In the parallel ICA model, we specified two groups (LTAA and NSAC) and two modalities (rs-fMRI NAcc seed correlation maps and EEG-coherence vectors). We chose to use the default mask for the imaging data (only voxels with non-zero values for all subjects were included in the analysis) and the number of estimated components was restricted to 8 for each modality. Since data were from different modalities and the units of measurement were different, data were scaled to unit standard deviation, yielding z-scores. We optimized the parallel ICA using the correlation between mixing coefficients of the two modalities.
