Fig 6. Task-rest correspondence: Composition of resting networks underlying psychological tasks.

As a use case for network co-occurrence modeling, an insufficiently understood question of human brain organization has been quantitatively revisited: the correspondence between neural activity during goal-directed tasks and idling mind-wandering. 40 sparse PCA networks were revealed in rest data and used for supervised classification (l1-penalized support vector machines, multi-class, one-versus-rest) of 18 psychological tasks from the ARCHI task battery. (1) Seven examples from the 40 spatiotemporal activity patterns drawn from task-unrelated resting-state fluctuations using sparse PCA decomposition. This enabled translation of whole-brain task activity maps (>60,000 voxels) from the ARCHI task data into 40 network component loadings per activity map. (2) These measures of network implication served as basis for statistical learning of a sparse classification model that disambiguates activity maps from the 18 psychological task. In the depicted matrix, each square represents an automatically determined classification model weight that corresponds to the importance of one specific large-scale network (x axis) during a given cognitive task (y axis). l1-penalization of the classification algorithm induced zero model weights (white) for automatic variable selection of the resting-state networks that are specifically associated with a given task (red or blue), in contrast to discrete selection of the k best network features (Figs 3 and 4). (3) The network weights of the fitted model is exploited for de-novo generation of realistic whole-brain activity maps for each of the 18 tasks. This is exemplified by gender judgments and trustworthiness judgments on visually presented faces: Consistent with previously published experimental fMRI studies [53], the default-mode network (black square), implicated in higher-order social processing, exhibited significant increase with trustworthiness judgments but decrease with gender judgments on faces. Both face discrimination tasks rely on the visual cortex (yellow square), the limbic system (red square), and the reward- and choice-related ventromedial prefrontal cortex (brown square). The dorsal attention/visual network (orange square) was detected as non-discriminatory for the two facial judgments tasks (i.e., weight is zero). Further, the frontoparietal network with extensive dorsolateral prefrontal cortex implication (purple square) was only associated with gender judgments, whereas the sensorimotor network (blue square) was only associated with trustworthiness judgments.