RESUMEN
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.
RESUMEN
BACKGROUND: Expanding the spectrum of organisms to model human brain phenotypes is critical for our improved understanding of the pathobiology of neuropsychiatric disorders. Given the clear limitations of existing mammalian models, there is an urgent need for low-cost, high-throughput in-vivo technologies for drug and gene discovery. NEW METHOD: Here, we introduce a new automated method for generating 3D (X,Y,Z) swim trajectories in adult zebrafish (Danio rerio), to improve their neurophenotyping. RESULTS: Based on the Track3D module of EthoVision XT video tracking software (Noldus Information Technology), this tool enhances the efficient, high-throughput 3D analyses of zebrafish behavioral responses. Applied to adult zebrafish behavior, this 3D method is highly sensitive to various classes of psychotropic drugs, including selected psychostimulant and hallucinogenic agents. COMPARISON WITH EXISTING METHODS: Our present method offers a marked advance in the existing 2D and 3D methods of zebrafish behavioral phenotyping, minimizing research time and recording high-resolution, automatically synchronized videos with calculated, high-precision object positioning. CONCLUSIONS: Our novel approach brings practical simplicity and 'integrative' capacity to the often complex and error-prone quantification of zebrafish behavioral phenotypes. Illustrating the value of 3D swim path reconstructions for identifying experimentally-evoked phenotypic profiles, this method fosters innovative, ethologically relevant, and fully automated small molecule screens using adult zebrafish.
Asunto(s)
Acelerometría/métodos , Descubrimiento de Drogas/métodos , Reconocimiento de Normas Patrones Automatizadas/métodos , Psicotrópicos/farmacología , Natación/fisiología , Pez Cebra/fisiología , Acelerometría/instrumentación , Animales , Fenómenos Biomecánicos , Descubrimiento de Drogas/instrumentación , Diseño de Equipo , Femenino , Dietilamida del Ácido Lisérgico/farmacología , Masculino , Actividad Motora/efectos de los fármacos , Actividad Motora/fisiología , Fenciclidina/farmacología , Fenotipo , Programas Informáticos , Vacunas/farmacología , Grabación en Video/métodosRESUMEN
The automated measurement of rodent behaviour is crucial to advance research in neuroscience and pharmacology. Rats and mice are used as models for human diseases; their behaviour is studied to discover and develop new drugs for psychiatric and neurological disorders and to establish the effect of genetic variation on behavioural changes. Such behaviour is primarily labelled by humans. Manual annotation is labour intensive, error-prone and subject to individual interpretation. We present a system for automated behaviour recognition (ABR) that recognises the rat behaviours 'drink', 'eat', 'sniff', 'groom', 'jump', 'rear unsupported', 'rear wall', 'rest', 'twitch' and 'walk'. The ABR system needs no on-site training; the only inputs needed are the sizes of the cage and the animal. This is a major advantage over other systems that need to be trained with hand-labelled data before they can be used in a new experimental setup. Furthermore, ABR uses an overhead camera view, which is more practical in lab situations and facilitates high-throughput testing more easily than a side-view setup. ABR has been validated by comparison with manual behavioural scoring by an expert. For this, animals were treated with two types of psychopharmaca: a stimulant drug (Amphetamine) and a sedative drug (Diazepam). The effects of drug treatment on certain behavioural categories were measured and compared for both analysis methods. Statistical analysis showed that ABR found similar behavioural effects as the human observer. We conclude that our ABR system represents a significant step forward in the automated observation of rodent behaviour.