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

Lu Wei, Yi Huang, Guosheng Yin, Fode Zhang, Manxue Zhang +1 more
9/22/2024
q-bio.NCcs.LGstat.AP

No official code found. Best implementation: lobsterlulu/ASD-Diagnosis-Using-Multimodal-MRI (7 stars, last commit 2025-04-10).

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.

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Code Implementations(1)

lobsterlulu/ASD-Diagnosis-Using-Multimodal-MRICommunity64%

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

70Aug 30, 20241 years ago

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Cite this paper

@article{wei2024diagnosis,
  title  = {Diagnosis and Pathogenic Analysis of Autism Spectrum Disorder Using Fused Brain Connection Graph},
  author = {Lu Wei and Yi Huang and Guosheng Yin and Fode Zhang and Manxue Zhang and Bin Liu},
  year   = {2024},
  eprint = {2410.07138},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2410.07138v1}
}

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