STARFormer: A Novel Spatio-Temporal Aggregation Reorganization Transformer of FMRI for Brain Disorder Diagnosis
Journal:
arXiv
Published Date:
Dec 31, 2024
Abstract
Many existing methods that use functional magnetic resonance imaging (fMRI)
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 eorganization
ransformer (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 the
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 codes will be available at:
https://github.com/NZWANG/STARFormer.
