EEG gamma frequency and sleep–wake scoring in mice: Comparing two types of supervised classifiers

SlumberNet: deep learning classification of sleep stages using residual neural networks

Sleep research is fundamental to understanding health and well-being, as proper sleep is essential for maintaining optimal physiological function. Here we present SlumberNet, a novel deep learning model based on residual network (ResNet) architecture, designed to classify sleep states in mice using electroencephalogram (EEG) and electromyogram (EMG) signals. Our model was trained and tested on data from mice undergoing baseline sleep, sleep deprivation, and recovery sleep, enabling it to handle a wide range of sleep conditions. Employing k-fold cross-validation and data augmentation techniques, SlumberNet achieved high levels of overall performance (accuracy = 97%; F1 score = 96%) in predicting sleep stages and showed robust performance even with a small and diverse training dataset. Comparison of SlumberNet's performance to manual sleep stage classification revealed a significant reduction in analysis time (~ 50 × faster), without sacrificing accuracy. Our study showcases the potential of deep learning to facilitate sleep research by providing a more efficient, accurate, and scalable method for sleep stage classification. Our work with SlumberNet further demonstrates the power of deep learning in mouse sleep research.

Somnotate: A probabilistic sleep stage classifier for studying vigilance state transitions

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 current computational annotation methods ignore intermediate states because the classification features become ambiguous, even though intermediate states contain important information regarding vigilance state dynamics. To address this problem, we have developed "Somnotate"-a probabilistic classifier based on a combination of linear discriminant analysis (LDA) with a hidden Markov model (HMM). First we demonstrate that Somnotate sets new standards in polysomnography, exhibiting annotation accuracies that exceed human experts on mouse electrophysiological data, remarkable robustness to errors in the training data, compatibility with different recording configurations, and an ability to maintain high accuracy during experimental interventions. However, the key feature of Somnotate is that it quantifies and reports the certainty of its annotations. We leverage this feature to reveal that many intermediate vigilance states cluster around state transitions, whereas others correspond to failed attempts to transition. This enables us to show for the first time that the success rates of different types of transition are differentially affected by experimental manipulations and can explain previously observed sleep patterns. Somnotate is open-source and has the potential to both facilitate the study of sleep stage transitions and offer new insights into the mechanisms underlying sleep-wake dynamics.

A wireless and battery-less implant for multimodal closed-loop neuromodulation in small animals

Fully implantable wireless systems for the recording and modulation of neural circuits that do not require physical tethers or batteries allow for studies that demand the use of unconstrained and freely behaving animals in isolation or in social groups. Moreover, feedback-control algorithms that can be executed within such devices without the need for remote computing eliminate virtual tethers and any associated latencies. Here we report a wireless and battery-less technology of this type, implanted subdermally along the back of freely moving small animals, for the autonomous recording of electroencephalograms, electromyograms and body temperature, and for closed-loop neuromodulation via optogenetics and pharmacology. The device incorporates a system-on-a-chip with Bluetooth Low Energy for data transmission and a compressed deep-learning module for autonomous operation, that offers neurorecording capabilities matching those of gold-standard wired systems. We also show the use of the implant in studies of sleep-wake regulation and for the programmable closed-loop pharmacological suppression of epileptic seizures via feedback from electroencephalography. The technology can support a broader range of applications in neuroscience and in biomedical research with small animals.

Disrupted sleep in dystonia depends on cerebellar function but not motor symptoms in mice

Although dystonia is the third most common movement disorder, patients often also experience debilitating nonmotor defects including impaired sleep. The cerebellum is a central component of a "dystonia network" that plays various roles in sleep regulation. Importantly, the primary driver of sleep impairments in dystonia remains poorly understood. The cerebellum, along with other nodes in the motor circuit, could disrupt sleep. However, it is unclear how the cerebellum might alter sleep and mobility. To disentangle the impact of cerebellar dysfunction on motion and sleep, we generated two mouse genetic models of dystonia that have overlapping cerebellar circuit miswiring but show differing motor phenotype severity: Ptf1a ^Cre ;Vglut2 ^fx/fx and Pdx1 ^Cre ;Vglut2 ^fx/fx mice. In both models, excitatory climbing fiber to Purkinje cell neurotransmission is blocked, but only the Ptf1a ^Cre ;Vglut2 ^fx/fx mice have severe twisting. Using in vivo ECoG and EMG recordings we found that both mutants spend greater time awake and in NREM sleep at the expense of REM sleep. The increase in awake time is driven by longer awake bouts rather than an increase in bout number. We also found a longer latency to reach REM in both mutants, which is similar to what is reported in human dystonia. We uncovered independent but parallel roles for cerebellar circuit dysfunction and motor defects in promoting sleep quality versus posture impairments in dystonia.

Respiratory entrainment of units in the mouse parietal cortex depends on vigilance state

Synchronous oscillations are essential for coordinated activity in neuronal networks and, hence, for behavior and cognition. While most network oscillations are generated within the central nervous system, recent evidence shows that rhythmic body processes strongly influence activity patterns throughout the brain. A major factor is respiration (Resp), which entrains multiple brain regions at the mesoscopic (local field potential) and single-cell levels. However, it is largely unknown how such Resp-driven rhythms interact or compete with internal brain oscillations, especially those with similar frequency domains. In mice, Resp and theta (θ) oscillations have overlapping frequencies and co-occur in various brain regions. Here, we investigated the effects of Resp and θ on neuronal discharges in the mouse parietal cortex during four behavioral states which either show prominent θ (REM sleep and active waking (AW)) or lack significant θ (NREM sleep and waking immobility (WI)). We report a pronounced state-dependence of spike modulation by both rhythms. During REM sleep, θ effects on unit discharges dominate, while during AW, Resp has a larger influence, despite the concomitant presence of θ oscillations. In most states, unit modulation by θ or Resp increases with mean firing rate. The preferred timing of Resp-entrained discharges (inspiration versus expiration) varies between states, indicating state-specific and different underlying mechanisms. Our findings show that neurons in an associative cortex area are differentially and state-dependently modulated by two fundamentally different processes: brain-endogenous θ oscillations and rhythmic somatic feedback signals from Resp.

Respiration and rapid eye movement ( REM) sleep substructure: short versus long episodes

Rapid eye movement (REM) sleep in rodents is defined by the presence of theta rhythm in the absence of movement. The amplitude and frequency of theta oscillations have been used to distinguish between tonic and phasic REM sleep. However, tonic REM sleep has not been further subdivided, although characteristics of network oscillations such as cross-frequency coupling between theta and gamma vary within this sub-state. Recently, it has been shown that theta-gamma coupling depends on an optimal breathing rate of ~5 Hz. The frequency of breathing varies strongly throughout REM sleep, and the duration of single REM sleep episodes ranges from several seconds to minutes, whereby short episodes predominate. Here we studied the relation between breathing frequency, accelerometer activity, and the length of REM sleep periods. We found that small movements detected with three-dimensional accelerometry positively correlate with breathing rate. Interestingly, breathing is slow in short REM sleep episodes, while faster respiration regimes exclusively occur after a certain delay in longer REM sleep episodes. Thus, merging REM sleep episodes of different lengths will result in a predominance of slow respiration due to the higher occurrence of short REM sleep periods. Moreover, our results reveal that not only do phasic REM sleep epochs predominantly occur during long REM sleep episodes, but that the long episodes also have faster theta and higher gamma activity. These observations suggest that REM sleep can be further divided from a physiological point of view depending on its duration. Higher levels of arousal during REM sleep, indicated by higher breathing rates, can only be captured in long REM sleep episodes.

Discriminating Sleep From Freezing With Cortical Spindle Oscillations

In-vivo longitudinal recordings require reliable means to automatically discriminate between distinct behavioral states, in particular between awake and sleep epochs. The typical approach is to use some measure of motor activity together with extracellular electrophysiological signals, namely the relative contribution of theta and delta frequency bands to the Local Field Potential (LFP). However, these bands can partially overlap with oscillations characterizing other behaviors such as the 4 Hz accompanying rodent freezing. Here, we first demonstrate how standard methods fail to discriminate between sleep and freezing in protocols where both behaviors are observed. Then, as an alternative, we propose to use the smoothed cortical spindle power to detect sleep epochs. Finally, we show the effectiveness of this method in discriminating between sleep and freezing in our recordings.

Differential modulation of parietal cortex activity by respiration and θ-oscillations

The simultaneous, local integration of information from widespread brain regions is an essential feature of cortical computation and particularly relevant for multimodal association areas such as the posterior parietal cortex. Slow, rhythmic fluctuations in the local field potentials (LFP) are assumed to constitute a global signal aiding interregional communication through the long-range synchronization of neuronal activity. Recent work demonstrated the brain-wide presence of a novel class of slow neuronal oscillations which are entrained by nasal respiration. However, whether there are differences in the influence of the respiration-entrained rhythm (RR) and the endogenous theta (θ) rhythm over local networks is unknown. In this work, we aimed at characterizing the impact of both classes of oscillations on neuronal activity in the posterior parietal cortex of mice. We focused our investigations on a θ-dominated state (REM sleep) and an RR-dominated state (wake immobility). Using linear silicon probes implanted along the dorsoventral cortical axis, we found that the LFP-depth distributions of both rhythms show differences in amplitude and coherence but no phase shift. Using tetrode recordings, we demonstrate that a substantial fraction of parietal neurons is modulated by either RR or θ or even by both rhythms simultaneously. Interestingly, the phase and cortical depth-dependence of spike-field coupling differ for these oscillations. We further show through intracellular recordings in urethane-anesthetized mice that synaptic inhibition is likely to play a role in generating respiration-entrainment at the membrane potential level. We conclude that θ and respiration differentially affect neuronal activity in the parietal cortex.

Automated sleep state classification of wide-field calcium imaging data via multiplex visibility graphs and deep learning

BACKGROUND

Wide-field calcium imaging (WFCI) allows for monitoring of cortex-wide neural dynamics in mice. When applied to the study of sleep, WFCI data are manually scored into the sleep states of wakefulness, non-REM (NREM) and REM by use of adjunct EEG and EMG recordings. However, this process is time-consuming and often suffers from low inter- and intra-rater reliability and invasiveness. Therefore, an automated sleep state classification method that operates on WFCI data alone is needed.

NEW METHOD

A hybrid, two-step method is proposed. In the first step, spatial-temporal WFCI data is mapped to multiplex visibility graphs (MVGs). Subsequently, a two-dimensional convolutional neural network (2D CNN) is employed on the MVGs to be classified as wakefulness, NREM and REM.

RESULTS

Sleep states were classified with an accuracy of 84% and Cohen's κ of 0.67. The method was also effectively applied on a binary classification of wakefulness/sleep (accuracy=0.82, κ = 0.62) and a four-class wakefulness/sleep/anesthesia/movement classification (accuracy=0.74, κ = 0.66). Gradient-weighted class activation maps revealed that the CNN focused on short- and long-term temporal connections of MVGs in a sleep state-specific manner. Sleep state classification performance when using individual brain regions was highest for the posterior area of the cortex and when cortex-wide activity was considered.

COMPARISON WITH EXISTING METHOD

On a 3-hour WFCI recording, the MVG-CNN achieved a κ of 0.65, comparable to a κ of 0.60 corresponding to the human EEG/EMG-based scoring.

CONCLUSIONS

The hybrid MVG-CNN method accurately classifies sleep states from WFCI data and will enable future sleep-focused studies with WFCI.

An artificial neural network for automated behavioral state classification in rats

Accurate behavioral state classification is critical for many research applications. Researchers typically rely upon manual identification of behavioral state through visual inspection of electrophysiological signals, but this approach is time intensive and subject to low inter-rater reliability. To overcome these limitations, a diverse set of algorithmic approaches have been put forth to automate the classification process. Recently, novel machine learning approaches have been detailed that produce rapid and highly accurate classifications. These approaches however, are often computationally expensive, require significant expertise to implement, and/or require proprietary software that limits broader adoption. Here we detail a novel artificial neural network that uses electrophysiological features to automatically classify behavioral state in rats with high accuracy, sensitivity, and specificity. Common parameters of interest to sleep scientists, including state-dependent power spectra and homeostatic non-REM slow wave activity, did not significantly differ when using this automated classifier as compared to manual scoring. Flexible options enable researchers to further increase classification accuracy through manual rescoring of a small subset of time intervals with low model prediction certainty or further decrease researcher time by generalizing trained networks across multiple recording days. The algorithm is fully open-source and coded within a popular, and freely available, software platform to increase access to this research tool and provide additional flexibility for future researchers. In sum, we have developed a readily implementable, efficient, and effective approach for automated behavioral state classification in rats.

Optogenetic activation of DRN 5-HT neurons induced active wakefulness, not quiet wakefulness

There has been a long-standing controversy regarding the physiological role of serotonin (5-HT) neurons in the dorsal raphe nucleus (DRN) in sleep/wake architecture. Some studies have reported that 5-HT acts as a sleep-promoting agent, but several studies have suggested that DRN 5-HT neurons function predominantly to promote wakefulness and inhibit rapid eye movement (REM) sleep. Furthermore, recent studies have reported that there is a clear neurobiological difference between a waking state that includes alertness and active exploration (i.e., active wakefulness) and a waking state that is devoid of locomotion (i.e., quiet wakefulness). These states have also been shown to differ clinically in terms of memory consolidation. However, the effects of 5-HT neurons on the regulation of these two different waking states have not been fully elucidated. In the present study, we attempted to examine the physiological role of DRN 5-HT neurons in various sleep/wake states using optogenetic methods that allowed manipulation of cell-type specific neuronal activation with high temporal and anatomical precision. We crossed TPH2-tTA and TetO-ChR2(C128S) mice to obtain mice with channelrhodopsin-2 (ChR2) [C128S]-expressing central 5-HT neurons, and we activated DRN-5HT neurons or medullary 5-HT neurons. Optogenetic activation of DRN 5-HT neurons caused rapid transition from non-REM sleep to active wakefulness, not quiet wakefulness, whereas activation of medullary 5-HT neurons did not appear to affect sleep/wake states or locomotor activity. Our results may shed light on the physiological role of DRN 5-HT neurons in sleep/wake architecture and encourage further investigations of the cortical functional connectivity involved in sleep/wake state regulation.

Theta-gamma coupling during REM sleep depends on breathing rate

Temporal coupling between theta and gamma oscillations is a hallmark activity pattern of several cortical networks and becomes especially prominent during REM sleep. In a parallel approach, nasal breathing has been recently shown to generate phase-entrained network oscillations which also modulate gamma. Both slow rhythms (theta and respiration-entrained oscillations) have been suggested to aid large-scale integration but they differ in frequency, display low coherence, and modulate different gamma sub-bands. Respiration and theta are therefore believed to be largely independent. In the present work, however, we report an unexpected but robust relation between theta-gamma coupling and respiration in mice. Interestingly, this relation takes place not through the phase of individual respiration cycles, but through respiration rate: the strength of theta-gamma coupling exhibits an inverted V-shaped dependence on breathing rate, leading to maximal coupling at breathing frequencies of 4-6 Hz. Noteworthy, when subdividing sleep epochs into phasic and tonic REM patterns, we find that breathing differentially relates to theta-gamma coupling in each state, providing new evidence for their physiological distinctiveness. Altogether, our results reveal that breathing correlates with brain activity not only through phase-entrainment but also through rate-dependent relations with theta-gamma coupling. Thus, the link between respiration and other patterns of cortical network activity is more complex than previously assumed.

Temporal Relations between Cortical Network Oscillations and Breathing Frequency during REM Sleep

Nasal breathing generates a rhythmic signal which entrains cortical network oscillations in widespread brain regions on a cycle-to-cycle time scale. It is unknown, however, how respiration and neuronal network activity interact on a larger time scale: are breathing frequency and typical neuronal oscillation patterns correlated? Is there any directionality or temporal relationship? To address these questions, we recorded field potentials from the posterior parietal cortex of mice together with respiration during REM sleep. In this state, the parietal cortex exhibits prominent θ and γ oscillations while behavioral activity is minimal, reducing confounding signals. We found that the instantaneous breathing frequency strongly correlates with the instantaneous frequency and amplitude of both θ and γ oscillations. Cross-correlograms and Granger causality revealed specific directionalities for different rhythms: changes in θ activity precede and Granger-cause changes in breathing frequency, suggesting control by the functional state of the brain. On the other hand, the instantaneous breathing frequency Granger causes changes in γ frequency, suggesting that γ is influenced by a peripheral reafference signal. These findings show that changes in breathing frequency temporally relate to changes in different patterns of rhythmic brain activity. We hypothesize that such temporal relations are mediated by a common central drive likely to be located in the brainstem.

A review of the current state of knowledge on sex differences in sleep and circadian phenotypes in rodents

Sleep is a vital part of our lives as it is required to maintain health and optimal cognition. In humans, sex differences are relatively well-established for many sleep phenotypes. However, precise differences in sleep phenotypes between male and female rodents are less documented. The main goal of this article is to review sex differences in sleep architecture and electroencephalographic (EEG) activity during wakefulness and sleep in rodents. The effects of acute sleep deprivation on sleep duration and EEG activity in male and female rodents will also be covered, in addition to sex differences in specific circadian phenotypes. When possible, the contribution of the female estrous cycle to the observed differences between males and females will be described. In general, male rodents spend more time in non-rapid eye movement sleep (NREMS) in comparison to females, while other differences between sexes in sleep phenotypes are species- and estrous cycle phase-dependent. Altogether, the review illustrates the need for a sex-based perspective in basic sleep and circadian research, including the consideration of sex chromosomes and gonadal hormones in sleep and circadian phenotypes.

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In rodents, males spend less time awake, and more time in NREMS than females.

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The recovery from sleep deprivation is also dependent on biological sex.

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Gonadal hormones modulate sleep and circadian phenotypes in rodents.

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A more systematic comparison of sex in basic sleep/circadian research is needed.

Machine learning prediction of sleep stages in dairy cows from heart rate and muscle activity measures

Sleep is important for cow health and shows promise as a tool for assessing welfare, but methods to accurately distinguish between important sleep stages are difficult and impractical to use with cattle in typical farm environments. The objective of this study was to determine if data from more easily applied non-invasive devices assessing neck muscle activity and heart rate (HR) alone could be used to differentiate between sleep stages. We developed, trained, and compared two machine learning models using neural networks and random forest algorithms to predict sleep stages from 15 variables (features) of the muscle activity and HR data collected from 12 cows in two environments. Using k-fold cross validation we compared the success of the models to the gold standard, Polysomnography (PSG). Overall, both models learned from the data and were able to accurately predict sleep stages from HR and muscle activity alone with classification accuracy in the range of similar human models. Further research is required to validate the models with a larger sample size, but the proposed methodology appears to give an accurate representation of sleep stages in cattle and could consequentially enable future sleep research into conditions affecting cow sleep and welfare.

Identification of the sleep-wake states in rats using the high-frequency activity of the electroencephalogram

The electroencephalographic signal constitutes the main sign classically used for the identification of states of alertness. However, activities in the high frequency (>100 Hz) range have not been properly studied despite their high potential for sleep scoring in rodents. In the present study, we designed a method for the identification of the sleep-wake states in rats by exclusively using high-frequency activities of the electroencephalogram. By calculating the ratio between the amplitude of the electroencephalographic signal from 110 to 200 Hz and from 110 to 300 Hz, we obtained an index that had values that were low during wakefulness, intermediate during non-REM sleep and high during REM sleep. This high-frequency index (HiFI) allowed the identification of each state without the need to study other signs such as muscle activity or eye movements. To evaluate the performance of the index, we compared it with the conventional scoring of the sleep-wake cycle based upon the study of the electromyogram and delta (0.5-4 Hz), theta (6-9 Hz) and sigma (10-14 Hz) bands of the electroencephalogram. The index had an accuracy of 90.43 ± 1.91% (Cohen's kappa value of 0.82), confirming that the study of the high-frequency activities of the electroencephalogram was sufficient to reliably identify alertness states in the rat. Compared to other sleep-scoring methods, the HiFI has several advantages. It only requires one electroencephalography electrode, thus reducing the severity of the surgical preparation of the experimental animal, and its calculation is very simple, so it can be easily implemented online to classify sleep-wake states in real time.

OSERR: an open-source standalone electrophysiology recording system for rodents

Behavioral assessment of rodents is critical for investigation of brain function in health and disease. In vivo neurophysiological recordings are powerful tools to mechanistically dissect neural pathways that underlie behavioral changes, and serve as markers for dynamics, efficacy and safety of potential therapeutic approaches. However, most in vivo recording systems require tethers or telemetry receivers, limiting their compatibility with some behavioral tests. Here, we developed an open-source standalone electrophysiology recording system for rodents (OSERR). It is a tether-free, standalone recording device with two channels, a reference and a ground, that acquires, amplifies, filters and stores data all in itself. Thus, it does not require any cable or receiver. It is also compact and light-weight, and compatible with juvenile mice, as well as multiple recording modalities and standard electrode implantation methods. In addition, we provide the complete design of hardware, and software for operation. As an example, we demonstrated that this standalone system, when configured with a bandwidth of 1–120 Hz and gain of 1000, successfully collected EEG signals during induced seizure, extended recording, anesthesia, and social interactions in mice. The design of this system is practical, economical, and freely available. Thus, this system could enable recording of brain activity during diverse behavioral assays in a variety of arenas and settings, and allow simultaneous recordings from multiple subjects to examine social behaviors. Importantly, with the open-source documentation, researchers could customize the design of the system to their specific needs.

Sleep problems in Rett syndrome animal models: A systematic review

Due to the discovery of Rett Syndrome (RTT) genetic mutations, animal models have been developed. Sleep research in RTT animal models may unravel novel neural mechanisms for this severe neurodevelopmental heritable rare disease. In this systematic literature review we summarize the findings on sleep research of 13 studies in animal models of RTT. We found disturbed efficacy and continuity of sleep in all genetically mutated models of mice, cynomolgus monkeys, and Drosophila. Models presented highly fragmented sleep with distinct differences in 24-hr sleep/wake cyclicity and circadian arrhythmicity. Overall, animal models mimic sleep complaints reported in individuals with RTT. However, contrary to human studies, in mutant mice, attenuated sleep delta waves, and sleep apneas in non-rapid eye movement sleep were reported. Future studies may focus on sleep structure and EEG alterations, potential central mechanisms involved in sleep fragmentation and the occurrence of sleep apnea across different sleep stages. Given that locomotor dysfunction is characteristic of individuals with RTT, studies may consider to integrate its potential impact on the behavioral analysis of sleep.

Supervised and unsupervised machine learning for automated scoring of sleep-wake and cataplexy in a mouse model of narcolepsy

Despite commercial availability of software to facilitate sleep-wake scoring of electroencephalography (EEG) and electromyography (EMG) in animals, automated scoring of rodent models of abnormal sleep, such as narcolepsy with cataplexy, has remained elusive. We optimize two machine-learning approaches, supervised and unsupervised, for automated scoring of behavioral states in orexin/ataxin-3 transgenic mice, a validated model of narcolepsy type 1, and additionally test them on wild-type mice. The supervised learning approach uses previously labeled data to facilitate training of a classifier for sleep states, whereas the unsupervised approach aims to discover latent structure and similarities in unlabeled data from which sleep stages are inferred. For the supervised approach, we employ a deep convolutional neural network architecture that is trained on expert-labeled segments of wake, non-REM sleep, and REM sleep in EEG/EMG time series data. The resulting trained classifier is then used to infer on the labels of previously unseen data. For the unsupervised approach, we leverage data dimensionality reduction and clustering techniques. Both approaches successfully score EEG/EMG data, achieving mean accuracies of 95% and 91%, respectively, in narcoleptic mice, and accuracies of 93% and 89%, respectively, in wild-type mice. Notably, the supervised approach generalized well on previously unseen data from the same animals on which it was trained but exhibited lower performance on animals not present in the training data due to inter-subject variability. Cataplexy is scored with a sensitivity of 85% and 57% using the supervised and unsupervised approaches, respectively, when compared to manual scoring, and the specificity exceeds 99% in both cases.

Intra-"cortical" activity during avian non-REM and REM sleep: variant and invariant traits between birds and mammals

Several mammalian-based theories propose that the varying patterns of neuronal activity occurring in wakefulness and sleep reflect different modes of information processing. Neocortical slow-waves, hippocampal sharp-wave ripples, and thalamocortical spindles occurring during mammalian non-rapid eye-movement (NREM) sleep are proposed to play a role in systems-level memory consolidation. Birds show similar NREM and REM (rapid eye-movement) sleep stages to mammals; however, it is unclear whether all neurophysiological rhythms implicated in mammalian memory consolidation are also present. Moreover, it is unknown whether the propagation of slow-waves described in the mammalian neocortex occurs in the avian "cortex" during natural NREM sleep. We used a 32-channel silicon probe connected to a transmitter to make intracerebral recordings of the visual hyperpallium and thalamus in naturally sleeping pigeons (Columba livia). As in the mammalian neocortex, slow-waves during NREM sleep propagated through the hyperpallium. Propagation primarily occurred in the thalamic input layers of the hyperpallium, regions that also showed the greatest slow-wave activity (SWA). Spindles were not detected in both the visual hyperpallium, including regions receiving thalamic input, and thalamus, using a recording method that readily detects spindles in mammals. Interestingly, during REM sleep fast gamma bursts in the hyperpallium (when present) were restricted to the thalamic input layers. In addition, unlike mice, the decrease in SWA from NREM to REM sleep was the greatest in these layers. Taken together, these variant and invariant neurophysiological aspects of avian and mammalian sleep suggest that there may be associated mechanistic and functional similarities and differences between avian and mammalian sleep.

MC-SleepNet: Large-scale Sleep Stage Scoring in Mice by Deep Neural Networks

Automated sleep stage scoring for mice is in high demand for sleep research, since manual scoring requires considerable human expertise and efforts. The existing automated scoring methods do not provide the scoring accuracy required for practical use. In addition, the performance of such methods has generally been evaluated using rather small-scale datasets, and their robustness against individual differences and noise has not been adequately verified. This research proposes a novel automated scoring method named “MC-SleepNet”, which combines two types of deep neural networks. Then, we evaluate its performance using a large-scale dataset that contains 4,200 biological signal records of mice. The experimental results show that MC-SleepNet can automatically score sleep stages with an accuracy of 96.6% and kappa statistic of 0.94. In addition, we confirm that the scoring accuracy does not significantly decrease even if the target biological signals are noisy. These results suggest that MC-SleepNet is very robust against individual differences and noise. To the best of our knowledge, evaluations using such a large-scale dataset (containing 4,200 records) and high scoring accuracy (96.6%) have not been reported in previous related studies.

Is xenon a suitable euthanasia agent for mice?

OBJECTIVE

To compare behavioural and electrophysiological variables of mice undergoing gas euthanasia with either xenon (Xe) or carbon dioxide (CO₂).

STUDY DESIGN

Single animals chronically instrumented for electroencephalography (EEG) recording were randomized to undergo euthanasia with either CO₂ or Xe (n = 6 animals per group).

ANIMALS

Twelve adult (>6 weeks old) male C57Bl6/n mice.

METHODS

Mice were surgically instrumented with EEG and electromyogram electrodes. Following a 7-day recovery period, animals were placed individually in a sealed chamber and a 5-minute baseline recorded in 21% O₂. Gas [100% Xe (n = 6) or 100% CO₂ (n = 6)] was then added to the chamber at 30% chamber volume minute^-1 (2.8 L minute^-1) until cessation of breathing. EEG, behaviour (jumping and freezing) and locomotion speed were recorded throughout.

RESULTS

Mice undergoing single gas euthanasia with Xe did not show jumping or freezing behaviours and had reduced locomotion speed compared to baseline, in contrast to CO₂, which resulted in increases in these variables. EEG recordings revealed sedative effects from Xe but heightened arousal from CO₂.

CONCLUSIONS

Our data suggest that Xe may be less aversive than CO₂ when using a 30% chamber volume minute^-1 fill rate and could improve the welfare of mice undergoing gas euthanasia.

Nitrogen gas produces less behavioural and neurophysiological excitation than carbon dioxide in mice undergoing euthanasia

Carbon dioxide (CO₂) is one of the most commonly used gas euthanasia agents in mice, despite reports of aversion and nociception. Inert gases such as nitrogen (N₂) may be a viable alternative to carbon dioxide. Here we compared behavioural and electrophysiological reactions to CO₂ or N₂ at either slow fill or rapid fill in C57Bl/6 mice undergoing gas euthanasia. We found that mice euthanised with CO₂ increased locomotor activity compared to baseline, whereas mice exposed to N₂ decreased locomotion. Furthermore, mice exposed to CO₂ showed significantly more vertical jumps and freezing episodes than mice exposed to N₂. We further found that CO₂ exposure resulted in increased theta:delta of the EEG, a measure of excitation, whereas the N₂ decreased theta:delta. Differences in responses were not oxygen-concentration dependent. Taken together, these results demonstrate that CO₂ increases both behavioural and electrophysiological excitation as well as producing a fear response, whereas N₂ reduces behavioural activity and central neurological depression and may be less aversive although still produces a fear response. Further studies are required to evaluate N₂ as a suitable euthanasia agent for mice.

Harnessing olfactory bulb oscillations to perform fully brain-based sleep-scoring and real-time monitoring of anaesthesia depth

Real-time tracking of vigilance states related to both sleep or anaesthesia has been a goal for over a century. However, sleep scoring cannot currently be performed with brain signals alone, despite the deep neuromodulatory transformations that accompany sleep state changes. Therefore, at heart, the operational distinction between sleep and wake is that of immobility and movement, despite numerous situations in which this one-to-one mapping fails. Here we demonstrate, using local field potential (LFP) recordings in freely moving mice, that gamma (50–70 Hz) power in the olfactory bulb (OB) allows for clear classification of sleep and wake, thus providing a brain-based criterion to distinguish these two vigilance states without relying on motor activity. Coupled with hippocampal theta activity, it allows the elaboration of a sleep scoring algorithm that relies on brain activity alone. This method reaches over 90% homology with classical methods based on muscular activity (electromyography [EMG]) and video tracking. Moreover, contrary to EMG, OB gamma power allows correct discrimination between sleep and immobility in ambiguous situations such as fear-related freezing. We use the instantaneous power of hippocampal theta oscillation and OB gamma oscillation to construct a 2D phase space that is highly robust throughout time, across individual mice and mouse strains, and under classical drug treatment. Dynamic analysis of trajectories within this space yields a novel characterisation of sleep/wake transitions: whereas waking up is a fast and direct transition that can be modelled by a ballistic trajectory, falling asleep is best described as a stochastic and gradual state change. Finally, we demonstrate that OB oscillations also allow us to track other vigilance states. Non-REM (NREM) and rapid eye movement (REM) sleep can be distinguished with high accuracy based on beta (10–15 Hz) power. More importantly, we show that depth of anaesthesia can be tracked in real time using OB gamma power. Indeed, the gamma power predicts and anticipates the motor response to stimulation both in the steady state under constant anaesthetic and dynamically during the recovery period. Altogether, this methodology opens the avenue for multi-timescale characterisation of brain states and provides an unprecedented window onto levels of vigilance.

Real-time tracking of vigilance states related to wake, sleep, and anaesthesia has been a goal for over a century. However identification of wakefulness and different sleep states cannot currently be performed routinely with brain signals and instead relies on motor activity. Here we demonstrate that 50–70 Hz electrical oscillations in the olfactory bulb (OB) of mice are a reliable indicator for global brain states. Recording this activity with an implanted electrode allows for clear classification of sleep and wake, without the need for motor activity monitoring. We construct a fully automatic sleep scoring algorithm that relies on brain activity alone and is robust throughout time, between animals, and after drug administration. Our method also tracks in real time the depth of anaesthesia both in the steady state under constant anaesthetic and dynamically during the recovery period from anaesthesia. Furthermore, this index predicts responsiveness to noxious stimulation under anaesthesia. Altogether, this methodology opens the avenue for characterisation of vigilance states based on OB recordings.

Circadian and Brain State Modulation of Network Hyperexcitability in Alzheimer's Disease

Network hyperexcitability is a feature of Alzheimer' disease (AD) as well as numerous transgenic mouse models of AD. While hyperexcitability in AD patients and AD animal models share certain features, the mechanistic overlap remains to be established. We aimed to identify features of network hyperexcitability in AD models that can be related to epileptiform activity signatures in AD patients. We studied network hyperexcitability in mice expressing amyloid precursor protein (APP) with mutations that cause familial AD, and compared a transgenic model that overexpresses human APP (hAPP) (J20), to a knock-in model expressing APP at physiological levels (APP^NL/F). We recorded continuous long-term electrocorticogram (ECoG) activity from mice, and studied modulation by circadian cycle, behavioral, and brain state. We report that while J20s exhibit frequent interictal spikes (IISs), APP^NL/F mice do not. In J20 mice, IISs were most prevalent during daylight hours and the circadian modulation was associated with sleep. Further analysis of brain state revealed that IIS in J20s are associated with features of rapid eye movement (REM) sleep. We found no evidence of cholinergic changes that may contribute to IIS-circadian coupling in J20s. In contrast to J20s, intracranial recordings capturing IIS in AD patients demonstrated frequent IIS in non-REM (NREM) sleep. The salient differences in sleep-stage coupling of IIS in APP overexpressing mice and AD patients suggests that different mechanisms may underlie network hyperexcitability in mice and humans. We posit that sleep-stage coupling of IIS should be an important consideration in identifying mouse AD models that most closely recapitulate network hyperexcitability in human AD.

Parallel detection of theta and respiration-coupled oscillations throughout the mouse brain

Slow brain oscillations are usually coherent over long distances and thought to link distributed cell assemblies. In mice, theta (5–10 Hz) stands as one of the most studied slow rhythms. However, mice often breathe at theta frequency, and we recently reported that nasal respiration leads to local field potential (LFP) oscillations that are independent of theta. Namely, we showed respiration-coupled oscillations in the hippocampus, prelimbic cortex, and parietal cortex, suggesting that respiration could impose a global brain rhythm. Here we extend these findings by analyzing LFPs from 15 brain regions recorded simultaneously with respiration during exploration and REM sleep. We find that respiration-coupled oscillations can be detected in parallel with theta in several neocortical regions, from prefrontal to visual areas, and also in subcortical structures such as the thalamus, amygdala and ventral hippocampus. They might have escaped attention in previous studies due to the absence of respiration monitoring, the similarity with theta oscillations, and the highly variable peak frequency. We hypothesize that respiration-coupled oscillations constitute a global brain rhythm suited to entrain distributed networks into a common regime. However, whether their widespread presence reflects local network activity or is due to volume conduction remains to be determined.

Cycle-Triggered Cortical Stimulation during Slow Wave Sleep Facilitates Learning a BMI Task: A Case Report in a Non-Human Primate

Slow wave sleep (SWS) has been identified as the sleep stage involved in consolidating newly acquired information. A growing body of evidence has shown that delta (1-4 Hz) oscillatory activity, the characteristic electroencephalographic signature of SWS, is involved in coordinating interaction between the hippocampus and the neocortex and is thought to take a role in stabilizing memory traces related to a novel task. This case report describes a new protocol that uses neuroprosthetics training of a non-human primate to evaluate the effects of surface cortical electrical stimulation triggered from SWS cycles. The results suggest that stimulation phase-locked to SWS oscillatory activity promoted learning of the neuroprosthetic task. This protocol could be used to elucidate mechanisms of synaptic plasticity underlying off-line learning during sleep and offers new insights into the role of brain oscillations in information processing and memory consolidation.

Selective entrainment of gamma subbands by different slow network oscillations

Theta oscillations (4-12 Hz) are thought to provide a common temporal reference for the exchange of information among distant brain networks. On the other hand, faster gamma-frequency oscillations (30-160 Hz) nested within theta cycles are believed to underlie local information processing. Whether oscillatory coupling between global and local oscillations, as showcased by theta-gamma coupling, is a general coding mechanism remains unknown. Here, we investigated two different patterns of oscillatory network activity, theta and respiration-induced network rhythms, in four brain regions of freely moving mice: olfactory bulb (OB), prelimbic cortex (PLC), parietal cortex (PAC), and dorsal hippocampus [cornu ammonis 1 (CA1)]. We report differential state- and region-specific coupling between the slow large-scale rhythms and superimposed fast oscillations. During awake immobility, all four regions displayed a respiration-entrained rhythm (RR) with decreasing power from OB to CA1, which coupled exclusively to the 80- to 120-Hz gamma subband (γ₂). During exploration, when theta activity was prevailing, OB and PLC still showed exclusive coupling of RR with γ₂ and no theta-gamma coupling, whereas PAC and CA1 switched to selective coupling of theta with 40- to 80-Hz (γ₁) and 120- to 160-Hz (γ₃) gamma subbands. Our data illustrate a strong, specific interaction between neuronal activity patterns and respiration. Moreover, our results suggest that the coupling between slow and fast oscillations is a general brain mechanism not limited to the theta rhythm.

Hydroalcoholic extract of Myrtus communis can alter anxiety and sleep parameters: a behavioural and EEG sleep pattern study in mice and rats

CONTEXT

Myrtus communis L. (Myrtaceae), myrtle, is an evergreen shrub with strong antibacterial, anti-inflammatory, antihyperglycemic and antioxidant activities. Also, it is used as a sedative-hypnotic plant in Iranian traditional medicine.

OBJECTIVE

This study evaluates the effect of 80% ethanolic extract of M. communis leaves on sleep and anxiety in mice and rats.

MATERIALS AND METHODS

Male NMRI mice were subjected to open field, righting reflex, grip strength and pentylentetrazole-induced seizure tests. Male Wistar rats were used to evaluate the alterations in rapid eye movement (REM) and non-REM (NREM) sleep. They were treated with 25-400 mg/kg doses of the extract intraperitoneally.

RESULTS

The applied doses (50-200 mg/kg) of M. communis extract increased vertical (ED50 = 40.2 ± 6.6 mg/kg) and vertical and horizontal activity (ED50 = 251 ± 55 mg/kg), while treatment with 200 and 400 mg/kg attenuated muscle tone significantly compared to vehicle treated animals (p < 0.001 for all) in a dose-independent manner. Also, a significant hypnotic and not anticonvulsant effect was observed when animals were treated with 200 mg/kg of the extract (p < 0.01). In this regard, electroencephalography results showed that REM sleep time was decreased (2.4 ± 0.5%), while total and NREM sleep times were increased significantly compared to the control group of mice (82.5 ± 7.6%).

DISCUSSION AND CONCLUSION

The data show the anxiolytic and muscle relaxant effect of the extract without anticonvulsant activities. The anxiolytic, myorelaxant and hypnotic effects without effect on seizure threshold are in line with the effect of a alpha 2 GABA receptor agonist.

Olfactory Bulb Field Potentials and Respiration in Sleep-Wake States of Mice

It is well established that local field potentials (LFP) in the rodent olfactory bulb (OB) follow respiration. This respiration-related rhythm (RR) in OB depends on nasal air flow, indicating that it is conveyed by sensory inputs from the nasal epithelium. Recently RR was found outside the olfactory system, suggesting that it plays a role in organizing distributed network activity. It is therefore important to measure RR and to delineate it from endogenous electrical rhythms like theta which cover similar frequency bands in small rodents. In order to validate such measurements in freely behaving mice, we compared rhythmic LFP in the OB with two respiration-related biophysical parameters: whole-body plethysmography (PG) and nasal temperature (thermocouple; TC). During waking, all three signals reflected respiration with similar reliability. Peak power of RR in OB decreased with increasing respiration rate whereas power of PG increased. During NREM sleep, respiration-related TC signals disappeared and large amplitude slow waves frequently concealed RR in OB. In this situation, PG provided a reliable signal while breathing-related rhythms in TC and OB returned only during microarousals. In summary, local field potentials in the olfactory bulb do reliably reflect respiratory rhythm during wakefulness and REM sleep but not during NREM sleep.

Unsupervised Estimation of Mouse Sleep Scores and Dynamics Using a Graphical Model of Electrophysiological Measurements

The proportion, number of bouts, and mean bout duration of different vigilance states (Wake, NREM, REM) are useful indices of dynamics in experimental sleep research. These metrics are estimated by first scoring state, sometimes using an algorithm, based on electrophysiological measurements such as the electroencephalogram (EEG) and electromyogram (EMG), and computing their values from the score sequence. Isolated errors in the scores can lead to large discrepancies in the estimated sleep metrics. But most algorithms score sleep by classifying the state from EEG/EMG features independently in each time epoch without considering the dynamics across epochs, which could provide contextual information. The objective here is to improve estimation of sleep metrics by fitting a probabilistic dynamical model to mouse EEG/EMG data and then predicting the metrics from the model parameters. Hidden Markov models (HMMs) with multivariate Gaussian observations and Markov state transitions were fitted to unlabeled 24-h EEG/EMG feature time series from 20 mice to model transitions between the latent vigilance states; a similar model with unbiased transition probabilities served as a reference. Sleep metrics predicted from the HMM parameters did not deviate significantly from manual estimates except for rapid eye movement sleep (REM) ([Formula: see text]; Wilcoxon signed-rank test). Changes in value from Light to Dark conditions correlated well with manually estimated differences (Spearman’s rho 0.43–0.84) except for REM. HMMs also scored vigilance state with over 90% accuracy. HMMs of EEG/EMG features can therefore characterize sleep dynamics from EEG/EMG measurements, a prerequisite for characterizing the effects of perturbation in sleep monitoring and control applications.

Multiple classifier systems for automatic sleep scoring in mice

BACKGROUND

Electroencephalogram (EEG) and electromyogram (EMG) recordings are often used in rodents to study sleep architecture and sleep-associated neural activity. These recordings must be scored to designate what sleep/wake state the animal is in at each time point. Manual sleep-scoring is very time-consuming, so machine-learning classifier algorithms have been used to automate scoring.

NEW METHOD

Instead of using single classifiers, we implement a multiple classifier system. The multiple classifier is built from six base classifiers: decision tree, k-nearest neighbors, naïve Bayes, support vector machine, neural net, and linear discriminant analysis. Decision tree and k-nearest neighbors were improved into ensemble classifiers by using bagging and random subspace. Confidence scores from each classifier were combined to determine the final classification. Ambiguous epochs can be rejected and left for a human to classify.

RESULTS

Support vector machine was the most accurate base classifier, and had error rate of 0.054. The multiple classifier system reduced the error rate to 0.049, which was not significantly different from a second human scorer. When 10% of epochs were rejected, the remaining epochs' error rate dropped to 0.018.

COMPARISON WITH EXISTING METHOD(S)

Compared with the most accurate single classifier (support vector machine), the multiple classifier reduced errors by 9.4%. The multiple classifier surpassed the accuracy of a second human scorer after rejecting only 2% of epochs.

CONCLUSIONS

Multiple classifier systems are an effective way to increase automated sleep scoring accuracy. Improvements in autoscoring will allow sleep researchers to increase sample sizes and recording lengths, opening new experimental possibilities.

Impaired theta-gamma coupling in APP-deficient mice

Amyloid precursor protein (APP) is critically involved in the pathophysiology of Alzheimer's disease, but its physiological functions remain elusive. Importantly, APP knockout (APP-KO) mice exhibit cognitive deficits, suggesting that APP plays a role at the neuronal network level. To investigate this possibility, we recorded local field potentials (LFPs) from the posterior parietal cortex, dorsal hippocampus and lateral prefrontal cortex of freely moving APP-KO mice. Spectral analyses showed that network oscillations within the theta- and gamma-frequency bands were not different between APP-KO and wild-type mice. Surprisingly, however, while gamma amplitude coupled to theta phase in all recorded regions of wild-type animals, in APP-KO mice theta-gamma coupling was strongly diminished in recordings from the parietal cortex and hippocampus, but not in LFPs recorded from the prefrontal cortex. Thus, lack of APP reduces oscillatory coupling in LFP recordings from specific brain regions, despite not affecting the amplitude of the oscillations. Together, our findings reveal reduced cross-frequency coupling as a functional marker of APP deficiency at the network level.

SegWay: A simple framework for unsupervised sleep segmentation in experimental EEG recordings

Sleep analysis in animal models typically involves recording an electroencephalogram (EEG) and electromyogram (EMG) and scoring vigilance state in brief epochs of data as Wake, REM (rapid eye movement sleep) or NREM (non-REM) either manually or using a computer algorithm. Computerized methods usually estimate features from each epoch like the spectral power associated with distinctive cortical rhythms and dissect the feature space into regions associated with different states by applying thresholds, or by using supervised/unsupervised statistical classifiers; but there are some factors to consider when using them:•

Most classifiers require scored sample data, elaborate heuristics or computational steps not easily reproduced by the average sleep researcher, who is the targeted end user.

•

Even when prediction is reasonably accurate, small errors can lead to large discrepancies in estimates of important sleep metrics such as the number of bouts or their duration.

•

As we show here, besides partitioning the feature space by vigilance state, modeling transitions between the states can give more accurate scores and metrics.

An unsupervised sleep segmentation framework, “SegWay”, is demonstrated by applying the algorithm step-by-step to unlabeled EEG recordings in mice. The accuracy of sleep scoring and estimation of sleep metrics is validated against manual scores.

Noninvasive dissection of mouse sleep using a piezoelectric motion sensor

BACKGROUND

Changes in autonomic control cause regular breathing during NREM sleep to fluctuate during REM. Piezoelectric cage-floor sensors have been used to successfully discriminate sleep and wake states in mice based on signal features related to respiration and other movements. This study presents a classifier for noninvasively classifying REM and NREM using a piezoelectric sensor.

NEW METHOD

Vigilance state was scored manually in 4-s epochs for 24-h EEG/EMG recordings in 20 mice. An unsupervised classifier clustered piezoelectric signal features quantifying movement and respiration into three states: one active; and two inactive with regular and irregular breathing, respectively. These states were hypothesized to correspond to Wake, NREM, and REM, respectively. States predicted by the classifier were compared against manual EEG/EMG scores to test this hypothesis.

RESULTS

Using only piezoelectric signal features, an unsupervised classifier distinguished Wake with high (89% sensitivity, 96% specificity) and REM with moderate (73% sensitivity, 75% specificity) accuracy, but NREM with poor sensitivity (51%) and high specificity (96%). The classifier sometimes confused light NREM sleep - characterized by irregular breathing and moderate delta EEG power - with REM. A supervised classifier improved sensitivities to 90, 81, and 67% and all specificities to over 90% for Wake, NREM, and REM, respectively.

COMPARISON WITH EXISTING METHODS

Unlike most actigraphic techniques, which only differentiate sleep from wake, the proposed piezoelectric method further dissects sleep based on breathing regularity into states strongly correlated with REM and NREM.

CONCLUSIONS

This approach could facilitate large-sample screening for genes influencing different sleep traits, besides drug studies or other manipulations.

Interictal spikes during sleep are an early defect in the Tg2576 mouse model of β-amyloid neuropathology

It has been suggested that neuronal hyperexcitability contributes to Alzheimer's disease (AD), so we asked how hyperexcitability develops in a common mouse model of β-amyloid neuropathology - Tg2576 mice. Using video-EEG recordings, we found synchronized, large amplitude potentials resembling interictal spikes (IIS) in epilepsy at just 5 weeks of age, long before memory impairments or β-amyloid deposition. Seizures were not detected, but they did occur later in life, suggesting that IIS are possibly the earliest stage of hyperexcitability. Interestingly, IIS primarily occurred during rapid-eye movement (REM) sleep, which is notable because REM is associated with increased cholinergic tone and cholinergic impairments are implicated in AD. Although previous studies suggest that cholinergic antagonists would worsen pathophysiology, the muscarinic antagonist atropine reduced IIS frequency. In addition, we found IIS occurred in APP51 mice which overexpress wild type (WT)-APP, although not as uniformly or as early in life as Tg2576 mice. Taken together with results from prior studies, the data suggest that surprising and multiple mechanisms contribute to hyperexcitability. The data also suggest that IIS may be a biomarker for early detection of AD.

An automated sleep-state classification algorithm for quantifying sleep timing and sleep-dependent dynamics of electroencephalographic and cerebral metabolic parameters

INTRODUCTION

Rodent sleep research uses electroencephalography (EEG) and electromyography (EMG) to determine the sleep state of an animal at any given time. EEG and EMG signals, typically sampled at >100 Hz, are segmented arbitrarily into epochs of equal duration (usually 2-10 seconds), and each epoch is scored as wake, slow-wave sleep (SWS), or rapid-eye-movement sleep (REMS), on the basis of visual inspection. Automated state scoring can minimize the burden associated with state and thereby facilitate the use of shorter epoch durations.

METHODS

We developed a semiautomated state-scoring procedure that uses a combination of principal component analysis and naïve Bayes classification, with the EEG and EMG as inputs. We validated this algorithm against human-scored sleep-state scoring of data from C57BL/6J and BALB/CJ mice. We then applied a general homeostatic model to characterize the state-dependent dynamics of sleep slow-wave activity and cerebral glycolytic flux, measured as lactate concentration.

RESULTS

More than 89% of epochs scored as wake or SWS by the human were scored as the same state by the machine, whether scoring in 2-second or 10-second epochs. The majority of epochs scored as REMS by the human were also scored as REMS by the machine. However, of epochs scored as REMS by the human, more than 10% were scored as SWS by the machine and 18 (10-second epochs) to 28% (2-second epochs) were scored as wake. These biases were not strain-specific, as strain differences in sleep-state timing relative to the light/dark cycle, EEG power spectral profiles, and the homeostatic dynamics of both slow waves and lactate were detected equally effectively with the automated method or the manual scoring method. Error associated with mathematical modeling of temporal dynamics of both EEG slow-wave activity and cerebral lactate either did not differ significantly when state scoring was done with automated versus visual scoring, or was reduced with automated state scoring relative to manual classification.

CONCLUSIONS

Machine scoring is as effective as human scoring in detecting experimental effects in rodent sleep studies. Automated scoring is an efficient alternative to visual inspection in studies of strain differences in sleep and the temporal dynamics of sleep-related physiological parameters.

Circadian cycle-dependent EEG biomarkers of pathogenicity in adult mice following prenatal exposure to in utero inflammation

Intrauterine infection or inflammation in preterm neonates is a known risk for adverse neurological outcomes, including cognitive, motor and behavioral disabilities. Our previous data suggest that there is acute fetal brain inflammation in a mouse model of intrauterine exposure to lipopolysaccharides (LPS). We hypothesized that the in utero inflammation induced by LPS produces long-term electroencephalogram (EEG) biomarkers of neurodegeneration in the exposed mice that could be determined by using continuous quantitative video/EEG/electromyogram (EMG) analyses. A single LPS injection at E17 was performed in pregnant CD1 dams. Control dams were injected with same volumes of saline (LPS n=10, Control n=8). At postnatal age of P90-100, 24-h synchronous video/EEG/EMG recordings were done using a tethered recording system and implanted subdural electrodes. Behavioral state scoring was performed blind to treatment group, on each 10s EEG epoch using synchronous video, EMG and EEG trace signatures to generate individual hypnograms. Automated EEG power spectrums were analyzed for delta and theta-beta power ratios during wake vs. sleep cycles. Both control and LPS hypnograms showed an ultradian wake/sleep cycling. Since rodents are nocturnal animals, control mice showed the expected diurnal variation with significantly longer time spent in wake states during the dark cycle phase. In contrast, the LPS-treated mice lost this circadian rhythm. Sleep microstructure also showed significant alteration in the LPS mice specifically during the dark cycle, caused by significantly longer average non-rapid eye movement (NREM) cycle durations. No significance was found between treatment groups for the delta power data; however, significant activity-dependent changes in theta-beta power ratios seen in controls were absent in the LPS-exposed mice. In conclusion, exposure to in utero inflammation in CD1 mice resulted in significantly altered sleep architecture as adults that were circadian cycle and activity state dependent.

Mapping pathological phenotypes in a mouse model of CDKL5 disorder

Mutations in cyclin-dependent kinase-like 5 (CDKL5) cause early-onset epileptic encephalopathy, a neurodevelopmental disorder with similarities to Rett Syndrome. Here we describe the physiological, molecular, and behavioral phenotyping of a Cdkl5 conditional knockout mouse model of CDKL5 disorder. Behavioral analysis of constitutive Cdkl5 knockout mice revealed key features of the human disorder, including limb clasping, hypoactivity, and abnormal eye tracking. Anatomical, physiological, and molecular analysis of the knockout uncovered potential pathological substrates of the disorder, including reduced dendritic arborization of cortical neurons, abnormal electroencephalograph (EEG) responses to convulsant treatment, decreased visual evoked responses (VEPs), and alterations in the Akt/rpS6 signaling pathway. Selective knockout of Cdkl5 in excitatory and inhibitory forebrain neurons allowed us to map the behavioral features of the disorder to separable cell-types. These findings identify physiological and molecular deficits in specific forebrain neuron populations as possible pathological substrates in CDKL5 disorder.

FASTER: an unsupervised fully automated sleep staging method for mice

Identifying the stages of sleep, or sleep staging, is an unavoidable step in sleep research and typically requires visual inspection of electroencephalography (EEG) and electromyography (EMG) data. Currently, scoring is slow, biased and prone to error by humans and thus is the most important bottleneck for large-scale sleep research in animals. We have developed an unsupervised, fully automated sleep staging method for mice that allows less subjective and high-throughput evaluation of sleep. Fully Automated Sleep sTaging method via EEG/EMG Recordings (FASTER) is based on nonparametric density estimation clustering of comprehensive EEG/EMG power spectra. FASTER can accurately identify sleep patterns in mice that have been perturbed by drugs or by genetic modification of a clock gene. The overall accuracy is over 90% in every group. 24-h data are staged by a laptop computer in 10 min, which is faster than an experienced human rater. Dramatically improving the sleep staging process in both quality and throughput FASTER will open the door to quantitative and comprehensive animal sleep research.

Anxiety-related mechanisms of respiratory dysfunction in a mouse model of Rett syndrome

Rett syndrome (RTT) is a severe neurological disorder that is associated with mutations in the methyl-CpG binding protein 2 (MECP2) gene. RTT patients suffer from mental retardation and behavioral disorders, including heightened anxiety and state-dependent breathing irregularities, such as hyperventilation and apnea. Many symptoms are recapitulated by the Mecp2-null male mice (Mecp2(-/y)). To characterize developmental progression of the respiratory phenotype and explore underlying mechanisms, we examined Mecp2(-/y) and wild-type (WT) mice from presymptomatic periods to end-stage disease. We monitored breathing patterns of unrestrained mice during wake-sleep states and while altering stress levels using movement restraint or threatening odorant (trimethylthiazoline). Respiratory motor patterns generated by in situ working heart-brainstem preparations (WHBPs) were measured to assess function of brainstem respiratory networks isolated from suprapontine structures. Data revealed two general stages of respiratory dysfunction in Mecp2(-/y) mice. At the early stage, respiratory abnormalities were limited to wakefulness, correlated with markers of stress (increased fecal deposition and blood corticosterone levels), and alleviated by antalarmin (corticotropin releasing hormone receptor 1 antagonist). Furthermore, the respiratory rhythm generated by WHBPs was similar in WT and Mecp2(-/y) mice. During the later stage, respiratory abnormalities were evident during wakefulness and sleep. Also, WHBPs from Mecp2(-/y) showed central apneas. We conclude that, at early disease stages, stress-related modulation from suprapontine structures is a significant factor in the Mecp2(-/y) respiratory phenotype and that anxiolytics may be effective. At later stages, abnormalities of brainstem respiratory networks are a significant cause of irregular breathing patterns and central apneas.

Global slowing of network oscillations in mouse neocortex by diazepam

Benzodiazepines have a broad spectrum of clinical applications including sedation, anti-anxiety, and anticonvulsive therapy. At the cellular level, benzodiazepines are allosteric modulators of GABA(A) receptors; they increase the efficacy of inhibition in neuronal networks by prolonging the duration of inhibitory postsynaptic potentials. This mechanism of action predicts that benzodiazepines reduce the frequency of inhibition-driven network oscillations, consistent with observations from human and animal EEG. However, most of existing data are restricted to frequency bands below ∼30 Hz. Recent data suggest that faster cortical network rhythms are critically involved in several behavioral and cognitive tasks. We therefore analyzed diazepam effects on a large range of cortical network oscillations in freely moving mice, including theta (4-12 Hz), gamma (40-100 Hz) and fast gamma (120-160 Hz) oscillations. We also investigated diazepam effects over the coupling between theta phase and the amplitude fast oscillations. We report that diazepam causes a global slowing of oscillatory activity in all frequency domains. Oscillation power was changed differently for each frequency domain, with characteristic differences between active wakefulness, slow-wave sleep and REM sleep. Cross-frequency coupling strength, in contrast, was mostly unaffected by diazepam. Such state- and frequency-dependent actions of benzodiazepines on cortical network oscillations may be relevant for their specific cognitive effects. They also underline the strong interaction between local network oscillations and global brain states.