Fig 7. Task-rest correspondence: Reconstructing two similar tasks from two different datasets based on the same resting networks.

40 sparse PCA networks were discovered from the same rest data and used for feature engineering as a basis for classification (l1-penalized support vector machines, multi-class, one-versus-rest) of 18 psychological tasks from HCP (left) and from ARCHI (right). Middle column: Examples of resting-state networks derived from decomposing rest data using sparse PCA. Networks B and C might be related to semantics processing in the anterior temporal lobe [54], network D covers extended parts of the parietal cortex, while networks E and F appear to be variants of the so-called “salience” network [10]. Left/Right column: Examples of task-specific neural activity generated from network co-occurrence models of the HCP/ARCHI task batteries. Arrows: A diagnostic subanalysis indicated what rest networks were automatically ranked top-five in distinguishing a given task from the respective 17 other tasks (i.e., k = 5 analogous to analyses in Figs 3 and 4). Although the experimental tasks in the HCP and ARCHI repositories, “story versus math” and “sentences versus computation” were the most similar cognitive contrasts in both datasets. For these four experimental conditions the model-derived task maps are highly similar. Consequently, two independent classification problems in two independent datasets with a six-fold difference in sample size resulted in two independent explicit models that, nevertheless, generated comparable task-specific maps. This indicated that network co-occurrence modeling indeed captures genuine aspects of neurobiology rather than arbitrary discriminatory aspects of the data.