Autism spectrum disorder (ASD) has been associated with structural alterations across cortical and subcortical regions. Quantitative neuroimaging enables large-scale analysis of these neuroanatomical patterns. This project used structural MRI (T1-weighted) data from the publicly available ABIDE I dataset (n = 1,112) to classify ASD and control participants using a hybrid model. A 3D convolutional neural network (CNN) was trained to learn neuroanatomical feature representations, which were then passed to a support vector machine (SVM) for final classification. Gradient-weighted class activation mapping (Grad-CAM) was applied to the CNN to visualize the brain regions that contributed most to the model predictions. The Grad-CAM difference maps showed strongest relevance along cortical boundary regions, with additional emphasis in midline frontal-temporal-parietal areas, which is broadly consistent with prior ASD neuroimaging findings.
翻译：自闭症谱系障碍（ASD）与皮质及皮质下区域的结构性改变相关。定量神经影像学使得对这些神经解剖模式的大规模分析成为可能。本项目利用公开可用的ABIDE I数据集（n = 1,112）中的结构磁共振成像（T1加权）数据，采用混合模型对ASD参与者与对照组参与者进行分类。我们训练了一个三维卷积神经网络（CNN）以学习神经解剖特征表示，随后将这些特征输入支持向量机（SVM）进行最终分类。对CNN应用梯度加权类激活映射（Grad-CAM）以可视化对模型预测贡献最大的脑区。Grad-CAM差异图显示，最强的相关性出现在皮质边界区域，并在中线额-颞-顶叶区域有额外强调，这与先前的ASD神经影像学研究结果基本一致。
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