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 vectors, followed by ICA) is similar to that proposed by Sui84, except that we return to the full feature space (as described above) for the ICA stage, rather than staying in the PCA-reduced space. The ICA result is extremely robust, with split-half (cross-subjects) reproducibility across the 9 ICA components of r>0.89. Interestingly, 5 of these ICA modes (including modes 7, 8 and 9, shown in Fig. 7) are virtually unchanged if the de-confounding step was omitted (correlation of variable-weights vectors: r>0.8).
