116 research outputs found
Sleep and vigilance linked to melanism in wild barn owls.
Understanding the function of variation in sleep requires studies in the natural ecological conditions in which sleep evolved. Sleep has an impact on individual performance and hence may integrate the costs and benefits of investing in processes that are sensitive to sleep, such as immunity or coping with stress. Because dark and pale melanic animals differentially regulate energy homeostasis, immunity and stress hormone levels, the amount and/or organization of sleep may covary with melanin-based colour. We show here that wild, cross-fostered nestling barn owls (Tyto alba) born from mothers displaying more black spots had shorter non-REM (rapid eye movement) sleep bouts, a shorter latency until the occurrence of REM sleep after a bout of wakefulness and more wakefulness bouts. In male nestlings, the same sleep traits also correlated with their own level of spotting. Because heavily spotted male nestlings and the offspring of heavily spotted biological mothers switched sleep-wakefulness states more frequently, we propose the hypothesis that they could be also behaviourally more vigilant. Accordingly, nestlings from mothers displaying many black spots looked more often towards the nest entrance where their parents bring food and towards their sibling against whom they compete. Owlets from heavily spotted mothers might invest more in vigilance, thereby possibly increasing associated costs due to sleep fragmentation. We conclude that different strategies of the regulation of brain activity have evolved and are correlated with melanin-based coloration
Evaluation of two minimally invasive techniques for electroencephalogram recording in wild or freely behaving animals
Insight into the function of sleep may be gained by studying animals in the ecological context in which sleep evolved. Until recently, technological constraints prevented electroencephalogram (EEG) studies of animals sleeping in the wild. However, the recent development of a small recorder (Neurologger 2) that animals can carry on their head permitted the first recordings of sleep in nature. To facilitate sleep studies in the field and to improve the welfare of experimental animals, herein, we test the feasibility of using minimally invasive surface and subcutaneous electrodes to record the EEG in barn owls. The EEG and behaviour of four adult owls in captivity and of four chicks in a nest box in the field were recorded. We scored a 24-h period for each adult bird for wakefulness, slow-wave sleep (SWS), and rapid-eye movement (REM) sleep using 4s epochs. Although the quality and stability of the EEG signals recorded via subcutaneous electrodes were higher when compared to surface electrodes, the owls' state was readily identifiable using either electrode type. On average, the four adult owls spent 13.28h awake, 9.64h in SWS, and 1.05h in REM sleep. We demonstrate that minimally invasive methods can be used to measure EEG-defined wakefulness, SWS, and REM sleep in owls and probably other animal
Galanin neurons unite sleep homeostasis and α2-adrenergic sedation
Our urge to sleep increases with time spent awake, until sleep becomes inescapable. The sleep following sleep deprivation is longer and deeper, with an increased power of delta (0.5 - 4 Hz) oscillations, a phenomenon termed sleep homeostasis [1-4]. Although widely-expressed genes regulate sleep homeostasis [1, 4-10], and the process is tracked by somnogens and phosphorylation [1, 3, 7, 11-14], at the circuit level sleep homeostasis has remained mysterious. Previously we found that sedation induced with 2 adrenergic agonists (e.g. dexmedetomidine) and sleep homeostasis both depend on the preoptic (PO) hypothalamus [15, 16]. Dexmedetomidine, increasingly used for long-term sedation in intensive care units [17], induces a NREM-like sleep but with undesirable hypothermia [18, 19]. Within the PO, various neuronal subtypes (e.g. GABA/galanin and glutamate/NOS1) induce NREM sleep [20-22] and concomitant body cooling [21, 22]. This could be because NREM sleep’s restorative effects depend on lower body temperature [23, 24]. Here, we show that mice with lesioned PO galanin neurons have reduced sleep homeostasis: in the recovery sleep following sleep deprivation, there is a diminished increase in delta power, and the mice catch up little on lost sleep. Furthermore, dexmedetomidine cannot induce high-power delta oscillations or sustained hypothermia. Some hours after dexmedetomidine administration to wild-type mice there is a rebound in delta power when they enter normal NREM sleep, reminiscent of emergence from torpor. This delta rebound is reduced in mice lacking PO galanin neurons. Thus, sleep homeostasis and dexmedetomidine-induced sedation require PO galanin neurons and likely share common mechanisms
Hidden Markov models identify major movement modes in accelerometer and magnetometer data from four albatross species
Funding was provided by an NSF CAREER award to L. Thorne under award number 79804, and by a Minghua Zhang Early Career Faculty Innovation award to L Thorne.Background : Inertial measurement units (IMUs) with high-resolution sensors such as accelerometers are now used extensively to study fine-scale behavior in a wide range of marine and terrestrial animals. Robust and practical methods are required for the computationally-demanding analysis of the resulting large datasets, particularly for automating classification routines that construct behavioral time series and time-activity budgets. Magnetometers are used increasingly to study behavior, but it is not clear how these sensors contribute to the accuracy of behavioral classification methods. Development of effective classification methodology is key to understanding energetic and life-history implications of foraging and other behaviors. Methods : We deployed accelerometers and magnetometers on four species of free-ranging albatrosses and evaluated the ability of unsupervised hidden Markov models (HMMs) to identify three major modalities in their behavior: ‘flapping flight’, ‘soaring flight’, and ‘on-water’. The relative contribution of each sensor to classification accuracy was measured by comparing HMM-inferred states with expert classifications identified from stereotypic patterns observed in sensor data. Results : HMMs provided a flexible and easily interpretable means of classifying behavior from sensor data. Model accuracy was high overall (92%), but varied across behavioral states (87.6, 93.1 and 91.7% for ‘flapping flight’, ‘soaring flight’ and ‘on-water’, respectively). Models built on accelerometer data alone were as accurate as those that also included magnetometer data; however, the latter were useful for investigating slow and periodic behaviors such as dynamic soaring at a fine scale. Conclusions : The use of IMUs in behavioral studies produces large data sets, necessitating the development of computationally-efficient methods to automate behavioral classification in order to synthesize and interpret underlying patterns. HMMs provide an accessible and robust framework for analyzing complex IMU datasets and comparing behavioral variation among taxa across habitats, time and space.Publisher PDFPeer reviewe
Cloud cover amplifies the sleep-suppressing effect of artificial light at night in geese
In modern society the night sky is lit up not only by the moon but also by artificial light devices. Both of these light sources can have a major impact on wildlife physiology and behaviour. For example, a number of bird species were found to sleep several hours less under full moon compared to new moon and a similar sleep-suppressing effect has been reported for artificial light at night (ALAN). Cloud cover at night can modulate the light levels perceived by wildlife, yet, in opposite directions for ALAN and moon. While clouds will block moon light, it may reflect and amplify ALAN levels and increases the night glow in urbanized areas. As a consequence, cloud cover may also modulate the sleep-suppressing effects of moon and ALAN in different directions. In this study we therefore measured sleep in barnacle geese (Branta leucopsis) under semi-natural conditions in relation to moon phase, ALAN and cloud cover. Our analysis shows that, during new moon nights stronger cloud cover was indeed associated with increased ALAN levels at our study site. In contrast, light levels during full moon nights were fairly constant, presumably because of moonlight on clear nights or because of reflected artificial light on cloudy nights. Importantly, cloud cover caused an estimated 24.8% reduction in the amount of night-time NREM sleep from nights with medium to full cloud cover, particularly during new moon when sleep was unaffected by moon light. In conclusion, our findings suggest that cloud cover can, in a rather dramatic way, amplify the immediate effects of ALAN on wildlife. Sleep appears to be highly sensitive to ALAN and may therefore be a good indicator of its biological effects.ISSN:0269-7491ISSN:1878-2450ISSN:1873-642
Prefrontal cortical control of a brainstem social behavior circuit
The prefrontal cortex helps adjust an organism's behavior to its environment. In particular, numerous studies have implicated the prefrontal cortex in the control of social behavior, but the neural circuits that mediate these effects remain unknown. Here we investigated behavioral adaptation to social defeat in mice and uncovered a critical contribution of neural projections from the medial prefrontal cortex to the dorsal periaqueductal gray, a brainstem area vital for defensive responses. Social defeat caused a weakening of functional connectivity between these two areas, and selective inhibition of these projections mimicked the behavioral effects of social defeat. These findings define a specific neural projection by which the prefrontal cortex can control and adapt social behavior
Ostriches Sleep like Platypuses
Mammals and birds engage in two distinct states of sleep, slow wave sleep (SWS) and rapid eye movement (REM) sleep. SWS is characterized by slow, high amplitude brain waves, while REM sleep is characterized by fast, low amplitude waves, known as activation, occurring with rapid eye movements and reduced muscle tone. However, monotremes (platypuses and echidnas), the most basal (or ‘ancient’) group of living mammals, show only a single sleep state that combines elements of SWS and REM sleep, suggesting that these states became temporally segregated in the common ancestor to marsupial and eutherian mammals. Whether sleep in basal birds resembles that of monotremes or other mammals and birds is unknown. Here, we provide the first description of brain activity during sleep in ostriches (Struthio camelus), a member of the most basal group of living birds. We found that the brain activity of sleeping ostriches is unique. Episodes of REM sleep were delineated by rapid eye movements, reduced muscle tone, and head movements, similar to those observed in other birds and mammals engaged in REM sleep; however, during REM sleep in ostriches, forebrain activity would flip between REM sleep-like activation and SWS-like slow waves, the latter reminiscent of sleep in the platypus. Moreover, the amount of REM sleep in ostriches is greater than in any other bird, just as in platypuses, which have more REM sleep than other mammals. These findings reveal a recurring sequence of steps in the evolution of sleep in which SWS and REM sleep arose from a single heterogeneous state that became temporally segregated into two distinct states. This common trajectory suggests that forebrain activation during REM sleep is an evolutionarily new feature, presumably involved in performing new sleep functions not found in more basal animals
Captivating behaviour: mouse models, experimental genetics and reductionist returns in the neurosciences
This a post-print, author-produced version of an article accepted for publication in The Sociological Review. Copyright © 2010 Wiley Blackwell. The definitive version is available at www3.interscience.wiley.comNo Abstract availabl
Differentiation and quantification of synthetic phosphatidylethanol (PEth) homologues by 1H- and 13C-NMR in polar organic solvents
Validation of ‘Somnivore’, a Machine Learning Algorithm for Automated Scoring and Analysis of Polysomnography Data
Manual scoring of polysomnography data is labor-intensive and time-consuming, and most existing software does not account for subjective differences and user variability. Therefore, we evaluated a supervised machine learning algorithm, SomnivoreTM, for automated wake–sleep stage classification. We designed an algorithm that extracts features from various input channels, following a brief session of manual scoring, and provides automated wake-sleep stage classification for each recording. For algorithm validation, polysomnography data was obtained from independent laboratories, and include normal, cognitively-impaired, and alcohol-treated human subjects (total n = 52), narcoleptic mice and drug-treated rats (total n = 56), and pigeons (n = 5). Training and testing sets for validation were previously scored manually by 1–2 trained sleep technologists from each laboratory. F-measure was used to assess precision and sensitivity for statistical analysis of classifier output and human scorer agreement. The algorithm gave high concordance with manual visual scoring across all human data (wake 0.91 ± 0.01; N1 0.57 ± 0.01; N2 0.81 ± 0.01; N3 0.86 ± 0.01; REM 0.87 ± 0.01), which was comparable to manual inter-scorer agreement on all stages. Similarly, high concordance was observed across all rodent (wake 0.95 ± 0.01; NREM 0.94 ± 0.01; REM 0.91 ± 0.01) and pigeon (wake 0.96 ± 0.006; NREM 0.97 ± 0.01; REM 0.86 ± 0.02) data. Effects of classifier learning from single signal inputs, simple stage reclassification, automated removal of transition epochs, and training set size were also examined. In summary, we have developed a polysomnography analysis program for automated sleep-stage classification of data from diverse species. Somnivore enables flexible, accurate, and high-throughput analysis of experimental and clinical sleep studies
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