Spatio-Temporal Hybrid Attentive Graph Network for Diagnosis of Mental Disorders on fMRI Time-Series Data

• Published in: IEEE transactions on emerging topics in computational intelligence 2024-12, Vol.8 (6), p.4046-4058
• Main Authors: Liu, Rui, Huang, Zhi-An, Hu, Yao, Huang, Lei, Wong, Ka-Chun, Tan, Kay Chen
• Format: Article
• Language: English
• Subjects: Attention deficit hyperactivity disorder; Autism; autism spectrum disorder; Brain; brain graph construction; Brain modeling; CAD; Computer aided design; Computer aided diagnosis; Correlation; Diagnosis; Functional magnetic resonance imaging; graph learning; Graph representations; Graph theory; Graphical models; Graphical representations; Magnetic resonance imaging; Medical imaging; Mental disorders; Neuroimaging; Spatiotemporal data; Spatiotemporal phenomena; Transformers; Urban areas
• ISSN: 2471-285X; 2471-285X
• DOI: 10.1109/TETCI.2024.3386612

Facing the prevalence of mental disorders around the world, the burden of healthcare services becomes increasingly imminent. To lessen patients' suffering, the timely diagnosis and therapy of mental disorders are particularly essential. Functional magnetic resonance imaging (fMRI), as the de facto non-invasive neuroimaging technique, can effectively examine the spatial and temporal patterns of brain activity. Recently, computer-aided diagnosis (CAD) approaches have emerged to assist doctors in interpreting fMRI images. However, existing CAD methods cannot fully exploit the spatio-temporal dependence in fMRI signals, possibly leading to inaccurate diagnosis. In this study, we propose a spatio-temporal hybrid attentive graph network (ST-HAG) for diagnosing autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) from fMRI data. Specifically, a hybrid graph convolution network is developed to effectively capture complex spatio-temporal dynamics. Meanwhile, a Transformer-based self-attention module helps ST-HAG to extract the full-scale temporal correlation. Finally, we use a gated fusion unit to learn discriminative spatio-temporal graph representations for classification. Cross-validation experiments demonstrate that the proposed ST-HAG achieves state-of-the-art performance with a mean accuracy of 71.9% and 74.8% for ASD and ADHD on ABIDE (1035 subjects) and ADHD-200 (939 subjects) datasets, respectively. Moreover, thanks to the adopted dynamic graph attentive representation, the potent interpretability enables ST-HAG to detect the remarkable temporal association patterns among different brain regions based on dynamic functional connectivity networks.