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Lookup NU author(s): Professor John-Paul TaylorORCiD, Dr Dag Aarsland
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2025 The Authors Background: Abnormalities in resting-state electroencephalogram (rsEEG) posterior alpha rhythm are promising biomarkers of neurodegenerative diseases (NDDs), often assessed via spectral analysis, ignoring the signal's non-rhythmic (aperiodic) component. Evidence assessing aperiodic and oscillatory rsEEG abnormalities across NDDs is scarce and often underpowered. Multicenter studies could tackle these limitations, but data pooling might introduce site-related rsEEG differences (batch effects). This study aims to characterize rsEEG oscillatory and aperiodic patterns across NDDs, minimizing potential batch effects. Methods: RsEEGs (n = 639; 11 sites) were automatically preprocessed. Signals comprised healthy controls (HC = 153), Lewy Body Dementias (LBD = 95), Parkinson's Disease (PD = 71), Alzheimer's Disease (AD = 186), Frontotemporal Dementia (FTD = 23), Mild Cognitive Impairment (MCI) in positive Lewy Bodies pathology or PD (MCI-LBD = 34), and MCI in positive AD pathology (MCI-AD = 77). Power spectrum batch effects were harmonized using reComBat (age, sex, and diagnosis-adjusted). Harmonization was evaluated with functional and mass-univariate ANOVAs. Oscillatory and aperiodic parameters were extracted from the batch-harmonized power spectrum. NDDs-related differences were estimated with functional and mass-univariate tests, bootstrapped pairwise comparisons, and logistic regressions. Results: Statistical testing showed reduced batch effects after harmonization. Significantly steeper aperiodic parameters and lower oscillatory center frequency were observed in LBD compared to other NDDs. The oscillatory extended alpha power was lower in AD comparisons (except AD vs. LBD). Conclusions: Batch effects in the rsEEG power spectrum can be mitigated with harmonization. Oscillatory alpha power reduction may better reflect AD abnormalities, whereas pronounced oscillatory frequency slowing and greater aperiodic activity characterize LBD.
Author(s): Jaramillo-Jimenez A, Mantilla-Ramos Y-J, Tovar-Rios DA, Lopera F, Aguillon D, Ochoa-Gomez JF, Paquet C, Gaubert S, Pardini M, Arnaldi D, Taylor J-P, Fladby T, Bronnick K, Aarsland D, Bonanni L
Publication type: Article
Publication status: Published
Journal: Computers in Biology and Medicine
Year: 2025
Volume: 197
Issue: Part B
Print publication date: 01/10/2025
Online publication date: 17/09/2025
Acceptance date: 09/09/2025
Date deposited: 01/10/2025
ISSN (print): 0010-4825
ISSN (electronic): 1879-0534
Publisher: Elsevier Ltd
URL: https://doi.org/10.1016/j.compbiomed.2025.111080
DOI: 10.1016/j.compbiomed.2025.111080
Data Access Statement: Publicly available repositories host the open datasets (refer to the Participants section). Access to in-house clinical rsEEGs is restricted due to ethical considerations and can only be granted upon approval of a project proposal by the E-DLB Steering Committee; for inquiries, please contact the corresponding author. All codes for data preprocessing and analysis are publicly available at https://github.com/alberto-jj/edlb_recombat_psd
PubMed id: 40967145
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