Keywords: Diagnosis/Prediction, Machine Learning/Artificial Intelligence, Dynamic Functional Connectivity
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.
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