SSR-GPCsT: Deep Learning Models Based on Functional Connectivity Maps in Autism Research

Autism is a neurodevelopmental disorder characterized by difficulties in social interaction, communication, and sensory sensitivity. Functional magnetic resonance imaging (fMRI) is a commonly used brain imaging technique to obtain functional connectivity information in individuals with autism. However, the heterogeneity of functional MRI data from different imaging centers poses a modeling challenge, and the lack of interpretability in the models hinders the identification of biomarkers. To address these issues, this study proposes a deep learning model based on a dynamic functional connectivity matrix that extracts shared features across multiple centers, mitigates the multicentre problem, enhances the representation of the data, and combines features from multiple pathways to capture a comprehensive view of brain connectivity patterns. In addition, two complementary approaches are designed to enhance the interpretability of the model and further explore the presence of biomarkers by analyzing changes in important features of the model. The code will be released soon.