EEG spectral and connectivity parameters as cognitive biomarkers in Parkinson disease
Bibliographic record
Abstract
Abstract Background Cognitive decline is a common presentation of Parkinson’s disease (PD) and a continued search exists for a reliable biomarker for early identification and management of this clinical problem. The objective of this study is to select the most useful biomarker in assessment of PD-related cognitive decline. This cross-sectional study included 47 patients with PD and 47 matched healthy controls. All participants were assessed by quantitative electroencephalography (QEEG) spectral (relative power and background peak frequency) and connectivity measures (coherence and phase lag degree), in addition to clinical evaluation using Unified Parkinson’s Disease Rating Scale (UPDRS)and Modified Hoehn and Yahr staging scale and neuropsychological assessment of the patients using Montreal Cognitive Assessment (MoCA). Results PD patients showed significantly higher relative power in all frequency bands over the right temporal region with no significant changes in peak frequency, coherence and phase lag degree compared to healthy controls. PD patients with impaired cognition (MoCA < 26) had significantly lower global relative power, more marked in alpha and beta frequency bands compared to PD patients with normal cognition. Alpha and beta relative power in frontal and temporal regions showed significant correlation with different cognitive domains of MoCA score. Conclusions QEEG measures especially spectral relative power could be used as adjunct to neuropsychological assessment in evaluation of PD-related cognitive decline.
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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 | 0.001 |
| 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".