Advanced Telecommunications Research Institute International

Hiroshima University

Giuseppe Lisi

Jun Morimoto

Mitsuo Kawato

Tomoyuki Noda

Noriaki Yahata

Asuka Takai

Tatsuya Teramae

Naho Ichikawa

Yasumasa Okamoto

Takashi Yamada

Hiroshi Imamizu

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

A motor teaching system is provided with a force presentation robot for teaching motion in accordance with a predetermined motor pattern by guiding movement of a movable part of the body of a test subject, a brainwave sensor, and a computer that classifies a cerebral activity pattern into one of a plurality of classes that include motor imagery, based on a brainwave signal. The computer guides evoking contents of the test subject prior to teaching of the motor pattern, such that a classification result obtained in the period of teaching the motor pattern will be the motor imagery class.

Motion teaching system and motion teaching method

[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

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Soraku-gun, Japan

Giuseppe Lisi