In this study, we used parallel ICA to complete a multivariate, multimodal analysis of rs-fMRI and EEG. Multimodal analysis of fMRI and EEG is an active area of research, with different approaches targeted toward the goals of each study. A recently published paper (Hacker et al., 2017) investigated frequency-specific ECoG (electrocorticography) correlates of resting state fMRI networks in epileptic patients, using a technique that mapped the fMRI signal onto the ECoG electrode locations, filtered the timeseries to compute band-limited power, computed correlations between all electrode pairs (separately for ECoG and mapped fMRI signals), then compared the resulting correlation maps between modalities. They found that theta band-limited power corresponded more strongly to fMRI in the default mode network and fronto-parietal control system, while alpha band-limited power corresponded more strongly to fMRI in the sensorimotor and dorsal attention network. Another recent paper (Case et al., 2017) examined simultaneously recorded EEG-fMRI and preprocessed EEG using either a microstate analysis or spontaneous power analysis to create normalized frequency-specific timecourses for convolution with the BOLD timeseries for use as regressors in a general linear model
