The Significance of EEG Alpha Oscillation Spectral Power and Beta Oscillation Phase Synchronization for Diagnosing Probable Alzheimer Disease

Haifeng Zhang; Xinling Geng; Yuanyuan Wang; Yanjun Guo; Ya Gao; Shouzi Zhang; Wenjin Du; Lixin Liu; Mingyan Sun; Fubin Jiao; Fang Yi; Xiaoli Li; Luning Wang

doi:10.3389/fnagi.2021.631587

The Significance of EEG Alpha Oscillation Spectral Power and Beta Oscillation Phase Synchronization for Diagnosing Probable Alzheimer Disease

Front Aging Neurosci. 2021 Jun 7:13:631587. doi: 10.3389/fnagi.2021.631587. eCollection 2021.

Authors

Haifeng Zhang^{1

2

3}, Xinling Geng⁴, Yuanyuan Wang², Yanjun Guo⁵, Ya Gao⁶, Shouzi Zhang⁷, Wenjin Du⁸, Lixin Liu⁷, Mingyan Sun⁹, Fubin Jiao^{1

2

3}, Fang Yi^{2

10}, Xiaoli Li¹¹, Luning Wang^{1

2}

Affiliations

¹ Medical School of Chinese People's Liberation Army, Beijing, China.
² Department of Neurology, The 2nd Medical Center, National Clinical Research Center for Geriatric Disease, Chinese People's Liberation Army General Hospital, Beijing, China.
³ Health Service Department of the Guard Bureau of the Joint Staff Department, Joint Staff of the Central Military Commission of Chinese PLA, Beijing, China.
⁴ School of Biomedical Engineering, Capital Medical University, Beijing, China.
⁵ Department of Neurology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
⁶ Department of Geriatrics, The Second Hospital of Hebei Medical University, Shijiazhuang, China.
⁷ The Psycho Department of Beijing Geriatric Hospital, Beijing, China.
⁸ Department of Neurology, Air Force Medical Center, Chinese People's Liberation Army, Beijing, China.
⁹ Ninth Health Care Department of the Second Medical Center of PLA General Hospital, Beijing, China.
¹⁰ Department of Neurology, Lishilu Outpatient, Jingzhong Medical District, Chinese People's Liberation Army General Hospital, Beijing, China.
¹¹ State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.

Abstract

Alzheimer disease (AD) is the most common cause of dementia in geriatric population. At present, no effective treatments exist to reverse the progress of AD, however, early diagnosis and intervention might delay its progression. The search for biomarkers with good safety, repeatable detection, reliable sensitivity and community application is necessary for AD screening and early diagnosis and timely intervention. Electroencephalogram (EEG) examination is a non-invasive, quantitative, reproducible, and cost-effective technique which is suitable for screening large population for possible AD. The power spectrum, complexity and synchronization characteristics of EEG waveforms in AD patients have distinct deviation from normal elderly, indicating these EEG features can be a promising candidate biomarker of AD. However, current reported deviation results are inconsistent, possibly due to multiple factors such as diagnostic criteria, sample sizes and the use of different computational measures. In this study, we collected two neurological tests scores (MMSE and MoCA) and the resting-state EEG of 30 normal control elderly subjects (NC group) and 30 probable AD patients confirmed by Pittsburgh compound B positron emission tomography (PiB-PET) inspection (AD group). We calculated the power spectrum, spectral entropy and phase synchronization index features of these two groups' EEG at left/right frontal, temporal, central and occipital brain regions in 4 frequency bands: δ oscillation (1-4 Hz), θ oscillation (4-8 Hz), α oscillation (8-13 Hz), and β oscillation (13-30 Hz). In most brain areas, we found that the AD group had significant differences compared to NC group: (1) decreased α oscillation power and increased θ oscillation power; (2) decreased spectral entropy in α oscillation and elevated spectral entropy in β oscillation; and (3) decrease phase synchronization index in δ, θ, and β oscillation. We also found that α oscillation spectral power and β oscillation phase synchronization index correlated well with the MMSE/MoCA test scores in AD groups. Our study suggests that these two EEG features might be useful metrics for population screening of probable AD patients.

Keywords: Alzheimer disease; electroencephalogram (EEG); phase synchronization index; power spectrum; spectral entropy (SE).