Biology subjects

Huang, Y.-G.

Publications and source records attributed to Huang, Y.-G..

4 recordsLinked to original sources

bioRxiv2025

Automated sleep scoring in hibernating and non-hibernating American black bears

Hibernating bears show remarkable metabolic suppression. Their decline in core body temperature (Tb) is moderate (from 38{degrees}C to 30-35{degrees}C), but their metabolism declines as much as 75%. To understand the role of sleep in this hypometabolic state, we recorded biotelemetrically EEG, EOG and EMG data over 3500 days from 16 captive American black bears in and out of hibernation under semi-natural conditions. This data set is too large to score manually for Wake, REM- and NREM sleep, so we tested two machine learning classifiers: (1) Somnotate trained on multiple one-day recordings, and (2) Somnivore, trained on a small subset from each recording. As automated scoring methods have not been applied to hibernating species before, a major concern is the effect changing brain temperature has on the EEG and on the machine learning based detection. Therefore, we selected reference data using consensus by 3 manual sleep scorers from each of 6 bears, two one-day recordings at the highest and lowest body temperatures during hibernation when Tb was oscillating in multiday cycles, and a non-hibernating one-day recording in summer. Somnotate results were excellent when trained separately for hibernating and non-hibernating data. Training Somnotate separately for high and low Tb within hibernation did not improve results further. Sleep times in hibernation were about 2x that in summer for both automated scores and manual scores (p<0.0001). There were no significant differences in occupancy of vigilance states between automated and manual scores in hibernation (p>0.05), but a small overestimate of sleep time in summer (p<0.05). Both applications yielded F-measures against manual scores in the 0.90-0.98 range. Outliers in the 0.67-0.88 range were correlated between the two applications, indicating that specific files are more challenging to annotate. We conclude that both applications have accuracies approaching that of manual scorers when trained on high quality data.

neuroscience↗

bioRxiv2021

Somnotate: A robust automated sleep stage classifier that exceeds human performance and identifies ambiguous states in mice

Electrophysiological recordings from freely behaving animals are a widespread and powerful mode of investigation in sleep research. These recordings generate large amounts of data that require sleep stage annotation (polysomnography), in which the data is parcellated according to three vigilance states: awake, rapid eye movement (REM) sleep, and non-REM (NREM) sleep. Manual and computational annotation methods currently ignore intermediate states because the classification features become ambiguous. However, these intermediate states contain important information regarding vigilance state dynamics. Here, we present a new classifier, "Somnotate", which produces automated annotation accuracies that exceed human expert performance on mouse electrophysiological data, is robust to errors in the training data, compatible with different recording configurations, and maintains high performance during experimental interventions. Somnotate is a probabilistic classifier based on a combination of linear discriminant analysis (LDA) with a hidden Markov model (HMM). A unique feature of Somnotate is that it quantifies and reports the certainty of its annotations, enabling the experimenter to identify ambiguous recording periods in a principled manner. We leverage this feature to identify epochs that exhibit intermediate vigilance states, revealing that many of these cluster around state transitions, whereas others correspond to failed attempts to transition. We show that the success rates of different transitions can be experimentally manipulated and explain previously observed sleep patterns. Somnotate can thus facilitate the study of sleep stage transitions and offers new insight into the mechanisms underlying sleep-wake dynamics. Author summaryTypically, the three different vigilance states - awake, REM sleep, and non-REM sleep - exhibit distinct features that are readily recognised in electrophysiological recordings. However, particularly around vigilance state transitions, epochs often exhibit features from more than one state. These intermediate vigilance states pose challenges for existing manual and automated classification methods, and are hence often ignored. Here, we present Somnotate - an open-source, highly accurate and robust sleep stage classifier, which supports research into intermediate states and sleep stage dynamics. Somnotate quantifies and reports the certainty of its annotations, enabling the experimenter to identify abnormal epochs in a principled manner. We use this feature to identify intermediate states and to detect unsuccessful attempts to switch between vigilance states. This provides new insights into the mechanisms of vigilance state transitions in mice, and creates new opportunities for future experiments.

neuroscience↗

bioRxiv2020

The role of the hypothalamus in cortical arousaland sleep homeostasis

Sleep and wakefulness are not simple homogenous all-or-none states, but instead are characterized by rich dynamics of brain activity across many temporal and spatial scales. Rapid global state transitions between waking and sleeping are believed to be controlled by hypothalamic circuits, but the contribution of the hypothalamus to within-state changes of sleep and wake "intensity" remains largely unexplored. Here we show that stimulation of inhibitory neurons in the preoptic hypothalamus does not merely trigger awakening from sleep, but the resulting awake state is also characterized by increased cortical activity. This activation is associated with a faster build-up of sleep pressure, proportional to the arousal level. These findings show that hypothalamic systems thought to exclusively control global state switching, also regulate within-state "intensity", which we propose as a key intrinsic variable in shaping the architecture of sleep/wake states across the 24h day.

neuroscience↗

bioRxiv2020

The relationship between fasting-induced torpor,sleep and wakefulness in the laboratory mouse

Torpor is a regulated reversible state of metabolic suppression used by many mammalian species to conserve energy. Although torpor has been studied extensively in terms of general physiology, metabolism and neuroendocrinology, the effects of hypometabolism and associated hypothermia on brain activity and states of vigilance have received little attention. Here we performed continuous monitoring of electroencephalogram (EEG), electromyogram (EMG) and peripheral body temperature in adult, male C57BL/6 mice over consecutive days of scheduled restricted feeding. All animals showed prominent bouts of hypothermia that became progressively deeper and longer as fasting progressed. EEG and EMG were markedly affected by hypothermia, although the typical electrophysiological signatures of NREM sleep, REM sleep and wakefulness allowed us to perform vigilance-state classification in all cases. Invariably, hypothermia bouts were initiated from a state indistinguishable from NREM sleep, with EEG power decreasing gradually in parallel with decreasing body temperature. Furthermore, during deep hypothermia REM sleep was largely abolished, but we observed brief and intense bursts of muscle activity, which resembled the regular motor discharges seen during early ontogeny associated with immature sleep patterns. We conclude that torpor and sleep are electrophysiologically on a continuum, and that, in order for torpor to occur, mice need to first transition through euthermic sleep.

neuroscience↗