  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.
