Multivariate Classification of fMRI Time Series with Fused Window Transformers

Functional MRI (fMRI) experiments serve a key role in advancing our understanding of human brain function during normal and disease states. Analysis of high-dimensional fMRI data can significantly benefit from recent deep learning approaches, yet existing methods are insufficiently sensitive to the contextual representations in fMRI data across diverse time scales. Here, we present a novel transformer model for fMRI analysis that effectively captures local and global dependencies in fMRI data. Comprehensive demonstrations are provided that show the superior performance of BolT in gender and disease detection against state-of-the-art learning-based methods.