Brain Connectivity Classifier for Cross-Site Depression Stratification
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Solution Overview
Problem
Existing methods for developing biomarkers for neurological/mental disorders, particularly depression, face challenges such as small sample sizes leading to overfitting and poor generalization across different imaging sites, limiting the effectiveness of classifiers in predicting therapeutic effects and classifying depression levels.
Innovation Solution
A discriminating device and method using machine learning to generate classifiers based on functional connectivities between specific brain regions, including the left dorsolateral prefrontal cortex, left precuneus, left posterior cingulate cortex, left inferior frontal gyrus opercular part, right dorsomedial prefrontal cortex, and right supplementary motor area, to objectively determine depressive symptoms, their levels, and therapeutic effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If classifiers are generated using machine learning based on functional connectivities from small sample sizes, then the classification accuracy for depressive symptoms is improved, but the generalization capability across different imaging sites deteriorates
Solution Approach 1:
The patent segments the brain into specific regions of interest (prefrontal cortex, precuneus, posterior cingulate cortex, inferior frontal gyrus, supplementary motor area) and calculates functional connectivities between these segmented regions. This segmentation approach allows the classifier to focus on specific neural circuits relevant to depression while reducing the impact of site-specific variations in whole-brain connectivity patterns.
Solution Approach 2:
The patent develops a classifier with universal applicability across multiple imaging sites by training on data from multiple centers and validating its generalization capability. The classifier uses functional connectivity features that are relatively invariant across sites, enabling it to function reliably in different clinical settings without site-specific recalibration.
2Reliability
If classifiers are trained on data from multiple imaging sites, then the generalization capability is improved, but the measurement precision for individual site characteristics deteriorates
Solution Approach 1:
The patent applies local quality by selecting specific brain regions and functional connectivity patterns that are particularly relevant to depression pathophysiology. Rather than using all available connectivity data uniformly, the method focuses on local neural circuits (e.g., default mode network, frontoparietal control network) that show consistent depression-related alterations across sites, thereby maintaining precision while achieving generalization.
3Device complexity
If feature selection is performed to identify relevant functional connectivities, then the classifier complexity is reduced, but the information loss about depressive symptoms increases
Solution Approach 1:
The patent extracts and selects specific functional connectivity features that are most relevant to depression classification using feature selection methods. By taking out only the most informative connectivity patterns (e.g., between prefrontal cortex and default mode network regions), the method reduces classifier complexity while preserving the essential information needed for accurate depression detection and stratification.
Data Source
AI summary
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.