Long‐Range Temporal Correlations in Electroencephalography for Parkinson's Disease Progression
Bibliographic record
Abstract
BACKGROUND: Patients with Parkinson's disease (PD) present progressive deterioration in both motor and non-motor manifestations. However, the absence of clinical biomarkers for disease progression hinders clinicians from tailoring treatment strategies effectively. OBJECTIVES: To identify electroencephalography (EEG) biomarker that can track disease progression in PD. METHODS: A total of 116 patients with PD were initially enrolled, whereas 63 completed 2-year follow-up evaluation. Fifty-eight age- and sex-matched healthy individuals were recruited as the control group. All participants underwent EEG and clinical assessments. Long-range temporal correlations (LRTC) of EEG data were analyzed using the detrended fluctuation analysis. RESULTS: Patients with PD exhibited higher LRTC in left parietal θ oscillations (P = 0.0175) and lower LRTC in centro-parietal γ oscillations (P = 0.0258) compared to controls. LRTC in parietal γ oscillations inversely correlated with changes in Unified Parkinson's Disease Rating Scale (UPDRS) part III scores over 2 years (Spearman ρ = -0.34, P = 0.0082). Increased LRTC in left parietal θ oscillations were associated with rapid motor progression (P = 0.0107), defined as an annual increase in UPDRS part III score ≥3. In cognitive assessments, LRTC in parieto-occipital α oscillations exhibited a positive correlation with changes in Mini-Mental State Examination and Montreal Cognitive Assessment scores over 2 years (Spearman ρ = 0.27-0.38, P = 0.0037-0.0452). CONCLUSIONS: LRTC patterns in EEG potentially predict rapid progression of both motor and non-motor manifestations in PD patients, enhancing clinical assessment and understanding of the disease. © 2024 International Parkinson and Movement Disorder Society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".