Mega‐Analysis of Oscillatory and Aperiodic Resting‐State EEG Alterations in Neurodegenerative Diseases
Bibliographic record
Abstract
BACKGROUND: Resting-state EEG (rsEEG) alterations in the posterior alpha rhythm are promising biomarkers of neurodegenerative diseases (NDDs). However, spectral analysis often overlooks the rsEEG non-rhythmic (aperiodic) component. Studies assessing the oscillatory and aperiodic activity are scarce and frequently underpowered. While multicenter data pooling (mega-analysis) can enhance statistical power, it may introduce site-related differences (batch effects). This mega-analysis differentiates rsEEG oscillatory and aperiodic alterations across NDDs while mitigating batch effects. METHOD: RsEEGs from 1750 subjects across 12 sites were preprocessed. We pooled signals from healthy controls (HC = 583), Parkinson's Disease (PD = 131), Lewy Body Dementias (LBD = 96), Alzheimer's Disease (AD = 403), Frontotemporal Dementia (FTD = 36), Mild Cognitive Impairment (MCI) in Lewy Bodies pathology or PD (MCI-LBD = 34), MCI in AD spectrum (MCI-AD = 245), and Vascular Dementia (VD = 222); Figure 1A. Batch effects harmonization of the posterior power spectrum was performed with reComBat (age and diagnosis-adjusted). We evaluated harmonization through functional and mass-univariate permutation ANOVAs. Oscillatory and aperiodic parameters were extracted from the harmonized spectrum with specparam. Group differences across NDDs were estimated with bootstrap pairwise comparisons, mass-univariate permutation tests, and logistic regression models (age-adjusted). RESULT: Visualizations and statistical testing supported reduced batch effects after harmonization; Figure 1B and 1C. As consistent results in the unharmonized and harmonized data, steeper aperiodic parameters and lower oscillatory center frequency characterized LBD compared to all other groups. Besides, oscillatory extended alpha power was lower in AD than in HC and PD; Figure 2. Harmonized oscillatory center frequency and aperiodic parameters improved the separation of LBD compared to unharmonized parameters; Figure 3. CONCLUSION: Harmonization mitigates batch effects in the rsEEG posterior power spectrum. LBD is characterized by pronounced oscillatory frequency slowing and increased aperiodic activity, while AD displays both oscillatory and aperiodic abnormalities with smaller effect sizes.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".