GINNA, a 33 resting-state networks atlas with meta-analytic decoding-based cognitive characterization

Abstract

Since resting-state networks were first observed using magnetic resonance imaging (MRI), their cognitive relevance has been widely suggested. These networks have often been labeled based on their visual resemblance to task activation networks, suggesting possible functional equivalence. 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 that the GINNA networks are consistently detected across subjects. We further explored the cognitive relevance of each GINNA network using meta-analytic decoding and generative null hypothesis testing, linking each network with cognitive terms derived from Neurosynth meta-analytic maps. Six independent authors then assigned one or two cognitive processes to each network based on significant terms. The GINNA atlas showcases a diverse range of topological profiles, including cortical, subcortical, and cerebellar gray matter, 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, informed by two decades of task-related functional magnetic resonance imaging, thus providing opportunities for empirical validation.