Role of sleep for memory consolidation and general cognition in patients with Parkinson's disease
Notice bibliographique
Résumé
Parkinson’s disease is a neurodegenerative disease that is initially diagnosed on the basis of motor symptoms such as rest tremor and bradykinesia but also involves symptoms in other domains such as cognition, sleep and autonomic function. It is the second most common neurodegenerative disease and it is estimated that over 100,000 Canadians are living with Parkinson’s disease according to the Public Health Agency of Canada (Mapping Connections, 2014). The disease process of Parkinson’s disease is characterized by early loss of dopaminergic neurons in the brainstem and this progresses over time to involve other neurotransmitter systems and other brain regions. Cognitive impairment is frequent and greatly reduces quality of life, however there are currently no known treatments to effectively target cognitive function in patients. This is partly due to a lack of understanding of the mechanisms which underlie cognitive dysfunction in patients. Sleep is significantly impaired in Parkinson’s disease and though the importance of sleep in maintaining healthy cognition is well-established, the contribution of sleep disturbances to the cognitive dysfunction of Parkinson’s disease is poorly understood. The overall goal of this thesis is to determine whether a better understanding of the period of sleep can provide a window into understanding the cognitive dysfunction of Parkinson’s disease.The first part of this thesis aims to understand how dopamine contributes to specific sleep-dependent cognitive processes. It is well-established that sleep plays a crucial role in memory consolidation – the process by which newly acquired information is integrated into long-term memory (Dudai et al., 2015). Chapter 2 examines if dopamine deficiency in Parkinson’s disease interferes with the process of overnight consolidation of motor memories. Though it has been demonstrated that motor memory consolidation is modulated by dopamine, it is unclear if this process is impaired in patients and if dopamine medication may remediate this. Chapter 3 aims to examine the relationship between sleep-dependent memory consolidation and specific features of sleep micro-architecture known to be important for consolidation and known to be altered in PD. Specifically, we were interested in sleep spindles because these are oscillations known to be crucial for the process of consolidation during sleep (Rasch & Born, 2013; Schabus et al., 2004), and because these are among the oscillations that are altered in patients (Christensen et al., 2015; Latreille et al., 2015). Chapter 4 aims to better understand the neural mechanisms underlying the association between sleep oscillations and more general cognitive performance. We were interested in how functional connectivity in different oscillations contributes to broader cognitive function in patients, as this may be the mechanism underlying the relationship between sleep oscillations and cognition. Importantly, sleep is a potentially modifiable factor and interventions, such as pharmacological therapies and non-invasive stimulation using sound, that enhance various aspects of sleep already exist. Studies have even shown that is possible to influence specific sleep oscillations, enhancing their spectral power, their density and potentially their connectivity across the scalp. Considering sleep disturbances often appear before the appearance of cognitive deficits, targeting sleep might offer a way to reduce the burden and even delay cognitive deficits in PD. This is particularly important as treatments for cognition in PD are currently lacking
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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 | 0,001 |
| 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 | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».