We examined patterns of differences in FC and GMV between healthy controls and patients with mTLE, using partial least squares analyses. Detailed descriptions of PLS analysis can be found elsewhere (Krishnan et al., 2011; McIntosh et al., 1996). In brief, PLS uses singular value decomposition (SVD) to extract ranked latent variables (LVs) from the covariance matrix of brain data and experimental groups. For the current study, the brain data matrices for the FC-PLS contained 190 correlation coefficients (every possible connection between the 20 DMN ROIs) per subject and were grouped into healthy controls and patients with left and right mTLE. For the GMV-PLS, brain data matrices contained 20 GMV values from the 20 DMN ROIs per subject and were grouped again into healthy controls and patients with left and right mTLE. The resulting LVs express patterns of brain data (e.g., strength of FC or amount of relative GMV) associated with each group. Statistical significance of the LVs was assessed using permutation testing. In this procedure, each subject's data was randomly reassigned (without replacement) to different experimental groups, and a null
