GINNA, a 33 resting-state networks atlas with meta-analytic decoding-based cognitive characterization
Achille Gillig1, Sandrine Cremona1, Laure Zago1
1GIN, IMN-UMR5293, Université de Bordeaux, CEA, CNRS, Bordeaux, France.
Communications Biology
|February 18, 2025
Summary
This study introduces the Groupe d'Imagerie Neurofonctionnelle Network Atlas (GINNA), a brain atlas of 33 resting-state networks. The atlas links these networks to cognitive processes, enhancing our understanding of brain function.
Area of Science:
- Neuroscience
- Cognitive Neuroscience
- Neuroimaging
Background:
- Resting-state networks (RSNs) observed via magnetic resonance imaging (MRI) are theoretically linked to cognition.
- Empirical cognitive characterization of RSNs remains limited.
- A standardized atlas is needed to bridge RSNs and cognitive functions.
Purpose of the Study:
- Introduce the Groupe d'Imagerie Neurofonctionnelle Network Atlas (GINNA).
- Provide a comprehensive brain atlas of 33 RSNs.
- Characterize the cognitive relevance of each RSN.
Main Methods:
- Developed the GINNA atlas using resting-state MRI data from 1812 participants.
- Classified independent components for consistent between-subject detection.
- Utilized Neurosynth-based meta-analytic decoding and generative null hypothesis testing to determine cognitive relevance.
Main Results:
- The GINNA atlas presents 33 distinct RSNs with diverse topological profiles.
- Each network was associated with specific cognitive terms, synthesized into cognitive processes.
- Network-associated cognitive processes align with the standard Cognitive Atlas ontology.
Conclusions:
- The GINNA atlas offers a robust framework for understanding the cognitive repertoire of RSNs.
- It facilitates empirical validation of RSN-cognitive links.
- This atlas advances the cognitive characterization of brain networks.
