Clinical symptoms and neuroanatomical substrates of daytime sleepiness in Parkinson’s disease
Bibliographic record
Abstract
Clinical and neuroanatomical correlates of daytime sleepiness in Parkinson's disease (PD) remain inconsistent in the literature. Two studies were conducted here. The first evaluated the interrelation between non-motor and motor symptoms, using a principal component analysis, associated with daytime sleepiness in PD. The second identified the neuroanatomical substrates associated with daytime sleepiness in PD using magnetic resonance imaging (MRI). In the first study, 77 participants with PD completed an extensive clinical, cognitive testing and a polysomnographic recording. In the second study, 29 PD participants also underwent MRI acquisition of T1-weighted images. Vertex-based cortical and subcortical surface analysis, deformation-based morphometry, and voxel-based morphometry were performed to assess the association between daytime sleepiness severity and structural brain changes in participants. In both studies, the severity of daytime sleepiness and the presence of excessive daytime sleepiness (EDS; total score >10) were measured using the Epworth Sleepiness Scale. We found that individuals with EDS had a higher score on a component including higher dosage of dopamine receptor agonists, motor symptoms severity, shorter sleep latency, and greater sleep efficiency. Moreover, increased daytime sleepiness severity was associated with a larger surface area in the right insula, contracted surfaces in the right putamen and right lateral amygdala, and a larger surface in the right posterior amygdala. Hence, daytime sleepiness in PD was associated with dopaminergic receptor agonists dosage, motor impairment, and objective sleep measures. Moreover, neuroanatomical changes in cortical and subcortical regions related to vigilance, motor, and emotional states were associated with more severe daytime sleepiness.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".