STARFormer: A novel spatio-temporal aggregation reorganization transformer of FMRI for brain disorder diagnosis.
Authors
Affiliations (7)
Affiliations (7)
- Laboratory of Digital Image and Intelligent Computation, College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, China. Electronic address: [email protected].
- Laboratory of Digital Image and Intelligent Computation, College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, China; Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong SAR, 100872, China. Electronic address: [email protected].
- Laboratory of Digital Image and Intelligent Computation, College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, China. Electronic address: [email protected].
- Laboratory of Digital Image and Intelligent Computation, College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, China. Electronic address: [email protected].
- Department of Neurology, Affiliated Lianyungang Hospital of Xuzhou Medical University, Lianyungang, 222002, China. Electronic address: [email protected].
- Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong SAR, 100872, China. Electronic address: [email protected].
- Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong SAR, 100872, China. Electronic address: [email protected].
Abstract
Many existing methods that use functional magnetic resonance imaging (fMRI) to classify brain disorders, such as autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD), often overlook the integration of spatial and temporal dependencies of the blood oxygen level-dependent (BOLD) signals, which may lead to inaccurate or imprecise classification results. To solve this problem, we propose a spatio-temporal aggregation reorganization transformer (STARFormer) that effectively captures both spatial and temporal features of BOLD signals by incorporating three key modules. The region of interest (ROI) spatial structure analysis module uses eigenvector centrality (EC) to reorganize brain regions based on effective connectivity, highlighting critical spatial relationships relevant to the brain disorder. The temporal feature reorganization module systematically segments the time series into equal-dimensional window tokens and captures multiscale features through variable window and cross-window attention. The spatio-temporal feature fusion module employs a parallel transformer architecture with dedicated temporal and spatial branches to extract integrated features. The proposed STARFormer has been rigorously evaluated on two publicly available datasets for the classification of ASD and ADHD. The experimental results confirm that STARFormer achieves state-of-the-art performance across multiple evaluation metrics, providing a more accurate and reliable tool for the diagnosis of brain disorders and biomedical research. The official implementation codes are available at: https://github.com/NZWANG/STARFormer.