Resting-state EEG measures—lower dominant frequency, reduced alpha prevalence (DFP-alpha), and lower individual alpha peak frequency—distinguish Lewy body disease patients with cognitive fluctuations from those without, with anterior DFP-alpha yielding an AUC ≈0.85.

This identifies a noninvasive, readily obtainable biomarker for detecting and stratifying cognitive fluctuations in Lewy body disease, aiding diagnosis and clinical trial enrichment, though it offers limited direct mechanistic or therapeutic targets.

BACKGROUND: Cognitive fluctuations (CF) are a core feature of Lewy body dementia (LBD) that negatively impact quality of life. Despite their prevalence and negative impact, CF are challenging to identify clinically. The goal of this study was to identify easily obtainable EEG features associated with CF across the spectrum of Lewy body disease (LB). METHODS: We conducted a cross-sectional study of 53 patients prospectively enrolled through the outpatient clinics of the Department of Neurology at Virginia Commonwealth University. Based on the Clinician Assessment of Fluctuations, participants were categorized as LB disease without CF (LB-CF) or LB disease with CF (LB + CF). All participants underwent a resting-state EEG recording with eyes closed for at least 3 minutes. EEG data were preprocessed and the following features were extracted: dominant frequency (DF), dominant frequency variability (DFV), dominant frequency prevalence (DFP), and individual alpha peak frequency (IAF). We applied Kruskal-Wallis tests and logistic regression models to evaluate differences in EEG features between study groups. RESULTS: We analyzed EEG features for 29 LB + CF and 24 LB-CF participants. Compared to the LB-CF group, the LB + CF group had significantly lower DF, DFP-alpha, and IAF in all derivations (all p < 0.05). In logistic regression analysis adjusting for age, sex, and MoCA score, these EEG features remained significant predictors of group (LB + CF vs. LB-CF, all q < 0.05). The elastic-net logistic regression model identified five predictors (anterior DFP-alpha, anterior DFP-theta, anterior DF, temporal DF, temporal IAF, and posterior DFP-pre-alpha) achieving an AUC of 0.86, whereas anterior DFP-alpha alone achieved an AUC of 0.85. CONCLUSION: Among patients with LB disease, resting-state EEG features were associated with CF and were significant predictors of CF even after adjustment for age, sex, and MoCA. The development of an EEGbased biomarker of CF using this set of features could improve diagnosis of Lewy body dementia.