Diagnosis and Pathogenic Analysis of Autism Spectrum Disorder Using Fused Brain Connection Graph

TL;DR

This paper presents a novel multimodal MRI-based graph neural network approach for diagnosing ASD, integrating brain connectivity data and analyzing centrality measures to identify pathological regions, thereby improving diagnostic accuracy and understanding of ASD.

Contribution

It introduces a fused graph classification model using GNNs with a new loss function and centrality analysis for ASD diagnosis and neurobiological insights.

Findings

High diagnostic accuracy achieved with the proposed model.

Significant differences in centrality measures between ASD and controls.

Identified brain regions linked to ASD pathology.

Abstract

We propose a model for diagnosing Autism spectrum disorder (ASD) using multimodal magnetic resonance imaging (MRI) data. Our approach integrates brain connectivity data from diffusion tensor imaging (DTI) and functional MRI (fMRI), employing graph neural networks (GNNs) for fused graph classification. To improve diagnostic accuracy, we introduce a loss function that maximizes inter-class and minimizes intra-class margins. We also analyze network node centrality, calculating degree, subgraph, and eigenvector centralities on a bimodal fused brain graph to identify pathological regions linked to ASD. Two non-parametric tests assess the statistical significance of these centralities between ASD patients and healthy controls. Our results reveal consistency between the tests, yet the identified regions differ significantly across centralities, suggesting distinct physiological interpretations. These findings enhance our understanding of ASD's neurobiological basis and offer new directions for clinical diagnosis.