Resting‐State EEG Features of Cognitive Fluctuations in Patients with Lewy Body Dementia
Bibliographic record
Abstract
Abstract Background Lewy body dementia (LBD) is characterized by fluctuations in arousal and alertness, i.e., cognitive fluctuations (CF). Although CF significantly impacts quality of life, its neurophysiological basis remains poorly understood. This study's objective was to identify specific EEG features associated with cognitive fluctuations in patients with LBD. Method We conducted a cross‐sectional study of 35 patients prospectively enrolled through the outpatient clinics of the Department of Neurology at Virginia Commonwealth University. Based on the Clinician Assessment of Fluctuations, participants with Parkinson’s disease, Parkinson disease with dementia, and dementia with Lewy bodies were categorized as Lewy body disease without CF (LBwoCF) and Lewy body dementia with CF (LBDwCF). All patients underwent resting‐state EEG recording with eyes closed and eyes open for 3 minutes each. EEG data were preprocessed and cleaned, and the following features were extracted: dominant frequency (DF), dominant frequency variability (DFV), dominant frequency prevalence (DFP) within the alpha band (8 to 13 Hz), individual alpha peak frequency (IAF), and alpha reactivity. Kruskal‐Wallis tests and logistic regression models were used to evaluate the relationship between EEG features and group (LBDwCF vs. LBwoCF). Result We analyzed EEG features for 17 LBDwCF and 18 LBwoCF. For both EEG conditions, eyes open and eyes closed, the LBDwCF group had significantly lower DF, posterior DF, DFP (alpha), and IAF but not alpha reactivity or DFV, compared to LBwoCF (all P<0.003, Table 1). The EEG feature with the largest effect size (ε²=0.43) was DFP within the alpha band measured from the posterior electrodes in the eyes closed condition. These EEG features, except for IAF, remained significant predictors of group (LBDwCF vs. LBwoCF, all P<0.03, Table 2) in logistic regression models adjusting for age and Montreal Cognitive Assessment (MoCA) score. Conclusion Among patients with LBD and PD, resting‐state EEG features were associated with CF. These features were significant predictors of CF even after adjustment for MoCA scores and age. The development of an EEG‐based biomarker of cognitive fluctuations may improve diagnosis of this clinical feature and thus diagnosis of LBD.
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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".