Our goal in this paper is to identify EEG networks that are correlated with the appetitive drive and executive function networks identified in our previous alcohol studies. We use parallel ICA (Liu et al., 2009, Meda et al., 2014, Narayanan et al., 2015) for multimodal data fusion between the rs-fMRI and resting state EEG for the 20 LTAA and 21 NSAC from our prior study (Camchong et al., 2013b) that had both usable fMRI and 64-channel EEG data. This approach is well accepted in the medical image processing community and has been used for joint analysis of fMRI, structural MRI, EEG, and genetic data. Parallel ICA allows us to consider two sets of extracted features from each subject's data (e.g., fMRI seed connectivity map and the resting state EEG coherence maps for each subject) and identify components that contribute in a similar way to each subject and are “linked.” In addition, we examined social deviance proneness, antisocial disposition, and both lifetime and current antisocial symptoms in our participants, to determine whether identified networks were accompanied by behavioral changes that implied enhanced “top-down” control.
