Biology subjects

Bovy, L.

Publications and source records attributed to Bovy, L..

5 recordsLinked to original sources

bioRxiv2026

Night-to-night sleep EEG variability over one year

This study aimed to explore night-to-night variability of multiscale sleep patterns by analyzing subcutaneous electroencephalography (sqEEG) from 20 healthy participants over one year (205-388 nights per participant, 6,429 nights in total). We utilized the time series of aperiodic slopes, sigma and slow-wave power as a new whole-night unit of sleep macrostructure. Using dynamic time warping, we calculated the distances (differences) between those time series to assess night-to-night sleep macrostructure dissimilarity. We found that the overall sleep macrostructural patterns were relatively similar across nights (20% dissimilarity), while their temporal alignment was quite variable (time series warped by [~]60% for the best alignment). Lower variation in macrostructure dissimilarity was associated with better subjective sleep quality (r=-0.25). Then, we qualitatively compared yearlong variation in macroscale, microscale (sleep stage proportions, mean spectral power) and mesoscale (sleep cycle duration) metrics. We found that intra-individual night-to-night variation was "low" (coefficients of variation < 20%) for spectral power, sleep duration, N2 and REM sleep; "medium" (20-40%) - for N3 and macrostructure dissimilarity; and "high" (>40%) - for sleep cycle duration, wake and N1. In summary, different sleep metrics showed differential night-to-night variability, which was more metric-specific than scale-dependent. This might reflect a distinction between more trait-like versus more dynamically varying features of sleep, although this assumption needs further clarification. Significance statementThe degree of the intra- and inter-individual night-to-night variability of sleep metrics at different scales shows more metric-specific than scale-dependent patterns and potentially distinguishes between trait-like and state-like features of sleep. Detailed description of the multiscale sleep structure and its night-to-night variability/stability might eventually lead to identification of electrophysiological fingerprints and bring insights about their physiological significance. The potential implications of this include the development of personalized time-sensitive recommendations for people facing (social) jet lag, shift work and other deviations from normal sleep patterns. In clinical settings, this might advance the tools to diagnose and monitor sleep alterations, predict nocturnal seizures in epilepsy care, instability in breathing control in apnea etc.

neuroscience↗

bioRxiv2023

Validation of the sleep EEG headband ZMax

Polysomnography (PSG) is the gold standard for recording sleep. However, the standard PSG systems are bulky, expensive, and often confined to lab environments. These systems are also time-consuming in electrode placement and sleep scoring. Such limitations render standard PSG systems less suitable for large-scale or longitudinal studies of sleep. Recent advances in electronics and artificial intelligence enabled wearable PSG systems. Here, we present a study aimed at validating the performance of ZMax, a widely-used wearable PSG that includes frontal electroencephalography (EEG) and actigraphy but no submental electromyography (EMG). We analyzed 135 nights with simultaneous ZMax and standard PSG recordings amounting to over 900 hours from four different datasets, and evaluated the performance of the headbands proprietary automatic sleep scoring (ZLab) alongside our open-source algorithm (DreamentoScorer) in comparison with human sleep scoring. ZLab and DreamentoScorer compared to human scorers with moderate and substantial agreement and Cohens kappa scores of 59.61% and 72.18%, respectively. We further analyzed the competence of these algorithms in determining sleep assessment metrics, as well as shedding more lights on the bandpower computation, and morphological analysis of sleep microstructural features between ZMax and standard PSG. Relative bandpower computed by ZMax implied an error of 5.5% (delta), 4.5% (theta), 1.6% (alpha), 0.5% (sigma), 0.8% (beta), and 0.2% (gamma), compared to standard PSG. In addition, the microstructural features detected in ZMax did not represent exactly the same characteristics as in standard PSG. Besides similarities and discrepancies between ZMax and standard PSG, we measured and discussed the technology acceptance rate, feasibility of data collection with ZMax, and highlighted essential factors for utilizing ZMax as a reliable tool for both monitoring and modulating sleep.

neuroscience↗

bioRxiv2023

Divergent associations of slow-wave sleep vs. REM sleep with plasma amyloid-beta

BackgroundRecent evidence shows that during slow-wave sleep (SWS), the brain is cleared from potentially toxic metabolites, such as the amyloid-beta protein. Poor sleep or elevated cortisol levels can worsen amyloid-beta clearance, potentially leading to the formation of amyloid plaques, a neuropathological hallmark of Alzheimers disease. Here, we explore how nocturnal neural and endocrine activity affects amyloid-beta fluctuations in the peripheral blood as a reflection of cerebral clearance. MethodsSimultaneous polysomnography and all-night blood sampling were acquired in 60 healthy volunteers aged 20-68 years old. Nocturnal plasma concentrations of two amyloid-beta species (amyloid-beta-40 and amyloid-beta-42), cortisol, and growth hormone were assessed every 20 minutes from 23:00-7:00. Amyloid-beta fluctuations were modeled with sleep stages, (non)-oscillatory power, and hormones as predictors while controlling for age and multiple comparisons. Time lags between the predictors and amyloid-beta ranged from 20 to 120min. FindingsThe amyloid-beta-40 and amyloid-beta-42 levels correlated positively with growth hormone concentrations, SWS proportion, slow-wave (0.3-4Hz) oscillatory and high-band (30-48Hz) non-oscillatory power, but negatively with cortisol concentrations and rapid eye movement sleep (REM) proportion measured 40-100min before (all t-values>|3|, p-values<0.003). Older participants showed higher amyloid-beta-40 levels. InterpretationSlow-wave oscillations are associated with higher plasma amyloid-beta levels, reflecting their contribution to cerebral amyloid-beta clearance across the blood-brain barrier. REM sleep is related to decreased amyloid-beta plasma levels; however, this link may reflect passive aftereffects of SWS and not REMs effects per se. Strong associations between cortisol, growth hormone, and amyloid-beta presumably reflect the sleep-regulating role of the corresponding releasing hormones. A positive association between age and amyloid-beta-40 may indicate that peripheral clearance becomes less efficient with age. Our study provides important insights into the specificity of different sleep features effects on brain clearance and suggests that cortisol nocturnal fluctuations may serve as a new marker of clearance efficiency.

neuroscience↗

bioRxiv2021

Non-REM sleep in major depressive disorder

Disturbed sleep is a key symptom in major depressive disorder (MDD). REM sleep alterations are well described in the current fliterature, but little is known about non-REM sleep alterations. Additionally, sleep disturbances relate to a variety of cognitive symptoms in MDD, but which features of non-REM sleep EEG contribute to this, remains unknown. We comprehensively analyzed non-REM sleep EEG features in three independently collected datasets (N=284). These included MDD patients with a broad age range, varying duration and severity of depression, unmedicated or medicated, age- and gender-matched to healthy controls. We explored changes in sleep architecture including sleep stages and cycles, spectral power, sleep spindles, slow waves (SW), and SW-spindle coupling. Next, we analyzed the association of these sleep features with acute measures of depression severity and overnight consolidation of procedural memory. Overall, no major systematic alterations in non-REM sleep architecture were found in patients compared to controls. For the microstructure of non-REM sleep, we observed a higher spindle amplitude in unmedicated patients compared to controls, and after the start of antidepressant medication longer SWs with lower amplitude and a more dispersed SW-spindle coupling. In addition, long-term, but not short-term medication seemed to lower spindle density. Overnight procedural memory consolidation was impaired in medicated patients and associated with lower sleep spindle density. Our results suggest that alterations in non-REM sleep EEG might be more subtle than previously reported. We discuss these findings in the context of antidepressant medication intake and age. Statement of SignificanceDepression affects large and diverse populations worldwide, including their sleep. Most sleep is non-REM sleep, which is vital to cognitive function, including memory. How non-REM is affected during a depression or medical treatment remains poorly investigated. We classified non-REM sleep of depressive patients against healthy controls in unprecedented analysis detail and confidence using the largest dataset published so far while also test sleep alterations associations with impaired memory. Surprisingly, severe depression alone did not alter sleep. We observed severe non-REM sleep alterations only worsening under patient medication, which ultimately coincided with 24-hour memory impairments. Though causal influences of medication on sleep in depressive patients remains to be investigated, this cautions common clinical practice in long-term treatment with antidepressants.

neuroscience↗

bioRxiv2019

The influence of transcranial magnetic stimulation of the medial prefrontal cortex on emotional memory schemas

Memory bias for negative information is a critical characteristic of major depression, but the underlying neural mechanisms are largely unknown. The recently revived concept of memory schemas may shed new light on memory bias in depression: negative schemas might enhance the encoding and consolidation of negative experiences, thereby contributing to the genesis and perpetuation of depressive pathology. To investigate this relationship, we aimed to transiently perturb processing in the medial prefrontal cortex (mPFC), a core region involved in schema memory, using neuronavigated transcranial magnetic stimulation (TMS) targeting the mPFC. Forty healthy volunteers first underwent a negative mood induction to activate negative schema processing after which they received either active inhibitory (N = 20) or control (N = 20) stimulation to the mPFC. Then, all participants performed the encoding of an emotional false memory task. Recall and recognition performance was tested the following morning. Polysomnographic data was recorded continuously during the night before and after encoding. Secondary measures included sleep and mood questionnaires. We observed a significantly lower number of false recognition of negative critical lures following mPFC perturbation compared to the control group, whereas no differences in veridical memory performance were observed. These findings were supported by reaction time data. No relation between REM sleep and (false) emotional memory performance was observed. These findings support previous causal evidence for a role of the mPFC in schema memory processing and further suggest a role of the mPFC in memory bias.

neuroscience↗