Achille Gillig

The brain’s 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, using a comprehensive set of behavioral measures together with resting-state functional magnetic resonance imaging from the human connectome project, we assessed whether connectivity patterns predictive of behavior reflected segregation or integration based on Groupe d’Imagerie Fonctionnelle Network Atlas, a 33 resting-state-networks atlas with cognitive characterization. We found that connectivity relevant for behavior organizes into three main latent dimensions, summarizing Cognition, Positive Affect, and Negative Affect. Crucially, we found that connectivity predictive of Cognition, but not Affect, was associated with global network segregation and reduced network integration. We further reveal differential resting-state-network 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 suggest that cognition may rest upon a segregated, modular intrinsic brain architecture.

The brain’s 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, using a comprehensive set of behavioral measures together with resting-state functional magnetic resonance imaging from the human connectome project, we assessed whether connectivity patterns predictive of behavior reflected segregation or integration based on GINNA, a 33 resting-state-networks atlas with cognitive characterization. We found that connectivity relevant for behavior organizes into 3 main latent dimensions, summarizing Cognition , Positive Affect and Negative Affect . Crucially, we found that connectivity predictive of Cognition , but not Affect , was associated with global network segregation and reduced network integration. 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 suggest that cognition may rest upon a segregated, modular intrinsic brain architecture. Author summary: How psychological processes are maintained when the brain is not engaged in an explicit task remains unclear. Resting-state networks offer a candidate intrinsic architecture, but whether interindividual differences in diverse behavioral domains relate to this architecture in similar ways is unknown. We combined Human Connectome Project resting-state fMRI with 58 behavioral measures to identify functional connectivity patterns predictive of behavior and examine how they relate to network segregation and integration. These patterns organized into three latent dimensions summarizing Cognition, Positive Affect, and Negative Affect. Cognition-related patterns aligned with a segregated intrinsic architecture, showing stronger within-network and weaker between-network connectivity, particularly among higher-order resting-state networks, with complementary integration of visual networks. Affective patterns showed less consistent network signatures, suggesting a more distributed or context-dependent intrinsic organization.

Since resting-state networks were first observed using magnetic resonance imaging (MRI), their cognitive relevance has been widely suggested. However, to date, the empirical cognitive characterization of these networks has been limited. The present study introduces the Groupe d’Imagerie Neurofonctionnelle Network Atlas, a comprehensive brain atlas featuring 33 resting-state networks. Based on the resting-state data of 1812 participants, the atlas was developed by classifying independent components extracted individually, ensuring consistent between-subject detection. We further explored the cognitive relevance of each GINNA network using Neurosynth-based meta-analytic decoding and generative null hypothesis testing. Significant cognitive terms for each network were then synthesized into appropriate cognitive processes through the consensus of six authors. The GINNA atlas showcases a diverse range of topological profiles, reflecting a broad spectrum of the known human cognitive repertoire. The processes associated with each network are named according to the standard Cognitive Atlas ontology, thus providing opportunities for empirical validation.