Brain structure-function interaction network via graph convolution network for Parkinson’s disease classification

Motivation: Brain functional connectivity (FC) and structural connectivity (SC) have distinct neural mechanisms for Parkinson’s disease (PD). Furthermore, the interactions between SC and FC could reveal underlying mechanisms and enhance classification performance. Goal(s): We aim to utilize structure-function interactions for PD classification. Approach: We propose a brain structure-function interaction model via graph convolution network to incorporate both modality-specific embeddings and structure-function interactions. Results: Results on 72 PD patients and 69 normal controls demonstrate that our method outperforms other state-of-the-art methods. We identify strong structure-function couplings in the precentral gyrus, prefrontal, superior temporal, cingulate cortices, and cerebellum that are associated with PD. Impact: We proposed a novel brain structure-function interaction network based on GCN to utilize modality-specific features and interactions of SC and FC for PD classification. Our method identified the coupling strengths between SC and FC associated with PD.