Mapping sleep's oscillatory events as a biomarker of Alzheimer's disease

Abstract

INTRODUCTIONMemory‐associated neural circuits produce oscillatory events including theta bursts (TBs), sleep spindles (SPs), and slow waves (SWs) in sleep electroencephalography (EEG). Changes in the “coupling” of these events may indicate early Alzheimer's disease (AD) pathogenesis.METHODSWe analyzed 205 aging adults using single‐channel sleep EEG, cerebrospinal fluid (CSF) AD biomarkers, and Clinical Dementia Rating® (CDR®) scale. We mapped SW‐TB and SW‐SP neural circuit coupling precision to amyloid positivity, cognitive impairment, and CSF AD biomarkers.RESULTSCognitive impairment correlated with lower TB spectral power in SW‐TB coupling. Cognitively unimpaired, amyloid positive individuals demonstrated lower precision in SW‐TB and SW‐SP coupling compared to amyloid negative individuals. Significant biomarker correlations were found in oscillatory event coupling with CSF Aβ42/Aβ40, phosphorylated‐ tau181, and total‐tau.DISCUSSIONSleep‐dependent memory processing integrity in neural circuits can be measured for both SW‐TB and SW‐SP coupling. This breakdown associates with amyloid positivity, increased AD pathology, and cognitive impairment.Highlights At‐home sleep EEG is a potential biomarker of neural circuits linked to memory. Circuit precision is associated with amyloid positivity in asymptomatic aging adults. Levels of CSF amyloid and tau also correlate with circuit precision in sleep EEG. Theta burst EEG power is decreased in very early mild cognitive impairment. This technique may enable inexpensive wearable EEGs for monitoring brain health.

Remote Wearable Neuroimaging Devices for Health Monitoring and Neurophenotyping: A Scoping Review

“…In [49], participants' EEG data were collected at home while they slept using a singlechannel EEG (employing AF7, AF8, and Fpz electrodes) for 3-6 nights. The research focused on detecting memory-relevant events from sleep EEGs, particularly the coupling of slow waves with theta bursts and sleep spindles.…”

On Monitoring Brain Health from the Depths of Sleep: Feature Engineering and Machine Learning Insights for Digital Biomarker Development

“…All signal processing steps were performed within MATLAB R2021b (MathWorks, Inc., Natick, MA). SWs were identified via automated zero-crossing detection as previously described 32,42,50 . Briefly, SW detection was performed from C3-A2 electrode pair in sleep stages N2 and N3.…”

“…Time-frequency (TF) wavelet spectrograms of SW-associated time windows were created via established methods 32,50 . Briefly, the troughs of each SW were centered in 5-s windows of EEG data and matched baseline intervals.…”

“…We processed individual spectrograms through a series of image processing steps in the MATLAB image processing toolbox to obtain image-based feature metrics as previously described 32 . Briefly, each spectrogram, resized to a 100x100 pixel image, was converted into a binary image.…”

“…Several types of these oscillatory events have been described, including slow waves, spindles, theta bursts, and ripples 6,[23][24][25][26] . The morphology of oscillatory event waveforms, and the temporal coupling of these events to one-another, demonstrate distinct patterns with aging and neurodegenerative disease [27][28][29][30][31][32] . There are also well-characterized oscillatory event properties that occur in association with biological processes and are highly related to SWS physiology, including memory performance 33 , sex differences [34][35][36][37][38] , and insulin sensitivity 11,39,40 .…”