Deep neural networks constrained by neural mass models improve electrophysiological source imaging of spatiotemporal brain dynamics.

ABSTRACT: Many efforts have been made to image the spatiotemporal electrical activity of the brain with the purpose of mapping its function and dysfunction as well as aiding the management of brain disorders. Here, we propose a non-conventional deep learning-based source imaging framework (DeepSIF) that provides robust and precise spatiotemporal estimates of underlying brain dynamics from noninvasive high-density electroencephalography (EEG) recordings. DeepSIF employs synthetic training data generated by biophysical models capable of modeling mesoscale brain dynamics. The rich characteristics of underlying brain sources are embedded in the realistic training data and implicitly learned by DeepSIF networks, avoiding complications associated with explicitly formulating and tuning priors in an optimiz

SUBMITTER: Sun R 

PROVIDER: S-EPMC9351497 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature