A statistical model for brain networks inferred from large-scale electrophysiological signals.

ABSTRACT: Network science has been extensively developed to characterize the structural properties of complex systems, including brain networks inferred from neuroimaging data. As a result of the inference process, networks estimated from experimentally obtained biological data represent one instance of a larger number of realizations with similar intrinsic topology. A modelling approach is therefore needed to support statistical inference on the bottom-up local connectivity mechanisms influencing the formation of the estimated brain networks. Here, we adopted a statistical model based on exponential random graph models (ERGMs) to reproduce brain networks, or connectomes, estimated by spectral coherence between high-density electroencephalographic (EEG) signals. ERGMs are made up by different local

SUBMITTER: Obando C 

PROVIDER: S-EPMC5378129 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature