Formal Models of the Network Co-occurrence Underlying Mental Operations

Systems neuroscience has identified a set of canonical large-scale networks in humans. These have predominantly been characterized by resting-state analyses of the task-unconstrained, mind-wandering brain. Their explicit relationship to defined task performance is largely unknown and remains challenging. The present work contributes a multivariate statistical learning approach that can extract the major brain networks and quantify their configuration during various psychological tasks. The method is validated in two extensive datasets (n = 500 and n = 81) by model-based generation of synthetic activity maps from recombination of shared network topographies. To study a use case, we formally revisited the poorly understood difference between neural activity underlying idling versus goal-directed behavior. We demonstrate that task-specific neural activity patterns can be explained by plausible combinations of resting-state networks. The possibility of decomposing a mental task into the relative contributions of major brain networks, the "network co-occurrence architecture" of a given task, opens an alternative access to the neural substrates of human cognition.Author Summary: Assuming the central importance of canonical brain networks for realizing human cognitive processes, the present work demonstrates the quantifiability of relative neural networks involvements during psychological tasks. This is achieved by a machine-learning approach that combines exploratory network discovery and inferential task prediction. We show that activity levels of network sets can be automatically derived from task batteries of two large reference datasets. The evidence supports the often-held suspicion that task-specific neural activity might be due in large part to distinct recombinations of the same underlying brain network units. The results further discourage the frequently embraced dichotomy between exteroceptive task-associated versus interoceptive task-unspecific brain systems. Standard fMRI brain scans can thus be used to reconstruct and quantitatively compare the entire set of major network engagements to test targeted hypotheses. In the future, such network co-occurrence signatures could perhaps be useful as biomarkers in psychiatric and neurological research.