Fig. 1.
Analysis schematic depicting the multimodal canonical correlation analysis (mCCA). For mCCA, each TRAC measure (x1, x2, x3) and imaging modality (y1, y2, y3) were entered into the model. For the fMRI measure of affective anticipation (y1), we entered the whole-brain beta estimates from the preprocessed unpleasant > pleasant image contrast maps. For the FDG-PET measure of verbal memory functioning, FDG metabolism-maps were entered (y2). For the sMRI measure of grey-matter volume, voxel-based morphometry (VBM) maps were used (y3). On a voxel-by-voxel basis, the mCCA analysis derives latent variables (i.e., canonical variates[CVs]) representing the maximized linear association between the variables on one side and the opposing CV. The output (Rxy) is a voxel-wise brain map of the canonical correlation coefficient between the TRAC CV and the neuroimaging CV.