carry out an eigenvalue decomposition. The two resulting (IDP and non-IDP) matrices of size 5034×200 are then fed into standard CCA (“canoncorr” in Matlab), resulting in 200 CCA modes being estimated. The CCA aims to identify symmetric linear relations between the two sets of variables. Each significant CCA mode identifies a linear combination of IDPs and a linear combination of non-IDPs, where the variation in mode strength across subjects is maximally correlated. That is, CCA finds modes that relate sets of brain measures to sets of subjects’ non-brain-imaging measures; for a graphical illustration of this approach, see Smith et al34 (Supplementary information).
