Changing cognitive chimera states in human brain networks with age: Variations in cognitive integration and segregation
Notice bibliographique
Résumé
Abstract The aging process profoundly impacts the human brain, leading to notable changes in cognitive abilities. Although the brain’s structural and functional alterations with age are individually well documented, how differences in cognitive abilities emerge from variations in the underlying spatio-temporal patterns of regional brain activity is largely unknown. Patterns of increased synchronization between brain regions are taken as enhanced cognitive integration, while decreased synchronization is indicative of cognitive segregation. The ability to dynamically switch between different levels of integration and segregation across different cognitive systems is believed to be crucial for overall cognitive performance. Building on a recently proposed cognitively informed, synchronization-based framework, we study here age-related variations in dynamical flexibility between segregation and integration, as captured by changes in the variable patterns of partial synchronization or chimera states. Leveraging personalized brain network models based on large-scale, multisite datasets of cross-sectional healthy cohorts, we systematically show how regional brain stimulation produces distinct patterns of synchronization. We find that chimera states play a crucial role in regulating the balance between cognitive integration and segregation as the brain ages, providing new insights into the mechanisms underlying cognitive decline and preservation in aging. Whereas the emergent synchronization behavior of brain regions belonging to the same cognitive system often show the same aging trends, different cognitive systems can demonstrate distinct trends. This supports the idea that aging affects cognitive systems differently and that understanding this variability is essential for a more comprehensive view of neuro-cognitive aging. At the same time, dynamical flexibility increases in the oldest age groups across most cognitive systems. This may reflect compensatory mechanisms to counteract age-related cognitive declines and points towards a phenomenon of dedifferentiation. Yet, the multiplicity of behaviors highlights that whereas dedifferentiation emerges in certain cognitive systems, differentiation can also occur in others. This illustrates that these processes, though seemingly oppositional, can coexist and unfold in parallel across different cognitive systems. Author summary The healthy brain relies on a dynamic balance between two complementary processes—integration and segregation. Integration enables coordination between distributed brain regions, while segregation reflects specialization. Optimal cognitive function emerges from a highly flexible balance between these processes as implicated by greater cognitive performance. As we age, changes in cognitive abilities occur, especially at older age, while at the same time the brain reorganizes. The exact relationship between the two is largely unknown and our work aims to tackle this challenge. Using a data-informed computational model we examine age-related differences in dynamical flexibility between segregation and integration, as captured by changes in the variable patterns of partial synchronization called chimera states—patterns where some brain regions cooperate while others remain largely independent. Our research shows that chimera states are key to maintaining the brain’s balance between integration and segregation as we age. This balance shifts differently in brain systems responsible for distinct cognitive skills, which may provide new insights into the mechanisms underlying cognitive decline and preservation in aging. In particular, it supports the idea that aging affects brain systems differently and that understanding this variability is essential for a more comprehensive view of neuro-cognitive aging.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
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