title: Parallel independent component analysis using an optimized neurovascular coupling for concurrent EEG-fMRI sources.
abstract: The complexity of the human brain and the limitation of any one imaging approach motivates the need for multimodal measurements to better understand cerebral processing. A very natural goal is to integrate electrophysiological and hemodynamic activity. Among them, concurrent EEG-fMRI studies have shown great promise for understanding intrinsic brain properties yet analyzing such data presents a significant methodological challenge. Here, we propose a multivariate parallel ICA decomposition incorporating dynamic neurovascular coupling for concurrent EEG-fMRI recordings. The goal of our algorithm is to fuse multimodal EEG-fMRI information and detect/interpret the relationship between electrophysiological and hemodynamic sources via a temporal neurovascular connection enhancement. We analyze the performance of the algorithm on a valid simulation based on real EEG and fMRI components (sources) from our previous works and a neurovascular coupling built from an extended 'balloon model'. The results from our simulations yield an accurate source tracking and linkage for concurrent EEG-fMRI, and provide a novel and efficient way to combine EEG and hemodynamic responses.
authors.affiliation: Mind Research Network and Electrical and Computer Engineering Department, University of New Mexico, Albuquerque, NM 87106, USA. lwu@mrn.org
authors.forename: Lei
authors.initials: L
authors.lastname: Wu
authors.forename: Tom
authors.initials: T
authors.lastname: Eichele
authors.forename: Vince
authors.initials: V
authors.lastname: Calhoun
mesh_terms: D000465:Algorithms; D002540:Cerebral Cortex / Q000098:blood supply*; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015999:Multivariate Analysis; D025341:Principal Component Analysis