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Modeling BOLD-fMRI Hemodynamics via Multidimensional Decomposition of Electrophysiology Data: A Simulation Study

We propose a framework for studying the electrophysiological correlates of BOLD-fMRI. This framework relies on structured coupled matrix-tensor factorization (sCMTF), a joint multidimensional decomposition which reveals dynamical interactions between LFP/EEG oscillatory features and BOLD-fMRI data. We test whether LFP/EEG-BOLD co-fluctuations and regional hemodynamic response functions can be estimated by sCMTF using whole-brain modelling of restingstate activity. We produce permuted datasets to show that our framework extracts EEG/LFP temporal patterns that correlate significantly with BOLD signal fluctuations. Our framework is also capable of estimating HRFs that accurately embodies simulated hemodynamics, with a word of caution regarding initialization of the sCMTF algorithm.