Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023;91(4):1557-1572.
doi: 10.3233/JAD-221152.

EEG Entropy in REM Sleep as a Physiologic Biomarker in Early Clinical Stages of Alzheimer's Disease

Hamed Azami  1 , Sebastian Moguilner  1 , Hector Penagos  2 , Rani A Sarkis  3 , Steven E Arnold  1 , Stephen N Gomperts  1 , Alice D Lam  1
Affiliations

EEG Entropy in REM Sleep as a Physiologic Biomarker in Early Clinical Stages of Alzheimer's Disease

Hamed Azami et al. J Alzheimers Dis. 2023.

Abstract

Background: Alzheimer's disease (AD) is associated with EEG changes across the sleep-wake cycle. As the brain is a non-linear system, non-linear EEG features across behavioral states may provide an informative physiologic biomarker of AD. Multiscale fluctuation dispersion entropy (MFDE) provides a sensitive non-linear measure of EEG information content across a range of biologically relevant time-scales.

Objective: To evaluate MFDE in awake and sleep EEGs as a potential biomarker for AD.

Methods: We analyzed overnight scalp EEGs from 35 cognitively normal healthy controls, 23 participants with mild cognitive impairment (MCI), and 19 participants with mild dementia due to AD. We examined measures of entropy in wake and sleep states, including a slow-to-fast-activity ratio of entropy (SFAR-entropy). We compared SFAR-entropy to linear EEG measures including a slow-to-fast-activity ratio of power spectral density (SFAR-PSD) and relative alpha power, as well as to cognitive function.

Results: SFAR-entropy differentiated dementia from MCI and controls. This effect was greatest in REM sleep, a state associated with high cholinergic activity. Differentiation was evident in the whole brain EEG and was most prominent in temporal and occipital regions. Five minutes of REM sleep was sufficient to distinguish dementia from MCI and controls. Higher SFAR-entropy during REM sleep was associated with worse performance on the Montreal Cognitive Assessment. Classifiers based on REM sleep SFAR-entropy distinguished dementia from MCI and controls with high accuracy, and outperformed classifiers based on SFAR-PSD and relative alpha power.

Conclusion: SFAR-entropy measured in REM sleep robustly discriminates dementia in AD from MCI and healthy controls.

Keywords: Alzheimer’s disease; EEG; REM sleep; entropy; mild cognitive impairment; sleep.

PubMed Disclaimer

Conflict of interest statement

The authors have no relevant conflicts of interest to report. Sebastian Moguilner has received research funding from the Alzheimer’s Association. Rani Sarkis received grants from the National Institutes of Health (K23 NS119798) during the submitted work, and research support from Biogen. Stephen Gomperts has served on Advisory Boards of Jannsen, Acadia, Sanofi, and EIP, has received consulting fees from EIP Pharma, and receives funding from the NIH (R01AG077611, R01AG054551, R01AG066171, 1R56AG070827, U01NS119562, R41NS122576, P30AG062421), the DOD CDMRP, the Michael J. Fox Foundation, the FFFPRI, and the Lewy Body Dementia Association. Alice Lam has received consulting fees from Sage Therapeutics, Neurona Therapeutics, and Cognito Therapeutics, and has received research funding from the NIH (K23NS101037, R21AG064413), the Alzheimer’s Association, and Sage Therapeutics.

Figures

Figure 1.
Figure 1.. Assessment of whole-brain averaged MFDE across the sleep-wake cycle in AD.
(A) Whole brain-averaged multiscale fluctuation dispersion entropy (MFDE) measured from EEG across awake and sleep states in 35 HC (black), 23 MCI (blue), and 19 DEM (red) participants. (B) Slow-to-fast activity ratio for MFDE (SFAR-entropy) across awake and sleep states for HC, MCI, and DEM. ANOVA p-values are shown for each boxplot (Bonferroni corrected). Statistically significant post-hoc comparisons with p-values < 0.01, 0.001, and 0.0001 are shown with **, ***, and ****, respectively.
Figure 2.
Figure 2.. Evaluation of regional REM sleep-associated MFDE changes in AD.
(A) Topoplots of averaged MFDE values during REM sleep at scale 5 (top row) and scale 20 (middle row), and the ratio of MFDE at scale 20 to scale 5 (SFAR-entropy, bottom row) for HC, MCI, and DEM. (B) SFAR-entropy in the frontal (ANOVA, p = 1e-03), temporal (ANOVA, p = 2e-05), central (ANOVA, p = 2e-03) parietal (ANOVA, p = 1e-03), and occipital (ANOVA, p = 3e-05) regions for HC, MCI, and DEM. ANOVA p-values are Bonferroni corrected. Group differences with p-values < 0.05, 0.01, 0.001, 0.0001, and 0.00001 are shown with *, **, ***, ****, and *****, respectively.
Figure 3.
Figure 3.. Assessment of SFAR-entropy and SFAR-PSD measures across REM cycles in the night.
(A) The slow-to-fast activity ratio of MFDE (SFAR-entropy) for the first, middle, and last 5 minutes of REM sleep, for HC, MCI, and DEM. MFDE was measured in the temporal region. (B) SFAR-PSD for the first, middle, and last 5 minutes of REM sleep, for HC, MCI, and DEM. (C) Relative alpha power for the first, middle, and last 5 minutes of REM sleep, for HC, MCI, and DEM. The ANOVA omnibus p-value is listed in each boxplot (Bonferroni corrected). The Tukey post-hoc comparisons with p-values smaller than 0.05, 0.01, 0.001, 0.0001, and 0.00001 are shown with *, **, ***, ****, and *****, respectively.
Figure 4.
Figure 4.. Comparison of MFDE to additional entropy and spectral measures in REM sleep to discriminate Alzheimer’s dementia.
(A) Comparison of slow-to-fast activity ratio of various entropy measures, including MFDE, multiscale fuzzy entropy (MFE), and multiscale dispersion entropy (MDE), to discriminate HC, MCI, and DEM in REM sleep. (B) Comparison of spectral measures, including slow-to-fast activity ratio of the PSD (SFAR-PSD), relative delta power, and relative alpha power, to discriminate HC, MCI, and DEM in REM sleep. For (A) and (B), ANOVA p values are listed, and differences with p-values smaller than 0.05, 0.01, 0.001, 0.0001, and 0.00001 are respectively shown with *, **, ***, ****, and *****. (C) Relative power spectral density curves for HC (black), MCI (blue), and DEM (red).
Figure 5.
Figure 5.. Correlation between SFAR-entropy, SFAR-PSD, and relative alpha with MoCA scores.
SFAR-entropy (left), SFAR-PSD (middle), and relative alpha (right) were calculated from the occipital region during REM sleep. Spearman correlations are shown.
Figure 6.
Figure 6.. Classification performance of SFAR-entropy and SFAR-PSD in REM sleep in discriminating HC, MCI, and DEM.
ROC (top row) and PR (bottom row) curves for logistic regression classifiers based on SFAR-entropy (blue) and SFAR-PSD (red), for discrimination between DEM vs. HC (left), DEM vs. MCI (middle), and MCI vs. HC (right). Both SFAR-entropy and SFAR-PSD were calculated from the occipital region during REM sleep. Shaded regions represent the 95% confidence intervals for each curve based on bootstrapping. Black dashed lines represent the expected performance for a random classifier.
See this image and copyright information in PMC

References

    1. Gaugler J, James B, Johnson T, Marin A, Weuve J (2019) 2019 Alzheimer’s disease facts and figures. Alzheimer’s Dement 15, 321–387.
    1. Scheltens P, Blennow K, Breteler MMB, de Strooper B, Frisoni GB, Salloway S, Van der Flier WM (2016) Alzheimer’s disease. Lancet (London, England) 388, 505–517. - PubMed
    1. Masters CL, Bateman R, Blennow K, Rowe CC, Sperling RA, Cummings JL (2015) Alzheimer’s disease. Nat Rev Dis Prim 1, 15056. - PubMed
    1. Tan C-C, Yu J-T, Tan L (2014) Biomarkers for preclinical Alzheimer’s disease. J Alzheimer’s Dis 42, 1051–1069. - PubMed
    1. Hansson O (2021) Biomarkers for neurodegenerative diseases. Nat Med 27, 954–963. - PubMed

Publication types