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

Gillig A(1), Cremona S(1), Zago L(1), Mellet E(1), Thiebaut de Schotten M(1),
Joliot M(2), Jobard G(1).

Author information:
(1)GIN, IMN-UMR5293, Université de Bordeaux, CEA, CNRS, Bordeaux, France.
(2)GIN, IMN-UMR5293, Université de Bordeaux, CEA, CNRS, Bordeaux, France.
.

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.

© 2025. The Author(s).

DOI: 10.1038/s42003-025-07671-2
PMCID: PMC11836461
PMID: 39966659 [Indexed for MEDLINE]

Conflict of interest statement: Competing interests: Michel Thiebaut de Schotten
is an Editorial Board Member for Communications Biology, but was not involved in
the editorial review of, nor the decision to publish this article. The remaining
authors declare no competing interests.

Auteurs Bordeaux Neurocampus