Novel EEG tools to predict early cognitive impairments in Parkinson’s disease
Bibliographic record
Abstract
Abstract Background Parkinson’s disease (PD) is a neurodegenerative disorder mainly characterised by motor symptoms but often also associated with dementia, increasing in likelihood with duration of disease. Electroencephalography (EEG) can aid in the early diagnosis of those at risk of developing dementia. Therefore, we analysed EEG recordings across conscious states from PD patients with and without cognitive symptoms (Latreille et al in 2016, https://doi.org/10.1093/brain/aww018). Method Baseline data (REM sleep, NREM sleep, WAKE) from 38 PD patients (15 with and 23 without mild cognitive impairment) and 27 healthy subjects provided power spectra (qEEG), relative band power, as well as dominant frequency and its standard deviation to investigate markers for cognitive impairment. At follow‐up (an average 4.5 years after baseline), 11 patients developed dementia (PDD). Connectivity measures for left and right occipital and central electrodes were obtained as described in Crouch et al, 2018, https://doi.org/10.1038/s41598‐018‐19707‐1. Result qEGG analyses confirmed previous reports of enhanced theta power during REM, NREM and WAKE for patients who developed PDD. A reduction in alpha power during WAKE was observed for MCI and PDD groups when compared to controls. Alpha rhythms generally decrease with age, alongside enhanced theta power, and this outcome may contribute to the global cognitive status of patients. A reduced and less variable dominant frequency was evident for MCI and PDD patients during WAKE, indicative of reduced flexibility of the network. Finally, rPDC detected disease‐specific changes, such as enhanced cross‐hemispheric connectivity in PDD in the alpha band during NREM sleep that indicate a lack of dynamism in the network. Also noted was the diminished interhemispheric communication in delta power between both central and occipital channels in PDD during WAKE. Conclusion Together, we here confirm that changes of theta and alpha power are optimal discriminatory biomarkers for PD with cognitive decline (with MCI and at risk of dementia). Also, spectral and rPDC analyses indicate that dementia in PD is preceded by a loss of dynamism in EEG activity during MCI stages, both at the electrode and network level.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".