# Sleep and Electroencephalography Biomarkers of Alzheimer's Disease

> **NIH NIH R01** · WASHINGTON UNIVERSITY · 2021 · $384,934

## Abstract

Project Summary
 Alzheimer's Disease (AD) is a growing epidemic, and potential treatments are unlikely to be effective
unless deployed during the earliest stages of AD, prior to cognitive symptoms. Currently there are no
inexpensive, non-invasive biomarkers for effective AD screening necessary for early recognition and treatment
on a broad scale. Sleep is abnormal in preclinical AD, even prior to cognitive symptoms, and disrupted sleep
may in turn accelerate AD pathological mechanisms. Electroencephalography (EEG) directly measures brain
function, and the stereotyped nature of sleep EEG offers a particularly rich opportunity to identify biomarkers of
brain dysfunction due to AD. The central hypothesis of the proposed study is that sleep-wake brain
mechanisms are abnormal very early in AD, and can be detected via subtle but distinct sleep and EEG
changes. The objective is to develop sleep and EEG biomarkers of AD, to enable non-invasive and
inexpensive screening on a large scale, through the following specific aims.
 Aim 1) Identify sleep-wake patterns across the 24-hour day characteristic of AD pathology.
Ambulatory sleep-EEG data will be recorded over the 24-hour period in the home setting from a large, diverse,
community-based cohort, with the hypothesis that increased sleep-wake transitions over the 24-hour day are
characteristic of preclinical-to-mild AD. Aim 2) Assess slow wave integrity measures as biomarkers of AD
pathology. EEG abnormalities of slow wave sleep are particularly associated with elevated amyloid-β levels
and plaques. Novel analytic techniques will extract bihemispheric slow wave coherence, slow wave velocity,
and slow wave intradaily ratio from EEG data collected during sleep and wake. The hypothesis is that amyloid
plaques present in early AD will reduce slow wave integrity by all three measures. Aim 3) Determine the EEG
signature of AD using machine learning. Machine learning techniques will be applied to EEG from a full
attended overnight polysomnogram, to identify a “signature” of AD pathology. The goal is to identify a
“signature” that can be detected with spatially limited EEG data that could be collected at home.
 The expected outcome of these aims is to identify sleep-EEG biomarkers of AD that can be detected
noninvasively and inexpensively at home. The impact of our work will be the ability to screen large populations
easily for AD pathology, so that affected individuals can be identified and treated. Moreover, sleep-EEG
biomarkers could be used to track disease progression and treatment response in clinical trials for AD. Lastly,
since sleep disturbance has a direct effect on AD pathology, by identifying sleep-EEG changes very early in
the pathological process, we may be able to intervene, improve sleep, and potentially change the trajectory of
AD.
 !

## Key facts

- **NIH application ID:** 10167595
- **Project number:** 5R01AG059507-04
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Yo-El S Ju
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $384,934
- **Award type:** 5
- **Project period:** 2018-09-15 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10167595

## Citation

> US National Institutes of Health, RePORTER application 10167595, Sleep and Electroencephalography Biomarkers of Alzheimer's Disease (5R01AG059507-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10167595. Licensed CC0.

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