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

Quantifying Locomotor Dysfunction in Spinal Cord Injury Mice Using 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.

Author Spotlight: Using the MouseWalker to Quantify Locomotor Dysfunction in a Mouse Model of Spinal Cord Injury  

Using the MouseWalker to Quantify Locomotor Dysfunction in a Mouse Model of Spinal Cord Injury

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  

In the computer, open MATLAB. Add the folder containing the MouseWalker script to the working directory and run MouseWalker. M on the main command line.Load the video folder as the input directory. Go to the Settings window where all the calibration and threshold parameters are located. Test the effect of changing some parameters by clicking the Preview button.After adjusting the threshold parameters, check that the video is ready for automated tracking. Go to the first frame and click auto to start tracking. After the tracking is completed, if required, perform a manual correction by selecting the appropriate footprint.Save the changes by pressing the Save button. Then, click evaluate to generate the output files from the tracked video. Check that all the graphical output data plots are saved in the Results folder.Next, check that all quantitative measurements generated by the MouseWalker software are saved on an Excel spreadsheet and summarized on 1.Information_Sheet. Use the MouseMultievaluate. M script to congregate the measurements from all the runs into a new file for analysis.To perform a principal component analysis, PCA, open pcaplotgenerator. py in Spyder and run the code by clicking on the Play button. Select the Excel file to analyze and the sheet name in the automatic window.If the sheet name is not altered, write Sheet 1. In the digital ink assay, a lack of support for the hind paws was detected. With a decrease in the footprint area for both the left and right hind paws.The overall stance traces displayed several unique features. After spinal cord injury, the hind paws had shorter stance traces and more random pore positioning at both touchdown and liftoff from 15 days post-injury. Principal component analysis of all the kinematic motor parameters showed a 40%variance in the data in the first component, which segregated the group of animals that had a spinal cord injury from the rest at all time points.Using other scripts such as the heat map script, it was found that the spinal cord injury animals showed alterations in the gait strategies at all time points. The spinal cord injury mice also displayed a lower stance straightness index in both the forelimbs and hindlimbs.

mousewalker-mw-video-tracking-and-kinematic-data-analysis

Research

JoVE Journal

Neuroscience

194 Views.  Faculdade de Medicina da Universidade de Lisboa. In the computer, open MATLAB. Add the folder containing the MouseWalker script to the working directory and run MouseWalker. M on the main command line.Load the video folder as the input directory. Go to the Settings window where all the calibration and threshold parameters are located. Test the effect of changing some parameters by clicking the Preview button.After adjusting the threshold parameters, check that the video is ready for automated tracking. Go to the first frame an...