Formal Models of the Network Co-occurrence Underlying Mental Operations

Abstract : 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-uncon-strained, 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.
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Danilo Bzdok, Gaël Varoquaux, Olivier Grisel, Michael Eickenberg, Cyril Poupon, et al.. Formal Models of the Network Co-occurrence Underlying Mental Operations. PLoS Computational Biology, Public Library of Science, 2016, ⟨10.1371/journal.pcbi.1004994⟩. ⟨hal-01338307⟩

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