Protecting Episodic Memory After Sleep Loss: Similar Benefits of Exercise and Naps via Distinct Neural Contributions
Notice bibliographique
Résumé
ABSTRACT Sleep benefits episodic memory, which is critical for everyday cognition, future planning and decision-making. Sleep loss, a widespread issue across all ages and a major public health concern, impairs the brain’s capacity to encode episodic memories. This, in turn, disrupts cognitive functions that rely on episodic memory, such as decision-making based on past experiences, encoding new events, or recalling critical safety protocols posing risks, particularly in safety-sensitive occupations. Cognitive impairment due to sleep loss at such workplaces raises safety concerns and causes accidents. Merely implementing sleep hygiene techniques may not be effective or practical in such settings. Finding cost-effective strategies to preserve episodic memory after sleep loss is critical. We compared the effect of exercise and naps to reduce the impact of sleep loss and investigated mechanisms underlying their potential benefits. Fifty-four healthy young (18-35 years) individuals were subjected to 30 hours of continuously monitored wakefulness after which they were randomized into a 90-minute nap (NAP) (n= 18), 20-minute exercise (EXE) (n= 18) or do nothing (Control: CON) (n= 17, 1 excluded) groups. Following this, all participants were shown images (encoding), and three days later their memory was tested in a Yes-No recognition paradigm by presenting a mix of previously shown images and new ones. Electroencephalography (n= 43) from the encoding session was analyzed for pre-stimulus sleep pressure and fatigue markers: delta/theta spectral power; and episodic memory encoding markers: event-related beta desynchronization (beta-ERD), event-related delta/theta synchronization (SW ERS) and P300 component of event-related potential. Both EXE and NAP groups had higher memory for the encoded images than the CON group (Cohen’s d 1 and 0.91, respectively; with average improvements of 22% over the CON group), and both intervention groups had similar memory scores. Contrary to the literature in normal wakefulness, beta-ERD and P300 amplitude did not differ significantly between EXE and CON groups, and only in the EXE group these two markers were associated with memory. In the CON group, in contrast, P300 was associated with fatigue. While all the groups showed delta and SW ERS, only in the NAP group were these markers associated with memory. Regression analyses revealed that the best neural predictors of memory performance in the EXE group were P300 and beta-ERD on remembered trials (Rsq. adj. 0.64). In contrast, in the NAP group, memory performance was best predicted by sleep pressure markers and SW ERS on remembered trials (Rsq. adj. 0.77). None of these predictors explained memory performance in the CON group. In summary, we demonstrate that exercise and napping benefit episodic memory performance after sleep loss with equal magnitude, but through different neural contributions within each group. Under a sleep-deprived state, exercise facilitates efficient neural processing while napping makes the brain state conducive to new learning, which contributes to memory encoding. Our mechanistic findings strengthen the principle of neural degeneracy. These results have important societal and policy implications for preserving performance under sleep-deprived conditions.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 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 | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».