Connectome transformer with anatomically inspired attention for Parkinson's diagnosis

Abstract

Parkinson's disease (PD) is the second most prevalent neurodegenerative disease in the United States. The structural or functional connectivity between regions of interest (ROIs) in the brain and their changes captured in brain connectomes could be potential biomarkers for PD. To effectively model the complex non-linear characteristic connectomic patterns related to PD and exploit the long-range feature interactions between ROIs, we propose a connectome transformer model for PD patient classification and biomarker identification. The proposed connectome transformer learns the key connectomic patterns by leveraging the global scope of the attention mechanism guided by an additional skip-connection from the input connectome and the local level focus of the CNN techniques. Our proposed model significantly outperformed the benchmarking models in the classification task and was able to visualize key feature interactions between ROIs in the brain.