Using generative models to make probabilistic statements about hippocampal engagement in MEG.

ABSTRACT: Magnetoencephalography (MEG) enables non-invasive real time characterization of brain activity. However, convincing demonstrations of signal contributions from deeper sources such as the hippocampus remain controversial and are made difficult by its depth, structural complexity and proximity to neocortex. Here, we demonstrate a method for quantifying hippocampal engagement probabilistically using simulated hippocampal activity and realistic anatomical and electromagnetic source modelling. We construct two generative models, one which supports neuronal current flow on the cortical surface, and one which supports neuronal current flow on both the cortical and hippocampal surface. Using Bayesian model comparison, we then infer which of the two models provides a more likely explanation of the

SUBMITTER: Meyer SS 

PROVIDER: S-EPMC5387160 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature