Fig. 3. Functional topography of association networks predicts individual differences in multiple cognitive domains in unseen data.

Results of ridge regression models predicting individual differences in executive function (a–d) and learning/memory (e–h). Panels a/e: Association between actual and predicted executive function (a) or learning/memory (e) using two-fold cross-validation (2F-CV) across both the discovery (black scatterplot) and replication (gray scatterplot) samples. Inset histograms represent the distributions of prediction accuracies from a permutation test. Repeated random 2F-CV (100 runs) provided evidence of stable prediction accuracy across many splits of the data for both executive function (b) and learning/memory (f), which was far better than a null distribution with permuted data (inset). The PFNs with the highest prediction accuracies for executive function (c, d) and learning/memory (g, h) were found in association cortex and were maximal in the ventral attention and fronto-parietal control networks. Prediction accuracy is shown for seventeen models trained on each PFN independently for the discovery sample (dark bars) and replication sample (light bars) in (c, g). Note that all p-values associated with prediction accuracies are significant after Bonferroni correction for multiple comparisons. (FP Fronto-Parietal, VA Ventral Attention, DA Dorsal Attention, DM Default Mode, AU Auditory, SM Somatomotor, VS Visual).