Brain Connectivity Estimation Model for Multi-Network Disorder Likelihood
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Solution Overview
Problem
Conventional neurofeedback training primarily focuses on changes in specific brain regions or networks, lacking an efficient method for estimating disorders associated with multiple brain networks, which hinders applications in diagnosis and therapy selection.
Innovation Solution
An estimation system that simultaneously measures brain wave and functional magnetic resonance imaging data to calculate functional connectivity across channels and networks, using machine learning to determine disorder-likelihood scores, enabling more accurate estimation of disorders through neurofeedback training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional neurofeedback training focuses on specific brain regions or networks, then the training can be implemented with simpler methods, but it becomes difficult to estimate disorders associated with multiple brain networks effectively
Solution Approach 1:
The patent segments the complex disorder estimation problem into multiple independent functional connectivity components (first functional connectivity from EEG, second functional connectivity from fMRI). Each connectivity type is calculated separately for specific brain networks, allowing the system to handle multiple brain networks without overwhelming complexity. The segmentation enables modular processing where each component can be computed and integrated independently.
Solution Approach 2:
The patent introduces functional connectivity as an intermediary variable that mediates between raw measurement data (EEG and fMRI) and the final disorder likelihood estimation. Instead of directly analyzing complex multi-network interactions, the system first computes functional connectivity metrics that serve as simplified representations, making the subsequent disorder estimation tractable while maintaining accuracy.
2Ease of manufacture
If simultaneous EEG and fMRI measurement is used to create estimation model, then the cost can be reduced and viability enhanced, but the computation complexity increases
Solution Approach 1:
The patent extracts only the essential and effective features from the simultaneous EEG and fMRI data - specifically, the functional connectivity metrics. Rather than processing all raw measurement data, the system extracts functional connectivity as the key informative component, discarding redundant information. This extraction reduces computation complexity while preserving the diagnostic value needed for viable neurofeedback training.
Solution Approach 2:
The patent applies partial action by focusing computation only on relevant brain networks and connectivity patterns rather than analyzing all possible brain region interactions. The system selectively computes functional connectivity for specific networks associated with the disorder of interest, performing only the necessary computations required for accurate estimation without unnecessary overhead.
3Measurement precision
If functional connectivity is calculated for each channel combination in EEG data, then the estimation accuracy improves, but the quantity of computation increases significantly
Solution Approach 1:
The patent applies local quality by focusing computational resources on specific, clinically relevant brain networks rather than uniformly processing all channel combinations. The system identifies and prioritizes functional connectivity calculations for brain networks that are locally relevant to the disorder being assessed, allocating computational effort where it provides maximum diagnostic value rather than distributing it evenly across all possible connections.
Data Source
AI summary
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.