Detection of early behavioral markers of Huntington's disease in R6/2 mice employing an automated social home cage

Rudenko, Olga; Tkach, Vadim; Berezin, Vladimir; Bock, Elisabeth

doi:10.1016/j.bbr.2009.04.034

Cited by 67 publications

(54 citation statements)

References 53 publications

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“…Thanks to the use of noninvasive behavioral recording techniques, we confirmed that R6/2 mice sacrificed at 4 weeks of age are at a premotor stage of the disease (15,31,32). Therefore, it was remarkable that increased phosphocreatine and creatine levels were detected in 4-week-old R6/2 mice, preceding the decrease of ATP.…”

Section: Discussionmentioning

confidence: 53%

Early Alterations of Brain Cellular Energy Homeostasis in Huntington Disease Models

Mochel

Durant²,

Meng³

et al. 2012

Journal of Biological Chemistry

105

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Background:We hypothesized cerebral energy deficit in Huntington disease (HD). Results: We found increased brain phosphocreatine and creatine in two HD mouse models, preceding ATP decrease and motor symptoms. Conclusion:We demonstrated chronic and early alterations in energy homeostasis associated with reduced phosphocreatine utilization in HD. Significance: These findings suggest that energy deficit is a relevant therapeutic target in HD.

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Section: Discussionmentioning

confidence: 53%

Early Alterations of Brain Cellular Energy Homeostasis in Huntington Disease Models

Mochel

Durant²,

Meng³

et al. 2012

Journal of Biological Chemistry

105

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“…Previous automated systems (see refs 8, 9, 11, 12 and Supplementary Note ) rely mostly on the use of simple detectors such as infrared beams to monitor behaviour. Th ese sensor-based approaches tend to be limited in the complexity of the behaviour that they can measure, even in the case of costly commercial systems using transponder technologies 13 . Although such systems can be used eff ectively to monitor locomotor activity and perform operant conditioning, they cannot be used to study homecage behaviours such as grooming, hanging, jumping and smaller movements (termed ' micromovements ' below).…”

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confidence: 99%

Automated home-cage behavioural phenotyping of mice

et al. 2010

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Neurobehavioural analysis of mouse phenotypes requires the monitoring of mouse behaviour over long periods of time. In this study, we describe a trainable computer vision system enabling the automated analysis of complex mouse behaviours. We provide software and an extensive manually annotated video database used for training and testing the system. Our system performs on par with human scoring, as measured from ground-truth manual annotations of thousands of clips of freely behaving mice. As a validation of the system, we characterized the home-cage behaviours of two standard inbred and two non-standard mouse strains. From these data, we were able to predict in a blind test the strain identity of individual animals with high accuracy. Our video-based software will complement existing sensor-based automated approaches and enable an adaptable, comprehensive, high-throughput, fi ne-grained, automated analysis of mouse behaviour. A utomated quantitative analysis of mouse behaviour will have a signifi cant role in comprehensive phenotypic analyses -both on the small scale of detailed characterization of individual gene mutants and on the large scale of assigning gene function across the entire mouse genome 1 . One key benefi t of automating behavioural analysis arises from inherent limitations of human assessment, namely, cost, time and reproducibility. Although automation in and of itself is not a panacea for neurobehavioural experiments 2 , it allows for addressing an entirely new set of questions about mouse behaviour and to conduct experiments on time scales that are orders of magnitude larger than those traditionally assayed. For example, reported tests of grooming behaviour span time scales of minutes 3,4 , whereas an automated analysis will allow for analysis of this behaviour over hours or even days and weeks.Indeed, the signifi cance of alterations in home-cage behaviour has recently gained attention as an eff ective means of detecting perturbations in neural circuit function -both in the context of disease detection and more generally to measure food consumption and activity parameters 5 -10 . Previous automated systems (see refs 8, 9, 11, 12 and Supplementary Note ) rely mostly on the use of simple detectors such as infrared beams to monitor behaviour. Th ese sensor-based approaches tend to be limited in the complexity of the behaviour that they can measure, even in the case of costly commercial systems using transponder technologies 13 . Although such systems can be used eff ectively to monitor locomotor activity and perform operant conditioning, they cannot be used to study homecage behaviours such as grooming, hanging, jumping and smaller movements (termed ' micromovements ' below). Visual analysis is a potentially powerful complement to these sensor-based approaches for the recognition of such fi ne animal behaviours.Advances in computer vision and machine learning over the last decade have yielded robust computer vision systems for the recognition of objects 14,15 and human actions (see Moeslund et ...

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“…Automated video analysis techniques used pattern recognition software to detect specific mouse behaviours including rearing, sniffing and hanging (Steele et al, 2007b;Baiguera et al, 2012;Mochel et al, 2012). RFID-based homecage systems typically focus on identifying the animals' location and movement (Lewejohann et al, 2009;Howerton et al, 2012), can be paired with other monitoring equipment such as lickometers (Krackow et al, 2010;Voikar et al, 2010;Bellmann-Sickert et al, 2011) and some systems can be programmed to respond differentially to each mouse and present stimuli such as mild aversive airpuffs (Rudenko et al, 2009). Automated homecage behavioural analysis systems may combine technologies, including systems that detect floor movement, infrared beams, automated video analysis and/or food/water consumption Brodkin et al, 2014).…”

Section: What Are Automated Behavioural Homecage Technologies?mentioning

confidence: 99%

The power of automated behavioural homecage technologies in characterizing disease progression in laboratory mice: A review

Richardson

2015

Applied Animal Behaviour Science

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a b s t r a c tBehavioural changes that occur as animals become sick have been characterized in a number of species and include the less frequent occurrence of 'luxury behaviours' such as playing, grooming and socialization. 'Sickness behaviours' or behavioural changes following exposure to infectious agents, have been particularly well described; animals are typically less active, sleep more, exhibit postural changes and consume less food/water. Disease is frequently induced in laboratory mice to model pathophysiological processes and investigate potential therapies but despite what is known about behavioural changes as animals become sick, behavioural phenotyping of mice involved in disease studies is relatively rare. A detailed understanding of how behaviour changes as mice get sick could be applied to improve welfare of laboratory mice and support the underlying biomedical research. Specifically, characterizing behavioural changes in ill health could help those working with laboratory mice to recognize when refinements should be introduced, when severity limits are being approached and when humane endpoints should be implemented. Understanding how behaviour changes with illness may also help to identify compounds that have a clinical effect as well as when these agents act. There are an increasing number of automated systems to monitor the behaviour of laboratory mice in their homecages incorporating technologies such as the quantification of cage movement, automated video analysis and radiofrequency identification transponders/readers. Mouse models of neurodegenerative diseases particularly Huntington's disease have been well characterized using these systems and behavioural biomarkers of pathology, including changes in the animals' use of environmental enrichment, changes in food/water consumption and alterations in circadian rhythms, are now monitored by laboratories worldwide and used to refine studies and develop therapies. In contrast, automated behavioural technologies have not been used to characterize the behaviour of mice with systemic diseases such as cancer and liver disease. In this review, common behavioural changes that occur in animals with declining health will be discussed with an emphasis on progressive disease studies involving mice. Automated homecage behaviour recording technologies will then be summarized, studies in which these systems have been used to characterize the behaviour of mice with progressive diseases will be reviewed and the potential to apply automated technologies to refine disease studies involving mice will be discussed.

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Detection of early behavioral markers of Huntington's disease in R6/2 mice employing an automated social home cage

Cited by 67 publications

References 53 publications

Early Alterations of Brain Cellular Energy Homeostasis in Huntington Disease Models

Early Alterations of Brain Cellular Energy Homeostasis in Huntington Disease Models

Automated home-cage behavioural phenotyping of mice

The power of automated behavioural homecage technologies in characterizing disease progression in laboratory mice: A review

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