Extensive spectral coherence data sets may be reduced by PCA to a much smaller number of factors accounting for a large fraction of underlying variance. Such factors, when treated as variables, significantly separate C-group from ASD-group children by DFA. Moreover, DFA-derived discriminant functions reliably classify individual control-group and ASD-group subjects prospectively as demonstrated by jackknifing and repetitive split half replication.
