Altered neural oscillations during complex sequential movements in patients with Parkinson’s disease
Bibliographic record
Abstract
The sequelae of Parkinson's disease (PD) includes both motor- and cognitive-related symptoms. Although traditionally considered a subcortical disease, there is increasing evidence that PD has a major impact on cortical function as well. Prior studies have reported alterations in cortical neural function in patients with PD during movement, but to date such studies have not examined whether the complexity of multicomponent movements modulate these alterations. In this study, 23 patients with PD (medication "off" state) and 27 matched healthy controls performed simple and complex finger tapping sequences during magnetoencephalography (MEG), and the resulting MEG data were imaged to identify the cortical oscillatory dynamics serving motor performance. The patients with PD were significantly slower than controls at executing the sequences overall, and both groups took longer to complete the complex sequences than the simple. In terms of neural differences, patients also exhibited weaker beta complexity-related effects in the right medial frontal gyrus and weaker complexity-related alpha activity in the right posterior and inferior parietal lobules, suggesting impaired motor sequence execution. Characterizing the cortical pathophysiology of PD could inform current and future therapeutic interventions that address both motor and cognitive symptoms.
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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 | 0.000 | 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) | 0.001 | 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".