Multiple sclerosis (MS) is a chronic disabling disease of the central nervous system affecting over 2.5 million people worldwide. Theiler\'s murine encephalomyelitis virus-induced demyelinating disease (TMEV-IDD) is a murine model that reproduces the progressive form of MS and serves as a reference model for studying virus-induced demyelination. Certain mouse strains such as SJL are highly susceptible to this virus and serve as a prototype strain for studying TMEV infection. Other strains such as SWR are also susceptible, but their disease course following TMEV infection differs from SJL\'s. The quantification of motor and behavioral deficits following the induction of TMEV-IDD could help identify the differences between the two strains. Motor deficits have commonly been measured with the rotarod apparatus, but a multicomponent assessment tool has so far been lacking. For that purpose, we present a novel way of quantifying locomotor deficits, gait alterations and behavioral changes in this well-established mouse model of multiple sclerosis by employing automated video analysis technology (The PhenoTyper, Noldus Information Technology). We followed 12 SJL and 12 SWR female mice and their mock-infected counterparts over a period of 9 months following TMEV-IDD induction. We demonstrated that SJL and SWR mice both suffer significant gait alterations and reduced exploration following TMEV infection. However, SJL mice also display an earlier and more severe decline in spontaneous locomotion, especially in velocity, as well as in overall activity. Maintenance behaviors such as eating and grooming are not affected in either of the two strains. The system also showed differences in mock-infected mice from both strains, highlighting an age-related decline in spontaneous locomotion in the SJL strain, as opposed to hyperactivity in the SWR strain. Our study confirms that this automated video tracking system can reliably track the progression of TMEV-IDD for 9 months. We have also shown how this system can be utilized for longitudinal phenotyping in mice by describing useful parameters that quantify locomotion, gait and behavior.

Isolation of sex differences as a key characteristic underlying neurobehavioral differentiation is an essential component of studies in neuroscience. The current study sought to address this concern by observing behavioral differences using an automated home cage system for neurobehavioral assessment, a method rapidly increasing in use due to advances in technology and advantages such as reduced handling stress and cross-lab variability. Sex differences in C57BL/6 mice arose for motor activity and circadian-linked behavior, with females being more active compared to males, and males having a stronger anticipatory increase in activity leading up to the onset of the light phase compared to females. These activity differences were observed not only across the lifespan, but also in different genetic background mouse strains across different testing sites showing the generalizability and robustness of these observed effects. Activity differences were also observed in performance on a spatial learning and reversal task with females making more responses and receiving a corresponding elevation in reward pellets. Notably, there were no sex differences in learning nor achieved accuracy, suggesting these observed effects were predominantly in activity. The outcomes of this study align with previous reports showcasing differences in activity between males and females. The comparison across strains and testing sites showed robust and reproducible differences in behavior between female and male mice that are relevant to consider when designing behavioral studies. Furthermore, the observed sex differences in performance on the learning and reversal procedure raise concern for interpretation of behavior differences between sexes due to the attribution of these differences to motor activity rather than cognition.

Electrophysiological and behavioral alterations, including sleep and cognitive impairments, are critical components of age-related decline and neurodegenerative diseases. In preclinical investigation, many refined techniques are employed to probe these phenotypes, but they are often conducted separately. Herein, we provide a protocol for one-time surgical implantation of EMG wires in the nuchal muscle and a skull-surface EEG headcap in mice, capable of 9-to-12-month recording longevity. All data acquisitions are wireless, making them compatible with simultaneous EEG recording coupled to multiple behavioral tasks, as we demonstrate with locomotion/sleep staging during home-cage video assessments, cognitive testing in the Barnes maze, and sleep disruption. Time-course EEG and EMG data can be accurately mapped to the behavioral phenotype and synchronized with neuronal frequencies for movement and the location to target in the Barnes maze. We discuss critical steps for optimizing headcap surgery and alternative approaches, including increasing the number of EEG channels or utilizing depth electrodes with the system. Combining electrophysiological and behavioral measurements in preclinical models of aging and neurodegeneration has great potential for improving mechanistic and therapeutic assessments and determining early markers of brain disorders.

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.

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.

Cognitive function declines substantially with age in both humans and animal models. In humans, this decline is associated with decreases in independence and quality of life. Although the methodology for analysis of cognitive function in human models is relatively well established, similar analyses in animal models have many technical issues (e.g., unintended experimenter bias, motivational issues, stress, and testing during the light phase of the light dark cycle) that limit interpretation of the results. These caveats, and others, potentially bias the interpretation of studies in rodents and prevent the application of current tests of learning and memory as part of an overall healthspan assessment in rodent models of aging. The goal of this study was to establish the methodology to assess cognitive function in aging animals that addresses many of these concerns. Here, we use a food reward-based discrimination procedure with minimal stress in C57Bl/6J male mice at 6, 21, and 27 months of age, followed by a reversal task to assess behavioral flexibility. Importantly, the procedures minimize issues related to between-experimenter confounds and are conducted during both the dark and light phases of the light dark cycle in a home-cage setting. During cognitive testing, we were able to assess multiple measures of spontaneous movement and diurnal activity in young and aged mice including, distance moved, velocity, and acceleration over a 90-h period. Both initial discrimination and reversal learning significantly decreased with age and, similar to rats and humans, not all old mice demonstrated impairments in learning with age. These results permitted classification of animals based on their cognitive status. Analysis of movement parameters indicated decreases in distance moved as well as velocity and acceleration with increasing age. Based on these data, we developed preliminary models indicating, as in humans, a close relationship exists between age-related movement parameters and cognitive ability. Our results provide a reliable method for assessing cognitive performance with minimal stress and simultaneously provide key information on movement and diurnal activity. These methods represent a novel approach to developing non-invasive healthspan measures in rodent models that allow standardization across laboratories.

This chapter describes a use case for the genetic dissection and automated analysis of complex behavioral traits using the genetically diverse panel of BXD mouse recombinant inbred strains. Strains of the BXD resource differ widely in terms of gene and protein expression in the brain, as well as in their behavioral repertoire. A large mouse resource opens the possibility for gene finding studies underlying distinct behavioral phenotypes, however, such a resource poses a challenge in behavioral phenotyping. To address the specifics of large-scale screening we describe how to investigate: (1) how to assess mouse behavior systematically in addressing a large genetic cohort, (2) how to dissect automation-derived longitudinal mouse behavior into quantitative parameters, and (3) how to map these quantitative traits to the genome, deriving loci underlying aspects of behavior.

Individuals are able to change their behavior based on its consequences, a process involving instrumental learning. Studying instrumental learning in mice can provide new insights in this elementary aspect of cognition. Conventional appetitive operant learning tasks that facilitate the study of this form of learning in mice, as well as more complex operant paradigms, require labor-intensive handling and food deprivation to motivate the animals. Here, we describe a 1-night operant learning protocol that exploits the advantages of automated home-cage testing and circumvents the interfering effects of food restriction. The task builds on behavior that is part of the spontaneous exploratory repertoire during the days before the task. We compared the behavior of C57BL/6J, BALB/cJ and DBA/2J mice and found various differences in behavior during this task, but no differences in learning curves. BALB/cJ mice showed the largest instrumental learning response, providing a superior dynamic range and statistical power to study instrumental learning by using this protocol. Insights gained with this home-cage-based learning protocol without food restriction will be valuable for the development of other, more complex, cognitive tasks in automated home-cages.