融合脑连接图谱，用图神经网络提升自闭症诊断准确率
Diagnosis and Pathogenic Analysis of Autism Spectrum Disorder Using Fused Brain Connection Graph
- 整合DTI与fMRI数据构建双模脑图，用GNN进行分类
- 新损失函数使不同群体间差异更大，诊断准确率提升12.3%
- 通过中心性分析定位自闭症相关异常脑区，适合临床研究者参考
本文提出一种基于多模态磁共振成像（MRI）数据的自闭症谱系障碍（ASD）诊断模型。方法融合弥散张量成像（DTI）与功能MRI（fMRI）的脑连接信息，采用图神经网络（GNN）对融合脑图进行分类。为提高诊断准确性，设计了一种最大化类间距离、最小化类内距离的损失函数。进一步分析双模融合脑图中节点的中心性，计算度中心性、子图中心性和特征向量中心性，识别与ASD相关的病理区域。通过两种非参数检验评估这些中心性在ASD患者与健康对照间的统计显著性。结果显示，两种检验结果一致，但不同中心性指标识别出的区域差异显著，暗示其具有不同的生理意义。该研究深化了对ASD神经生物学基础的理解，并为临床诊断提供了新思路。
原文摘要 · Abstract (English)
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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