M-Track: A New Software for Automated Detection of Grooming Trajectories in Mice

Grooming is a complex and robust innate behavior, commonly performed by most vertebrate species. In mice, grooming consists of a series of stereotyped patterned strokes, performed along the rostro-caudal axis of the body. The frequency and duration of each grooming episode is sensitive to changes in stress levels, social interactions and pharmacological manipulations, and is therefore used in behavioral studies to gain insights into the function of brain regions that control movement execution and anxiety. Traditional approaches to analyze grooming rely on manually scoring the time of onset and duration of each grooming episode, and are often performed on grooming episodes triggered by stress exposure, which may not be entirely representative of spontaneous grooming in freely-behaving mice. This type of analysis is time-consuming and provides limited information about finer aspects of grooming behaviors, which are important to understand movement stereotypy and bilateral coordination in mice. Currently available commercial and freeware video-tracking software allow automated tracking of the whole body of a mouse or of its head and tail, not of individual forepaws. Here we describe a simple experimental set-up and a novel open-source code, named M-Track, for simultaneously tracking the movement of individual forepaws during spontaneous grooming in multiple freely-behaving mice. This toolbox provides a simple platform to perform trajectory analysis of forepaw movement during distinct grooming episodes. By using M-track we show that, in C57BL/6 wild type mice, the speed and bilateral coordination of the left and right forepaws remain unaltered during the execution of distinct grooming episodes. Stress exposure induces a profound increase in the length of the forepaw grooming trajectories. M-Track provides a valuable and user-friendly interface to streamline the analysis of spontaneous grooming in biomedical research studies.

[1]  Ann M. Graybiel,et al.  Neurobiology of rodent self-grooming and its value for translational neuroscience , 2015, Nature Reviews Neuroscience.

[2]  Stacey Reynolds,et al.  Effects of Environmental Enrichment on Repetitive Behaviors in the BTBR T+tf/J Mouse Model of Autism , 2013, Autism research : official journal of the International Society for Autism Research.

[3]  J. C. Fentress,et al.  Expressive Contexts, Fine Structure, and Central Mediation of Rodent Grooming a , 1988, Annals of the New York Academy of Sciences.

[4]  Kent C. Berridge,et al.  Progressive degradation of serial grooming chains by descending decerebration , 1989, Behavioural Brain Research.

[5]  Ashesh K Dhawale,et al.  Motor Cortex Is Required for Learning but Not for Executing a Motor Skill , 2015, Neuron.

[6]  D. Thiessen,et al.  Washing, drying, and anointing in adult humans (Homo sapiens): commonalities with grooming sequences in rodents. , 1991, Journal of comparative psychology.

[7]  Allan V. Kalueff,et al.  Contrasting grooming phenotypes in three mouse strains markedly different in anxiety and activity (129S1, BALB/c and NMRI) , 2005, Behavioural Brain Research.

[8]  D. Murphy,et al.  Analyzing grooming microstructure in neurobehavioral experiments , 2007, Nature Protocols.

[9]  J. C. Fentress,et al.  Grammar of a Movement Sequence in Inbred Mice , 1973, Nature.

[10]  Ann M. Graybiel,et al.  Neurobiology of rodent self-grooming and its value for translational neuroscience , 2016, Nature Reviews Neuroscience.

[11]  J. C. Fentress THE TONIC HYPOTHESIS AND THE PATTERNING OF BEHAVIOR * , 1977, Annals of the New York Academy of Sciences.

[12]  Chad B. Sandusky,et al.  Laboratory routines cause animal stress. , 2004, Contemporary topics in laboratory animal science.

[13]  J. W. Aldridge,et al.  Coding of Serial Order by Neostriatal Neurons: A “Natural Action” Approach to Movement Sequence , 1998, The Journal of Neuroscience.

[14]  Kristin Branson,et al.  Cortex commands the performance of skilled movement , 2015, eLife.

[15]  Olivier Mirat,et al.  ZebraZoom: an automated program for high-throughput behavioral analysis and categorization , 2013, Front. Neural Circuits.

[16]  A. Moyaho,et al.  Grooming and yawning trace adjustment to unfamiliar environments in laboratory Sprague-Dawley rats (Rattus norvegicus). , 2002, Journal of comparative psychology.

[17]  Pietro Perona,et al.  Robust Face Landmark Estimation under Occlusion , 2013, 2013 IEEE International Conference on Computer Vision.

[18]  François Y. Doré,et al.  Repeated subchronic exposure to phencyclidine elicits excessive atypical grooming in rats , 2006, Behavioural Brain Research.

[19]  B D Sachs,et al.  The Development of Grooming and Its Expression in Adult Animals a , 1988, Annals of the New York Academy of Sciences.

[20]  K. Berridge,et al.  Cortex, striatum and cerebellum: control of serial order in a grooming sequence , 2004, Experimental Brain Research.

[21]  J. C. Fentress,et al.  Natural syntax rules control action sequence of rats , 1987, Behavioural Brain Research.

[22]  Alan Mackay-Sim,et al.  Swimming behaviour and post-swimming activity in Vitamin D receptor knockout mice , 2006, Brain Research Bulletin.

[23]  J. Wayne Aldridge,et al.  Neuronal Coding of Serial Order: Syntax of Grooming in the Neostriatum , 1993 .

[24]  Pietro Perona,et al.  High-throughput Ethomics in Large Groups of Drosophila , 2009, Nature Methods.

[25]  T. Schmickl,et al.  Development of a New Method to Track Multiple Honey Bees with Complex Behaviors on a Flat Laboratory Arena , 2014, PloS one.

[26]  A. Moyaho,et al.  Induced grooming transitions and open field behaviour differ in high- and low-yawning sublines of Sprague-Dawley rats , 1995, Animal Behaviour.

[27]  A. Kalueff,et al.  Grooming analysis algorithm for neurobehavioural stress research. , 2004, Brain research. Brain research protocols.

[28]  A. Kalueff,et al.  Mouse grooming microstructure is a reliable anxiety marker bidirectionally sensitive to GABAergic drugs. , 2005, European journal of pharmacology.

[29]  Kristin Branson,et al.  JAABA: interactive machine learning for automatic annotation of animal behavior , 2013, Nature Methods.