Cognition, but not affect, rests upon a segregated intrinsic network architecture

The brains intrinsic organization into resting-state networks has long been suggested to be fundamental for the offline support of mental processes. Extensive task-based evidence support the relevance of the crosstalk between network segregation, supporting systems specialization, and network integration, allowing to flexibly implement complex behavior. However, only scarce evidence focusing on few behavioral measures directly link changes in these network properties at rest with interindividual differences in behavior. In this work, we investigated whether the maintenance of behavior is associated with a segregated intrinsic resting-state networks organization. Using a comprehensive set of behavioral measures spanning cognition, emotion, and personality together with resting-state functional magnetic resonance imaging from the human connectome project, we performed functional connectivity prediction of behavior combined with model interpretability and latent connectivity-behavior factors extraction. We then assessed whether connectivity-behavior patterns were associated with changes in segregation or integration based on GINNA, a 33 resting-state-networks atlas with cognitive characterization, providing opportunities for comparison of the involved cognitive processes. We found that connectivity relevant for behavior organizes into 3 main latent dimensions, summarizing Cognition, Positive Affect and Negative Affect. Crucially, we show that only Cognition, but not Affect, was associated with global network segregation and reduced network integration, suggesting that Cognition is supported by an intrinsic segregated network architecture, necessary for modular specialization, while Affect may rely on distributed mechanisms across intrinsic brain networks. We further reveal differential resting-state-networks involvements, with Cognition associated with the segregation of higher-level resting-state-networks, and the integration of lower-level, visual networks. All in all, the present results reinforce the view that cognition rests upon a segregated intrinsic brain architecture, fostering the maintenance of specialized cognitive modules.