Brain Activity Training Using Discriminator Feedback
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Solution Overview
Problem
Current brain activity analysis using functional brain imaging and neurofeedback techniques are not yet practical for treating neurological and mental disorders, particularly due to the lack of established biomarkers for predicting diagnostic results and developing therapeutic agents, and the need for more effective training methods to achieve desirable brain states.
Innovation Solution
A brain activity training apparatus and system that utilizes correlations among brain regions measured by functional brain imaging as feedback information, employing a discriminator generated from pre-measured signals to calculate reward values and present them to subjects, enabling training to change these correlations and potentially treat neurological and mental disorders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If functional brain imaging is used to measure brain activities, then brain activity patterns can be detected, but the lack of established biomarkers prevents practical application for treating neurological and mental disorders
Solution Approach 1:
The system performs preliminary measurement of brain activities from multiple second subjects to generate a discriminator before actual training. This pre-computation of the discriminator (which encodes target brain state patterns) enables subsequent real-time feedback to reliably guide subjects toward desirable brain states, resolving the lack of established biomarkers for treatment
2Ease of operation
If real-time neurofeedback is implemented, then subjects can receive feedback about brain activities, but the complexity of generating effective feedback signals from multiple brain regions remains unresolved
Solution Approach 1:
The system extracts a condensed discriminator signal from complex multi-region brain activity patterns of multiple second subjects. This extracted discriminator serves as a simplified feedback target that captures essential brain state information, making real-time neurofeedback practical while avoiding the complexity of processing individual region correlations directly
Solution Approach 2:
The discriminator acts as an intermediary between raw brain activity measurements and feedback presentation to subjects. It translates complex multi-dimensional brain correlation data into a manageable feedback signal that can be processed in real-time and presented to guide subjects toward target brain states
3Manufacturing precision
If correlations of brain activities from multiple subjects are used to generate a discriminator, then target brain states can be defined, but the computational complexity increases
Solution Approach 1:
The system merges brain activity data from multiple second subjects to generate a consensus discriminator. By combining measurements across multiple subjects, the system defines more robust target brain states that represent desirable patterns, while the merging process itself averages out individual variations and reduces computational burden compared to processing each subject separately
Data Source
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AI summary
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.