QEEG: The tentative biomarker for early screening of preclinical Alzheimer’s disease or progressiveness of subjective cognitive decline

Ho, SeongHee; Yang, Dong Won; Hong, Yun Jeong; Jeong, Jee Hyang; Park, Kee Hyung; Kim, SangYun; Wang, Minjung; Choi, Seong Hye; Han, SangUk; Kang, Seung Wan; Lee, Hannah; Kim, Seon Myeong

doi:10.1002/alz.042834

Cited by 2 publications

(4 citation statements)

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“…Mander et al (2015) found correlation between non-REM sleep slow wave activity and beta-amyloid burden 30 . On the other hand, Ho et al (2020) reported that SCD with Aβ + showed stronger delta and theta waves in resting-state EEG compared to normal, which has similarly been reported for amnestic MCI or AD dementia 31 . Kang et al found that AD dementia showed an increase in theta and decrease in beta at both scalp and cortical level compared to non-dementia AD subjects and discriminated them accurately with QEEG 32 .…”

Section: Introductionmentioning

confidence: 76%

“…We conducted several preliminary studies to detect the specific EEG patterns related to AD and observed the characteristics of EEG patterns as being similar to this study where relative theta increased in the frontal or temporal area and decreased in mid-beta [12 ~ 20 Hz] in both centroparietal areas. It was demonstrated that the Aβ + SCD group showed stronger theta activity in the frontotemporal area compared to the Aβ- SCD group, and this pattern was more prominent in the APOE ε4 allele (+) subgroup 31 . A similar patten was enhanced when AD dementia was compared to the non-dementia AD group 32 .…”

Section: Discussionmentioning

confidence: 99%

“…Power spectral density of the EEG rhythms was computed using FFT analysis with 0.25 Hz of frequency resolution using iSyncBrain ® . Then the signal was decomposed into the following frequency bands: delta (1-4 Hz), theta (4)(5)(6)(7)(8), alpha 1 (8-10 Hz), alpha 2 (10-12 Hz), beta 1 (12-15 Hz), beta 2 (15)(16)(17)(18)(19)(20), beta 3 (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45). For each channel and frequency band, the average power during the recoding was calculated and treated as a feature.…”

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confidence: 99%

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PET-validated EEG-machine learning algorithm predicts brain amyloid pathology in pre-dementia Alzheimer’s disease

Kim

Park

Yang

et al. 2023

Sci Rep

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Developing reliable biomarkers is important for screening Alzheimer’s disease (AD) and monitoring its progression. Although EEG is non-invasive direct measurement of brain neural activity and has potentials for various neurologic disorders, vulnerability to noise, difficulty in clinical interpretation and quantification of signal information have limited its clinical application. There have been many research about machine learning (ML) adoption with EEG, but the accuracy of detecting AD is not so high or not validated with Aβ PET scan. We developed EEG-ML algorithm to detect brain Aβ pathology among subjective cognitive decline (SCD) or mild cognitive impairment (MCI) population, and validated it with Aβ PET. 19-channel resting-state EEG and Aβ PET were collected from 311 subjects: 196 SCD(36 Aβ +, 160 Aβ −), 115 MCI(54 Aβ +, 61Aβ −). 235 EEG data were used for training ML, and 76 for validation. EEG features were standardized for age and sex. Multiple important features sets were selected by 6 statistics analysis. Then, we trained 8 multiple machine learning for each important features set. Meanwhile, we conducted paired t-test to find statistically different features between amyloid positive and negative group. The best model showed 90.9% sensitivity, 76.7% specificity and 82.9% accuracy in MCI + SCD (33 Aβ +, 43 Aβ −). Limited to SCD, 92.3% sensitivity, 75.0% specificity, 81.1% accuracy (13 Aβ +, 24 Aβ −). 90% sensitivity, 78.9% specificity and 84.6% accuracy for MCI (20 Aβ +, 19 Aβ −). Similar trends of EEG power have been observed from the group comparison between Aβ + and Aβ −, and between MCI and SCD: enhancement of frontal/ frontotemporal theta; attenuation of mid-beta in centroparietal areas. The present findings suggest that accurate classification for beta-amyloid accumulation in the brain based on QEEG alone could be possible, which implies that QEEG is a promising biomarker for beta-amyloid. Since QEEG is more accessible, cost-effective, and safer than amyloid PET, QEEG-based biomarkers may play an important role in the diagnosis and treatment of AD. We expect specific patterns in QEEG could play an important role to predict future progression of cognitive impairment in the preclinical stage of AD. Further feature engineering and validation with larger dataset is recommended.

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Section: Introductionmentioning

confidence: 76%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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PET-validated EEG-machine learning algorithm predicts brain amyloid pathology in pre-dementia Alzheimer’s disease

Kim

Park

Yang

et al. 2023

Sci Rep

Self Cite

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“…Tau aggregation, another hallmark of AD, is also modulated by the changes in the sleep/wake cycle [68,69]. Longitudinal studies have identified changes in specific EEG markers as predictors of the rate of beta-amyloid accumulation over several years [70,71].…”

Section: Alzheimer's Disease (Ad)mentioning

confidence: 99%

REM Sleep Loss-Induced Elevated Noradrenaline Plays a Significant Role in Neurodegeneration: Synthesis of Findings to Propose a Possible Mechanism of Action from Molecule to Patho-Physiological Changes

Giri,

Mehta,

Mallick

2023

Brain Sciences

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Wear and tear are natural processes for all living and non-living bodies. All living cells and organisms are metabolically active to generate energy for their routine needs, including for survival. In the process, the cells are exposed to oxidative load, metabolic waste, and bye-products. In an organ, the living non-neuronal cells divide and replenish the lost or damaged cells; however, as neuronal cells normally do not divide, they need special feature(s) for their protection, survival, and sustenance for normal functioning of the brain. The neurons grow and branch as axons and dendrites, which contribute to the formation of synapses with near and far neurons, the basic scaffold for complex brain functions. It is necessary that one or more basic and instinct physiological process(es) (functions) is likely to contribute to the protection of the neurons and maintenance of the synapses. It is known that rapid eye movement sleep (REMS), an autonomic instinct behavior, maintains brain functioning including learning and memory and its loss causes dysfunctions. In this review we correlate the role of REMS and its loss in synaptogenesis, memory consolidation, and neuronal degeneration. Further, as a mechanism of action, we will show that REMS maintains noradrenaline (NA) at a low level, which protects neurons from oxidative damage and maintains neuronal growth and synaptogenesis. However, upon REMS loss, the level of NA increases, which withdraws protection and causes apoptosis and loss of synapses and neurons. We propose that the latter possibly causes REMS loss associated neurodegenerative diseases and associated symptoms.

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QEEG: The tentative biomarker for early screening of preclinical Alzheimer’s disease or progressiveness of subjective cognitive decline

Cited by 2 publications

References 0 publications

PET-validated EEG-machine learning algorithm predicts brain amyloid pathology in pre-dementia Alzheimer’s disease

PET-validated EEG-machine learning algorithm predicts brain amyloid pathology in pre-dementia Alzheimer’s disease

REM Sleep Loss-Induced Elevated Noradrenaline Plays a Significant Role in Neurodegeneration: Synthesis of Findings to Propose a Possible Mechanism of Action from Molecule to Patho-Physiological Changes

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