Advanced Telecommunications Research Institute International

Sony Corporation

Honda Motor Co., Ltd.

Hiroshima University

Shionogi Company Limited

Mitsuo Kawato

Jun Morimoto

Okito Yamashita

Noriaki Yahata

Takeo Watanabe

Kazuhisa Shibata

Yuka Sasaki

Giuseppe Lisi

Ayumu Yamashita

Kiyoto Kasai

Nobumasa Kato

Ryuichiro Hashimoto

Kentaro Yamada

Akihiro Toda

Masaaki Sato

Tatsuya Okabe

Jun Nakanishi

Gen Endo

Masahiro Kimura

Gordon Cheng

Takahito Tamagawa

Yasumasa Okamoto

Naho Ichikawa

Takashi Yamada

Hiroshi Imamizu

Yuuto Kashiwagi

Junichiro Yoshimoto

Yuki Sakai

Takanori Kochiyama

Yuji Takahara

Tomoki Tokuda

Yusuke Takeda

Takeshi Ogawa

Motoaki Kawanabe

Toshinori Yoshioka

Megumi Fukuda

Provided is a method of analyzing brain activities for realizing a biomarker for neurological/mental disorder, based on brain function imaging. From measured data of resting-state functional connectivity MRI of a healthy group and a patient group, correlation matrix () of degree of brain activities among prescribed brain regions is derived for each subject. Feature extraction is executed by regularized canonical correlation analysis () on the correlation matrix () and attributes of the subject including a disease/healthy label of the subject. Based on the result of regularized canonical correlation analysis, by discriminant analysis () through sparse logistic regression, a discriminator () is generated.

Brain activity analyzing apparatus, brain activity analyzing method and biomarker apparatus

A harmonization system for a brain activity classifier harmonizing brain measurement data obtained at a plurality of sites to realize a discrimination process based on brain functional imaging: obtains data, for a plurality of traveling subjects as common objects of measurements at each of the plurality of measurement sites, resulting from measurements of brain activities of a predetermined plurality of brain regions of each of the traveling subjects; calculates, for each of the traveling subjects, prescribed elements of a brain functional connectivity matrix representing the temporal correlation of brain activities of a set of the plurality of brain regions; using a generalized linear mixed model, calculates measurement bias datafor each element of the brain functional connectivity matrix, as a fixed effect at each measurement site with respect to an average of the corresponding element across the plurality of measurement sites and across the plurality of traveling subjects; and thereby executes a harmonizing process.

Brain functional connectivity correlation value adjustment method, brain functional connectivity correlation value adjustment system, brain activity classifier harmonization method, brain activity classifier harmonization system, and brain activity biomarker system

A brain functional connectivity correlation value clustering device for clustering subjects having a prescribed attribute on the basis of brain measurement data obtained from a plurality of facilities, wherein a plurality of MRI devices capture resting state fMRI image data of a healthy cohort and a patient cohort; a computing systemperforms generation of an identifier as ensemble learning of 'supervised learning' between harmonized component values of correlation matrixes and disease labels of each of the subjects, selects, during the ensemble learning, features for clustering in accordance with importance from the features specified for generating an identifier for a disease label, and performs multiple co-clustering by “unsupervised learning.”

Brain functional connectivity correlation value clustering device, brain functional connectivity correlation value clustering system, brain functional connectivity correlation value clustering method, brain functional connectivity correlation value classifier program, brain activity marker classification system and clustering classifier model for brain functional connectivity correlation values

Objective discrimination of a disease label of a depressive symptom with respect to an active state of a brain is achieved. One means for solving the problems of the present invention is to provide a discriminating device for assisting in determination of whether a subject has a depressive symptom. The discriminating device includes a storage device for storing information for identifying a classifier generated by classifier generation processing based on a signal obtained by using a brain activity detecting apparatus to measure, in advance and time-sequentially, a signal indicating a brain activity of a plurality of predetermined regions of each brain of a plurality of participants in a resting state, the plurality of participants including healthy individuals and patients with depression. The classifier is generated so as to discriminate a disease label of a depressive symptom based on a weighted sum of a plurality of functional connectivities selected by feature selection as being relevant to the disease label of the depressive symptom through machine learning from among functional connectivities of the plurality of predetermined regions. The discriminating device further includes a processor configured to execute discriminating processing of generating a classification result for the depressive symptom of the subject by using the classifier.

Differentiation device, differentiation method for depression symptoms, determination method for level of depression symptoms, stratification method for depression patients, determination method for effects of treatment of depression symptoms, and brain activity training device

[Object] An object is to provide a brain activity analyzing method for realizing a biomarker using brain function imaging for neurological/mental disorders. [Solution] From data of resting-state functional connectivity MRI obtained by measuring groups of healthy subjects and patients, a correlation matrix of degrees of activities among prescribed brain regions is derived. By a sparse canonical correlation analysis (SCCA) of attributes of subjects and the correlation matrix, elements of correlation matrix that connect to canonical variables corresponding only to the diagnosis label are extracted. By sparse logistic regression (SLR) during Leave-One-Out Cross Validation of a first sum-set of elements of correlation matrix obtained as a result of feature extraction by a sparse regularized canonical correlation analysis, a second sum-set of elements of the correlation matrix is extracted. By discrimination analysis using sparse logistic regression on the second sum-set, a discriminator is generated.

Brain activity analyzing apparatus, brain activity analyzing method, program and biomarker apparatus

A training apparatususing a method of decoding nerve activity includes: a brain activity detecting devicefor detecting brain activity at a prescribed area within a brain of a subject; and an output devicefor presenting neurofeedback information (presentation information) to the subject. A processing devicedecodes a pattern of cranial nerve activity, generates a reward value based on a degree of similarity of the decoded pattern with respect to a target activation pattern obtained in advance for the event as the object of training, and generates presentation information corresponding to the reward value.

Apparatus and method for supporting brain function enhancement

A brain information output apparatus includes an intention determination information storage unit in which two or more pieces of intention determination information can be stored, with each intention determination information including a pair of an intention identifier, and a learning feature amount group including one or more feature amounts extracted from second learning data that is obtained by converting first learning data into intracerebral brain activity data, the first leaning data being acquired from the outside of the cranium of a user when the user performs a trial according to one intention; a first brain activity data acquiring unit that acquires first brain activity data from the outside of the cranium of a user; a second brain activity data acquiring unit that converts the first brain activity data to intracerebral brain activity data, and acquires second brain activity data; a feature amount group acquiring unit that acquires, from the second brain activity data, an input feature amount group including one or more feature amounts; an intention identifier acquiring unit that acquires an intention identifier corresponding to the input feature amount group based on the two or more pieces of intention determination information; and an intention identifier output unit that outputs the intention identifier.

Brain information output apparatus, robot, and brain information output method

The motion of the movable sections of the robot is taken for a periodic motion so that the attitude of the robot can be stably controlled in a broad sense of the word by regulating the transfer of the movable sections. More specifically, one or more than one phase generators are used for the robot system and one of the plurality of controllers is selected depending on the generated phase. Then, the controller controls the drive of the movable sections according to continuous phase information. Additionally, the actual phase is estimated from the physical system and the frequency and the phase of the phase generator are regulated by using the estimated value, while the physical phase and the phase generator of the robot system are subjected to mutual entrainment so that consequently, it is possible to control the motion of the robot by effectively using the dynamics of the robot.

Robot and attitude control method of robot

Growing community of inventors

Soraku-gun, Japan

Mitsuo Kawato