Ozbek, YagmurFide, EzgiYener, Görsev2023-06-162023-06-1620211388-24571872-8952https://doi.org/10.1016/j.clinph.2021.05.012https://hdl.handle.net/20.500.14365/1129Objectives: The present study aims to compare early-onset Alzheimer's disease (EOAD) patients with healthy controls (HC), and late-onset Alzheimer's disease (LOAD) patients using resting-state delta, theta, alpha, and beta oscillations and provide a cut-off score of alpha/theta ratio to discriminate individuals with EOAD and young HC. Methods: Forty-seven individuals with EOAD, 51 individuals with LOAD, and demographically-matched 49 young and 51 older controls were included in the study. Spectral-power analysis using Fast-Fourier Transformation (FFT) is performed on resting-state electroencephalography (EEG) data. Delta, theta, alpha, and beta oscillations compared between groups and Receiver Operating Characteristic (ROC) curve analysis was conducted. Results: Compared to healthy controls individuals with EOAD showed an increase in slow frequency bands and a decrease in fast frequency bands. Frontal alpha/theta power ratio is the best discriminating value between EOAD and young HC with the sensitivity and specificity greater than 80% with area under the curve (AUC) 0.881. Conclusions: EOAD display more widespread and severe electrophysiological abnormalities than LOAD and HC which may reflect more pronounced pathological burden and cholinergic deficits in EOAD. Additionally, the alpha/theta ratio can discriminate EOAD and young HC successfully. Significance: This study is the first to report that resting-state EEG power can be a promising marker for diagnostic accuracy between EOAD and healthy controls. (C) 2021 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.eninfo:eu-repo/semantics/closedAccessResting-state EEGEarly-onset ADLate-onset ADFFTAlpha/theta ratioPowerMild Cognitive ImpairmentCerebrospinal-Fluid BiomarkersSubjective Memory ComplaintsFunctional ConnectivityQuantitative EegHippocampal AtrophyAlpha-RhythmsGray-MatterMetabolic FunctionBand OscillationsArticle10.1016/j.clinph.2021.05.012