Athens, Greece, 20193.

Seki K, Gain control of spinal proprioceptive reflex in awake, behaving monkeys, Society for
Neuroscience 2019, Chicago, USA, 20194.

Daisuke Ishii, Kiyoshige Ishibashi, Yuki Kaku, Hiroshi Yuine, Satoshi Yamamoto, Arito Yozu, Yutaka
Kohno, Afferent pathway to the ipsilateral somatosensory cortex in human, Chicago, USA, 20195.

H. Kogami, Q. An, H. Yamakawa, N. Yang, S. Shimoda, M. Kinomoto, N. Hattori, K. Takahashi, T.
Fujii, H. Otomune, I. Miyai, S. Ishiguro, T. Saigusa, Y. Nozaki, H. Maruyama, A. Yamashita, H. Aasama,
Assistive Chair to Support Hip Rising of Elderly People Improves Body Movement of Sit-to-Stand
Motion, 1st Workshop on Robot Control, Daejeon, Korea, 2019

6.

Y.Koike, Motor Learning and Control Mechanism of the Brain, 2019 Workshop on Brain-inspired
Artificial Intelligence, Shanghai, China, 20197.

Wen Wen, Ryu Ohata, Masaru Tanaka, Atsushi Yamashita, Hajime Asama, and Hiroshi Imamizu, Twodissociable processes for detecting gaining and losing control in human brain, The 23rd Annual
Meeting of Association for the Scientific Study of Consciousness (ASSC23), London, Canada, 20198.

Kei Aoyagi, Wen Wen, Qi An, Shunsuke Hamasaki, Hiroshi Yamakawa, Yusuke Tamura, Atsushi
Yamashita and Hajime Asama, Improvement of Sense of Agency During Upper-Limb Movement for
Motor Rehabilitation Using Virtual Reality, The 41st Annual International Conference of the IEEE
Engineering in Medicine and Biology Society (EMBC2019), Berlin, Germany, 20199.

Takakusaki K, Takahashi M, Nakajima T, Chiba R, Obara K, Multi-faced functions of the reticulospinal
systems involved in motor control in relation to behavioral states of animals, Society for Neuroscience
2019, Chicago, USA, 2019

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 (Director, NCNP)

co-investigator

Ryosuke Chiba (Associate Professor, Asahikawa Medical University)

co-investigator

Shiro Yano (Assistant Professor,
Tokyo University of Agriculture and Technology)

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 (Assistant Professor, The University of Tokyo)

co-investigator

Arito Yozu (Associate Professor,
Ibaraki Prefectural University of Health Sciences)

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)
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)

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)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 (Director, NCNP)

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,
Tokyo University of Agriculture and Technology)

Research Project B01: Systems modelling of hyper-adaptation mechanism for reconstruction of neural
structure
Principal investigator

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

Funded co-investigator Ryosuke Chiba (Associate Professor, Asahikawa Medical University)
Funded co-investigator Shiro Yano (Assistant Professor,
Tokyo University of Agriculture and Technology)
Co-investigator

Koji Ito (Emeritus Professor, Tokyo Institute of Technology)

Co-investigator

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

Masafumi Yano (Professor Emeritus, Tohoku University)

Co-investigator

Atsushi Yamashita (Associate Professor, The University of Tokyo)

Co-investigator

Hiroyuki Hamada (Research Associate, Bunkyo Gakuin University)

Co-investigator

Ningjia Yang (PhD student, 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,
Ibaraki Prefectural University of Health Sciences)
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)