Permutation testing is then applied to estimate (family-wise-error, multiple-comparison-corrected) p-values for the CCA modes estimated. Nine modes are found to be significant (Pcorrected<0.002, with all later modes having Pcorrected>0.05). Because CCA can in general only unambiguously estimate distinct modes up to an orthogonal rotation amongst them (by direct analogy to PCA), we identify a non-ambiguous unmixing of the modes using ICA to optimize the final set of modes reported. Because we expect meaningful population modes to be much more structured (for example, sparser) in the cross-variable dimension than in the cross-subject dimension, we calculate ICA components that are statistically independent from each other in the cross-variable dimension. In order to take full advantage of the numbers of variables originally prepared, we first multiply the nine CCA subject-weight vectors into the original IDP and non-IDP data matrices (after concatenating these across variables), resulting in nine CCA variable-weight vectors of length 2501+1100=3601. These nine vectors are then fed into FastICA33 in order to estimate nine population data sources having maximal statistical independence. This general approach (CCA, followed by concatenation of CCA weight
