A Brain-to-Population Graph Learning Framework for Diagnosing Brain Disorders

TL;DR

This paper introduces a novel two-stage graph learning framework that leverages brain atlas knowledge and phenotypic data to improve the accuracy and interpretability of brain disorder diagnosis from functional connectivity data.

Contribution

The proposed B2P-GL framework integrates semantic brain region information and population graph modeling, addressing atlas reliance and variability issues in brain disorder diagnosis.

Findings

Outperforms state-of-the-art methods in prediction accuracy.

Enhances interpretability of brain disorder diagnosis.

Effective across multiple datasets (ABIDE I, ADHD-200, Rest-meta-MDD).

Abstract

Recent developed graph-based methods for diagnosing brain disorders using functional connectivity highly rely on predefined brain atlases, but overlook the rich information embedded within atlases and the confounding effects of site and phenotype variability. To address these challenges, we propose a two-stage Brain-to-Population Graph Learning (B2P-GL) framework that integrates the semantic similarity of brain regions and condition-based population graph modeling. In the first stage, termed brain representation learning, we leverage brain atlas knowledge from GPT-4 to enrich the graph representation and refine the brain graph through an adaptive node reassignment graph attention network. In the second stage, termed population disorder diagnosis, phenotypic data is incorporated into population graph construction and feature fusion to mitigate confounding effects and enhance diagnosis performance. Experiments on the ABIDE I, ADHD-200, and Rest-meta-MDD datasets show that B2P-GL outperforms state-of-the-art methods in prediction accuracy while enhancing interpretability. Overall, our proposed framework offers a reliable and personalized approach to brain disorder diagnosis, advancing clinical applicability.