Extracting electrophysiological correlates of functional magnetic resonance imaging data using the canonical polyadic decomposition.

ABSTRACT: The relation between electrophysiology and BOLD-fMRI requires further elucidation. One approach for studying this relation is to find time-frequency features from electrophysiology that explain the variance of BOLD time-series. Convolution of these features with a canonical hemodynamic response function (HRF) is often required to model neurovascular coupling mechanisms and thus account for time shifts between electrophysiological and BOLD-fMRI data. We propose a framework for extracting the spatial distribution of these time-frequency features while also estimating more flexible, region-specific HRFs. The core component of this method is the decomposition of a tensor containing impulse response functions using the Canonical Polyadic Decomposition. The outputs of this decomposition provide

SUBMITTER: Mann-Krzisnik D 

PROVIDER: S-EPMC9374895 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature