Fig. 3: Visualization of single-subject and group-level contrast maps, as well as similarity (or dissimilarity) among subjects.

a Unthresholded and thresholded task activations for GAMBLING REWARD contrasts are presented for sample atypical and typical subjects. In each task contrast map, the left column represents an atypical subject, while the right column represents a typical subject, defined by their similarity to the corresponding group average task activations. For each method, unthresholded task activations are displayed at the top, and thresholded activations (top 25% most activated voxels) are shown at the bottom. Dice AUC scores for unthresholded maps and Dice scores for thresholded maps are provided below the corresponding images. The circled areas show activation patterns replicated better in DeepTaskGen compared to linear regression and group-average task contrasts. Sample atypical and typical subject images for six additional task contrasts are given in Supplementary Figs. 12–18. b Group-level contrast maps for seven representative tasks were generated for predicted maps on three datasets (HCP Young Adult, HCP Development, and UK Biobank) and compared to actual task contrast maps from HCP-YA. Activations and deactivations are displayed using scaled z-scores, with colors indicating the magnitude of the effect (shown in the color gradient bar). c To visualize within- and between-task variance among subjects, we employed Uniform Manifold Approximation and Projection (UMAP), a non-linear dimensionality reduction technique. UMAP projects high-dimensional task contrast maps into two dimensions for clear visualization, allowing us to assess if the projected datasets contain individual- and task-specific information necessary for downstream analyses. Each dot represents a subject’s task contrast map for the seven tasks, colored accordingly. Similar subjects are positioned closer together, indicating similarity, while distant dots represent dissimilarity. Accordingly, wider spread within tasks and greater distance between tasks represent higher within- and between-task variability. Note that we fitted a separate UMAP model to each dataset, as indicated in each column and row.