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

• Published in: NeuroImage (Orlando, Fla.) Vol. 277; no. C; p. 120211
• Main Authors: Nagle, Alliot, Gerrelts, Josh P., Krause, Bryan M., Boes, Aaron D., Bruss, Joel E., Nourski, Kirill V., Banks, Matthew I., Van Veen, Barry
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.08.2023; Elsevier Limited; Elsevier
• Subjects: Algorithms; Brain - physiology; Brain mapping; Brain Mapping - methods; Brain research; Cognition; Computer Simulation; EEG; Effective connectivity; Electrocorticography - methods; Electroencephalography - methods; Estimates; Functional connectivity; Functional magnetic resonance imaging; Humans; Intracranial electrophysiology; Localization; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Neural networks; Neuroimaging; Neurosciences & Neurology; Partial directed coherence; Radiology, Nuclear Medicine & Medical Imaging; Sleep; Functional connectivity; Partial directed coherence; Magnetic resonance imaging; Intracranial electrophysiology; Effective connectivity
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2023.120211

•Multielectrode iEEG recordings can elucidate brain network organization.•Effective connectivity is derived from MVAR model fits to high-dimensional iEEG data.•Very long data segments are required to estimate high-dimensional MVAR models without prior information.•Weighted group LASSO regularization using fMRI information enables reliable MVAR modeling in limited data regime.•Methods are broadly applicable to high-dimensional time series data in neuroscience. 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.