Role of sleep for memory consolidation and general cognition in patients with Parkinson's disease
Bibliographic record
Abstract
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 sleepdependent 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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".