Multivariate autoregressive model estimation for high-dimensional intracranial electrophysiological data.

ABSTRACT: Fundamental to elucidating the functional organization of the brain is the assessment of causal interactions between different brain regions. Multivariate autoregressive (MVAR) modeling techniques applied to multisite electrophysiological recordings are a promising avenue for identifying such causal links. They estimate the degree to which past activity in one or more brain regions is predictive of another region's present activity, while simultaneously accounting for the mediating effects of other regions. Including as many mediating variables as possible in the model has the benefit of drastically reducing the odds of detecting spurious causal connectivity. However, effective bounds on the number of MVAR model coefficients that can be estimated reliably from limited data make exploiting

SUBMITTER: Endemann CM 

PROVIDER: S-EPMC9360562 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature