High-dimensional multivariate autoregressive model estimation of human electrophysiological data using fMRI priors.

ABSTRACT: Multivariate autoregressive (MVAR) model estimation enables assessment of causal interactions in brain networks. However, accurately estimating MVAR models for high-dimensional electrophysiological recordings is challenging due to the extensive data requirements. Hence, the applicability of MVAR models for study of brain behavior over hundreds of recording sites has been very limited. Prior work has focused on different strategies for selecting a subset of important MVAR coefficients in the model to reduce the data requirements of conventional least-squares estimation algorithms. Here we propose incorporating prior information, such as resting state functional connectivity derived from functional magnetic resonance imaging, into MVAR model estimation using a weighted group least absolute s

SUBMITTER: Nagle A 

PROVIDER: S-EPMC10528866 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature