Decoding Brain Dynamic Functional Connectivity Implicated in ADHD via Graph Neural Networks and Transformers

Motivation: Few studies have investigated the potential of using dynamic functional connectivity for Attention Deficit Hyperactivity Disorder (ADHD) diagnosis and biomarker discovery. Goal(s): The goal of this study is to effectively capture spatiotemporal dynamic features in resting state fMRI data for detection of ADHD subjects. Approach: We present a novel ensemble framework that combines the strengths of Graph Convolutional Networks (GCN), Graph Isomorphism Networks (GIN), and Transformers. Results: On ADHD-200 dataset, our framework outperforms other state-of-the-art models for ADHD detection. By using explainable AI, we generated biomarkers for ADHD which are consistent with the existing literature. Impact: Innovative integration of GCN, GIN, and Transformers in our proposed framework enables analysis of effective dynamic functional connectivity for ADHD diagnosis. The classification performance outperforms existing state-of-the-art models and generated biomarkers further affirm its usefulness of our methods.