Cognitive training effects are shaped more by individual brain dynamics than age – Evidence from younger and older women
Notice bibliographique
Résumé
Abstract Given the well-established structural and functional changes in the aging brain, it is widely assumed that cognitive aging is primarily driven by robust group-level differences between young and older adults. However, our individual-level EEG functional connectivity analysis challenges this notion. We investigated the impact of cognitive training on functional brain connectivity using task-switching paradigms in 39 younger (18–25 years) and 40 older (60–75 years) women. Participants were randomly assigned to either a training group, which completed eight individualized one-hour cognitive training sessions, or a no-contact control group. EEG was recorded at both pre- and post-training sessions across three task-switching paradigms (trained and near-transfer versions). Unique functional connectivity of different sources of variation was examined by calculating how much variance was shared across stable traits (e.g., individual, age, and common factors), or dynamic states (e.g., task and training effects). Our results revealed that age accounted for only a modest proportion of variance, whereas self-similarity was a dominant factor – particularly in older adults. Similarly, group-level training effects were small but strongly modulated by individual neural profiles, suggesting person-specific trajectories. Participants recruited distinct neural networks across tasks, and even within the same task engaged unique, individual-specific network configurations, reflecting personalised brain adaptations to cognitive demands. Importantly, older adults displayed a shift from common to individual network patterns, consistent with increased neural specialization and compensatory mechanisms. These findings underscore the importance of moving beyond group-level contrasts toward models that capture the complexity of individual brain dynamics in cognitive aging and training responsiveness. Significance Statement Why do cognitive training programs work for some individuals but not for others? And is aging really the main factor driving changes in brain function? Using individual-level EEG functional connectivity analyses, we show that individual differences – not chronological age, task, or training – are the primary drivers of similarity in brain connectomes. While training effects are modest on average, their impact strongly depends on the individual’s unique neural profile. Older adults, in particular, rely more on personalized brain network configurations. These results help explain why cognitive training studies often yield inconsistent findings and point toward a more individualized approach to understanding and enhancing brain function across the lifespan.
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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 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| 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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