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Cross multivariate correlation coefficients as screening tool for analysis of concurrent EEG‐fMRI recordings
Author(s) -
Ji Hong,
Petro Nathan M.,
Chen Badong,
Yuan Zejian,
Wang Jianji,
Zheng Nanning,
Keil Andreas
Publication year - 2018
Publication title -
journal of neuroscience research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.72
H-Index - 160
eISSN - 1097-4547
pISSN - 0360-4012
DOI - 10.1002/jnr.24217
Subject(s) - electroencephalography , functional magnetic resonance imaging , pattern recognition (psychology) , multivariate statistics , correlation , computer science , artificial intelligence , eeg fmri , canonical correlation , neurophysiology , psychology , neuroscience , machine learning , mathematics , geometry
Abstract Over the past decade, the simultaneous recording of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) data has garnered growing interest because it may provide an avenue towards combining the strengths of both imaging modalities. Given their pronounced differences in temporal and spatial statistics, the combination of EEG and fMRI data is however methodologically challenging. Here, we propose a novel screening approach that relies on a Cross Multivariate Correlation Coefficient (xMCC) framework. This approach accomplishes three tasks: (1) It provides a measure for testing multivariate correlation and multivariate uncorrelation of the two modalities; (2) it provides criterion for the selection of EEG features; (3) it performs a screening of relevant EEG information by grouping the EEG channels into clusters to improve efficiency and to reduce computational load when searching for the best predictors of the BOLD signal. The present report applies this approach to a data set with concurrent recordings of steady‐state‐visual evoked potentials (ssVEPs) and fMRI, recorded while observers viewed phase‐reversing Gabor patches. We test the hypothesis that fluctuations in visuo‐cortical mass potentials systematically covary with BOLD fluctuations not only in visual cortical, but also in anterior temporal and prefrontal areas. Results supported the hypothesis and showed that the xMCC‐based analysis provides straightforward identification of neurophysiological plausible brain regions with EEG‐fMRI covariance. Furthermore xMCC converged with other extant methods for EEG‐fMRI analysis.