Modeling motivational deficits in mouse models of schizophrenia: behavior analysis as a guide for neuroscience

In recent years it has become possible to develop animal models of psychiatric disease in genetically modified mice. While great strides have been made in the development of genetic and neurobiological tools with which to model psychiatric disease, elucidation of neural and molecular mechanisms thought to underlie behavioral phenotypes has been hindered by an inadequate analysis of behavior. This is unfortunate given the fact that the experimental analysis of behavior has created powerful methods for isolating and describing the functional properties of behavioral mechanisms that are capable of providing deep understanding of behavioral phenotypes. A better understanding of the biological basis of normal behavior and its disturbance in psychiatric disease will require the application of these rigorous behavior analytic tools to animal models. In this review we provide an example of a merging of genetic and behavioral methods and illustrate its utility in the analysis of a mouse model of the motivational deficits in schizophrenia. The synergy between basic behavior analysis, neuroscience, and animal models of psychiatric disease has great potential for achieving a deeper understanding of behavior and its neurobiological mechanisms as well as for leading to improvements in diagnosis and treatment in clinical settings.     

Sleep‐like behavior and 24‐h rhythm disruption in the Tc1 mouse model of Down syndrome

Down syndrome is a common disorder associated with intellectual disability in humans. Among a variety of severe health problems, patients with Down syndrome exhibit disrupted sleep and abnormal 24‐h rest/activity patterns. The transchromosomic mouse model of Down syndrome, Tc1, is a trans‐species mouse model for Down syndrome, carrying most of human chromosome 21 in addition to the normal complement of mouse chromosomes and expresses many of the phenotypes characteristic of Down syndrome. To date, however, sleep and circadian rhythms have not been characterized in Tc1 mice. Using both circadian wheel‐running analysis and video‐based sleep scoring, we showed that these mice exhibited fragmented patterns of sleep‐like behaviour during the light phase of a 12:12‐h light/dark (LD) cycle with an extended period of continuous wakefulness at the beginning of the dark phase. Moreover, an acute light pulse during night‐time was less effective in inducing sleep‐like behaviour in Tc1 animals than in wild‐type controls. In wheel‐running analysis, free running in constant light (LL) or constant darkness (DD) showed no changes in the circadian period of Tc1 animals although they did express subtle behavioural differences including a reduction in total distance travelled on the wheel and differences in the acrophase of activity in LD and in DD. Our data confirm that Tc1 mice express sleep‐related phenotypes that are comparable with those seen in Down syndrome patients with moderate disruptions in rest/activity patterns and hyperactive episodes, while circadian period under constant lighting conditions is essentially unaffected.     

Scoring atonia during normal and pathological rapid eye movement sleep: Visual and automatic quantification methods

One of the essential features of rapid eye movement (REM) sleep behavior disorder is REM sleep without atonia seen during nocturnal polysomnographic recordings. In this paper we provide an overview about the varied scoring criteria proposed for visual analysis of loss of atonia during REM sleep. The automatic quantification of loss of atonia overcomes many of the limitations of visual scoring and these new approaches are reviewed. Finally, the contributions of these automatic methods to the understanding of the complex mechanisms underlying muscle atonia and motor suppression during REM sleep are briefly illustrated.

     

A mathematical model of the sleep/wake cycle

We present a biologically-based mathematical model that accounts for several features of the human sleep/wake cycle. These features include the timing of sleep and wakefulness under normal and sleep-deprived conditions, ultradian rhythms, more frequent switching between sleep and wakefulness due to the loss of orexin and the circadian dependence of several sleep measures. The model demonstrates how these features depend on interactions between a circadian pacemaker and a sleep homeostat and provides a biological basis for the two-process model for sleep regulation. The model is based on previous “flip–flop” conceptual models for sleep/wake and REM/NREM and we explore whether the neuronal components in these flip–flop models, with the inclusion of a sleep-homeostatic process and the circadian pacemaker, are sufficient to account for the features of the sleep/wake cycle listed above. The model is minimal in the sense that, besides the sleep homeostat and constant cortical drives, the model includes only those nuclei described in the flip–flop models. Each of the cell groups is modeled by at most two differential equations for the evolution of the total population activity, and the synaptic connections are consistent with those described in the flip–flop models. A detailed analysis of the model leads to an understanding of the mathematical mechanisms, as well as insights into the biological mechanisms, underlying sleep/wake dynamics.     

XCI in preimplantation mouse and human embryos: first there is remodelling…

Female eutherians silence one of their X chromosomes to accomplish an equal dose of X-linked gene expression compared with males. The mouse is the most widely used animal model in XCI research and has proven to be of great significance for understanding the complex mechanism of X-linked dosage compensation. Although the basic principles of XCI are similar in mouse and humans, differences exist in the timing of XCI initiation, the genetic elements involved in XCI regulation and the form of XCI in specific tissues. Therefore, the mouse has its limitations as a model to understand early human XCI and analysis of human tissues is required. In this review, we describe these differences with respect to initiation of XCI in human and mouse preimplantation embryos, the extra-embryonic tissues and the in vitro model of the epiblast: the embryonic stem cells.     

Epilepsy: A heuristic model for relating nocturnal sleep EEG spike distributions to the risk of seizure

The cumulative temporal distribution of epileptiform events in the sleep EEG of epileptic patients is represented by a second-degree regression equation as a basis for summarizing properties of the spike distribution. This heuristic regression model attempts to provide a quantitative as well as an intuitive physical basis for understanding the relationship between epileptiform events and the state of the epileptic patient. The mathematical model is derived in this paper and the regression parameters associated with the model are interpreted. Computer implementation of the epileptiform event detection and the regression analysis have been carried out and typical results obtained from all-night sleep EEG recordings are described.     

Phenotypic screening for heart rate variability in the mouse

We developed a technology for heart rate (HR) variability (HRV) analysis in the mouse for characterization of HR dynamics, modulated by vagal and sympathetic activity. The mouse is the principal animal model for studying biological processes. Mouse strains are now available harboring gene mutations providing fundamental insights into molecular mechanisms underlying cardiac electrical diseases. Future progress depends on enhanced understanding of these fundamental mechanisms and the implementation of methods for the functional analysis of mouse cardiovascular physiology. By telemetric techniques, standard time and frequency-domain measures of HRV were computed with and without autonomic blockade, and baroreflex sensitivity testing was performed. HR modulation in the high-frequency component is predominantly mediated by the parasympathetic nervous system, whereas the low-frequency component is under the influence of both the parasympathetic and sympathetic systems. The presented technology and protocol allow for assessment of autonomic regulation of the murine HR. Phenotypic screening for HR regulation in mice will further enhance the value of the mouse as a model of heritable electrophysiological human disease.     

Virtues and limitations of the preimplantation mouse embryo as a model system

The mouse is the most widely used model of preimplantation embryo development, but is it a good model? Its small size, prolificacy and ease of handling make the mouse a relatively low cost, readily available and attractive alternative when embryos from other species are difficult or expensive to obtain. However, the real power of the mouse as a model lies in mouse genetics. The development of inbred mouse strains facilitated gene discovery as well as our understanding of gene function and regulation while the development of tools to introduce precise genetic modifications uniquely positioned the mouse as a powerful model system for uncovering gene function. However, all models have limitations; the small size of the mouse limits tissue availability and manipulations that can be performed and differences in physiology among species may make it inappropriate to extrapolate from the mouse to other species. Thus, rather than extrapolating directly from the mouse to other species, it may be more useful to use the mouse as a model system for developing and refining hypotheses to be tested directly in species of interest. In this brief review, the value of the preimplantation mouse embryo as a model is considered, both as a model for other species and as a model for the mouse, as understanding the virtues and limitations of the mouse as a model system is essential to its appropriate use.     

Commentary: models of sleep regulation: successes and continuing challenges

Quantitative models have been developed to describe salient aspects of human sleep regulation. The two-process model of sleep regulation and the thermoregulatory model of sleep control highlight the interaction between sleep homeostasis and circadian rhythmicity and the association between sleep and temperature regulation, respectively. These models have been successful and inspiring, but continuing progress remains dependent on rigorous testing of some of their basic assumptions. Whereas it has been established that EEG slow-wave activity is a marker of sleep homeostasis, its causal role in regulating the timing of sleep and wakefulness remains to be demonstrated conclusively. Likewise, the causal role of the temperature regulatory system in sleep timing requires further investigation. In both models, many parameters have yet to be associated with specific physiologic processes. This makes it challenging, at least within the framework of these models, to account for interindividual differences or age-related changes in such features as sleep duration and sleep timing, as well as changes in the phase angle between the sleep-wake cycle and accepted markers of the circadian pacemaker, such as the body temperature or melatonin rhythm. Although the models may describe adequately global sleep patterns and their circadian modulation, detailed modeling of the frequent short awakenings from, and the subsequent transitions back to, sleep, as well as the variation of the propensity to awaken across the ultradian non-REM-REM cycle, is not addressed. Incoporation of these aspects of sleep in mathematical models of sleep regulation may further our understanding of a key aspect of sleep regulation, that is, its timing.     

High-density EEG Recordings of the Freely Moving Mice using Polyimide-based Microelectrode

Electroencephalogram (EEG) indicates the averaged electrical activity of the neuronal populations on a large-scale level. It is widely utilized as a noninvasive brain monitoring tool in cognitive neuroscience as well as a diagnostic tool for epilepsy and sleep disorders in neurology. However, the underlying mechanism of EEG rhythm generation is still under the veil. Recently introduced polyimide-based microelectrode (PBM-array) for high resolution mouse EEG¹ is one of the trials to answer the neurophysiological questions on EEG signals based on a rich genetic resource that the mouse model contains for the analysis of complex EEG generation process. This application of nanofabricated PBM-array to mouse skull is an efficient tool for collecting large-scale brain activity of transgenic mice and accommodates to identify the neural correlates to certain EEG rhythms in conjunction with behavior. However its ultra-thin thickness and bifurcated structure cause a trouble in handling and implantation of PBM-array. In the presented video, the preparation and surgery steps for the implantation of PBM-array on a mouse skull are described step by step. Handling and surgery tips to help researchers succeed in implantation are also provided.     

Reciprocal interaction of sleep and synaptic plasticity

Synaptic plasticity underlying learning and memory has been proposed, on the basis of several experimental approaches, to be intimately related with sleep: 1) The idea that sleep contributes to stabilization of acquired memory arises from numerous studies depriving subjects or animals of sleep. 2) Evidence from developing technologies supports "offline" reprocessing of recent experiences during sleep. 3) Recent analysis of the thalamocortical system establishes the reciprocal observation that sleep itself is a plastic process affected by waking experience. This overview synthesizes these converging perspectives across a variety of brain regions and species. We propose the developing visual pathway as a fruitful model for comprehensive understanding of sleep and synaptic plasticity.     

Tracking the Sleep Onset Process: An Empirical Model of Behavioral and Physiological Dynamics

The sleep onset process (SOP) is a dynamic process correlated with a multitude of behavioral and physiological markers. A principled analysis of the SOP can serve as a foundation for answering questions of fundamental importance in basic neuroscience and sleep medicine. Unfortunately, current methods for analyzing the SOP fail to account for the overwhelming evidence that the wake/sleep transition is governed by continuous, dynamic physiological processes. Instead, current practices coarsely discretize sleep both in terms of state, where it is viewed as a binary (wake or sleep) process, and in time, where it is viewed as a single time point derived from subjectively scored stages in 30-second epochs, effectively eliminating SOP dynamics from the analysis. These methods also fail to integrate information from both behavioral and physiological data. It is thus imperative to resolve the mismatch between the physiological evidence and analysis methodologies. In this paper, we develop a statistically and physiologically principled dynamic framework and empirical SOP model, combining simultaneously-recorded physiological measurements with behavioral data from a novel breathing task requiring no arousing external sensory stimuli. We fit the model using data from healthy subjects, and estimate the instantaneous probability that a subject is awake during the SOP. The model successfully tracked physiological and behavioral dynamics for individual nights, and significantly outperformed the instantaneous transition models implicit in clinical definitions of sleep onset. Our framework also provides a principled means for cross-subject data alignment as a function of wake probability, allowing us to characterize and compare SOP dynamics across different populations. This analysis enabled us to quantitatively compare the EEG of subjects showing reduced alpha power with the remaining subjects at identical response probabilities. Thus, by incorporating both physiological and behavioral dynamics into our model framework, the dynamics of our analyses can finally match those observed during the SOP.     

Rhythmicity in Mice Selected for Extremes in Stress Reactivity: Behavioural,Endocrine and Sleep Changes Resembling Endophenotypes of Major Depression

Background

Dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis, including hyper- or hypo-activity of the stress hormone system, plays a critical role in the pathophysiology of mood disorders such as major depression (MD). Further biological hallmarks of MD are disturbances in circadian rhythms and sleep architecture. Applying a translational approach, an animal model has recently been developed, focusing on the deviation in sensitivity to stressful encounters. This so-called ‘stress reactivity’ (SR) mouse model consists of three separate breeding lines selected for either high (HR), intermediate (IR), or low (LR) corticosterone increase in response to stressors.

Methodology/Principle Findings

In order to contribute to the validation of the SR mouse model, our study combined the analysis of behavioural and HPA axis rhythmicity with sleep-EEG recordings in the HR/IR/LR mouse lines. We found that hyper-responsiveness to stressors was associated with psychomotor alterations (increased locomotor activity and exploration towards the end of the resting period), resembling symptoms like restlessness, sleep continuity disturbances and early awakenings that are commonly observed in melancholic depression. Additionally, HR mice also showed neuroendocrine abnormalities similar to symptoms of MD patients such as reduced amplitude of the circadian glucocorticoid rhythm and elevated trough levels. The sleep-EEG analyses, furthermore, revealed changes in rapid eye movement (REM) and non-REM sleep as well as slow wave activity, indicative of reduced sleep efficacy and REM sleep disinhibition in HR mice.

Conclusion/Significance

Thus, we could show that by selectively breeding mice for extremes in stress reactivity, clinically relevant endophenotypes of MD can be modelled. Given the importance of rhythmicity and sleep disturbances as biomarkers of MD, both animal and clinical studies on the interaction of behavioural, neuroendocrine and sleep parameters may reveal molecular pathways that ultimately lead to the discovery of new targets for antidepressant drugs tailored to match specific pathologies within MD.     

Comparison of Different Methods for the Evaluation of Treatment Effects from the Sleep EEG of Patients with Major Depression

In healthy subjects, sleep has a typical structure of three to five cyclic transitions between different sleep states. In major depression, this regular pattern is often destroyed but can be reestablished during successful treatment. The differences between healthy and abnormal sleep are generally assessed in a time-consuming process, which consists of determining the nightly variations of the sleep states (the hypnogram) based on visual inspection of the electroencephalogram (EEG), electrooculogram, and electromyogram. In this study, three different methods of sleep EEG analysis (spectrum, outlier, and recurrence analysis) have been examined with regard to their ability to extract information about treatment effects in patients with major depression. Our data suggest that improved sleep patterns during treatment with antidepressant medication can be identified with an appropriate analysis of the EEG. By comparing different methods, we have found that many treatment effects identified by spectrum analysis can be reproduced by the much simpler technique of outlier analysis. Finally, the cyclic structure of sleep and its modification by antidepressant treatment is best illustrated by a non-linear approach, the so-called recurrence method.     

Invited review: Identifying new mouse models of cardiovascular disease: a review of high-throughput screens of mutagenized and inbred strains.

The mouse is a proven model for studying human disease. Many strains exist that exhibit either natural or engineered genetic variation and thereby enable the elucidation of pathways involved in the development of cardiovascular disease. Although those mouse models have been fundamental to advancing our knowledge base, we are still at an early stage in understanding how genes contribute to complex disorders. There remains a need for new animal models that closely represent human disease. To expedite their development, we have established the Center for New Mouse Models of Heart, Lung, Blood, and Sleep Disorders at The Jackson Laboratory. We are using a phenotype-driven approach to identify mutations leading to atherosclerosis, hypertension, obesity, blood disorders, lung dysfunction, thrombosis, and disordered sleep. Our high-throughput, comprehensive phenotyping draws from two sources for new models: 1) the natural variation among over 40 inbred mouse strains and 2) chemically induced, whole-genome mutagenized mice. Here, we review our cardiovascular screens and present some hypertensive, obese, and cardiovascular models identified with this approach.     

Motoren des Schlafs

The triggers of sleep Sleep‐active sleep‐promoting neurons are key inhibitors of wakefulness and thus crucial triggers of sleep. They appear to be widespread in animals and may share a common evolutionary origin. Important steps towards understanding sleep control will be the dissection of neural circuits and molecular players for sleep induction across species. Given the high level of conservation of sleep across species, the use of model organisms to solve sleep is valid, and findings from various models can be merged to obtain a holistic view of sleep control.     

Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders

Recent neuroscience studies demonstrate that a deeper understanding of brain function requires a deeper understanding of behavior. Detailed behavioral measurements are now often collected using video cameras, resulting in an increased need for computer vision algorithms that extract useful information from video data. Here we introduce a new video analysis tool that combines the output of supervised pose estimation algorithms (e.g. DeepLabCut) with unsupervised dimensionality reduction methods to produce interpretable, low-dimensional representations of behavioral videos that extract more information than pose estimates alone. We demonstrate this tool by extracting interpretable behavioral features from videos of three different head-fixed mouse preparations, as well as a freely moving mouse in an open field arena, and show how these interpretable features can facilitate downstream behavioral and neural analyses. We also show how the behavioral features produced by our model improve the precision and interpretation of these downstream analyses compared to using the outputs of either fully supervised or fully unsupervised methods alone.     

Video tracking and analysis of sleep in Drosophila melanogaster

In the past decade, Drosophila has emerged as an ideal model organism for studying the genetic components of sleep as well as its regulation and functions. In fruit flies, sleep can be conveniently estimated by measuring the locomotor activity of the flies using techniques and instruments adapted from the field of circadian behavior. However, proper analysis of sleep requires degrees of spatial and temporal resolution higher than is needed by circadian scientists, as well as different algorithms and software for data analysis. Here I describe how to perform sleep experiments in flies using techniques and software (pySolo and pySolo-Video) previously developed in my laboratory. I focus on computer-assisted video tracking to monitor fly activity. I explain how to plan a sleep analysis experiment that covers the basic aspects of sleep, how to prepare the necessary equipment and how to analyze the data. By using this protocol, a typical sleep analysis experiment can be completed in 5-7 d.     

Mutants of purine metabolism in cultured mouse and hamster cells

The regulation of purine metabolism in mammalian cells is poorly understood presently. A variety of mutants in hamster and mouse is now available for these studies. It is clear that selective methods provide the potential for isolation of a greater variety and number of such mutants. It is predicted that the methods are now available for extensive study of the purine biosynthetic pathway with respect to both its genetic organization and cellular regulation. These mouse and hamster mutants will undoubtedly facilitate our understanding of this multistep pathway which until recently has been limited to the study of rare human mutants.     

Mathematical Models for Sleep-Wake Dynamics: Comparison of the Two-Process Model and a Mutual Inhibition Neuronal Model

Sleep is essential for the maintenance of the brain and the body, yet many features of sleep are poorly understood and mathematical models are an important tool for probing proposed biological mechanisms. The most well-known mathematical model of sleep regulation, the two-process model, models the sleep-wake cycle by two oscillators: a circadian oscillator and a homeostatic oscillator. An alternative, more recent, model considers the mutual inhibition of sleep promoting neurons and the ascending arousal system regulated by homeostatic and circadian processes. Here we show there are fundamental similarities between these two models. The implications are illustrated with two important sleep-wake phenomena. Firstly, we show that in the two-process model, transitions between different numbers of daily sleep episodes can be classified as grazing bifurcations. This provides the theoretical underpinning for numerical results showing that the sleep patterns of many mammals can be explained by the mutual inhibition model. Secondly, we show that when sleep deprivation disrupts the sleep-wake cycle, ostensibly different measures of sleepiness in the two models are closely related. The demonstration of the mathematical similarities of the two models is valuable because not only does it allow some features of the two-process model to be interpreted physiologically but it also means that knowledge gained from study of the two-process model can be used to inform understanding of the behaviour of the mutual inhibition model. This is important because the mutual inhibition model and its extensions are increasingly being used as a tool to understand a diverse range of sleep-wake phenomena such as the design of optimal shift-patterns, yet the values it uses for parameters associated with the circadian and homeostatic processes are very different from those that have been experimentally measured in the context of the two-process model.