Formal Models of the Network Co-occurrence Underlying Mental Operations
Fig 5
Network co-occurrence modeling: Comparing whole-brain reconstruction performance to region co-occurrence models.
40 networks from ICA or sparse PCA decomposition and 40 regions from ward or k-means clustering were discovered in HCP task data (upper rows) or ARCHI task data (lower rows) and used for classifying (l1-penalized support vector machines, multi-class, one-versus-rest) 18 psychological tasks in the remaining 50% of that same task data. For three exemplary tasks from HCP and ARCHI, the mean activity pattern across all participants is depicted (leftmost column). The corresponding whole-brain task activity derived from the network decomposition models ICA and sparse PCA capture proxies of functional brain networks by emphasis on functional integration (S4–S7 Figs). In contrast, task activity derived from the region parcellation models ward clustering (all region voxels are always spatially connected) and k-means clustering (no spatial constraint) capture proxies of functional brain regions by emphasis on regional specialization (S8–S10 Figs). The correlation values r quantify the voxel-wise similarity between the reconstructed activity map and the average activity map for each task and network decomposition method. This measure of recovery performance indicates the information loss incurred when first expressing activity maps as 40 network-wise loading values or 40 region-wise activity averages and then translating these values back into whole-brain space (cf. methods section). Consequently, learning network co-occurrence models outperformed region co-occurrence models in recovering realistic task activity, given an equal number of latent network and region components.