Abstract: An estimation system obtains brain wave measurement data and functional magnetic resonance imaging measurement data simultaneously measured from a subject, calculates first functional connectivity for each channel combination based on correlation between channels included in the brain wave measurement data, calculates second functional connectivity for each brain network based on correlation between regions of interest included in the functional magnetic resonance imaging measurement data, calculates a disorder-likelihood label by calculating a score representing disorder-likelihood to be estimated with the use of a plurality of second functional connectivities, and determines an estimation model for estimating disorder-likelihood based on prescribed first functional connectivity by machine learning using the first functional connectivity for each channel combination and the disorder-likelihood label.

Abstract: There is provided a therapy selection support device that generates a discriminator (identifier) as a diagnostic marker or a classifier as a stratification marker through machine learning on the basis of measurement data on brain activities and that uses the discriminator or the classifier as a biomarker. A therapy selection support system 300a, 300b, 500 includes a clustering device 300b that executes stratification in which the results of measurement of brain functional connectivity correlation values acquired from a plurality of second subjects are divided into a plurality of clusters through a clustering process.

Abstract: 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 system 300 performs 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.

Abstract: [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.

Abstract: 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 data 3108 for 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 harm

Abstract: A training apparatus 1000 using a method of decoding nerve activity includes: a brain activity detecting device 108 for detecting brain activity at a prescribed area within a brain of a subject; and an output device 130 for presenting neurofeedback information (presentation information) to the subject. A processing device 102 decodes 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.

Abstract: 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.

Abstract: Neurofeedback training executed by a brain activity training apparatus for performing a training to change the correlation of connectivity between brain areas utilizing correlation of measured connectivity between brain areas involves repetition of a plurality of trials. Each trial includes a resting period Trest, a self-regulation period TNF and a presenting period TScore presenting feedback information. The brain activity training apparatus calculates, from signals detected from a trainee in the resting period by a brain activity detecting device, a baseline level of degrees of activity of prescribed regions corresponding to the functional connectivity as the object of training, calculates, from signals detected in the self-regulation period and from the baseline level, time correlation of degrees of activity of the prescribed regions corresponding to the functional connectivity as the object of training, and calculates information to be fedback.

Abstract: [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.

Abstract: 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 (80) of degree of brain activities among prescribed brain regions is derived for each subject. Feature extraction is executed by regularized canonical correlation analysis (82) on the correlation matrix (80) 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 (86) through sparse logistic regression, a discriminator (88) is generated.

Abstract: 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 (80) of degree of brain activities among prescribed brain regions is derived for each subject. Feature extraction is executed by regularized canonical correlation analysis (82) on the correlation matrix (80) 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 (86) through sparse logistic regression, a discriminator (88) is generated.

Abstract: Provided is a brain activity training apparatus for training to cause a change in correlation of connectivity among brain regions, utilizing measured correlations of connections among brains regions as feedback information. From measured data of resting-state functional connectivity MRI of a healthy group and a patient group (S102), correlation matrix of degree of brain activities among prescribed brain regions is derived for each subject. Feature extraction is executed (S104) 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 (S108). The brain activity training apparatus feeds back a reward value to the subject based on the result of discriminator on the data of functional connectivity MRI of the subject.

Abstract: 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 (80) of degree of brain activities among prescribed brain regions is derived for each subject. Feature extraction is executed by regularized canonical correlation analysis (82) on the correlation matrix (80) 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 (86) through sparse logistic regression, a discriminator (88) is generated.

Abstract: 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

Abstract: A training apparatus 1000 using a method of decoding nerve activity includes: a brain activity detecting device 108 for detecting brain activity at a prescribed area within a brain of a subject; and an output device 130 for presenting neurofeedback information (presentation information) to the subject. A processing device 102 decodes 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.

Abstract: 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

Abstract: 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.

Abstract: 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.

Abstract: The invention provides a novel highly-adaptive agent learning machine comprising a plurality of learning modules each having a set of reinforcement learning system which works on an environment and determines an action output for maximizing a reward provided as a result thereof and an environment predicting system which predicts a change in the environment, wherein a responsibility signal is calculated such that the smaller a prediction error of the environment predicting system of each of the learning modules, the larger the value thereof, and the action output by the reinforcement learning system is weighted in proportion to the responsibility signal, thereby providing an action with regard to the environment. The machine switches and combines actions optimum to various states or operational modes of an environment without using any specific teacher signal and performs behavior learning flexibly without using any prior knowledge.

Abstract: A movement trajectory generating system of a dynamical system uses neural network units (1, 2, 3) including cascade connection of a first layer (11, 21, 31), a second layer (12, 22, 32), a third layer (13, 23, 33) and a fourth layer (14, 24, 34), to learn a vector field of differential equations indicating forward dynamics of a controlled object (4). Conditions concerning trajectories of a final point and a via-point of movement of the controlled object and locations of obstacles are given from a motor center (5). While smoothness of movement is ensured by couplings of electric synapses using errors with respect to those conditions as total energy, least dissipation of energy is attained, whereby trajectory formation and control input for realizing the trajectory are obtained simultaneously.