Multiple Kernel Learning Model for Relating Structural and Functional Connectivity in the Brain.

ABSTRACT: A challenging problem in cognitive neuroscience is to relate the structural connectivity (SC) to the functional connectivity (FC) to better understand how large-scale network dynamics underlying human cognition emerges from the relatively fixed SC architecture. Recent modeling attempts point to the possibility of a single diffusion kernel giving a good estimate of the FC. We highlight the shortcomings of the single-diffusion-kernel model (SDK) and propose a multi-scale diffusion scheme. Our multi-scale model is formulated as a reaction-diffusion system giving rise to spatio-temporal patterns on a fixed topology. We hypothesize the presence of inter-regional co-activations (latent parameters) that combine diffusion kernels at multiple scales to characterize how FC could arise from SC. We fo

SUBMITTER: Surampudi SG 

PROVIDER: S-EPMC5818607 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature