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High-dimensional multivariate autoregressive model estimation of human electrophysiological data using fMRI priors
Journal article   Open access   Peer reviewed

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

Alliot Nagle, Josh P. Gerrelts, Bryan M. Krause, Aaron D. Boes, Joel E. Bruss, Kirill V. Nourski, Matthew I. Banks and Barry Van Veen
NeuroImage (Orlando, Fla.), Vol.277, 120211
08/01/2023
DOI: 10.1016/j.neuroimage.2023.120211
PMCID: PMC10528866
PMID: 37385393
url
https://doi.org/10.1016/j.neuroimage.2023.120211View
Published (Version of record) Open Access

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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 shrinkage and selection operator (LASSO) regularization strategy. The proposed approach is shown to reduce data requirements by a factor of two relative to the recently proposed group LASSO method of Endemann et al (Neuroimage 254:119057, 2022) while resulting in models that are both more parsimonious and more accurate. The effectiveness of the method is demonstrated using simulation studies of physiologically realistic MVAR models derived from intracranial electroencephalography (iEEG) data. The robustness of the approach to deviations between the conditions under which the prior information and iEEG data is obtained is illustrated using models from data collected in different sleep stages. This approach allows accurate effective connectivity analyses over short time scales, facilitating investigations of causal interactions in the brain underlying perception and cognition during rapid transitions in behavioral state.
 Magnetic Resonance Imaging Effective connectivity Functional connectivity Intracranial electrophysiology Partial directed coherence

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