EEG and FMRI Coupling and Decoupling Based on Joint Independent Component Analysis (jICA)

Background: Meaningful integration of functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) requires knowing whether these measurements reflect the activity of the same neural sources, i.e., estimating the degree of coupling and decoupling between the neuroimaging modalities.

New Method: This paper proposes a method to quantify the coupling and decoupling of fMRI and EEG signals based on the mixing matrix produced by joint independent component analysis (jICA). The method is termed fMRI/EEG-jICA.

Results: fMRI and EEG acquired during a syllable detection task with variable syllable presentation rates (0.25-3 Hz) were separated with jICA into two spatiotemporally distinct components, a primary component that increased nonlinearly in amplitude with syllable presentation rate, putatively reflecting an obligatory auditory response, and a secondary component that declined nonlinearly with syllable presentation rate, putatively reflecting an auditory attention orienting response. The two EEG subcomponents were of similar amplitude, but the secondary fMRI subcomponent was ten folds smaller than the primary one.

Comparison To Existing Method: FMRI multiple regression analysis yielded a map more consistent with the primary than secondary fMRI subcomponent of jICA, as determined by a greater area under the curve (0.5 versus 0.38) in a sensitivity and specificity analysis of spatial overlap.

Conclusion: fMRI/EEG-jICA revealed spatiotemporally distinct brain networks with greater sensitivity than fMRI multiple regression analysis, demonstrating how this method can be used for leveraging EEG signals to inform the detection and functional characterization of fMRI signals. fMRI/EEG-jICA may be useful for studying neurovascular coupling at a macro-level, e.g., in neurovascular disorders.