Learning-Disability Recognition by Using Sparse Spatio-Temporal Graph Neural Networks

Yuxin Li, Xuequn Shang, Yupei Zhang

Abstract: This paper proposes a representation learning model for identifying learning disability in resting-state fMRI, with the potential to enhance our understanding of the human brain. The traditional GNN model is used to learn graph representation from fMRI but neglects to consider side information and sparsity. In this paper, we introduce Sparse Spatio-Temporal Graph Neural Networks (SSTGNN) for brain image representation. Specifically, SSTGNN consists of four parts: The ROI Feature Encoder aims to learn temporal ROI features from fMRI, then generate sparse spatial-temporal graphs based on the encoded features, employ GNN for brain image classification, and introduce side information regularization to narrow the gap between the generated graph and prior information. Our model is trained to minimize the cross-entropy (CE) loss. We conducted experiments on the publicly available Autism Brain Imaging Data Exchange dataset. The results demonstrate that the proposed SSTGNN benefits from the introduced side information regularization and sparsity, leading to improved performance in brain classification. This study not only presents an effective fMRI classification model but also has the potential to deepen our understanding of brain intelligence and assist patients with learning disabilities.