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Belonosov, G.

Publications and source records attributed to Belonosov, G..

2 recordsLinked to original sources

Sleep Renormalizes Negative Emotional Generalization

Overgeneralization of negative experiences, in which aversive responses spread to otherwise safe stimuli, often co-occurs with sleep disruption, and both are central features of anxiety and posttraumatic stress disorder (PTSD). Here, we show that sleep shifts emotional generalization away from the negative and toward the positive. Across three experiments combining behavior, fMRI, and sleep electrophysiology, participants learned to associate three faces with positive, negative, or neutral outcomes. Participants were tested using morphed faces that blended the original stimuli in varying proportions. Immediate generalization was assessed post-learning, and delayed generalization was assessed after overnight sleep or daytime wakefulness. Behaviorally, we found that sleep selectively promotes positive generalization, whereas prolonged daytime wakefulness favors generalization of the negative face. In the fMRI scanner, amygdala and limbic activity during outcome processing predicted stronger immediate generalization of the negative face, and subsequent shift from negative to positive face generalization that occurred only after sleep. Overnight sleep spindle activity, extracted from high-density sleep EEG, positively correlated both with positive face generalization and with the shift from negative to positive generalization following sleep. These findings reveal a potential neural mechanism by which sleep attenuates negative bias and enhances positive representations, suggesting a potential method to buffer against maladaptive generalization in anxiety and PTSD.

neuroscience↗

SleepEEGpy: a Python-based package for the preprocessing, analysis, and visualization of sleep EEG data

Sleep research uses electroencephalography (EEG) to infer brain activity in health and disease. Beyond standard sleep scoring, there is increased interest in advanced EEG analysis that require extensive preprocessing to improve the signal-to-noise ratio, and dedicated analysis algorithms. While many EEG software packages exist, sleep research has specific needs that require dedicated tools (e.g. particular artifacts, event detection). Currently, sleep investigators use different libraries for specific tasks in a fragmented configuration that is inefficient, prone to errors, and requires the learning of multiple software environments. This leads to a high initial complexity, creating a crucial barrier for beginners in sleep research. Here, we present SleepEEGpy, an open-source Python software "wrapper" package to facilitate sleep EEG data preprocessing and analysis. SleepEEGpy builds upon MNE-Python, YASA, and SpecParam tools to provide an all-in-one, beginner-friendly package for comprehensive yet straightforward sleep EEG research including (i) cleaning, (ii) independent component analysis, (iii) detection of sleep events, (iv) analysis of spectral features, and associated visualization tools. A dashboard visualization tool provides an overview to evaluate data and its preprocessing, which can be useful as an initial step prior to detailed analysis. We demonstrate the SleepEEGpy pipeline and its functionalities by applying it to overnight high-density EEG data in healthy participants, revealing multiple characteristic activity signatures typical of each vigilance state. These include alpha oscillations in wakefulness, sleep spindle and slow wave activities in NREM sleep, and theta activity in REM sleep. We hope that this software will be embraced and further developed by the sleep research community, and constitute a useful entry point tool for beginners in sleep EEG research.

neuroscience↗