Fig. 19.
Posterior classification rates for a multi-class SVM trained to distinguish between the different active-state conditions. The results on the left are when the off-diagonal elements of the network matrices are fed in, and the results on the right are when the amplitudes are used as features. Posterior densities are based on the number of correct and incorrect classifications out of the full set of 70 tests (14 subjects; 5 conditions), combined with Haldane’s uninformative beta prior (Haldane, 1932). The modes of the distributions are shown by the black bars, and the chance level is shown by the dashed blue line. The two p-values are calculated via McNemar’s test (mid-p variant) and Bonferroni corrected (Fagerland et al., 2013). For the PFM netmats, the variants are: PFMs: network matrices inferred as part of the PFM model, α(sr). PFM (BT): network matrices estimated as the partial correlations between the PFM BOLD time courses B(sr). PFM (CT): network matrices estimated as the partial correlations between the combined time courses .