Deep learning models based on resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used to diagnose brain diseases, particularly autism spectrum disorder (ASD). Existing studies have leveraged the functional connectivity (FC) of rs-fMRI, achieving notable classification performance. However, they have significant limitations, including the lack of adequate information while using linear low-order FC as inputs to the model, not considering individual characteristics (i.e., different symptoms or varying stages of severity) among patients with ASD, and the non-explainability of the decision process. To cover these limitations, we propose a novel explainability-guided region of interest (ROI) selection (EAG-RS) framework that identifies non-linear high-order functional associations among brain regions by leveraging an explainable artificial intelligence technique and selects class-discriminative regions for brain disease identification. The proposed framework includes three steps: (i) inter-regional relation learning to estimate non-linear relations through random seed-based network masking, (ii) explainable connection-wise relevance score estimation to explore high-order relations between functional connections, and (iii) non-linear high-order FC-based diagnosis-informative ROI selection and classifier learning to identify ASD. We validated the effectiveness of our proposed method by conducting experiments using the Autism Brain Imaging Database Exchange (ABIDE) dataset, demonstrating that the proposed method outperforms other comparative methods in terms of various evaluation metrics. Furthermore, we qualitatively analyzed the selected ROIs and identified ASD subtypes linked to previous neuroscientific studies.

基于静息态功能磁共振成像 (rs-fMRI) 的深度学习模型被广泛应用于诊断脑疾病，尤其是自闭症谱系障碍 (ASD)。本文提出了一种新颖的可解释的感兴趣区域 (ROI) 选择框架（EAG-RS），通过利用可解释的人工智能技术识别脑区之间的非线性高阶功能关联，并选择具有类别区分能力的脑区进行脑疾病识别。通过使用自闭症脑成像数据库交换（ABIDE）数据集进行实验证实了我们提出方法的有效性，证明其在各种评估指标上优于其他比较方法。此外，我们对选定的感兴趣区域进行定性分析，并鉴别与先前神经科学研究相关的ASD亚型。

EAG-RS: 一种基于可解释性的自闭症诊断的区域选择框架，通过区域间关系学习