A four-state Markov model of sleep-wakefulness dynamics along light/dark cycle in mice

Augmenting Circadian Biology Research With Data Science

The nature of biological research is changing, driven by the emergence of big data, and new computational models to parse out the information therein. Traditional methods remain the core of biological research but are increasingly either augmented or sometimes replaced by emerging data science tools. This presents a profound opportunity for those circadian researchers interested in incorporating big data and related analyses into their plans. Here, we discuss the emergence of novel sources of big data that could be used to gain real-world insights into circadian biology. We further discuss technical considerations for the biologist interested in including data science approaches in their research. We conversely discuss the biological considerations for data scientists so that they can more easily identify the nuggets of biological rhythms insight that might too easily be lost through application of standard data science approaches done without an appreciation of the way biological rhythms shape the variance of complex data objects. Our hope is that this review will make bridging disciplines in both directions (biology to computational and vice versa) easier. There has never been such rapid growth of cheap, accessible, real-world research opportunities in biology as now; collaborations between biological experts and skilled data scientists have the potential to mine out new insights with transformative impact.

Workflow for the unsupervised clustering of sleep stages identifies light and deep sleep in electrophysiological recordings in mice

BACKGROUND

Sleep physiology plays a critical role in brain development and aging. Accurate sleep staging, which categorizes different sleep states, is fundamental for sleep physiology studies. Traditional methods for sleep staging rely on manual, rule-based scoring techniques, which limit their accuracy and adaptability.

NEW METHOD

We describe, test and challenge a workflow for unsupervised clustering of sleep states (WUCSS) in rodents, which uses accelerometer and electrophysiological data to classify different sleep states. WUCSS utilizes unsupervised clustering to identify sleep states using six features, extracted from 4-second epochs.

RESULTS

We gathered high-quality EEG recordings combined with accelerometer data in diverse transgenic mouse lines (male ApoE3 versus ApoE4 knockin; male CNTNAP2 KO versus wildtype littermates). WUCSS showed high recall, precision, and F1-score against manual scoring on awake, NREM, and REM sleep states. Within NREM, WUCSS consistently identified two additional clusters that qualify as deep and light sleep states.

COMPARISON WITH EXISTING METHODS

The ability of WUCSS to discriminate between deep and light sleep enhanced the precision and comprehensiveness of the current mouse sleep physiology studies. This differentiation led to the discovery of an additional sleep phenotype, notably in CNTNAP2 KO mice, showcasing the method's superiority over traditional scoring methods.

CONCLUSIONS

WUCSS, with its unsupervised approach and classification of deep and light sleep states, provides an unbiased opportunity for researchers to enhance their understanding of sleep physiology. Its high accuracy, adaptability, and ability to save time and resources make it a valuable tool for improving sleep staging in both clinical and preclinical research.

Middle-Aged Lpaatδ-Deficient Mice Have Altered Metabolic Measures

Lysophosphatidic acid acyltransferases/acylglycerophosphate acyltransferases (LPAATs/AGPATs) are a group of homologous enzymes that catalyze the formation of phosphatidic acid (PA) from lysophosphatidic acid. We have previously reported that LPAATδ/AGPAT4 localizes to mitochondria, suggesting a potential role in energy metabolism. However, in prior studies of young Lpaatδ-deficient mice (age 9–12 weeks old), we found no differences in body weights, food intakes, activity levels, respiratory gas exchange, or energy expenditure compared to their wildtype (Wt) littermates. To test whether Lpaatδ−/− mice may develop differences in metabolic measures with advancing age, we recorded body weights and food intakes, and used metabolic chambers to assess ambulatory and locomotor activity levels, oxygen consumption (VO2), carbon dioxide production (VCO2), respiratory exchange ratio (RER), and total energy expenditure (heat). Fourteen-month-old Lpaatδ−/− mice had significantly lower mean body weights compared to Wt littermate controls (44.6 ± 1.08 g vs. 53.5 ± 0.42 g, respectively), but no significant differences in food intake or activity levels. This phenotypic difference was accompanied by significantly elevated 24 h daily, and 12 h light and dark photoperiod average VO2 (~20% higher) and VCO2 (~30% higher) measures, as well as higher RER and total energy expenditure (heat) values compared to Wt control littermates. Thus, an age-related metabolic phenotype is evident in Lpaatδ−/− mice. Future studies should examine the role of the lipid-modifying enzyme LPAATδ across the lifespan for greater insight into its role in normal and pathophysiology.

Sleep-wake dynamics pre- and post-exposure to chronic social stress

An analytical approach combining the statistical distributions of the sleep-wake bouts and the Markov transition matrix is used to explain the under-examined association between the microarchitecture of the sleep-wake cycle and susceptibility to chronic social stress in C57BL/6J mice. We separated the sleep-wake transitions into distinct sleep-wake sequences, NREM↔Wake and NREM→REM→Wake, which are controlled by independent neural circuits. Our findings imply greater pull toward the wake leading to early termination and fragmentation of the sleep bouts in the light in both sleep-wake sequences pre- and post-stress. Moreover, the stability of NREM in the NREM↔Wake transition was lower, and the probability of transitioning to wake was higher in susceptible relative to resilient or stress-naïve mice pre- and post-stress. Our findings help elucidate the mechanistic interplay between sleep and mood by suggesting the potential neural underpinnings of sleep disturbances responsible the aberrant transitions of sleep-wake bouts exhibited by the stress-susceptible phenotype.

Continuous long-term recording and triggering of brain neurovascular activity and behaviour in freely moving rodents

A minimally invasive, microchip-based approach enables continuous long-term recording of brain neurovascular activity, heart rate, and head movement in freely behaving rodents. This approach can also be used for transcranial optical triggering of cortical activity in mice expressing channelrhodopsin. The system uses optical intrinsic signal recording to measure cerebral blood volume, which under baseline conditions is correlated with spontaneous neuronal activity. The arterial pulse and breathing can be quantified as a component of the optical intrinsic signal. Multi-directional head movement is measured simultaneously with a movement sensor. A separate movement tracking element through a camera enables precise mapping of overall movement within an enclosure. Data is processed by a dedicated single board computer, and streamed from multiple enclosures to a central server, enabling simultaneous remote monitoring and triggering in many subjects. One application of this system described here is the characterization of changes in of cerebral blood volume, heart rate and behaviour that occur with the sleep-wake cycle over weeks. Another application is optical triggering and recording of cortical spreading depression (CSD), the slowly propagated wave of neurovascular activity that occurs in the setting of brain injury and migraine aura. The neurovascular features of CSD are remarkably different in the awake vs. anaesthetized state in the same mouse. With its capacity to continuously and synchronously record multiple types of physiological and behavioural data over extended time periods in combination with intermittent triggering of brain activity, this inexpensive method has the potential for widespread practical application in rodent research. KEY POINTS: Recording and triggering of brain activity in mice and rats has typically required breaching the skull, and experiments are often performed under anaesthesia A minimally invasive microchip system enables continuous recording and triggering of neurovascular activity, and analysis of heart rate and behaviour in freely behaving rodents over weeks This system can be used to characterize physiological and behavioural changes associated with the sleep-wake cycle over extended time periods This approach can also be used with mice expressing channelrhodopsin to trigger and record cortical spreading depression (CSD) in freely behaving subjects. The neurovascular responses to CSD are remarkably different under anaesthesia compared with the awake state. The method is inexpensive and straightforward to employ at a relatively large scale. It enables translational investigation of a wide range of physiological and pathological conditions in rodent models of neurological and systemic diseases.

Fear conditioning potentiates the hippocampal CA1 commissural pathway in vivo and increases awake phase sleep

The hippocampus is essential for spatial learning and memory. To assess learning we used contextual fear conditioning (cFC), where animals learn to associate a place with aversive events like foot‐shocks. Candidate memory mechanisms for cFC are long‐term potentiation (LTP) and long‐term depression (LTD), but there is little direct evidence of them operating in the hippocampus in vivo following cFC. Also, little is known about the behavioral state changes induced by cFC. To address these issues, we recorded local field potentials in freely behaving mice by stimulating in the left dorsal CA1 region and recording in the right dorsal CA1 region. Synaptic strength in the commissural pathway was monitored by measuring field excitatory postsynaptic potentials (fEPSPs) before and after cFC. After cFC, the commissural pathway's synaptic strength was potentiated. Although recordings occurred during the wake phase of the light/dark cycle, the mice slept more in the post‐conditioning period than in the pre‐conditioning period. Relative to awake periods, in non‐rapid eye movement (NREM) sleep the fEPSPs were larger in both pre‐ and post‐conditioning periods. We also found a significant negative correlation between the animal's speed and fEPSP size. Therefore, to avoid confounds in the fEFSP potentiation estimates, we controlled for speed‐related and sleep‐related fEPSP changes and still found that cFC induced long‐term potentiation, but no significant long‐term depression. Synaptic strength changes were not found in the control group that simply explored the fear‐conditioning chamber, indicating that exploration of the novel place did not produce the measurable effects caused by cFC. These results show that following cFC, the CA1 commissural pathway is potentiated, likely contributing to the functional integration of the left and right hippocampi in fear memory consolidation. In addition, the cFC paradigm produces significant changes in an animal's behavioral state, which are observable as proximal changes in sleep patterns.

Stability of nocturnal wake and sleep stages defines central nervous system disorders of hypersomnolence

STUDY OBJECTIVES

We determine if young people with narcolepsy type 1 (NT1), narcolepsy type 2 (NT2), and idiopathic hypersomnia (IH) have distinct nocturnal sleep stability phenotypes compared to subjectively sleepy controls.

METHODS

Participants were 5- to 21-year old and drug-naïve or drug free: NT1 (n = 46), NT2 (n = 12), IH (n = 18), and subjectively sleepy controls (n = 48). We compared the following sleep stability measures from polysomnogram recording between each hypersomnolence disorder to subjectively sleepy controls: number of wake and sleep stage bouts, Kaplan-Meier survival curves for wake and sleep stages, and median bout durations.

RESULTS

Compared to the subjectively sleepy control group, NT1 participants had more bouts of wake and all sleep stages (p ≤ .005) except stage N3. NT1 participants had worse survival of nocturnal wake, stage N2, and rapid eye movement (REM) bouts (p < .005). In the first 8 hours of sleep, NT1 participants had longer stage N1 bouts but shorter REM (all ps < .004). IH participants had a similar number of bouts but better survival of stage N2 bouts (p = .001), and shorter stage N3 bouts in the first 8 hours of sleep (p = .003). In contrast, NT2 participants showed better stage N1 bout survival (p = .006) and longer stage N1 bouts (p = .02).

CONCLUSIONS

NT1, NT2, and IH have unique sleep physiology compared to subjectively sleepy controls, with only NT1 demonstrating clear nocturnal wake and sleep instability. Overall, sleep stability measures may aid in diagnoses and management of these central nervous system disorders of hypersomnolence.

Stability of neocortical synapses across sleep and wake states during the critical period in rats

Sleep is important for brain plasticity, but its exact function remains mysterious. An influential but controversial idea is that a crucial function of sleep is to drive widespread downscaling of excitatory synaptic strengths. Here, we used real-time sleep classification, ex vivo measurements of postsynaptic strength, and in vivo optogenetic monitoring of thalamocortical synaptic efficacy to ask whether sleep and wake states can constitutively drive changes in synaptic strength within the neocortex of juvenile rats. We found that miniature excitatory postsynaptic current amplitudes onto L4 and L2/3 pyramidal neurons were stable across sleep- and wake-dense epochs in both primary visual (V1) and prefrontal cortex (PFC). Further, chronic monitoring of thalamocortical synaptic efficacy in V1 of freely behaving animals revealed stable responses across even prolonged periods of natural sleep and wake. Together, these data demonstrate that sleep does not drive widespread downscaling of synaptic strengths during the highly plastic critical period in juvenile animals. Whether this remarkable stability across sleep and wake generalizes to the fully mature nervous system remains to be seen.

Glutamatergic Neurons in the Preoptic Hypothalamus Promote Wakefulness, Destabilize NREM Sleep, Suppress REM Sleep, and Regulate Cortical Dynamics

Clinical and experimental data from the last nine decades indicate that the preoptic area of the hypothalamus is a critical node in a brain network that controls sleep onset and homeostasis. By contrast, we recently reported that a group of glutamatergic neurons in the lateral and medial preoptic area increases wakefulness, challenging the long-standing notion in sleep neurobiology that the preoptic area is exclusively somnogenic.

Clinical and experimental data from the last nine decades indicate that the preoptic area of the hypothalamus is a critical node in a brain network that controls sleep onset and homeostasis. By contrast, we recently reported that a group of glutamatergic neurons in the lateral and medial preoptic area increases wakefulness, challenging the long-standing notion in sleep neurobiology that the preoptic area is exclusively somnogenic. However, the precise role of these subcortical neurons in the control of behavioral state transitions and cortical dynamics remains unknown. Therefore, in this study, we used conditional expression of excitatory hM3Dq receptors in these preoptic glutamatergic (Vglut2⁺) neurons and show that their activation initiates wakefulness, decreases non-rapid eye movement (NREM) sleep, and causes a persistent suppression of rapid eye movement (REM) sleep. We also demonstrate, for the first time, that activation of these preoptic glutamatergic neurons causes a high degree of NREM sleep fragmentation, promotes state instability with frequent arousals from sleep, decreases body temperature, and shifts cortical dynamics (including oscillations, connectivity, and complexity) to a more wake-like state. We conclude that a subset of preoptic glutamatergic neurons can initiate, but not maintain, arousals from sleep, and their inactivation may be required for NREM stability and REM sleep generation. Further, these data provide novel empirical evidence supporting the hypothesis that the preoptic area causally contributes to the regulation of both sleep and wakefulness.

SIGNIFICANCE STATEMENT Historically, the preoptic area of the hypothalamus has been considered a key site for sleep generation. However, emerging modeling and empirical data suggest that this region might play a dual role in sleep-wake control. We demonstrate that chemogenetic stimulation of preoptic glutamatergic neurons produces brief arousals that fragment sleep, persistently suppresses REM sleep, causes hypothermia, and shifts EEG patterns toward a “lighter” NREM sleep state. We propose that preoptic glutamatergic neurons can initiate, but not maintain, arousal from sleep and gate REM sleep generation, possibly to block REM-like intrusions during NREM-to-wake transitions. In contrast to the long-standing notion in sleep neurobiology that the preoptic area is exclusively somnogenic, we provide further evidence that preoptic neurons also generate wakefulness.

Machine learning approaches reveal subtle differences in breathing and sleep fragmentation in Phox2b-derived astrocytes ablated mice

Modern neurophysiology research requires the interrogation of high-dimensionality data sets. Machine learning and artificial intelligence (ML/AI) workflows have permeated into nearly all aspects of daily life in the developed world but have not been implemented routinely in neurophysiological analyses. The power of these workflows includes the speed at which they can be deployed, their availability of open-source programming languages, and the objectivity permitted in their data analysis. We used classification-based algorithms, including random forest, gradient boosted machines, support vector machines, and neural networks, to test the hypothesis that the animal genotypes could be separated into their genotype based on interpretation of neurophysiological recordings. We then interrogate the models to identify what were the major features utilized by the algorithms to designate genotype classification. By using raw EEG and respiratory plethysmography data, we were able to predict which recordings came from genotype class with accuracies that were significantly improved relative to the no information rate, although EEG analyses showed more overlap between groups than respiratory plethysmography. In comparison, conventional methods where single features between animal classes were analyzed, differences between the genotypes tested using baseline neurophysiology measurements showed no statistical difference. However, ML/AI workflows successfully were capable of providing successful classification, indicating that interactions between features were different in these genotypes. ML/AI workflows provide new methodologies to interrogate neurophysiology data. However, their implementation must be done with care so as to provide high rigor and reproducibility between laboratories. We provide a series of recommendations on how to report the utilization of ML/AI workflows for the neurophysiology community.NEW & NOTEWORTHY ML/AI classification workflows are capable of providing insight into differences between genotypes for neurophysiology research. Analytical techniques utilized in the neurophysiology community can be augmented by implementing ML/AI workflows. Random forest is a robust classification algorithm for respiratory plethysmography data. Utilization of ML/AI workflows in neurophysiology research requires heightened transparency and improved community research standards.

Covert sleep-related biological processes are revealed by probabilistic analysis in Drosophila

Reduced sleep duration and disrupted sleep quality are correlated with adverse mental and physical health outcomes. Better tools for measuring the internal drives for sleep and wake in model organisms would facilitate understanding the role of sleep quality in health. We defined two conditional probabilities, P(Wake) and P(Doze), that can be calculated from recordings of Drosophila activity without disturbing the animal. We demonstrated that P(Wake) is a measure of sleep depth and that P(Doze) is a measure of sleep pressure. In parallel, we developed an automatic classifier for state-based analysis of Drosophila behavior. These analysis tools will improve our understanding of the pharmacology and neuronal regulation of behavioral drives in the Drosophila brain.

Sleep pressure and sleep depth are key regulators of wake and sleep. Current methods of measuring these parameters in Drosophila melanogaster have low temporal resolution and/or require disrupting sleep. Here we report analysis tools for high-resolution, noninvasive measurement of sleep pressure and depth from movement data. Probability of initiating activity, P(Wake), measures sleep depth while probability of ceasing activity, P(Doze), measures sleep pressure. In vivo and computational analyses show that P(Wake) and P(Doze) are largely independent and control the amount of total sleep. We also develop a Hidden Markov Model that allows visualization of distinct sleep/wake substates. These hidden states have a predictable relationship with P(Doze) and P(Wake), suggesting that the methods capture the same behaviors. Importantly, we demonstrate that both the Doze/Wake probabilities and the sleep/wake substates are tied to specific biological processes. These metrics provide greater mechanistic insight into behavior than measuring the amount of sleep alone.

SGK1.1 Reduces Kainic Acid-Induced Seizure Severity and Leads to Rapid Termination of Seizures

Approaches to control epilepsy, one of the most important idiopathic brain disorders, are of great importance for public health. We have previously shown that in sympathetic neurons the neuronal isoform of the serum and glucocorticoid-regulated kinase (SGK1.1) increases the M-current, a well-known target for seizure control. The effect of SGK1.1 activation on kainate-induced seizures and neuronal excitability was studied in transgenic mice that express a permanently active form of the kinase, using electroencephalogram recordings and electrophysiological measurements in hippocampal brain slices. Our results demonstrate that SGK1.1 activation leads to reduced seizure severity and lower mortality rates following status epilepticus, in an M-current–dependent manner. EEG is characterized by reduced number, shorter duration, and early termination of kainate-induced seizures in the hippocampus and cortex. Hippocampal neurons show decreased excitability associated to increased M-current, without altering basal synaptic transmission or other neuronal properties. Altogether, our results reveal a novel and selective anticonvulsant pathway that promptly terminates seizures, suggesting that SGK1.1 activation can be a potent factor to secure the brain against permanent neuronal damage associated to epilepsy.