Low-cost solution for rodent home-cage behaviour monitoring

Vibration-Based Non-Contact Activity Classification for Home Cage Monitoring Using a Tuned-Beam IMU Sensing Device

This work presents a vibration-based non-contact monitoring method to classify the physical activity of a mouse inside a home cage. A novel tuned-beam sensing device is developed to measure low-amplitude activity-induced cage vibrations. The sensing device uses a mechanical beam structure to enhance a six-axis IMU that increases the signal-to-noise ratio (SNR) by 20 to 40 times in a relevant environment. A sophisticated classification algorithm is developed to process vibration sequences with a variable time frame that utilizes multi-level discrete wavelet transformation (MLDWT) to extract time-frequency features and optimize signal properties. The extracted features are classified by a convolutional neural network-long short-term memory (CNN-LSTM) machine learning model to determine the activity class. The ground truth is obtained with a camera-based system using EthoVision XT from Noldus and a custom post-processor. The method is developed on a dataset containing 300 h of vibration measurements with camera-based reference and includes two separate home cages and two individual mice. The method classifies the activity types Resting, Stationary Activity, Walking, Activity in Feeder, and Drinking with an accuracy of 86.81% and an average F1 score of 0.798 using a 9 s time frame. In long-term monitoring, the proposed method reproduces behavioral patterns such as sleep and acclimatization as accurately as the reference method, enabling home cage monitoring in the husbandry environment with a low-cost sensor.

Assessing cognitive flexibility in mice using a custom-built touchscreen chamber

Automated touchscreen systems have become increasingly prevalent in rodent model screening. This technology has significantly enhanced cognitive and behavioral assessments in mice and has bridged the translational gap between basic research using rodent models and human clinical research. Our study introduces a custom-built touchscreen operant conditioning chamber powered by a Raspberry Pi and a commercially available computer tablet, which effectively addresses the significant cost barriers traditionally associated with this technology. In order to test our prototype, we decided to train C57BL/6 mice on a visual discrimination serial-reversal task, and both C57BL/6 and AppNL-G-Fstrain - an Alzheimer's Disease (AD) mouse model - on a new location discrimination serial-reversal task. The results demonstrated a clear progression toward asymptotic performance, particularly in the location discrimination task, which also revealed potential genotype-specific deficits, with AppNL-G-F mice displaying an increase in the average number of errors in the first reversal as well as in perseverative errors, compared to wild-type mice. These results validate the practical utility of our touchscreen apparatus and underline its potential to provide insights into the behavioral and cognitive markers of neurobiological disorders.

Orthologs of Drosophila pointed and Arginine kinase 1 impact sleep in mice

Model organisms such as Drosophila are powerful tools to study the genetic basis of sleep. Previously, we identified the genes pointed and Arginine kinase 1 using selective breeding for long and short sleep duration in an outbred population of Drosophila. pointed is a transcription factor that is part of the epidermal growth factor receptor signaling pathway, while Arginine kinase 1 is involved in proline and arginine metabolism. Conserved orthologs of these genes exist in mice, leading us to hypothesize that they would also impact sleep in a murine model. We generated mutations in the murine orthologs Ets1 and Ckm using CRISPR in a C57BL/6N background and used video analysis to measure sleep in the mice. Both mutations affected sleep parameters, and the effects were observed predominantly in female mice, with males showing fewer differences from littermate controls. The study of natural populations in flies therefore leads to candidate genes with functional conservation on sleep in mammals.

AI-powered home cage system for real-time tracking and analysis of rodent behavior

Researchers in animal behavior and neuroscience devote considerable time to observing rodents behavior and physiological responses, with AI monitoring systems reducing personnel workload. This study presents the RodentWatch (RW) system, which leverages deep learning to automatically identify experimental animal behaviors in home cage environments. A single multifunctional camera and edge device are installed inside the animal's home cage, allowing continuous real-time monitoring of the animal's behavior, position, and body temperature for extended periods. We investigated identifying the drinking and resting behaviors of rats, with recognition accuracy enhanced through contextual object labeling and modified non-maximum suppression (NMS) schemes. Two tests-a light cycle change test and a sucrose preference test-were conducted to evaluate the usability of this system in rat behavioral experiments. This system enables notable advancements in image-based behavior recognition for living rodents.

High quality, high throughput, and low-cost simultaneous video recording of 60 animals in operant chambers using PiRATeMC

BACKGROUND

The development of Raspberry Pi-based recording devices for video analyses of drug self-administration studies has been shown to be promising in terms of affordability, customizability, and capacity to extract in-depth behavioral patterns. Yet, most video recording systems are limited to a few cameras making them incompatible with large-scale studies.

NEW METHOD

We expanded the PiRATeMC (Pi-based Remote Acquisition Technology for Motion Capture) recording system by increasing its scale, modifying its code, and adding equipment to accommodate large-scale video acquisition, accompanied by data on throughput capabilities, video fidelity, synchronicity of devices, and comparisons between Raspberry Pi 3B+ and 4B models.

RESULTS

Using PiRATeMC default recording parameters resulted in minimal storage (∼350MB/h), high throughput (< ∼120 seconds/Pi), high video fidelity, and synchronicity within ∼0.02 seconds, affording the ability to simultaneously record 60 animals in individual self-administration chambers for various session lengths at a fraction of commercial costs. No consequential differences were found between Raspberry Pi models.

COMPARISON WITH EXISTING METHOD(S)

This system allows greater acquisition of video data simultaneously than other video recording systems by an order of magnitude with less storage needs and lower costs. Additionally, we report in-depth quantitative assessments of throughput, fidelity, and synchronicity, displaying real-time system capabilities.

CONCLUSIONS

The system presented is able to be fully installed in a month's time by a single technician and provides a scalable, low cost, and quality-assured procedure with a high-degree of customization and synchronicity between recording devices, capable of recording a large number of subjects and timeframes with high turnover in a variety of species and settings.

c-fos induction in the choroid plexus, tanycytes and pars tuberalis is an early indicator of spontaneous arousal from torpor in a deep hibernator

Hibernation is an extreme state of seasonal energy conservation, reducing metabolic rate to as little as 1% of the active state. During the hibernation season, many species of hibernating mammals cycle repeatedly between the active (aroused) and hibernating (torpid) states (T-A cycling), using brown adipose tissue (BAT) to drive cyclical rewarming. The regulatory mechanisms controlling this process remain undefined but are presumed to involve thermoregulatory centres in the hypothalamus. Here, we used the golden hamster (Mesocricetus auratus), and high-resolution monitoring of BAT, core body temperature and ventilation rate, to sample at precisely defined phases of the T-A cycle. Using c-fos as a marker of cellular activity, we show that although the dorsomedial hypothalamus is active during torpor entry, neither it nor the pre-optic area shows any significant changes during the earliest stages of spontaneous arousal. Contrastingly, in three non-neuronal sites previously linked to control of metabolic physiology over seasonal and daily time scales - the choroid plexus, pars tuberalis and third ventricle tanycytes - peak c-fos expression is seen at arousal initiation. We suggest that through their sensitivity to factors in the blood or cerebrospinal fluid, these sites may mediate metabolic feedback-based initiation of the spontaneous arousal process.

Evidence for chronic headaches induced by pathological changes of myodural bridge complex

Clinical studies have shown that there may be a certain relationship between pathological changes of the myodural bridge complex (MDBC) and chronic headaches of unknown cause. But there is still a lack of experimental evidence to explain the possible mechanism. This study aims to further confirm this relationship between MDBC and chronic headaches and explore its potential occurrence mechanism in rats. Bleomycin (BLM) or phosphate-buffered saline (PBS) was injected into the myodural bridge fibers of rats to establish the hyperplastic model of MDBC. After 4 weeks, the occurrence of headaches in rats was evaluated through behavioral scores. The immunohistochemistry staining method was applied to observe the expression levels of headache-related neurotransmitters in the brain. Masson trichrome staining results showed that the number of collagen fibers of MDBC was increased in the BLM group compared to those of the other two groups. It revealed hyperplastic changes of MDBC. The behavioral scores of the BLM group were significantly higher than those of the PBS group and the blank control group. Meanwhile, expression levels of CGRP and 5-HT in the headache-related nuclei of the brain were increased in the BLM group. The current study further confirms the view that there is a relationship between pathological changes of MDBC and chronic headaches of unknown cause. This study may provide anatomical and physiological explanations for the pathogenesis of some chronic headaches of unknown cause.

ReptiLearn: An automated home cage system for behavioral experiments in reptiles without human intervention

Understanding behavior and its evolutionary underpinnings is crucial for unraveling the complexities of brain function. Traditional approaches strive to reduce behavioral complexity by designing short-term, highly constrained behavioral tasks with dichotomous choices in which animals respond to defined external perturbation. In contrast, natural behaviors evolve over multiple time scales during which actions are selected through bidirectional interactions with the environment and without human intervention. Recent technological advancements have opened up new possibilities for experimental designs that more closely mirror natural behaviors by replacing stringent experimental control with accurate multidimensional behavioral analysis. However, these approaches have been tailored to fit only a small number of species. This specificity limits the experimental opportunities offered by species diversity. Further, it hampers comparative analyses that are essential for extracting overarching behavioral principles and for examining behavior from an evolutionary perspective. To address this limitation, we developed ReptiLearn-a versatile, low-cost, Python-based solution, optimized for conducting automated long-term experiments in the home cage of reptiles, without human intervention. In addition, this system offers unique features such as precise temperature measurement and control, live prey reward dispensers, engagement with touch screens, and remote control through a user-friendly web interface. Finally, ReptiLearn incorporates low-latency closed-loop feedback allowing bidirectional interactions between animals and their environments. Thus, ReptiLearn provides a comprehensive solution for researchers studying behavior in ectotherms and beyond, bridging the gap between constrained laboratory settings and natural behavior in nonconventional model systems. We demonstrate the capabilities of ReptiLearn by automatically training the lizard Pogona vitticeps on a complex spatial learning task requiring association learning, displaced reward learning, and reversal learning.

Behavioral phenotyping based on physical inactivity can predict sleep in female rats before, during, and after sleep disruption

BACKGROUND

A noninvasive method that can accurately quantify sleep before, during, and after sleep disruption (SD) has not been validated in female rats across their estrous cycle. In female rats, we hypothesized that the duration of physical inactivity (PIA) required to predict sleep would 1) change with the differences in baseline sleep between the circadian and estrous cycle phases and 2) predict sleep and the change in sleep (Δsleep) before, during, and after SD independent of circadian and estrous cycle phase.

NEW METHODS

EEG, EMG, physical activity and estrous cycle phase were measured in female Sprague-Dawley rats before, during, and after SD. Sleep was determined by two methods [EEG/EMG and a duration of continuous PIA (i.e., PIA criterion)]. Reliability between the methods was tested with a previously validated criterion (40 s). Sensitivity analyses and criterion-related validity analyses for sleep during SD and recovery were conducted across multiple PIA criteria (10 s-120 s). Predictability between the two methods and Δsleep was calculated.

RESULTS/COMPARISON WITH EXISTING METHODS

Three criteria (10 s, 20 s, 30 s) predicted baseline sleep independent of circadian and estrous cycle phase. Sleep during SD and recovery were predicted by two criteria (30 s and 10 s). Δsleep between study periods was not reliably predicted by a single PIA criterion.

CONCLUSION

PIA predicted sleep independent of estrous cycle phase in female rats. However, the specific criterion was dependent upon the study period (before, during, and after SD) and circadian phase. Thus, prior work validating a PIA criterion in male rodents is not applicable to the female rat.

Advancing preclinical chronic stress models to promote therapeutic discovery for human stress disorders

There is an urgent need to develop more effective treatments for stress-related illnesses, which include depression, post-traumatic stress disorder, and anxiety. We view animal models as playing an essential role in this effort, but to date, such approaches have generally not succeeded in developing therapeutics with new mechanisms of action. This is partly due to the complexity of the brain and its disorders, but also to inherent difficulties in modeling human disorders in rodents and to the incorrect use of animal models: namely, trying to recapitulate a human syndrome in a rodent which is likely not possible as opposed to using animals to understand underlying mechanisms and evaluating potential therapeutic paths. Recent transcriptomic research has established the ability of several different chronic stress procedures in rodents to recapitulate large portions of the molecular pathology seen in postmortem brain tissue of individuals with depression. These findings provide crucial validation for the clear relevance of rodent stress models to better understand the pathophysiology of human stress disorders and help guide therapeutic discovery. In this review, we first discuss the current limitations of preclinical chronic stress models as well as traditional behavioral phenotyping approaches. We then explore opportunities to dramatically enhance the translational use of rodent stress models through the application of new experimental technologies. The goal of this review is to promote the synthesis of these novel approaches in rodents with human cell-based approaches and ultimately with early-phase proof-of-concept studies in humans to develop more effective treatments for human stress disorders.

Abnormal patterns of sleep and waking behaviors are accompanied by neocortical oscillation disturbances in an Ank3 mouse model of epilepsy-bipolar disorder comorbidity

ANK3 is a leading bipolar disorder (BD) candidate gene in humans and provides a unique opportunity for studying epilepsy-BD comorbidity. Previous studies showed that deletion of Ank3-1b, a BD-associated variant of Ank3 in mice leads to increased firing threshold and diminished action potential dynamic range of parvalbumin (PV) interneurons and absence epilepsy, thus providing a biological mechanism linking epilepsy and BD. To explore the behavioral overlap of these disorders, we characterized behavioral patterns of Ank3-1b KO mice during overnight home-cage activity and examined network activity during these behaviors using paired video and EEG recordings. Since PV interneurons contribute to the generation of high-frequency gamma oscillations, we anticipated changes in the power of neocortical EEG signals in the gamma frequency range (> 25 Hz) during behavioral states related to human BD symptoms, including abnormal sleep, hyperactivity, and repetitive behaviors. Ank3-1b KO mice exhibited an overall increase in slow gamma (~25-45 Hz) power compared to controls, and slow gamma power correlated with seizure phenotype severity across behaviors. During sleep, increased slow gamma power correlated with decreased time spent in the rapid eye movement (REM) stage of sleep. Seizures were more common during REM sleep compared to non-REM (NREM) sleep. We also found that Ank3-1b KO mice were hyperactive and exhibited a repetitive behavior phenotype that co-occurred with increased slow gamma power. Our results identify a novel EEG biomarker associating Ank3 genetic variation with BD and epilepsy and suggest modulation of gamma oscillations as a potential therapeutic target.

High quality, high throughput, and low-cost simultaneous video recording of 60 animals in operant chambers using PiRATeMC

Background

The development of Raspberry Pi-based recording devices for video analyses of drug self-administration studies has shown to be promising in terms of affordability, customizability, and capacity to extract in-depth behavioral patterns. Yet, most video recording systems are limited to a few cameras making them incompatible with large-scale studies.

New Method

We expanded the PiRATeMC (Pi-based Remote Acquisition Technology for Motion Capture) recording system by increasing its scale, modifying its code, and adding equipment to accommodate large-scale video acquisition, accompanied by data on the throughput capabilities, video fidelity, synchronicity of devices, and comparisons between the Raspberry Pi 3B+ and 4B models.

Results

Using PiRATeMC default recording parameters resulted in minimal storage (~350MB/h), high throughput (< ~120 seconds/Pi), high video fidelity, and synchronicity within ~0.02 seconds, affording the ability to simultaneously record 60 animals in individual self-administration chambers at a fraction of current commercial costs. No consequential differences were found between Raspberry Pi 3B+ and 4B models.

Comparison with Existing Methods

This system allows greater acquisition of video data simultaneously than other video recording systems by an order of magnitude with less storage needs and lower costs. Additionally, we report in-depth quantitative assessments of throughput, fidelity, and synchronicity, displaying real-time system capabilities.

Conclusions

The system presented is able to be fully installed in a month's time by a single technician and provides a scalable, low cost, and quality-assured procedure with a high-degree of customization and synchronicity between recording devices, capable of recording a large number of subjects with high turnover in a variety of species and settings.

PiRATeMC: A highly flexible, scalable, and low-cost system for obtaining high quality video recordings for behavioral neuroscience

With the rapidly accelerating adoption of machine-learning based rodent behavioral tracking tools, there is an unmet need for a method of acquiring high quality video data that is scalable, flexible, and relatively low-cost. Many experimenters use webcams, GoPros, or other commercially available cameras that can be expensive, offer minimal flexibility of recording parameters, and not optimized for recording rodent behavior, leading to suboptimal and inconsistent video quality. Furthermore, commercially available products are not conducive for synchronizing multiple cameras, or interfacing with third-party equipment to allow time-locking of video to other equipment such as microcontrollers for closed-loop experiments. We present a low-cost, customizable ecosystem of behavioral recording equipment, PiRATeMC (Pi-based Remote Acquisition Technology for Motion Capture) based on Raspberry Pi Camera Boards with the ability to acquire high quality recordings in bright/low light, or dark conditions under infrared light. PiRATeMC offers users control over nearly every recording parameter, and can be fine-tuned to produce optimal videos in any behavioral apparatus. This setup can be scaled up for synchronous control of any number of cameras via a self-contained network without burdening institutional network infrastructure. The Raspberry Pi is an excellent platform with a large online community designed for novice and inexperienced programmers interested in using an open-source recording system. Importantly, PiRATeMC supports TTL and serial communication, allowing for synchronization and interfacing of video recording with behavioral or other third-party equipment. In sum, PiRATeMC minimizes the cost-prohibitive nature of conducting and analyzing high quality behavioral neuroscience studies, thereby increasing accessibility to behavioral neuroscience.

A conserved role for frizzled in sleep architecture

Previous studies of natural variants in Drosophila melanogaster implicated the Wnt signaling receptor frizzled in sleep. Given that the Wnt signaling pathway is highly conserved across species, we hypothesized that frizzled class receptor 1 (Fzd1), the murine homolog of frizzled, would also have a role in sleep. Using a CRISPR transgenic approach, we removed most of the Fzd1 coding region from C57BL/6N mice. We used a video assay to measure sleep characteristics in Fzd1-deficient mice. As Wnt signaling is known to affect visuospatial memory, we also examined the impact of the deletion on learning and memory using the novel object recognition (NOR) paradigm. Fzd1-deficient mice had altered sleep compared to littermate controls. The mice did not respond differently to the NOR paradigm compared to controls but did display anxiety-like behavior. Our strategy demonstrates that the study of natural variation in Drosophila sleep translates into candidate genes for sleep in vertebrate species such as the mouse.

Automated behavioral scoring: Do we even need humans?

The development of automated behavior scoring technology has been a tremendous boon to the study of social behavior. However, completely outsourcing behavioral analysis to a computer runs the risk of overlooking important nuances, and researchers risk distancing themselves from their very object of study. Here, I make the case that while automating analysis has been valuable, and overautomating analysis is risky, more effort should be spent automating the collection of behavioral data. Continuous automated behavioral observations conducted in situ have the promise to reduce confounding elements of social behavior research, such as handling stress, novel environments, one-time "snapshot" measures, and experimenter presence. Now that we have the capability to automatically process behavioral observations thanks to machine vision and machine learning, we would do well to leverage the same open-source ethos to increase the throughput of behavioral observation and collection. Fortunately, several such platforms have recently been developed. Repeated testing in the home environment will produce higher qualities and quantities of data, bringing us closer to realizing the ethological goals of studying animal behavior in a naturalistic context.

PiE: an open-source pipeline for home cage behavioral analysis

Over the last two decades a growing number of neuroscience labs are conducting behavioral assays in rodents. The equipment used to collect this behavioral data must effectively limit environmental and experimenter disruptions, to avoid confounding behavior data. Proprietary behavior boxes are expensive, offer limited compatible sensors, and constrain analysis with closed-source hardware and software. Here, we introduce PiE, an open-source, end-to-end, user-configurable, scalable, and inexpensive behavior assay system. The PiE system includes the custom-built behavior box to hold a home cage, as well as software enabling continuous video recording and individual behavior box environmental control. To limit experimental disruptions, the PiE system allows the control and monitoring of all aspects of a behavioral experiment using a remote web browser, including real-time video feeds. To allow experiments to scale up, the PiE system provides a web interface where any number of boxes can be controlled, and video data easily synchronized to a remote location. For the scoring of behavior video data, the PiE system includes a standalone desktop application that streamlines the blinded manual scoring of large datasets with a focus on quality control and assay flexibility. The PiE system is ideal for all types of behavior assays in which video is recorded. Users are free to use individual components of this setup independently, or to use the entire pipeline from data collection to analysis. Alpha testers have included scientists without prior coding experience. An example pipeline is demonstrated with the PiE system enabling the user to record home cage maternal behavior assays, synchronize the resulting data, conduct blinded scoring, and import the data into R for data visualization and analysis.

Galactic Cosmic Irradiation Alters Acute and Delayed Species-Typical Behavior in Male and Female Mice

Exposure to space galactic cosmic radiation is a principal consideration for deep space missions. While the effects of space irradiation on the nervous system are not fully known, studies in animal models have shown that exposure to ionizing radiation can cause neuronal damage and lead to downstream cognitive and behavioral deficits. Cognitive health implications put humans and missions at risk, and with the upcoming Artemis missions in which female crew will play a major role, advance critical analysis of the neurological and performance responses of male and female rodents to space radiation is vital. Here, we tested the hypothesis that simulated Galactic Cosmic Radiation (GCRSim) exposure disrupts species-typical behavior in mice, including burrowing, rearing, grooming, and nest-building that depend upon hippocampal and medial prefrontal cortex circuitry. Behavior comprises a remarkably well-integrated representation of the biology of the whole animal that informs overall neural and physiological status, revealing functional impairment. We conducted a systematic dose-response analysis of mature (6-month-old) male and female mice exposed to either 5, 15, or 50 cGy 5-ion GCRSim (H, Si, He, O, Fe) at the NASA Space Radiation Laboratory (NSRL). Behavioral performance was evaluated at 72 h (acute) and 91-days (delayed) postradiation exposure. Specifically, species-typical behavior patterns comprising burrowing, rearing, and grooming as well as nest building were analyzed. A Neuroscore test battery (spontaneous activity, proprioception, vibrissae touch, limb symmetry, lateral turning, forelimb outstretching, and climbing) was performed at the acute timepoint to investigate early sensorimotor deficits postirradiation exposure. Nest construction, a measure of neurological and organizational function in rodents, was evaluated using a five-stage Likert scale 'Deacon' score that ranged from 1 (a low score where the Nestlet is untouched) to 5 (a high score where the Nestlet is completely shredded and shaped into a nest). Differential acute responses were observed in females relative to males with respect to species-typical behavior following 15 cGy exposure while delayed responses were observed in female grooming following 50 cGy exposure. Significant sex differences were observed at both timepoints in nest building. No deficits in sensorimotor behavior were observed via the Neuroscore. This study revealed subtle, sexually dimorphic GCRSim exposure effects on mouse behavior. Our analysis provides a clearer understanding of GCR dose effects on species typical, sensorimotor and organizational behaviors at acute and delayed timeframes postirradiation, thereby setting the stage for the identification of underlying cellular and molecular events.

Socially Integrated Polysubstance (SIP) system: An open-source solution for continuous monitoring of polysubstance fluid intake in group housed mice

Despite impressive results from neuroscience research using rodent models, there is a paucity of successful translation from preclinical findings to effective pharmacological interventions for treatment of substance use disorder (SUD) in humans. One potential reason for lack of translation from animal models is difficulty in accurately replicating the lived experience of people who use drugs. Aspects of substance use in humans that are often not modeled in animal research include but are not limited to 1) voluntary timing and frequency of substance intake, 2) social environment during substance use, and 3) access to multiple substances and multiple concentrations of each substance. Critically, existing commercial equipment that allows for social housing and voluntary polysubstance use (e.g., home cage monitoring system) is prohibitively expensive and no open-source solutions exist. With these goals in mind, here we detail development of the Socially Integrated Polysubstance (SIP) system, an open-source and lower cost solution that allows for group housed rodents to self-administer multiple substances with continuous monitoring and measurement. In our current setup, each SIP cage contains four drinking stations, and each station is equipped with a RFID sensor and sipper tube connected to a unique fluid reservoir. Using this system, we can track which animal (implanted with unique RFID transponder) visits which drinking location and the amount they drink during each visit (in 20 ul increments). Using four flavors of Kool-Aid, here we demonstrate that the SIP system is reliable and accurate with high temporal resolution for long term monitoring of substance intake and behavior tracking in a social environment. The SIP cage system is a first step towards designing an accessible and flexible rodent model of substance use that more closely resembles the experience of people who use drugs.

PyMouseTracks: Flexible Computer Vision and RFID-Based System for Multiple Mouse Tracking and Behavioral Assessment

PyMouseTracks (PMT) is a scalable and customizable computer vision and radio frequency identification (RFID)-based system for multiple rodent tracking and behavior assessment that can be set up within minutes in any user-defined arena at minimal cost. PMT is composed of the online Raspberry Pi (RPi)-based video and RFID acquisition with subsequent offline analysis tools. The system is capable of tracking up to six mice in experiments ranging from minutes to days. PMT maintained a minimum of 88% detections tracked with an overall accuracy >85% when compared with manual validation of videos containing one to four mice in a modified home-cage. As expected, chronic recording in home-cage revealed diurnal activity patterns. In open-field, it was observed that novel noncagemate mouse pairs exhibit more similarity in travel trajectory patterns than cagemate pairs over a 10-min period. Therefore, shared features within travel trajectories between animals may be a measure of sociability that has not been previously reported. Moreover, PMT can interface with open-source packages such as DeepLabCut and Traja for pose estimation and travel trajectory analysis, respectively. In combination with Traja, PMT resolved motor deficits exhibited in stroke animals. Overall, we present an affordable, open-sourced, and customizable/scalable mouse behavior recording and analysis system.

Elevated estradiol during a hormone simulated pseudopregnancy decreases sleep and increases hypothalamic activation in female Syrian hamsters

Sleep disruptions are a common occurrence during the peripartum period. While physical and environmental factors associated with pregnancy and newborn care account for some sleep disruptions, there is evidence that peripartum fluctuations in estrogens may independently impact sleep. However, the impact of these large fluctuations in estrogens on peripartum sleep is unclear because it is difficult to tease apart the effects of estrogens on sleep from effects associated with the growth and development of the fetus or parental care. We therefore used a hormone-simulated pseudopregnancy (HSP) in female Syrian hamsters to test the hypothesis that pregnancy-like increases in estradiol decrease sleep in the absence of other factors. Adult female Syrian hamsters were ovariectomized and given daily hormone injections that simulate estradiol levels during early pregnancy, late pregnancy, and the postpartum period. Home cage video recordings were captured at seven timepoints and videos were analyzed for actigraphy. During "late pregnancy," total sleep time and sleep efficiency were decreased in hormone-treated animals during the white light period compared to pretest levels. Likewise, during "late pregnancy," locomotion was increased in the white light period for hormone-treated animals compared to pretest levels. These changes continued into the "postpartum period" for animals who continued to receive estradiol treatment, but not for animals who were withdrawn from estradiol. At the conclusion of the experiment, animals were euthanized and cFos expression was quantified in the ventral lateral preoptic area (VLPO) and lateral hypothalamus (LH). Animals who continued to receive high levels of estradiol during the "postpartum" period had significantly more cFos in the VLPO and LH than animals who were withdrawn from hormones or vehicle controls. Together, these data suggest that increased levels of estradiol during pregnancy are associated with sleep suppression, which may be mediated by increased activation of hypothalamic nuclei.

The importance of a multidimensional approach to the preclinical study of major depressive disorder and apathy

Both the neuropsychiatric syndrome of apathy and major depressive disorder comprise a heterogenous cluster of symptoms which span multiple behavioural domains. Despite this heterogeneity, there is a tendency in the preclinical literature to conclude a MDD or apathy-like phenotype from a single dimensional behavioural task used in isolation, which may lead to inaccurate phenotypic interpretation. This is significant, as apathy and major depressive disorder are clinically distinct with different underlying mechanisms and treatment approaches. At the clinical level, apathy and major depressive disorder can be dissociated in the negative valence (loss) domain of the Research Domain Criteria. Symptoms of MDD in the negative valence (loss) domain can include an exaggerated response to emotionally salient stimuli and low mood, while in contrast apathy is characterised by an emotionally blunted state. In this article, we highlight how using a single dimensional approach can limit psychiatric model interpretation. We discuss how integrating behavioural findings from both the positive and negative (loss) valence domains of the Research Domain Criteria can benefit interpretation of findings. We focus particularly on behaviours relating to the negative valence (loss) domain, which may be used to distinguish between apathy and major depressive disorder at the preclinical level. Finally, we consider how future approaches using home cage monitoring may offer a new opportunity to detect distinct behavioural profiles and benefit the overall translatability of findings.

The effect of Aβ seeding is dependent on the presence of knock-in genes in the App NL-G-F mice

Alzheimer's disease (AD) is characterized by the prion-like propagation of amyloid-β (Aβ). However, the role of Aβ in cognitive impairment is still unclear. To determine the causal role of Aβ in AD, we intracerebrally seeded the entorhinal cortex of a 2-month-old App NL-G-F mouse model with an Aβ peptide derived from patients who died from rapidly progressing AD. When the mice were 3 months of age or 1 month following seeding, spatial learning and memory were tested using the Morris water task. Immunohistochemical labeling showed seeding with the Aβ was found accelerate Aβ plaque deposition and microgliosis in the App NL-G-F mice, but this was dependent on the presence of the knocked-in genes. However, we found no correlation between pathology and spatial performance. The results of the present study show the seeding effects in the App NL-G-F knock-in model, and how these are dependent on the presence of a humanized App gene. But these pathological changes were not initially causal in memory impairment.

An open-source, low-cost voluntary running activity tracking tool for in vivo rodent studies

In vivo rodent behavioral and physiological studies often benefit from measurement of general activity. However, many existing instruments necessary to track such activity are high in cost and invasive within home cages, some even requiring extensive separate cage systems, limiting their widespread use to collect data. We present here a low-cost open-source alternative that measures voluntary wheel running activity and allows for modulation and customization, along with a reproducible and easy to set-up code pipeline for setup and analysis in Arduino IDE and R. Our robust, non-invasive scalable voluntary running activity tracker utilizes readily accessible magnets, Hall effect sensors, and an Arduino microcontroller. Importantly, it can interface with existing rodent home cages and wheel equipment, thus eliminating the need to transfer the mice to an unfamiliar environment. The system was validated both for accuracy by a rotating motor used to simulate mouse behavior, and in vivo. Our recorded data is consistent with results found in the literature showing that the mice run between 3 to 16 kilometers per night, and accurately captures speed and distance traveled continuously on the wheel. Such data are critical for analysis of highly variable behavior in mouse models and allow for characterization of behavioral metrics such as general activity. This system provides a flexible, low-cost methodology, and minimizes the cost, infrastructure, and personnel required for tracking voluntary wheel activity.

Pi USB Cam: A Simple and Affordable DIY Solution That Enables High-Quality, High-Throughput Video Capture for Behavioral Neuroscience Research

Video recording is essential for behavioral neuroscience research, but the majority of available systems suffer from poor cost-to-functionality ratio. Commercial options frequently come at high financial cost that prohibits scalability and throughput, whereas DIY solutions often require significant expertise and time investment unaffordable to many researchers. To address this, we combined a low-cost Raspberry Pi microcomputer, DIY electronics peripherals, freely available open-source firmware, and custom 3D-printed casings to create Pi USB Cam, a simple yet powerful and highly versatile video recording solution. Pi USB Cam is constructed using affordable and widely available components and requires no expertise to build and implement. The result is a system that functions as a plug-and-play USB camera that can be easily installed in various animal testing and housing sites and is readily compatible with popular behavioral and neural recording software. Here, we provide a comprehensive parts list and step-by-step instructions for users to build and implement their own Pi USB Cam system. In a series of benchmark comparisons, Pi USB Cam was able to capture ultra-wide fields of view of behaving rats given limited object distance and produced high image quality while maintaining consistent frame rates even under low-light and no-light conditions relative to a standard, commercially available USB camera. Video recordings were easily scaled using free, open-source software. Altogether, Pi USB Cam presents an elegant yet simple solution for behavioral neuroscientists seeking an affordable and highly flexible system to enable quality video recordings.

Blueprints for measuring natural behavior

Until recently laboratory tasks for studying behavior were highly artificial, simplified, and designed without consideration for the environmental or social context. Although such an approach offers good control over behavior, it does not allow for researching either voluntary responses or individual differences. Importantly for neuroscience studies, the activity of the neural circuits involved in producing unnatural, artificial behavior is variable and hard to predict. In addition, different ensembles may be activated depending on the strategy the animal adopts to deal with the spurious problem. Thus, artificial and simplified tasks based on responses, which do not occur spontaneously entail problems with modeling behavioral impairments and underlying brain deficits. To develop valid models of human disorders we need to test spontaneous behaviors consistently engaging well-defined, evolutionarily conserved neuronal circuits. Such research focuses on behavioral patterns relevant for surviving and thriving under varying environmental conditions, which also enable high reproducibility across different testing settings.

Protocol for non-invasive assessment of spontaneous movements of group-housed animals using remote video monitoring

Frequent monitoring of laboratory animals is critical for ensuring animal welfare and experimental data collection. To minimize the adverse and confounding effects caused by current monitoring protocols and human presence, we developed a low-cost, non-invasive, remotely accessible, extensible infrared video monitoring system. This protocol describes the construction and operation of the system, followed by applying deep-learning neural networks to track group-housed, unmarked mice for objective behavioral quantification. This system can be adapted to a variety of home-cage environments and species.

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Secure, low-cost, non-invasive, remote infrared video monitoring of animals

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Tracks group-housed, unmarked mice to quantify behavior and assess drug efficacy

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Extensible and requires no advanced technological or programming skills

CageView: A Smart Food Control and Monitoring System for Phenotypical Research In Vivo

The present work introduces an automated and smart system (named CageView) used to monitor a mouse, detect motion, and control access to food in accordance with experimental schedules. We describe the components of the CageView platform and give a summarized description on how we employed a convolutional neural network to detect and recognize a mouse in real time before presenting the results of a case study. In particular, CageView is a programmable and remotely operable system such that (1) an experimenter at a remote workstation may set up a feeding and fasting schedule that allows feeding and fasting without requiring the physical presence of a staff member, (2) the experimenter can control access to food in real time regardless of the preset schedule, (3) the experimenter has real-time access to a live video feed to assess the mouse, (4) an artificial intelligence system tracks the mouse’s location and physical activity, and (5) a record is kept of activity, which can be displayed as a 2D representation of mouse movement or a histogram showing mouse movement in 15-min blocks for the duration of the experiment.

Challenges of a small world analysis for the continuous monitoring of behavior in mice

Documenting a mouse's "real world" behavior in the "small world" of a laboratory cage with continuous video recordings offers insights into phenotypical expression of mouse genotypes, development and aging, and neurological disease. Nevertheless, there are challenges in the design of a small world, the behavior selected for analysis, and the form of the analysis used. Here we offer insights into small world analyses by describing how acute behavioral procedures can guide continuous behavioral methodology. We show how algorithms can identify behavioral acts including walking and rearing, circadian patterns of action including sleep duration and waking activity, and the organization of patterns of movement into home base activity and excursions, and how they are altered with aging. We additionally describe how specific tests can be incorporated within a mouse's living arrangement. We emphasize how machine learning can condense and organize continuous activity that extends over extended periods of time.

Measuring Locomotor Activity and Behavioral Aspects of Rodents Living in the Home-Cage

Automatization and technological advances have led to a larger number of methods and systems to monitor and measure locomotor activity and more specific behavior of a wide variety of animal species in various environmental conditions in laboratory settings. In rodents, the majority of these systems require the animals to be temporarily taken away from their home-cage into separate observation cage environments which requires manual handling and consequently evokes distress for the animal and may alter behavioral responses. An automated high-throughput approach can overcome this problem. Therefore, this review describes existing automated methods and technologies which enable the measurement of locomotor activity and behavioral aspects of rodents in their most meaningful and stress-free laboratory environment: the home-cage. In line with the Directive 2010/63/EU and the 3R principles (replacement, reduction, refinement), this review furthermore assesses their suitability and potential for group-housed conditions as a refinement strategy, highlighting their current technological and practical limitations. It covers electrical capacitance technology and radio-frequency identification (RFID), which focus mainly on voluntary locomotor activity in both single and multiple rodents, respectively. Infrared beams and force plates expand the detection beyond locomotor activity toward basic behavioral traits but discover their full potential in individually housed rodents only. Despite the great premises of these approaches in terms of behavioral pattern recognition, more sophisticated methods, such as (RFID-assisted) video tracking technology need to be applied to enable the automated analysis of advanced behavioral aspects of individual animals in social housing conditions.

Noncognitive species-typical and home-cage behavioral alterations in conditional presenilin 1/presenilin 2 double knockout mice

Impairments in activities of daily living (ADL) are common clinical symptoms of human Alzheimer's disease (AD). Describing the ADL in AD animal models might provide more insights into the mechanism/treatment of the disease. Here, we demonstrated that the forebrain presenilin 1(Psen1)/presenilin 2 (Psen2) conditional double knockout (DKO) mice exhibited deficits in nest building, marble burying and food burrowing starting at 3 months old and worsening at later ages. At 4 months of age, spontaneous activities in the home cage were also impaired in DKO mice, including physically demanding activities, habituation-like behaviors, and nourishment behaviors during the first two hours in the dark phase. These results indicated that loss of function of Psen1 and Psen2 in mice impaired a series of noncognitive behaviors in the early phase of neurodegeneration. This observation suggests that DKO mice are an ideal model for further mechanistic studies of Psen1 and Psen2 functions in regulating noncognitive behaviors.

High-throughput visual assessment of sleep stages in mice using machine learning

STUDY OBJECTIVES

Sleep is an important biological process that is perturbed in numerous diseases, and assessment its substages currently requires implantation of electrodes to carry out electroencephalogram/electromyogram (EEG/EMG) analysis. Although accurate, this method comes at a high cost of invasive surgery and experts trained to score EEG/EMG data. Here, we leverage modern computer vision methods to directly classify sleep substages from video data. This bypasses the need for surgery and expert scoring, provides a path to high-throughput studies of sleep in mice.

METHODS

We collected synchronized high-resolution video and EEG/EMG data in 16 male C57BL/6J mice. We extracted features from the video that are time and frequency-based and used the human expert-scored EEG/EMG data to train a visual classifier. We investigated several classifiers and data augmentation methods.

RESULTS

Our visual sleep classifier proved to be highly accurate in classifying wake, non-rapid eye movement sleep (NREM), and rapid eye movement sleep (REM) states, and achieves an overall accuracy of 0.92 +/- 0.05 (mean +/- SD). We discover and genetically validate video features that correlate with breathing rates, and show low and high variability in NREM and REM sleep, respectively. Finally, we apply our methods to non-invasively detect that sleep stage disturbances induced by amphetamine administration.

CONCLUSIONS

We conclude that machine learning based visual classification of sleep is a viable alternative to EEG/EMG based scoring. Our results will enable non-invasive high-throughput sleep studies and will greatly reduce the barrier to screening mutant mice for abnormalities in sleep.

Measuring Behavior in the Home Cage: Study Design, Applications, Challenges, and Perspectives

The reproducibility crisis (or replication crisis) in biomedical research is a particularly existential and under-addressed issue in the field of behavioral neuroscience, where, in spite of efforts to standardize testing and assay protocols, several known and unknown sources of confounding environmental factors add to variance. Human interference is a major contributor to variability both within and across laboratories, as well as novelty-induced anxiety. Attempts to reduce human interference and to measure more "natural" behaviors in subjects has led to the development of automated home-cage monitoring systems. These systems enable prolonged and longitudinal recordings, and provide large continuous measures of spontaneous behavior that can be analyzed across multiple time scales. In this review, a diverse team of neuroscientists and product developers share their experiences using such an automated monitoring system that combines Noldus PhenoTyper® home-cages and the video-based tracking software, EthoVision® XT, to extract digital biomarkers of motor, emotional, social and cognitive behavior. After presenting our working definition of a "home-cage", we compare home-cage testing with more conventional out-of-cage tests (e.g., the open field) and outline the various advantages of the former, including opportunities for within-subject analyses and assessments of circadian and ultradian activity. Next, we address technical issues pertaining to the acquisition of behavioral data, such as the fine-tuning of the tracking software and the potential for integration with biotelemetry and optogenetics. Finally, we provide guidance on which behavioral measures to emphasize, how to filter, segment, and analyze behavior, and how to use analysis scripts. We summarize how the PhenoTyper has applications to study neuropharmacology as well as animal models of neurodegenerative and neuropsychiatric illness. Looking forward, we examine current challenges and the impact of new developments. Examples include the automated recognition of specific behaviors, unambiguous tracking of individuals in a social context, the development of more animal-centered measures of behavior and ways of dealing with large datasets. Together, we advocate that by embracing standardized home-cage monitoring platforms like the PhenoTyper, we are poised to directly assess issues pertaining to reproducibility, and more importantly, measure features of rodent behavior under more ethologically relevant scenarios.

Developing Recommendations for Cumulative Endpoints and Lifetime Use for Research Animals

Research animals are important for scientific advancement, and therefore, their long-term welfare needs to be monitored to not only minimize suffering, but to provide positive affective states and experiences. Currently, there is limited guidance in countries around the world on cumulative and experimental endpoints. This paper aims to explore current opinions and institutional strategies regarding cumulative use and endpoints through a scoping survey and review of current regulations and welfare assessment tools, and ultimately to provide recommendations for assessment of cumulative and lifetime use of research animals. The survey found that only 36% of respondents indicated that their institution had cumulative use endpoint policies in place, but these policies may be informal and/or vary by species. Most respondents supported more specific guidelines but expressed concerns about formal policies that may limit their ability to make case-by-case decisions. The wide diversity in how research animals are used makes it difficult for specific policies to be implemented. Endpoint decisions should be made in an objective manner using standardized welfare assessment tools. Future research should focus on robust, efficient welfare assessment tools that can be used to support planning and recommendations for cumulative endpoints and lifetime use of research and teaching animals.

Motility Profile of Captive-Bred Marmosets Revealed by a Long-Term In-Cage Monitoring System

A quantitative evaluation of motility is crucial for studies employing experimental animals. Here, we describe the development of an in-cage motility monitoring method for new world monkeys using off-the-shelf components, and demonstrate its capability for long-term operation (e.g., a year). Based on this novel system, we characterized the motility of the common marmoset over different time scales (seconds, hours, days, and weeks). Monitoring of seven young animals belonging to two different age groups (sub-adult and young-adult) over a 231-day period revealed: (1) strictly diurnal activity (97.3% of movement during daytime), (2) short-cycle (∼20 s) transition in activity, and (3) bimodal diurnal activity including a "siesta" break. Additionally, while the mean duration of short-cycle activity, net daily activity, and diurnal activity changed over the course of development, 24-h periodicity remained constant. Finally, the method allowed for detection of progressive motility deterioration in a transgenic marmoset. Motility measurement offers a convenient way to characterize developmental and pathological changes in animals, as well as an economical and labor-free means for long-term evaluation in a wide range of basic and translational studies.

Miniaturized head-mounted microscope for whole-cortex mesoscale imaging in freely behaving mice

The advent of genetically encoded calcium indicators, along with surgical preparations such as thinned skulls or refractive index matched skulls, have enabled mesoscale cortical activity imaging in head-fixed mice. However, neural activity during unrestrained behavior substantially differs from neural activity in head-fixed animals. For whole-cortex imaging in freely behaving mice, we here present the “mini-mScope,” a wide-field, miniaturized, and head-mounted fluorescence microscope compatible with transparent polymer skull preparations. With a field of view of 8 mm x 10 mm and weighing less than 4 g, the mini-mScope can image most of the mouse dorsal cortex with resolution ranging from 39 to 56 μm. We have used the mini-mScope to record mesoscale calcium activity across the dorsal cortex during sensory-evoked stimuli, open field behaviors, social interactions, and transitions from wakefulness to sleep.

Jump takeoff in a small jumping spider

Jumping in animals presents an interesting locomotory strategy as it requires the generation of large forces and accurate timing. Jumping in arachnids is further complicated by their semi-hydraulic locomotion system. Among arachnids, jumping spiders (Family Salticidae) are agile and dexterous jumpers. However, less is known about jumping in small salticid species. Here we used Habronattus conjunctus, a small jumping spider (body length ~ 4.5 mm) to examine its jumping performance and compare it to that of other jumping spiders and insects. We also explored how legs are used during the takeoff phase of jumps. Jumps were staged between two raised platforms. We analyzed jumping videos with DeepLabCut to track 21 points on the cephalothorax, abdomen, and legs. By analyzing leg liftoff and extension patterns, we found evidence that H. conjunctus primarily uses the third legs to power jumps. We also found that H. conjunctus jumps achieve lower takeoff speeds and accelerations than most other jumping arthropods, including other jumping spiders. Habronattus conjunctus takeoff time was similar to other jumping arthropods of the same body mass. We discuss the mechanical benefits and drawbacks of a semi-hydraulic system of locomotion and consider how small spiders may extract dexterous jumps from this locomotor system.

The Promise of Automated Home-Cage Monitoring in Improving Translational Utility of Psychiatric Research in Rodents

Large number of promising preclinical psychiatric studies in rodents later fail in clinical trials, raising concerns about the efficacy of this approach to generate novel pharmacological interventions. In this mini-review we argue that over-reliance on behavioral tests that are brief and highly sensitive to external factors play a critical role in this failure and propose that automated home-cage monitoring offers several advantages that will increase the translational utility of preclinical psychiatric research in rodents. We describe three of the most commonly used approaches for automated home cage monitoring in rodents [e.g., operant wall systems (OWS), computerized visual systems (CVS), and automatic motion sensors (AMS)] and review several commercially available systems that integrate the different approaches. Specific examples that demonstrate the advantages of automated home-cage monitoring over traditional tests of anxiety, depression, cognition, and addiction-like behaviors are highlighted. We conclude with recommendations on how to further expand this promising line of preclinical research.

Three Pillars of Automated Home-Cage Phenotyping of Mice: Novel Findings, Refinement, and Reproducibility Based on Literature and Experience

Animal models of neurodegenerative and neuropsychiatric disorders require extensive behavioral phenotyping. Currently, this presents several caveats and the most important are: (i) rodents are nocturnal animals, but mostly tested during the light period; (ii) the conventional behavioral experiments take into consideration only a snapshot of a rich behavioral repertoire; and (iii) environmental factors, as well as experimenter influence, are often underestimated. Consequently, serious concerns have been expressed regarding the reproducibility of research findings on the one hand, and appropriate welfare of the animals (based on the principle of 3Rs-reduce, refine and replace) on the other hand. To address these problems and improve behavioral phenotyping in general, several solutions have been proposed and developed. Undisturbed, 24/7 home-cage monitoring (HCM) is gaining increased attention and popularity as demonstrating the potential to substitute or complement the conventional phenotyping methods by providing valuable data for identifying the behavioral patterns that may have been missed otherwise. In this review, we will briefly describe the different technologies used for HCM systems. Thereafter, based on our experience, we will focus on two systems, IntelliCage (NewBehavior AG and TSE-systems) and Digital Ventilated Cage (DVC®, Tecniplast)-how they have been developed and applied during recent years. Additionally, we will touch upon the importance of the environmental/experimenter artifacts and propose alternative suggestions for performing phenotyping experiments based on the published evidence. We will discuss how the integration of telemetry systems for deriving certain physiological parameters can help to complement the description of the animal model to offer better translation to human studies. Ultimately, we will discuss how such HCM data can be statistically interpreted and analyzed.

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.

Noninvasive three-state sleep-wake staging in mice using electric field sensors

STUDY OBJECTIVE

Validate a novel method for sleep-wake staging in mice using noninvasive electric field (EF) sensors.

METHODS

Mice were implanted with electroencephalogram (EEG) and electromyogram (EMG) electrodes and housed individually. Noninvasive EF sensors were attached to the exterior of each chamber to record respiration and other movement simultaneously with EEG, EMG, and video. A sleep-wake scoring method based on EF sensor data was developed with reference to EEG/EMG and then validated by three expert scorers. Additionally, novice scorers without sleep-wake scoring experience were self-trained to score sleep using only the EF sensor data, and results were compared to those from expert scorers. Lastly, ability to capture three-state sleep-wake staging with EF sensors attached to traditional mouse home-cages was tested.

RESULTS

EF sensors quantified wake, rapid eye movement (REM) sleep, and non-REM sleep with high agreement (>93%) and comparable inter- and intra-scorer error as EEG/EMG. Novice scorers successfully learned sleep-wake scoring using only EF sensor data and scoring criteria, and achieved high agreement with expert scorers (>91%). When applied to traditional home-cages, EF sensors enabled classification of three-state (wake, NREM and REM) sleep-wake independent of EEG/EMG.

CONCLUSIONS

EF sensors score three-state sleep-wake architecture with high agreement to conventional EEG/EMG sleep-wake scoring 1) without invasive surgery, 2) from outside the home-cage, and 3) and without requiring specialized training or equipment. EF sensors provide an alternative method to assess rodent sleep for animal models and research laboratories in which EEG/EMG is not possible or where noninvasive approaches are preferred.

Heme Oxygenase 1 and 2 Differentially Regulate Glucose Metabolism and Adipose Tissue Mitochondrial Respiration: Implications for Metabolic Dysregulation

Heme oxygenase (HO) consists of inducible (HO-1) and constitutive (HO-2) isoforms that are encoded by Hmox1 and Hmox2 genes, respectively. As an anti-inflammatory and antioxidant molecule, HO participates in the development of metabolic diseases. Whether Hmox deficiency causes metabolic abnormalities under basal conditions remains unclear. We hypothesized that HO-1 and HO-2 differentially affect global and adipose tissue metabolism. To test this hypothesis, we determined insulin sensitivity, glucose tolerance, energy expenditure, and respiratory exchange ratio in global Hmox1^-/- and Hmox2^-/- mice. Body weight was reduced in female but not male Hmox1^-/- and Hmox2^-/- mice. Reduced insulin sensitivity and physical activity were observed in Hmox1^-/- but not Hmox2^-/- mice. Deletion of either Hmox1 or Hmox2 had no effects on glucose tolerance, energy expenditure or respiratory exchange ratio. Mitochondrial respiration was unchanged in gonadal fat pads (white adipose tissue, WAT) of Hmox1^-/- mice. Hmox2 deletion increased proton leak and glycolysis in gonadal, but not interscapular fat tissues (brown adipose tissue, BAT). Uncoupling protein and Hmox1 genes were unchanged in gonadal fat pads of Hmox2^-/- mice. Conclusively, HO-1 maintains insulin sensitivity, while HO-2 represses glycolysis and proton leak in the WAT under basal condition. This suggests that HO-1 and HO-2 differentially modulate metabolism, which may impact the metabolic syndrome.

Phenotyping in Mice Using Continuous Home Cage Monitoring and Ultrasonic Vocalization Recordings

Over the last century, the study of mouse behavior has uncovered insights into brain molecular mechanisms while revealing potential causes of many neurological disorders. To this end, researchers have widely exploited the use of mutant strains, including those generated in mutagenesis screens and those produced using increasingly sophisticated genome engineering technologies. It is now relatively easy to access mouse models carrying alleles that faithfully recapitulate changes found in human patients or bearing variants of genes that provide data on those genes' functions. Concurrent with these developments has been an appreciation of the limitations of some current testing platforms, especially those monitoring complex behaviors. Out-of-cage observational testing is useful in describing overt persistent phenotypes but risks missing sporadic or intermittent events. Furthermore, measuring the progression of a phenotype, potentially over many months, can be difficult while relying on assays that may be susceptible to changes in the testing environment. In recent years, there has also been increasing awareness that measurement of behaviors in isolation can be limiting, given that mice attempt to hide behavioral cues of vulnerability. To overcome these limitations, laboratory animal science is capitalizing on progress in data capture and processing expertise. Moreover, as additional recording modes become commonplace, ultrasonic vocalization recording is an appealing focus, as mice use vocalizations in various social contexts. Using video and audio technologies, we record the voluntary, unprovoked behaviors and vocalizations of mice in social groups. Adoption of these approaches is undoubtedly set to increase, as they capture the round-the-clock behavior of mouse strains. © 2020 Wiley Periodicals LLC. Basic Protocol 1: Continuous recording of home cage activity using the Home Cage Analyzer (HCA) system Support Protocol: Subcutaneous insertion of a radio frequency identification microchip in the inguinal area Basic Protocol 2: Continuous recording of mouse ultrasonic vocalizations in the home cage.

Low acetylcholine during early sleep is important for motor memory consolidation

The synaptic homeostasis theory of sleep proposes that low neurotransmitter activity in sleep optimizes memory consolidation. We tested this theory by asking whether increasing acetylcholine levels during early sleep would weaken motor memory consolidation. We trained separate groups of adult mice on the rotarod walking task and the single pellet reaching task, and after training, administered physostigmine, an acetylcholinesterase inhibitor, to increase cholinergic tone in subsequent sleep. Post-sleep testing showed that physostigmine impaired motor skill acquisition of both tasks. Home-cage video monitoring and electrophysiology revealed that physostigmine disrupted sleep structure, delayed non-rapid-eye-movement sleep onset, and reduced slow-wave power in the hippocampus and cortex. Additional experiments showed that: (1) the impaired performance associated with physostigmine was not due to its effects on sleep structure, as 1 h of sleep deprivation after training did not impair rotarod performance, (2) a reduction in cholinergic tone by inactivation of cholinergic neurons during early sleep did not affect rotarod performance, and (3) stimulating or blocking muscarinic and nicotinic acetylcholine receptors did not impair rotarod performance. Taken together, the experiments suggest that the increased slow wave activity and inactivation of both muscarinic and nicotinic receptors during early sleep due to reduced acetylcholine contribute to motor memory consolidation.

Automated Analysis of Stroke Mouse Trajectory Data With Traja

Quantitative characterization of mouse activity, locomotion and walking patterns requires the monitoring of position and activity over long periods of time. Manual behavioral phenotyping, however, is time and skill-intensive, vulnerable to researcher bias and often stressful for the animals. We present examples for using a platform-independent open source trajectory analysis software, Traja, for semi-automated analysis of high throughput mouse home-cage data for neurobehavioral research. Our software quantifies numerous parameters of movement including traveled distance, velocity, turnings, and laterality which are demonstrated for application to neurobehavioral analysis. In this study, the open source software for trajectory analysis Traja is applied to movement and walking pattern observations of transient stroke induced female C57BL/6 mice (30 min middle cerebral artery occlusion) on an acute multinutrient diet intervention (Fortasyn). After stroke induction mice were single housed in Digital Ventilated Cages [DVC, GM500, Tecniplast S.p.A., Buguggiate (VA), Italy] and activity was recorded 24/7, every 250 ms using a DVC board. Significant changes in activity, velocity, and distance walked are computed with Traja. Traja identified increased walked distance and velocity in Control and Fortasyn animals over time. No diet effect was found in preference of turning direction (laterality) and distance traveled. As open source software for trajectory analysis, Traja supports independent development and validation of numerical methods and provides a useful tool for computational analysis of 24/7 mouse locomotion in home-cage environment for application in behavioral research or movement disorders.

The Role of BTBD9 in the Cerebellum, Sleep-like Behaviors and the Restless Legs Syndrome

Recent genome-wide association studies (GWAS) have found cerebellum as a top hit for sleep regulation. Restless legs syndrome (RLS) is a sleep-related sensorimotor disorder characterized by uncomfortable sensations in the extremities, generally at night, which are often relieved by movements. Clinical studies have found that RLS patients have structural and functional abnormalities in the cerebellum. However, whether and how cerebellar pathology contributes to sleep regulation and RLS is not known. GWAS identified polymorphisms in BTBD9 conferring a higher risk of sleep disruption and RLS. Knockout of the BTBD9 homolog in mice (Btbd9) and fly results in motor restlessness and sleep disruption. We performed manganese-enhanced magnetic resonance imaging on the Btbd9 knockout mice and found decreased neural activities in the cerebellum, especially in lobules VIII, X, and the deep cerebellar nuclei. Electrophysiological recording of Purkinje cells (PCs) from Btbd9 knockout mice revealed an increased number of non-tonic PCs. Tonic PCs showed increased spontaneous activity and intrinsic excitability. To further investigate the cerebellar contribution to RLS and sleep-like behaviors, we generated PC-specific Btbd9 knockout mice (Btbd9 pKO) and performed behavioral studies. Btbd9 pKO mice showed significant motor restlessness during the rest phase but not in the active phase. Btbd9 pKO mice also had an increased probability of waking at rest. Unlike the Btbd9 knockout mice, there was no increased thermal sensation in the Btbd9 pKO. Our results indicate that the Btbd9 knockout influences the PC activity; dysfunction in the cerebellum may contribute to the motor restlessness found in the Btbd9 knockout mice.