Biology subjects

Prerau, M. J.

Publications and source records attributed to Prerau, M. J..

3 recordsLinked to original sources

bioRxiv2026

The DYNAM-O Toolbox: Characterizing Individualized Neural Signatures in Sleep EEG

Here, we introduce the Dynamic Oscillation (DYNAM-O) Toolbox, an open-source, cross-platform (MATLAB, Python, and Rust) software package for data-driven characterization of individualized neural dynamics in sleep EEG. Conventional sleep electroencephalography (EEG) measures often rely on predefined bands, thresholds, and averages that incompletely capture transient oscillatory dynamics across the night. For example, sleep spindles, 12-16 Hz bursts of EEG activity related to memory consolidation and altered in disease states, are traditionally detected using fixed frequency and amplitude criteria. Recent studies, however, have shown that canonical spindles represent only a small subset of the tens of thousands of transient "spindle-like" oscillations occurring each night, comprising multiple event classes across a broad frequency range. Together, these classes of transient oscillations form highly individualized signatures of brain state with demonstrated utility as EEG biomarkers of disease. DYNAM-O substantially extends our transient oscillation framework and provides the first end-to-end pipeline for this analysis, from event detection to group-level statistics. It identifies transient oscillations as time-frequency peaks (TF-peaks) using a novel multi-resolution procedure that combines time- and frequency-optimized multitaper spectrograms to separate closely spaced events and refines each event's peak frequency to resolve oscillations separated by less than the spectrogram resolution. It then computes intrinsic and sleep-state-dependent extrinsic features for each event and represents the overnight distributions of TF-peaks as feature histograms spanning oscillation frequency, slow oscillation power, and slow oscillation phase, preserving continuous brain-state variation obscured by stage-based averaging. New parametric and spline-based dimensionality reduction methods distill these histograms into a small number of interpretable modes, and integrated whole-histogram statistical tests support exploratory and hypothesis-driven group analyses. As a demonstration, we analyzed sex differences in polysomnography recordings from 112 adults (C3 electrode; 67 females, 45 males; ages 20-35 years) from the Cleveland Family Study. In addition to replicating the established higher fast-spindle frequency in females, we identified greater low-alpha transient oscillatory activity in females, a novel sex difference in events ignored in traditional analyses. DYNAM-O provides an accessible and interpretable framework for studying individualized sleep physiology, with applications to longitudinal studies, group comparisons, and feature extraction for deep learning models.

bioinformatics↗

bioRxiv2024

Individualized temporal patterns dominate cortical upstate and sleep depth in driving human sleep spindle timing

Sleep spindles are critical for memory consolidation and strongly linked to neurological disease and aging. Despite their significance, the relative influences of factors like sleep depth, cortical up/down states, and spindle temporal patterns on individual spindle production remain poorly understood. Moreover, spindle temporal patterns are typically ignored in favor of an average spindle rate. Here, we analyze spindle dynamics in 1008 participants from the Multi-Ethnic Study of Atherosclerosis using a point process framework. Results reveal fingerprint-like temporal patterns, characterized by a refractory period followed by a period of increased spindle activity, which are highly individualized yet consistent night-to-night. We observe increased timing variability with age and distinct gender/age differences. Strikingly, and in contrast to the prevailing notion, individualized spindle patterns are the dominant determinant of spindle timing, accounting for over 70% of the statistical deviance explained by all of the factors we assessed, surpassing the contribution of slow oscillation (SO) phase ([~]14%) and sleep depth ([~]16%). Furthermore, we show spindle/SO coupling dynamics with sleep depth are preserved across age, with a global negative shift towards the SO rising slope. These findings offer novel mechanistic insights into spindle dynamics with direct experimental implications and applications to individualized electroencephalography biomarker identification.

neuroscience↗

bioRxiv2021

Sources of variation in the spectral slope of the sleep EEG

Building on previous work linking changes in the electroencephalogram (EEG) spectral slope to arousal level, Lendner et al. (2021) reported that wake, non rapid eye movement (NREM) sleep and rapid eye movement (REM) sleep exhibit progressively steeper 30-45 Hz slopes, interpreted in terms of increasing cortical inhibition. Here we sought to replicate Lendner et al.s scalp EEG findings (based on 20 individuals) in a larger sample of 11,630 individuals from multiple cohorts in the National Sleep Research Resource (NSRR). In a final analytic sample of N = 10,255 distinct recordings, there was unambiguous statistical support for the hypothesis that, within individuals, the mean spectral slope grows steeper going from wake to NREM to REM sleep. We found that the choice of mastoid referencing scheme modulated the extent to which electromyogenic or electrocardiographic artifacts were likely to bias 30-45 Hz slope estimates, as well as other sources of technical, device-specific bias. Nonetheless, within individuals, slope estimates were relatively stable over time. Both cross-sectionally and longitudinal, slopes tended to become shallower with increasing age, particularly for REM sleep; males tended to show flatter slopes than females across all states. Although conceptually distinct, spectral slope did not predict sleep state substantially better than other summaries of the high frequency EEG power spectrum (>20 Hz, in this context) including beta band power, however. Finally, to more fully describe sources of variation in the spectral slope and its relationship to other sleep parameters, we quantified state-dependent differences in the variances (both within and between individuals) of spectral slope, power and interhemispheric coherence, as well as their covariances. In contrast to the common conception of the REM EEG as relatively wake-like (i.e. paradoxical sleep), REM and wake were the most divergent states for multiple metrics, with NREM exhibiting intermediate profiles. Under a simplified modelling framework, changes in spectral slope could not, by themselves, fully account for the observed differences between states, if assuming a strict power law model. Although the spectral slope is an appealing, theoretically inspired parameterization of the sleep EEG, here we underscore some practical considerations that should be borne in mind when applying it in diverse datasets. Future work will be needed to fully characterize state-dependent changes in the aperiodic portions of the EEG power spectra, which appear to be consistent with, albeit not fully explained by, changes in the spectral slope.

neuroscience↗