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

Summary

An experimental pipeline to quantitatively describe the locomotor pattern of freely walking mice using the MouseWalker (MW) toolbox is provided, ranging from initial video recordings and tracking to post-quantification analysis. A spinal cord contusion injury model in mice is employed to demonstrate the usefulness of the MW system.

Abstract

The execution of complex and highly coordinated motor programs, such as walking and running, is dependent on the rhythmic activation of spinal and supra-spinal circuits. After a thoracic spinal cord injury, communication with upstream circuits is impaired. This, in turn, leads to a loss of coordination, with limited recovery potential. Hence, to better evaluate the degree of recovery after the administration of drugs or therapies, there is a necessity for new, more detailed, and accurate tools to quantify gait, limb coordination, and other fine aspects of locomotor behavior in animal models of spinal cord injury. Several assays have been developed over the years to quantitatively assess free-walking behavior in rodents; however, they usually lack direct measurements related to stepping gait strategies, footprint patterns, and coordination. To address these shortcomings, an updated version of the MouseWalker, which combines a frustrated total internal reflection (fTIR) walkway with tracking and quantification software, is provided. This open-source system has been adapted to extract several graphical outputs and kinematic parameters, and a set of post-quantification tools can be to analyze the output data provided. This manuscript also demonstrates how this method, allied with already established behavioral tests, quantitatively describes locomotor deficits following spinal cord injury.

Introduction

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 program^1,3,4, as well as proprioceptive feedback circuits⁵, are conserved between humans and other mammals 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 system^2,9,10. These connections culminate in the central pattern generators (CPGs), which are situated at a cervical and lumbar level for the forelimbs and hindlimbs, respectively^1,9,10. 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 grabs^16,17; 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 joint^4,20,21. 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.

Protocol

All handling, surgical, and post-operative care procedures were approved by Instituto de Medicina Molecular Internal Committee (ORBEA) and the Portuguese Animal Ethics Committee (DGAV) in accordance with the European Community guidelines (Directive 2010/63/EU) and the Portuguese law on animal care (DL 113/2013) under the license 0421/000/000/2022. Female C57Bl/6J mice aged 9 weeks were used for the present study. All efforts were made to minimize the number of animals and to decrease the suffering of the animals used in the study. The MATLAB script and the standalone version of the MW software are open-source and are available at the GitHub
repository (https://github.com/NeurogeneLocomotion/MouseWalker). While the MW software was developed in MATLAB R2012b, it has been adapted to run in MATLAB R2022b. Figure 1 illustrates the analysis workflow of the MW.

1. Setting up the MouseWalker (MW) apparatus

1. Assemble the MW apparatus as described previously²³, or adapt to the specific needs of the experimental design (see Table of Materials and Supplementary Figure 1 for more details on the setup).
   NOTE: The walking arena can be made wider to accommodate larger animals, such as rats.
2. Verify that the plexiglass where the animals walk is clean and scratch-free. Use a smooth cleaning cloth, and minimize the use of organic solvents such as ammonia or ethanol in high concentrations, which can damage the plexiglass (3% hydrogen peroxide, 7% ethanol, or any compatible and appropriate disinfectant for plexiglass is recommended). If necessary, replace the plexiglass.
3. Set up the high-speed camera with a fast lens and a large aperture (i.e., smaller F-stop values) to capture a large amount of light, as this helps to record the fTIR signals (see Table of Materials).
   NOTE: The lens should not generate optical distortions, particularly at the edges of the image. Optical distortions can be tested by recording a known pattern (e.g., stripes or squares) and then measuring the size of the blocks on ImageJ/FIJI²⁴ (use the line tool, and then click on Analyze > Measure). For example, a 1 cm sized square should have the same pixel dimensions both at the center of the image and on the edges. Variations should be smaller than 5%.
4. Light up the multicolored LED light strip from the background light box.
5. Light up the white LED light strip from the walkway light box.
   NOTE: A colored LED can also be used²⁵ to facilitate the distinction of the footprint/body/background.
6. With the room lights off, verify the light intensity of the background light box and walkway. Adjust the intensity, if necessary, using a potentiometer or semi-opaque plastic. These must be optimized so that pixel intensity increases in the following order: animal's body < background < footprints.
   1. To check the pixel intensity of the animal's body/background/footprints, open the image sequence on ImageJ/FIJI²⁴, and click on Analyze > Measure. The footprint signal should not be oversaturated, as this will prevent the boundaries of the footprint from being defined (i.e., toes and foot pads) (Supplementary Figure 2).
7. Adjust the image contrast of the walkway on the video recording software. The contrast can be adjusted in two ways: by dimming or increasing the lighting on the LED strip or by adjusting the camera lens aperture.
8. Position the lens correctly to be at the same height and in the center of the 45° reflecting mirror and perpendicular (90°) to the walkway. This will generate a constantly proportional image along the left-right walkway.
   NOTE: Avoid changing the camera position (distance, height, and orientation) across the multiple recording sessions. If necessary, mark the floor where the tripod should be placed. This will maintain the image features.
9. Focus the lens on the surface of the plexiglass. This can be tested using a non-damaging object touching the surface of the plexiglass.
   ​NOTE: With lower F-stop lens values, the depth of field will become smaller, thus making focusing harder.
10. Ensure all the settings remain unaltered during the assay, as they may change the pixel intensity of the recorded videos.

2. Video acquisition

1. Ensure the mice are familiar with the room and apparatus prior to testing. Save at least 1 day for habituation (day 0). To avoid excessive training, perform the MW test on a different day from the other behavioral tests (preferably the day after).
2. In the video recording software, ensure at least 50 cm of the walkway is visible.
3. Adjust the recording settings to truncate the walkway region. This will reduce the video size and optimize the video acquisition.
4. Take a picture or a short video of a regular ruler before each session. The number of pixels per centimeter will later be used in the "settings window" to calibrate the videos.
5. Start the video acquisition, and place the animal on the edge of the walkway by grasping the base of the tail to avoid injuries. Ensure the animals move forward to the extreme edge of the platform. Perform the video recordings with at least 100 frames/s to ensure smooth gait transitions.
   1. If needed, motivate the animals to move by gently tapping the walkway wall or snapping/clapping the fingers. However, avoid physical nudging, as this may affect the results.
   2. Save the videos directly as image sequences in TIFF (with LZW compression), JPEG, or PNG format. In case the camera records as a raw MOV file, convert the videos into image sequences by opening the file in ImageJ/FIJI²⁴ and clicking on File > Save as > Image sequence (or by using other software, such as LosslessCut²⁵).
      ​NOTE: Most animals start walking immediately after being put in the walkway; therefore, it is recommended to start the video acquisition before placing the animal.

3. Preparing the videos for the MW tracking software

1. Film enough complete runs of each individual mouse. The number of animals to film per condition and the number of complete runs must be decided according to each experimental design. A complete run is when the mouse walks the complete 50 cm of the walkway without prolonged stops (in this experiment, three complete runs were selected).
   NOTE: Depending on the image acquisition software, videos may need to be cropped to the smallest ROI. This will increase the speed of tracking and output generation.
2. In ImageJ/FIJI²⁴, select the frames in which the mouse is on the screen by clicking on Image > Stack > Tools > Make a substack. The tracking on the MW requires the head and the tail to be visible in all the frames. It is possible, however, to make several substacks from a single video recording, which will later represent each run.
3. Save each substack separately in different folders by clicking on File > Save as > Image sequence. The MW software later creates a subfolder automatically in each directory every time one starts analyzing a run.

4. Tracking

1. Open MATLAB, add the folder containing the MW script to the working directory, and run "MouseWalker.m" on the main command line.
   NOTE: Using the MW software under MATLAB allows tracking error messages to be viewed on MATLAB's main console and the desired output data to be selected (by opening the main script file "MouseEvaluate.m" and changing the outputs to either 1 or 0: the excel file, footstep plots, stance traces, and gait patterns).
2. Load the video folder as the "Input directory". One can also choose the output folder; however, this is not a requirement as the MW software creates a new folder called "Results" automatically inside the "Input directory".
3. Using the arrows "<<", "<", ">>", and ">" check if the video frames are all loaded correctly inside the MW software.
4. Go to the "Settings window" where all the calibration and threshold parameters are located. These settings can change depending on the pixel intensity of the background and footprints, as well as the minimal size of the body and footprints, amongst other factors (see example in Supplementary Figure 2). Test the effect of changing some parameters by clicking on the Preview button.
   1. Use the different plot styles, including "body + feet + tail", "body only", "feet only", and "tail only", to help discriminate body parts after adjusting the threshold parameters.
   2. Take advantage of the tools on the right-side panel to take measurements of the brightness or size (using the "brightness" and "ruler" buttons, respectively). All settings can be saved as "default" as long as the camera distance remains the same.
5. After adjusting the threshold parameters, check that the video is ready for automated tracking. Go to the first frame, and click on Auto to start tracking. This step can be followed in real time, and it takes a few minutes, depending on the size of the video and the computer's performance.
   1. If the auto-tracking incorrectly labels the body features, cancel the auto-tracking, enter new settings, and restart the process.
6. After the tracking is completed, check if a manual correction is needed. To correct, use the middle panel to select or deselect, and indicate the location of the right fore (RF), right hind (RH), left fore (LF), and left hind (LH) paw footprints, head, nose, body (divided into two segments), and tail positions (divided into four segments). Save the changes by pressing the Save button.
   NOTE: All buttons and most commands have a key shortcut (check the associated manual for details²³). To facilitate video scrolling and the execution of keyboard shortcuts, a hardware controller with programmable buttons and a shuttle wheel like the Contour ShuttlePro V2 can be used.
7. Click on Evaluate to generate the output files from the tracked video. Depending on the desired output selected (see step 4.1), this step can take a few minutes.
8. Check that all the graphical output data plots are saved in the "Results" folder. Verify the accuracy of the tracking by examining some of the graphical outputs, such as the "Stance traces", where one can check if all the paw positions are consistent.
   1. If an error is identified, manually correct the tracking (if possible; otherwise, eliminate the "Results" folder, and perform the auto-tracking again with new settings), and click on the Evaluate command again.
9. Check that all the quantitative measurements generated by the MW software are saved on an Excel spreadsheet and summarized on "1. Info_Sheet". Ensure that the excel options for the formula delimitations match the script. The decimal separator must be ",", and the thousand separators must be ";".
10. Use the "MouseMultiEvaluate.m" script to congregate the measurements from all the runs into a new file for analysis.
    1. To begin, generate a .txt file containing the folder paths for all the videos (e.g. "Videofiles.txt"). Ensure that each line corresponds to a single video.
    2. Then, write "MouseMultiEvaluate('Videofiles.txt')" into the command line. An excel file named "ResultSummary.xls" will be generated in the working directory (see an example in the GitHub repository).
       ​NOTE: Figure 2 represents the graphical outputs obtained by the MW software from the videos of one recorded animal.

5. Kinematic data analysis workflow

1. Edit the excel sheet generated in step 4.10, which contains the data for processing using the supplied Python scripts, according to the following prerequisites.
   1. In the first column header, specify the experimental condition. Name each line following the group/condition name (individuals from the same groups must have the same name). The first group must be the control or baseline (this is only mandatory for heatmap plotting, step 5.6).
   2. In the second column, specify the animal ID. This is mandatory, although this information will not be used for plot generation.
   3. In the third column onward, choose the motor parameters that will be used for the analysis. Ensure that the first line is the name of the parameter (these names will later appear in the plots).
2. Open Anaconda Navigator, and execute Spyder to open the supplied Python scripts.
   NOTE: All the scripts were developed with Python 3.9.13, were executed with Spyder 5.2.2 in Anaconda Navigator 2.1.4, and are available in the Table of Materials and the GitHub repository (where additional materials are included, such as a video example, an excel example file, and an FAQs document). It is possible to execute the scripts outside the Anaconda Navigator; however, this graphical user interface is more user-friendly.
3. Use the "Rawdata_PlotGenerator.py" to generate the raw data plots. This will allow the visualization of each parameter as a function of speed.
   1. Open "Rawdata_PlotGenerator.py" in Spyder, and run the code by clicking on the Play button.
   2. Select the Excel file to analyze and the sheet name in the automatic window. If the sheet name was not altered, write "Sheet1".
   3. The raw data plots will appear in the plot console (upper right panel). To save the plots, click on the Save image or Save all images button in the plot console.
4. Use the script "Residuals_DataAnalysis" to calculate the residuals for data analysis. This script will generate a CSV file with the calculations of the residuals for all the motor parameters.
   NOTE: Many of the measured gait parameters extracted by the MW vary with speed (e.g., swing speed, step length, stance duration, stance straightness, and gait indexes). Therefore, it is recommended to perform a best-fit regression model of each individual parameter versus speed for the baseline experiment and to then determine the residual values for each experimental group in relation to this regression model. The data are then expressed as the difference from the residual normalized line²⁶.
   1. Open "Residuals_DataAnalysis.py" in Spyder, and run the code by clicking on the Play button.
   2. Select the Excel file to analyze and the sheet name in the automatic window. If the sheet name was not altered, write "Sheet1".
   3. Save the CSV file in the same folder as the data. It is mandatory that the control (or baseline) is the first group in the Excel file.
5. Use the "PCA_PlotGenerator.py" script to perform a principal component analysis (PCA).
   NOTE: This unsupervised dimensionality reduction method is used to generate a more succinct representation^27,28,29 of the data (Figure 3A, B). The PCA script includes the following steps. The data is first pre-processed by centering and scaling, after which the PCA algorithm computes the covariance matrix to determine the correlations between the variables and calculate the eigenvectors and eigenvalues of the covariance matrix to identify the principal components. The first two or three principal components are chosen for the representation of the data in 2D or 3D plots, respectively. Each dot in the plots corresponds to an animal and represents a different abstract variable. Color-coded dots are used to distinguish the specific groups. As such, clusters of dots reflect similar walking patterns shared by the corresponding individuals.
   1. Open "PCA_PlotGenerator.py" in Spyder, and run the code by clicking on the Play button.
   2. Select the Excel file to analyze and the sheet name in the automatic window. If the sheet name was not altered, write "Sheet1".
   3. Ensure that the PCA 2D and 3D plots appear in the plot console (upper-right panel). Each color represents a different group, and the legend appears next to the plot. To save the plot, click on Save image in the plot console.
6. Use "Heatmap_PlotGenerator.py" to generate a heatmap. Ensure that the heatmap generator creates a table showing the statistical differences between the baseline group (or control group) and the other groups for each motor parameter²⁷ (Figure 4). Each column depicts one group, and each line relates to a specific motor parameter.
   NOTE: Statistical analysis was conducted with a one-way ANOVA followed by Tukey's post hoc test (for normal distributions) or a Kruskal-Wallis ANOVA followed by Dunn's post hoc test (for non-normal distributions). Outliers were excluded from the analysis. P-values are represented by a color code, with red and blue shades indicating an increase or decrease relative to control (or baseline), respectively. The color shade represents the statistical significance, with darker colors showing a higher significance, and lighter colors showing a lower significance. *** corresponds to P < 0.001; ** corresponds to P < 0.01; and * corresponds to P < 0.05. White indicates no variation.
   1. Open "Heatmap_PlotGenerator.py" in Spyder, and run the code by clicking on the Play button.
   2. Select the Excel file to analyze and the sheet name in the automatic window. If the sheet name was not altered, write "Sheet1".
   3. Select the type of data in the second automatic window: raw data or residuals data. If an option is not selected, residuals data is the default.
   4. The heatmap will appear in the plot console (upper-right panel). To save the plot, click on Save image in the plot console.
      NOTE: It is mandatory that the control (or baseline) is the first group in the Excel file.
7. Use "Boxplots_PlotGenerator.py" to generate the boxplots. This tool will allow the generation of boxplots that represent the distribution of values for all the motor parameters for each group (Figure 5, Figure 6, and Figure 7).
   NOTE: Each box contains the median as the middle line, and the lower and upper edges of the boxes represent the 25% and 75% quartiles, respectively. The whiskers represent the range of the full data set, excluding outliers. Outliers are defined as any value that is 1.5 times the interquartile range below or above the 25% and 75% quartiles, respectively.
   1. Open "Boxplots_PlotGenerator.py" in Spyder, and run the code by clicking on the Play button.
   2. Select the Excel file to analyze and the sheet name in the automatic window. If the sheet name was not altered, write "Sheet1".
   3. Select the type of data in the second automatic window: raw data or residuals data. If an option is not selected, residuals data is the default.
   4. The boxplots will appear in the plot console (upper-right panel). To save the plots, click on the Save image or Save all images button in the plot console.

Results

The standard BMS system describes the gross motor deficits after SCI¹⁴. Due to its subjective nature, other quantitative assays are generally performed alongside the BMS to produce a more detailed and fine assessment of locomotion. However, these tests fail to show specific information about step cycles, stepping patterns, and forelimb-hindlimb coordination, which is extremely important in understanding how the spinal circuitry maintains function and adapts to an incomplete SCI. This section shows...

Discussion

Here, the potential of the MouseWalker method is demonstrated by analyzing locomotor behavior after SCI. It provides new insights into specific alterations in stepping, footprint, and gait patterns that would otherwise be missed by other standard tests. In addition to providing an updated version of the MW package, data analysis tools are also described using the supplied Python scripts (see step 5).

As the MW generates a large dataset and a collection of kinematic parameters that reflect a hi...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
45º Mirror
2 aluminum extrusion (2 x 2 cm), 16 cm height, 1 on each side	Misumi
2 aluminum extrusion (2 x 2 cm), 23 cm, @ 45° , 1 on each side	Misumi
1 aluminum extrusion (2 x 2 cm), 83 cm long	Misumi
87 x 23 cm mirror	General glass supplier
black cardboard filler	General stationery supplier		We used 2, one with 69 x 6 cm and another with 69 x 3cm to limit the reflection on the mirror
Background backlight
109 x 23 cm plexiglass (0.9525 cm thick)	General hardware supplier
2 lateral aluminum extrusion (4 x 4 cm), 20 cm long, 1 on each side	Misumi
multicolor LED strip	General hardware supplier
white opaque paper to cover the plexyglass	General stationery supplier
fTIR Support base and posts
2 aluminum extrusion (4 x 4 cm), 100 cm height	Misumi
60 x 30 cm metric breadboard	Edmund Optics	#54-641
M6 12 mm screws	Edmund Optics
M6 hex nuts and wahers	Edmund Optics
fTIR Walkway
109 x 8.5 cm plexyglass (1.2 cm thick)	General hardware supplier		109 x 8.5 cm plexyglass (1.2 cm thick)
109 cm long Base-U-channel aluminum with 1.6 cm height x 1.9 cm depth thick folds (to hold the plexyglass)	General hardware supplier
2 lateral aluminum extrusion (4 x 4 cm) 20 cm length, 1 on each side	Misumi
black cardboard filler	General stationery supplier		we used 2 fillers on each side to cover the limits of the plexyglass, avoiding bright edges
M6 12 mm screws	Edmund Optics
High speed camera (on a tripod)
Blackfly S USB3	FLIR	#BFS-U3-32S4M-C	This is a reccomendation. The requirement is to record at least 100 frames per second
Infinite Horizon Impactor
Infinite Horizon Impactor	Precision Systems and Instrumentation, LLC.	#0400
Lens
Nikon AF Zoom-Nikkor 24-85mm	Nikon	#1929	This lens is reccomended, however other lens can be used. Make sure it contains a large aperture (i.e., smaller F-stop values), to capture fTIR signals
Software
MATLAB R2022b	MathWorks
Python 3.9.13	Python Software Foundation
Anaconda Navigator 2.1.4	Anaconda, Inc.
Spyder 5.1.5	Spyder Project Contributors
Walkway wall
2 large rectagular acrilics with 100 x 15 cm	Any bricolage convenience store
2 Trapezian acrilic laterals with 6-10 length x 15 cm height	Any bricolage convenience store
GitHub Materials
Folder name	URL
Boxplots	https://github.com/NeurogeneLocomotion/MouseWalker/tree/main/Boxplots		Script to create Boxplots
Docs	https://github.com/NeurogeneLocomotion/MouseWalker/tree/main/Docs		Additional documents
Heatmap	https://github.com/NeurogeneLocomotion/MouseWalker/tree/main/Heatmaps		Script to create heatmap
Matlat script	https://github.com/NeurogeneLocomotion/MouseWalker/tree/main/Matlab%20Script		MouseWalker matlab script
PCA	https://github.com/NeurogeneLocomotion/MouseWalker/tree/main/PCA%20plots		Script to perform Principal Component Analysis
Raw data Plots	https://github.com/NeurogeneLocomotion/MouseWalker/tree/main/Rawdata%20Plots		Script to create Raw data plots
Residual Analysis	https://github.com/NeurogeneLocomotion/MouseWalker/tree/main/Residual_Analysis		Code to compute residuals from Raw data