Diagnosis and Pathogenic Analysis of Autism Spectrum Disorder Using Fused Brain Connection Graph
This work addresses the problem of diagnosing ASD for clinicians and researchers, offering incremental improvements in accuracy and neurobiological insights.
The authors tackled the diagnosis of Autism Spectrum Disorder (ASD) by developing a model that fuses brain connectivity data from DTI and fMRI using graph neural networks, achieving improved diagnostic accuracy through a novel loss function and analyzing network centralities to identify pathological regions linked to ASD.
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.