Formal Models of the Network Co-occurrence Underlying Mental Operations

Danilo Bzdok, Gaël Varoquaux, Olivier Grisel, Michael Eickenberg, Cyril Poupon, Bertrand Thirion

Abstract: 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.