Attention-based fusion of multiple graphheat networks for structural to functional brain mapping.

ABSTRACT: Over the last decade, there has been growing interest in learning the mapping from structural connectivity (SC) to functional connectivity (FC) of the brain. The spontaneous fluctuations of the brain activity during the resting-state as captured by functional MRI (rsfMRI) contain rich non-stationary dynamics over a relatively fixed structural connectome. Among the modeling approaches, graph diffusion-based methods with single and multiple diffusion kernels approximating static or dynamic functional connectivity have shown promise in predicting the FC given the SC. However, these methods are computationally expensive, not scalable, and fail to capture the complex dynamics underlying the whole process. Recently, deep learning methods such as GraphHeat networks and graph diffusion have been s

SUBMITTER: Oota SR 

PROVIDER: S-EPMC10786906 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature