Brain Connectivity Alterations Are Associated with the Development of Dementia in Parkinson's Disease
Bibliographic record
Abstract
Dementia affects a high proportion of Parkinson's disease (PD) patients and poses a burden on caregivers and healthcare services. Electroencephalography (EEG) is a common nonevasive and nonexpensive technique that can easily be used in clinical settings to identify brain functional abnormalities. Only few studies had identified EEG abnormalities that can predict PD patients at higher risk for dementia. Brain connectivity EEG measures, such as multiscale entropy (MSE) and phase-locking value (PLV) analyses, may be more informative and sensitive to brain alterations leading to dementia than previously used methods. This study followed 62 dementia-free PD patients for a mean of 3.4 years to identify cerebral alterations that are associated with dementia. Baseline resting state EEG of patients who developed dementia (N = 18) was compared to those of patients who remained dementia-free (N = 44) and of 37 healthy subjects. MSE and PLV analyses were performed. Partial least squares statistical analysis revealed group differences associated with the development of dementia. Patients who developed dementia showed higher signal complexity and lower PLVs in low frequencies (mainly in delta frequency) than patients who remained dementia-free and controls. Conversely, both patient groups showed lower signal variability and higher PLVs in high frequencies (mainly in gamma frequency) compared to controls, with the strongest effect in patients who developed dementia. These findings suggest that specific disruptions of brain communication can be measured before PD patients develop dementia, providing a new potential marker to identify patients at highest risk of developing dementia and who are the best candidates for neuroprotective trials.
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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 | 0.000 |
| 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".