Keywords: Diagnosis/Prediction, Multimodal, functional connectivity, structural connectivity, graph convolution network, Parkinson's disease
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.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords