Name: MouseWalker (MW) Video Tracking and Kinematic Data Analysis
Uploaded: 2023-03-24T14:50:00
Description: Watch this Scientific Journal Video about MouseWalker MW Video Tracking and Kinematic Data Analysis at JoVE.com

The video demonstrates using MATLAB to analyze motion data from MouseWalker software, focusing on automated tracking and principal component analysis (PCA) of mice with spinal cord injuries. Key findings include changes in gait and stance characteristics, supported by quantitative measurements and visual data outputs saved for further analysis.

mousewalker (mw) video tracking and kinematic data analysis

Kvantifisering av lokomotorisk dysfunksjon hos mus med ryggmargsskade ved hjelp av MouseWalker

The effective coordination of four limbs is not unique to quadruped animals. Forelimb-hindlimb coordination in humans remains important to accomplish several tasks, such as swimming and alterations of speed while walking1. Various limb kinematic2 and motor program1,3,4, as well as proprioceptive feedback circuits5, are conserved between humans and other mammals&#160;and should be considered when analyzing therapeutic options for motor disorders, such as spinal cord injury (SCI)6,7,8.
In order to walk, several spinal connections from the forelimbs and hindlimbs need to be properly wired and rhythmically activated, which requires inputs from the brain and feedback from the somatosensory system2,9,10. These connections culminate in the central pattern generators (CPGs), which are situated at a cervical and lumbar level for the forelimbs and hindlimbs, respectively1,9,10. Often, after SCI, the disruption of neuronal connectivity and the formation of an inhibitory glial scar12 limit the recovery of locomotor function, with outcomes varying from total paralysis to restricted function of a group of limbs depending on the injury severity. Tools to precisely quantify locomotor function after SCI are critical for monitoring recovery and evaluating the effects of treatments or other clinical interventions6.
The standard metric assay for mouse contusion models of SCI is the Basso mouse scale (BMS)13,14, a non-parametric score that considers trunk stability, tail position, plantar stepping, and forelimb-hindlimb coordination in an open field arena. Even though the BMS is extremely reliable for most cases, it requires at least two experienced raters to observe all the angles of animal movement in order to account for natural variability and reduce bias.
Other assays have also been developed to assess motor performance after SCI quantitatively. These include the rotarod test, which measures time spent on a rotating cylinder15; the horizontal ladder, which measures the number of missed railings and positive ladder grabs16,17; and the beam walking test, which measures the time an animal takes and the number of failures it makes when crossing a narrow beam18. Despite reflecting a combination of motor deficits, none of these tests produce direct locomotor information about forelimb-hindlimb coordination.
To specifically and more thoroughly analyze walking behavior, other assays have been developed to reconstruct step cycles and gaiting strategies. One example is the footprint test, where the inked paws of an animal draw a pattern over a sheet of white paper19. Although simple in its execution, extracting kinematic parameters such as stride length is cumbersome and inaccurate. Moreover, the lack of dynamic parameters, such as the duration of the step cycle or leg-timed coordination, limits its applications; indeed, these dynamic parameters can only be acquired by analyzing frame-by-frame videos of rodents walking through a transparent surface. For SCI studies, researchers have analyzed walking behavior from a lateral view using a treadmill, including reconstructing the step cycle and measuring the angular variations of each leg joint4,20,21. Even though this approach can be extremely informative6, it remains focused on a specific set of limbs and lacks additional gait features, such as coordination.
To fill these gaps, Hamers and colleagues developed a quantitative test based on an optical touch sensor using frustrated total internal reflection (fTIR)22. In this method, light propagates through glass via internal reflection, becomes scattered upon paw pressing, and, finally, is captured by a high-speed camera. More recently, an open-source version of this method, called MouseWalker, was made available, and this approach combines an fTIR walkway with a tracking and quantification software package23. Using this method, the user can extract a large set of quantitative parameters, including step, spatial, and gait patterns, footprint positioning, and forelimb-hindlimb coordination, as well as visual outputs, such as footprint patterns (mimicking the inked paw assay6) or stance phases relative to the body axis. Importantly, due to its open-source nature, new parameters can be extracted by updating the MATLAB script package.
Here, the previously published assembly of the MouseWalker23 system is updated. A description of how to set it up is provided, with all the steps required to achieve the best video quality, tracking conditions, and parameter acquisition. Additional post-quantification tools are also shared to enhance the analysis of the MouseWalker (MW) output dataset. Finally, the usefulness of this tool is demonstrated by obtaining quantifiable values for general locomotor performance, specifically step cycles and forelimb-hindlimb coordination, in a spinal cord injury (SCI) context.

Forfatter Spotlight: Bruke MouseWalker til å kvantifisere lokomotorisk dysfunksjon i en musemodell av ryggmargsskade  

Bruke MouseWalker til å kvantifisere lokomotorisk dysfunksjon i en musemodell av ryggmargsskade

In this study, we use a mouse model of spinal cord injury to uncover ways of promoting repair of the spinal cord that could lead to effective locomotor and functional sensory recovery. With a mouse walker, we can move a step forward into a more quantitative analysis of spinal cord recovery by combining several graphical outputs and kinematic parameters with a set of post-quantification tools. High quality video cameras and sophisticated software packages allow a more direct and quantitative description of motor activity.The mouse walker is a good example of this. Currently two main challenges exist. First, the ability of laboratories to implement these technologies in their experimental pipeline.And second, to reduce the time and steps from video recordings to data generation. We realized that standard protocols were insufficient to describe all locomotive deficits we see after spinal cord injury. We also found that the mouse walker helped us measure coordination, one of the hardest parameters to assess in standard tests.Here, not only is the software free and open source, but the hardware is also easy to assemble with uncomplicated materials. We also provide open source tools to analyze the mouse walker data. The mouse walker is a valuable method for studying other motor related or motor dysfunctions, not just spinal cord injury.It can also be used in combination with already established behavior protocols.

Watch this Scientific Journal Video about MouseWalker MW Video Tracking and Kinematic Data Analysis at JoVE.com

MouseWalker (MW) Video Tracking and Kinematic Data Analysis  

MouseWalker (MW) videosporing og kinematisk dataanalyse

Åpne MATLAB på datamaskinen. Legg til mappen som inneholder MouseWalker-skriptet i arbeidsmappen og kjør MouseWalker. M på hovedkommandolinjen.Last inn videomappen som inndatakatalog. Gå til vinduet Innstillinger der alle kalibrerings- og terskelparametrene er plassert. Test effekten av å endre noen parametere ved å klikke på Forhåndsvisning-knappen.Når du har justert terskelparametrene, må du kontrollere at videoen er klar for automatisk sporing. Gå til den første rammen og klikk på auto for å starte sporing. Etter at sporingen er fullført, hvis nødvendig, utfør en manuell korrigering ved å velge riktig fotavtrykk.Lagre endringene ved å trykke på Lagre-knappen. Klikk deretter evaluere for å generere utdatafilene fra den sporede videoen. Kontroller at alle grafiske utdataplott er lagret i Resultater-mappen.Deretter kontrollerer du at alle kvantitative målinger generert av MouseWalker-programvaren er lagret i et Excel-regneark og oppsummert på 1.Information_Sheet. Bruk MouseMultievaluate. M script for å samle målingene fra alle løpene i en ny fil for analyse.For å utføre en prinsipal komponentanalyse, PCA, åpne pcaplotgenerator. py i Spyder og kjøre koden ved å klikke på Play-knappen. Velg Excel-filen som skal analyseres, og arknavnet i det automatiske vinduet.Hvis arknavnet ikke er endret, skriver du ark 1. I den digitale blekkanalysen ble det oppdaget mangel på støtte for bakpotene. Med en nedgang i fotavtrykkområdet for både venstre og høyre bakpote.De overordnede holdningssporene viste flere unike egenskaper. Etter ryggmargsskade hadde baklabbene kortere holdningsspor og mer tilfeldig poreposisjonering både ved touchdown og liftoff fra 15 dager etter skaden. Hovedkomponentanalyse av alle kinematiske motoriske parametere viste en 40% varians i dataene i den første komponenten, noe som skilte gruppen av dyr som hadde ryggmargsskade fra resten på alle tidspunkter.Ved hjelp av andre skript som varmekartskriptet, ble det funnet at ryggmargsskadedyrene viste endringer i gangstrategiene på alle tidspunkter. Ryggmargsskademusene viste også en lavere holdningsretthetsindeks i både forben og baklemmer.