This study was supported by the Merit Review Award # B1495-R (to Jeffrey R. Capadona) and the Presidential Early Career Award for Scientist and Engineers (PECASE) (to Jeffrey R. Capadona) from the United States Department of Veterans Affairs Rehabilitation Research and Development Service. Additionally, this work was supported in part by the Office of the Assistant Secretary of Defense for Health Affairs through the Peer Reviewed Medical Research Program under Award No. W81XWH-15-1-0608. The authors acknowledge SOURCE for its summer research funding support. The contents do not represent the views of the United States Department of Veterans Affairs or the United States Government. The authors would like to thank Hiroyuki Arakawa in the CWRU Rodent Behavior Core for his guidance in designing and testing rodent behavioral protocols. The authors would also like to thank James Drake and Kevin Talbot from the CWRU Department of Mechanical and Aerospace Engineering for their help in designing and manufacturing the rodent ladder test.

All procedures and animal care practices were approved by and performed in accordance with the Louis Stokes Cleveland Department of Veterans Affairs Medical Center Institutional Animal Care and Use Committee. The behavioral testing protocol closely follows previously published work12,13.
1. Behavioral Testing: Filming the Tasks
NOTE: Here, the animals were tested for 8 weeks to detect any chronic behavioral changes. The study duration is dependent on the application/injury/disease model used for the study.
<ol>
	<li>Open field grid testing 
	NOTE: The grid test has a 1 m2 area with 40 cm walls and was built in-house. The bottom is partitioned into nine squares via brightly colored tape (Figure 1A). For the use of the automated BVAS, it is important that the color of the line and the grid contrast with the rat. Here, white Sprague Dawley rats were used; thus, the grid background was painted black, and the lines were created with bright pink tape.
	<ol>
		<li>Test the animals 2x per week for all tests including the week before beginning the study in order to create a baseline score for use in later calculations.</li>
		<li>Clean all the testing equipment with a chlorine dioxide-based sterilant at the beginning of each testing session and between each animal.</li>
		<li>Bring the animals to the room for testing and allow them to adjust to the room for 20 min prior to starting the trials.</li>
		<li>Begin filming and place the animal in the center of the grid, facing away from the researcher, to begin the grid test.</li>
		<li>Allow the animal to run freely for 3 min while being video recorded from above.</li>
		<li>Stop filming when the 3 min time period has ended and return the animal to its home cage. Clean the grid with a chlorine dioxide-based sterilant, taking care to fully dry the surfaces before testing. Test each animal 1x per session.</li>
	</ol>
	</li>
	<li>Ladder testing 
	NOTE: The ladder test was built in-house and consists of two acrylic side walls, each 1 m in length, connected by 3 mm diameter rungs spaced at 2 cm intervals (Figure 2). The animals require a week of ladder training before beginning the official testing. There is no difference between the training and the testing protocol. Note that the training does not need to be filmed.
	<ol>
		<li>For the use with the BVAS, cover the wall behind the ladder with a black poster board and delineate the level of the rungs, start, and finish lines with bright tape to create a contrast with the white rats.</li>
		<li>Test the animals 2x per week for all tests including the week before beginning the study to create a baseline score for use in later calculations.</li>
		<li>Clean all the testing equipment with a chlorine dioxide-based sterilant at the beginning of each testing session and between each animal.</li>
		<li>Bring the animals to the room for testing and allow them to adjust to the room for 20 min prior to starting the trials.</li>
		<li>Place the animal in a clean temporary cage before the ladder testing. 
		NOTE: A temporary cage is a short-term cage to hold the animal while the animal&#39;s home cage is used at the end of the ladder. There is no age bias as all animals used in the study are the same age and complete testing over the same time period. The control used here is a na&#239;ve animal having never received surgery.</li>
		<li>Set the ladder apparatus up to span two cages; the start cage is a clean cage, and the end cage is the animal&#39;s living cage, an incentive to complete the run.</li>
		<li>Place the camera on a tripod, centered on the length of the ladder. Extend the tripod so the lens of the camera is at the height of the ladder rungs. Position the camera so that the rungs are exactly aligned with the lens as this is important for the slip detection algorithm in the BVAS code.</li>
		<li>Start the video recording and allow the rat to begin the run by holding their front paws over the first rung of the ladder. Allow the animal to move onto the ladder unassisted.</li>
		<li>Allow the animal to move from the start line to the finish line at their own pace while filming. End the video recording and remove the animal from the ladder once the animal has completed the run.</li>
		<li>Consider the run a failed run if the animal turns around or remains stagnant for 20 s. End the video recording and remove the animal from the ladder if the animal fails the run. Assign a penalty score for failed runs which is equivalent to the slowest time recorded during the pre-surgery baseline testing.</li>
		<li>Ensure that each animal completes 5 runs per testing day and give them a 1 min rest period between each trial. 
		NOTE: In this study, the animals were tested 2x per week for 8 weeks. However, the timeframe is up to the discretion of the researcher.</li>
	</ol>
	</li>
</ol>
2. File Storage and Naming
NOTE: The BVAS code uses specifically designated video file and folder naming conventions so that videos may be reliably parsed and analyzed properly. Different naming conventions are currently not supported. After the completion of a testing session, the video file is saved in the default location under a default name.
<ol>
	<li>Individual video naming
	<ol>
		<li>Name each video with the animal&#39;s designation for all grid testing videos.</li>
		<li>Collect 5 ladder videos per testing session for each animal. Select all five videos and rename the first video, using the animal&#39;s designation. For example, if the animal&#39;s name is &#34;C1NS&#34;, the files are named &#34;C1NS (1)&#34;, &#34;C1NS (2)&#34;, &#34;C1NS (3)&#34;, etc.</li>
	</ol>
	</li>
	<li>Storage folder naming
	<ol>
		<li>Take the videos now named after each animal and place the files in their own storage folder. Name this folder using the following convention: &#34;TestingWeek_TestMode_MM_DD_YY&#34;. 
		NOTE: For example, if this folder holds the first week of grid videos and was filmed on January 1st, 2018, the folder holding these videos is named &#34;Week1_GridTest_01_01_18&#34;.</li>
		<li>Place this specifically named folder in another folder that will denote the larger study that this set of experiments belongs to. 
		NOTE: There are no naming requirements for the folder tree system to keep studies and testing modes organized. The only folder the system selects is the folder holding videos named in step 2.2.1.</li>
	</ol>
	</li>
	<li>Spreadsheet file creation
	<ol>
		<li>Using an outside spreadsheet program (see the Table of Materials), create a new empty spreadsheet file to store the data for each study and testing mode. 
		NOTE: There are no naming or storage location requirements for this file. Each study requires two spreadsheet files for use with the system, one for grid testing and one for ladder testing. Leave these files empty for now.</li>
		<li>Check to ensure these spreadsheet files are not open on any computers while the BVAS is running. 
		NOTE: If the BVAS attempts to open a spreadsheet file that is already open, this will result in an error.</li>
	</ol>
	</li>
</ol>
3. System Installation
NOTE: The BVAS was built and tested on PC operating systems and leverages Runtime components from simple and readily available coding software. Other system configurations are currently not supported.
<ol>
	<li>Installing latest BVAS version
	<ol>
		<li>Run the installer by double-clicking on the installer icon labeled &#39;BVASX.XXinstaller&#39;. &#34;X.XX&#34; is the current version number, currently &#39;BVAS3.30installer&#39;. 
		NOTE: This is open source software. Contact the corresponding author for the most recent version.</li>
		<li>Follow the on-screen instructions to install the BVAS. 
		NOTE: The installer will check the system for the correct version of the software (e.g., MATLAB Runtime) and if it is not found, it will install the software from the internet.</li>
		<li>Once installed, launch the program by clicking the &#39;BVAS.exe&#39; file.</li>
	</ol>
	</li>
</ol>
4. System Use
<ol>
	<li>Opening the program
	<ol>
		<li>Double-click on the BVAS program icon to bring up the BVAS main menu which allows the following options: &#39;Grid Analysis&#39;, &#39;Ladder Analysis&#39;, &#39;Ladder Review&#39;, or &#39;Quit&#39;. 
		NOTE: There is also a drop-down menu located in the top left corner labeled &#39;Email Settings&#39; (Figure 3A).</li>
	</ol>
	</li>
	<li>Email settings
	<ol>
		<li>Click on the &#39;Email Settings&#39; drop-down menu and first select &#39;Change email destination&#39;.</li>
		<li>Enter a preferred email address to receive updates on the analysis and select &#39;OK&#39;. Select &#39;Cancel&#39; to not change the email currently saved.</li>
		<li>Click the &#39;Send Test Email&#39; button to send a test email to the email currently saved in the system. Close the &#39;Email Sent!&#39; window pop-up signifying the test&#39;s completion.</li>
	</ol>
	</li>
	<li>Open Field Grid Test Analysis
	<ol>
		<li>Select &#39;Grid Analysis&#39; from the top menu screen.</li>
		<li>Ensure the toggle button labeled &#39;Single Video/Whole Session/Plot Data&#39; is displaying Whole Session to complete the analysis on a whole set of testing session videos.</li>
		<li>Utilize the &#39;&#8230;&#39; button next to the upper input box to browse the file system to find the file of videos to analyze. Select the folder labeled in the style discussed in step 2.2.1.</li>
		<li>Utilize the &#8230; button next to the lower input box to browse the file system to select the spreadsheet file in which to store the data. 
		NOTE: Note that, as well as outputting the data to a spreadsheet file, the complete path data for each video is stored in a data file (.mat) in the same directory that holds the videos chosen in step 4.3.3.</li>
		<li>Ensure the toggle button mentioned in step 4.3.2 is set to &#39;Single Video&#39; to complete the analysis on a single video.</li>
		<li>Utilize the &#8230; button next to the upper input box to browse the file system to find the file of the video to analyze.</li>
		<li>Utilize the &#8230; button next to the lower input box to browse the file system to select a directory to save the data file (.mat) that results from the single video analysis. 
		NOTE: The data file will be named after the video, which is named after the animal.</li>
		<li>Select &#39;Output Data&#39; from the toggle mentioned in step 4.3.2. to output the data from a previously analyzed grid video. Examples of this plotted data are shown in Figures 1B and 1C.</li>
		<li>Utilize the &#8230; button next to the upper input box to browse the file system to select a previously created grid video data file. Select &#39;Go!&#39; to create the plot and data figure. 
		NOTE: This function also creates a text file, named the same and located in the same directory as the data file, which has the animal&#39;s run data in it.</li>
		<li>Once both necessary files have been located, select &#39;Go!&#39; to begin the video analysis. Note that the &#39;Return&#39; button will reopen the top menu and the &#39;Quit&#39; button will close the program completely. Choose the &#39;Debug Display&#39;&#160;toggle to show a visual representation of the tracking algorithm (Figures 3C and 3D).</li>
		<li>Select &#39;Go!&#39; to start the analysis process. Examine the images of each video on the reviewer screen. Use the &#39;Next&#39; and &#39;Previous&#39; page buttons if there are more than five videos.</li>
		<li>To bring up a manual selection menu, select the &#39;Edit&#39; button if there are no green lines or the green line does not appear to be in the correct square around the testing area. To edit the corners, select the &#39;Edit Corners&#39; button. 
		NOTE: The &#39;Next Frame&#39; button changes the representative image to the next frame of the video in question. Select the &#39;Next Frame&#39; button if the frame given automatically is obstructed or out of focus.</li>
		<li>Follow the instructions to the right of the representative image to select the testing area and hit &#39;Enter&#39; to complete the selection process. 
		NOTE: The selected green lines will now be shown on the representative image.</li>
		<li>Select &#39;Done&#39; to save the selection and return to the reviewer screen.</li>
		<li>Select the center &#39;Done&#39; button to start the complete analysis process once the grid area for each video is selected correctly. Allow the program to run and, upon completion, a success prompt will appear. 
		NOTE: The full analysis of a session of videos can take some time, approximately 10 min per animal.</li>
	</ol>
	</li>
	<li>Ladder test analysis 
	NOTE: The protocol for the initial ladder test analysis is very similar to the protocol outlined in steps 4.3.10&#8211;4.3.15. The notable differences prior to those steps are as follows:
	<ol>
		<li>Select &#39;Ladder Analysis&#39; from the top menu to open the ladder analysis menu.</li>
		<li>View the two options in the toggle, &#39;Single Video&#39; and &#39;Whole Session&#39; (Figure 3B).</li>
		<li>With &#39;Whole Session&#39;, select the upper input &#8230; button to select the folder with ladder videos named in step 2.2.1.Select the lower &#8230; button to select a directory to save a folder with data from the ladder videos to review later.</li>
		<li>If &#39;Single Video&#39; is selected, select a single video file with the upper &#8230; button and a location the same as in step 4.4.3.</li>
		<li>Select &#39;Go!&#39; to start the analysis process and bring up the ladder test area reviewer screen.</li>
		<li>Follow steps 4.3.10&#8211;4.3.15 the same way for the ladder as for the grid. Allow the program to run and, upon completion, a success prompt will appear. 
		NOTE: If something goes wrong during the analysis, an email is sent to the email address entered in step 4.2.2. Upon completion of an analysis of any kind, a completion email is sent to the same email address.</li>
	</ol>
	</li>
	<li>Ladder image review 
	NOTE: The following protocol is to review previously analyzed ladder videos to confirm any failures and paw slips. Note, spreadsheet files for a ladder crossing are not generated to view until &#39;Ladder Image Review&#39; (steps 4.5.1&#8211;4.5.5) is completed.
	<ol>
		<li>From the top menu, select &#39;Ladder Image Review&#39;. This brings up the Ladder Review file select screen.</li>
		<li>Select the top &#8230; button to select the folder of the data files. 
		NOTE: The files are saved in the location chosen in step 4.4.2 and are named in the following format: &#34;M1_D1_Y1 Ladder Video Session HH_MM M2_D2_YYYY&#34;. M1, D1, and Y1 are the date of the session being analyzed. &#34;Ladder Video Session&#34; will read &#34;Single Video Analysis&#34; if only a single video was analyzed. HH_MM and M2_D2_YYYY are the time and date that the review session was started.</li>
		<li>Select the bottom &#8230; to select the spreadsheet file created for the study.</li>
		<li>Select &#39;Go!&#39; to start the manual review process.</li>
		<li>View the automatically opened ladder data reviewer menu. If a slip is detected, use the &#39;Next Frame&#39; and &#39;Last Frame&#39; buttons to check any consecutively recorded frames if available.</li>
		<li>Utilize the slip toggle to denote what foot of the rat has slipped. Leave the toggle on &#39;No Slip&#39; if the detection is a false positive. Click &#39;Save/Continue&#39; to move on to the next detection (Figure 2A).</li>
		<li>In the case of failure, use the toggle to denote whether the failure is due to reversal (where the rat turned around during the run) or stagnation (where the rat failed to complete the run in a timely manner).</li>
		<li>Click &#39;Save/Continue&#39; to move on to the next detection. On the last detection, click &#39;Save/Continue&#39; to complete the analysis and save the data to the spreadsheet file chosen earlier. Dismiss the completion notice. 
		NOTE: After the review is completed, the folder that was reviewed is renamed &#34;REVIEWED M1_D1_Y1 Ladder Video Session HH_MM M2_D2_YYYY&#34;.</li>
	</ol>
	</li>
</ol>
5. Output Data Analysis
<ol>
	<li>Grid spreadsheet file
	<ol>
		<li>Locate the created data in the spreadsheet file from each grid video. 
		NOTE: The columns are left to right as follows: animal name, date of test, week of study number, total distance traveled, total time stopped, maximum velocity achieved, right turns, left turns, and grid line crosses (Figure 1D).</li>
		<li>Use these metrics to quantify the gross motor performance and create comparison plots between animals and time points.</li>
	</ol>
	</li>
	<li>Ladder spreadsheet file
	<ol>
		<li>Locate the created data in the spreadsheet file from each ladder video following the failure and paw slip review. 
		NOTE: The columns are left to right as follows: animal name, run number 1&#8211;5, week of study number, date of test, run type (either success or failure), time of run for successes, percent completion, paw slips on each paw, and failure mode. The time column is blank for a failure and the failure mode column will read either reversal or stagnation (Figure 2B).</li>
		<li>Use these metrics to quantify the fine motor performance and create comparison plots between animals and time points.</li>
	</ol>
	</li>
</ol>

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The authors have nothing to disclose.

The most critical portion of the protocol to ensure a strong analysis is the consistent filming. If the videos are well lit and filmed at the correct position as discussed in the first section of the protocol, the system will be able to do a precise analysis. As with any image-processing problem, the work done in preprocessing will make the post-processing more accurate and simple. As such, making sure that the apparatus and animals are well-lit during the testing and any shadows or other motion in the frame is kept to a minimum will mean the BVAS can function at a higher level of accuracy.
The protocol presented here can be used to efficiently and reproducibly analyze results from both gross and fine motor function testing in rodents via an open field grid and a ladder test. Additionally, it reduces the possibility of human error or bias in the analysis process as the data analysis is largely completed by the self-functioning computer without any user input. Because of this feature, the BVAS system can be used with the same level of accuracy by both expert and novice users. The BVAS program is self-checking, easy to implement, and inexpensive to use. Further, the code can be adapted to fit a researcher&#39;s individual needs. For example, the open field grid code can be used to analyze a variety of metrics including the total distance traveled, the maximum speed achieved, and the number of left and right turns, and could likely be easily modified for the tracking in a water maze or forced swim task. Additional modifications can be made to the code to account for varying room lighting, differences in grid and ladder line color, and differences in animal color. Although the testing apparatus and the BVAS code presented here were designed for their use in rat studies, we expect that either could be scaled up or down to be used with various sized animals, although this has not been verified to date.
As with all behavior testing and data analysis, it is important to maintain as much consistency as possible throughout the study and subsequent analyses. Although the presented BVAS greatly reduces any reliance on human input for the data analysis, human variance can come into play with the researcher working with the animals and handling the procedures15. Furthermore, changes in the testing location16 or housing and husbandry conditions17 can also influence the results. While the BVAS can be updated to account for lighting and camera angle, factors such as smells, personnel, or diet can only be accounted for at the time of the study. Therefore, researchers should take caution to remain as consistent as possible in the animal testing and housing conditions, testing personnel, and analytical methods, among others.
The BVAS is novel because of its compound detection system. Each frame of the video is passed through multiple image filters to create binary masks all calibrated to look for the rat in the frame. Each possible rat shape is then rated by the system on its likelihood of being the rat. This rating factors in size, the number of filters the shape is detected on, and the last known location and the predicted location of the animal based on the previous trajectory. This makes for a strong animal detection that can overcome most issues that arise based on shadows and changes in the lighting. Unlike current commercially available animal tracking systems, this method does not require any modifications to the animal to make it more visible and uses a single standard-speed webcam for the video recording. The BVAS system is also an improvement over conventional manually quantified behavior testing because all the metrics that are outputted are objectively measured from the videos. A human quantified behavioral study will have some subjectivity in nature as the researcher decides what constitutes a paw slip or grid line cross.
The system requires a somewhat specific setup to work to its fullest capabilities. While it is possible to adapt the system to changes in the apparatus, it would currently require some fluency in computer coding to edit the code. With some coding fluency, troubleshooting is made easy because the error notice emails include a full error information readout. If the videos fed into the system are not at the ideal resolution or frame rate (1080 p, 15 fps), the user is alerted via a warning message. This change will affect the accuracy of the system, but it still can run on lower resolution videos. Any lighting or shadow problems that cause an error during the analysis will lead to a dynamic error message sent to the user-entered email. The user can then use the debug viewer to watch the frame by frame analysis to understand what issue arose. The error message also includes an error report, so if it is a coding issue, it can be addressed simply.
In the future, the BVAS system may be able to be further adapted without any knowledge of the syntax from coding software. The addition of a tracking options menu could allow the user to select the color of the animal and grid lines simply and conveniently. The ladder test tracking will also increase in accuracy with the possible addition of a cascade object detector in the code and a better ladder lighting rig to accompany the ladder test and filming apparatus. Therefore, we expect that the BVAS system described here can be readily implemented into an array of behavioral and motor function tasks spanning a wide variety of disease and injury models.

There are many established methods to assess both functional and behavioral motor and cognitive impairments1,2,3. Some of the more commonly employed methods include testing fine motor function via paw placement, stepping, and limb coordination on a ladder test4, testing gross motor function and stress behavior via the open field grid test5,6, and testing for fear, depression, and despair via the forced swim test7,8 or rotor rod9. However, many of these methods rely on human researchers to &#34;score&#34; the animal or to judge its performance subjectively. The need for a subjective human assessment can slow the generation and analysis of the data, as well as present the opportunity for an intentional or unintentional influence of research bias in the study10. Further, subjective assessment of the data also presents the risk of inaccurate data representation, be it through forgetfulness, poor motivation, improper training, or negligence11.
We have recently reported the use of both an open field grid test and a ladder test in rats implanted with intracortical microelectrodes12,13. Due to the novelty of the findings in those studies, we immediately began employing those and additional functional testing in many ongoing studies in the laboratory. In anticipation of unintentional human-generated variability resulting from an increase in the number of subjective evaluators, and to improve the analysis throughput, we set out to create an automated, computer-assisted program to score behavioral testing, and greatly limit the potential for error.
Here, we report on the development of the BVAS. The BVAS uses computer analysis to score an open field grid test and a ladder test as metrics of gross and fine motor function, respectively. The results can be used to elucidate possible motor function deficits caused by injury or disease, regardless of the injury or disease model. The analysis codes can be adapted to account for changes in behavioral testing equipment or to score various metrics of motor function. Therefore, the BVAS can be implemented in many applications, beyond our intended use or the intended use of those currently employed by other laboratories.
Note that the open field grid and ladder tests require video recording. Therefore, each test will require a video camera [1080 p, minimum 15 frames per second (fps)], a laptop, and a room to store the video data. For both tests, place the camera in a centered position, allowing for the whole apparatus to be seen on the frame. Anchor the camera on a tripod or scaffolding so that it does not move during the testing. Keep the edges of the video frame as close to parallel with the edges of the testing apparatus as possible. Be sure the same personnel complete all testing and the room is well-lit with a temperature-controlled system. Use the same room for all animals throughout the course of the testing, with minimal changes to the room. Cereals or banana chips make good rewards to encourage the animals to complete the behavior tests.

<table><tbody><tr><td>Sprague Dawley rats, male, 201-225 g</td><td>Charles River</td><td>CD</td><td></td></tr><tr><td>Webcam HD Pro c920</td><td>Logitec</td><td>960-000764</td><td></td></tr><tr><td>Excel</td><td>Microsoft</td><td>N/A</td><td></td></tr><tr><td>Matalb 2017a, Computer Vision System Toolbox</td><td>Mathworks</td><td>N/A</td><td></td></tr><tr><td>Open field grid test</td><td>Made in-house at Case Western Reserve University</td><td>N/A</td><td></td></tr><tr><td>Ladder test</td><td>Made in-house at Case Western Reserve University</td><td>N/A</td><td></td></tr></tbody></table>

a novel single animal motor function tracking system using simple, readily available software

We have recently demonstrated that implanting intracortical microelectrodes in the motor corteces of rats&#160;results in immediate and lasting motor deficits. Motor impairments were manually quantified through an open field grid test to measure the gross motor function and through a ladder test to measure the fine motor function. Here, we discuss a technique for the automated quantification of the video-recorded tests using our custom Capadona Behavioral Video Analysis System: Grid and Ladder Test, or BVAS. Leveraging simple and readily available coding software (see the Table of Materials), this program allows for the tracking of a single animal on both the open field grid and the ladder tests. In open field grid tracking, the code thresholds the video for intensity, tracks the position of the rat over the 3 min duration of the grid test, and analyzes the path. It then computes and returns measurements for the total distance traveled, the maximum velocity achieved, the number of left- and right-handed turns, and the total number of grid lines crossed by the rat. In ladder tracking, the code again thresholds the video for intensity, tracks the movement of the rat across the ladder, and returns calculated measurements including the time it took the rat to cross the ladder, the number of paw slips occurring below the plane of the ladder rungs, and the incidence of failures due to stagnation or reversals. We envision that the BVAS developed here can be employed for the analysis of motor function in a variety of applications, including many injury or disease models.

Following the methods presented here, rats completed the open field grid and ladder tests 2x per week. The data were analyzed both by using BVAS and manually with a stop-watch by trained and novice reviewers. The results presented are an average of the raw weekly scores from a single non-implanted control animal over an 8 week study, where week &#34;0&#34; corresponds to the baseline testing. Note there was no testing during week 1 as this was a rest week for the surgery animals. Because the open field grid is tested 1x per day, the ladder is tested 5x per day, and there are two testing days per week, there is a sample of 16 trials for open field grid whereas there are 80 trials for the ladder test in the same 8 week time period.
BVAS vs. manual analysis: 
To confirm the consistency of the BVAS system and validate it against manual analysis, the results for grid lines crossed and ladder crossing time were compared to the manual results from three expert reviewers (n = 3) as a &#34;gold standard.&#34; Novice users (n = 7) also reviewed the data both manually and using the BVAS. For the validation, each reviewer examined the same one day of experiments for one animal (grid testing = one video/trial; ladder testing = five independent trials). The results for the open field grid test showed that expert reviewers were more consistent than novice reviewers were (a variance of 0 versus a variance of 17.1, respectively), but when using the BVAS, there was zero variance for both expert and novice users (Figure 4A). Similarly, the results for the ladder test showed that expert reviewers were more consistent than novice reviewers were (~3.5x larger: a variance of 0.120 versus a variance of 0.414, respectively), but when using the BVAS, there was again zero variance for both expert and novice users (Figure 4B).
Therefore, where possible, behavior metrics for both the open field grid and ladder test were quantified manually by the same expert user and compared to the results generated using the BVAS. Here, the results from all included animals throughout the duration of the experiments were evaluated. All error is reported as a standard error of the mean (SEM) unless reported otherwise. The difference between the two methods ranged from an average of 0.64 &#177; 0.06 s for the ladder test and 3.56 &#177; 0.53 lines for the grid test. The average difference for the open field grid test over the course of 8 weeks between the two methods was 11.13 &#177; 3.03%. The average difference for the ladder test over the course of 8 weeks between the two methods was 9.05 &#177; 1.07%. The percent difference between the two methods was calculated following Equation 1.
Equation 1: <img alt="Equation 1" src="/files/ftp_upload/57917/57917eq1.jpg" />
Note also for the ladder test timing, the manual data collection is only precise to 1 s (which can change based on the device used to keep time), while the BVAS is precise to the inverse of the frame rate of the video being reviewed. For example, if the video is filmed at 15 frames per second, the BVAS data is precise to 1/15th of a second.
Open field grid test: 
Following previously published protocols12,13, animals were allowed to run freely in an open field grid test for 3 min to measure their gross motor function and stress behavior. The video recorded during the testing was analyzed using the BVAS to quantify the raw scores for the maximum velocity, the total distance, the number of gridlines crossed, and the percentage of right turns achieved by the animal (Figure 5). While this data is only representative of one animal, trends were seen over the course of the study of increased maximum velocity achieved, increased total distance traveled, and an increased number of gridlines crossed. Unsurprisingly, when compared to each other, the total distance traveled, and the total gridlines crossed had a strong positive correlation. Comparing the manual quantification to the BVAS gridline quantification revealed comparable results (Figure 5C). For this particular animal, the percentage of right turns largely hovered between 40% and 50% (Figure 5D). Of note, the metrics for the total distance traveled, maximum velocity, and turn direction were unable to be quantified manually, and represent another added feature of the automated BVAS program.
Ladder test: 
As previously reported4,14, the ladder testing was completed to measure the coordinated grasp and fine motor function. The video recorded during the testing was analyzed using the BVAS to quantify the raw scores for the time to cross and the number of paw slips (Figure 6).In this animal, there was a decreasing trend in the time to cross the ladder, followed by an increase in time starting around week 4. Comparing the manual quantification to the BVAS crossing time quantification revealed comparable results (Figure 6A). As the animal presented was a non-surgical control, paw slips were infrequent, and comparable in number between the left and right paw (Figure 6B).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/57917/57917fig1.jpg" /> 
Figure 1: Example of open field grid test results. (A) This panel shows the behavioral testing 1 m2 open testing area with a black background and pink grid lines. (B) This is an example of a plot of a single animal&#39;s 3-min grid run. This animal made one circuit of the grid and then remained mostly stationary for the remainder of the time. The red O&#39;s and X&#39;s denote where the turn detection algorithm detected a left (O) or a right (X) turn. (C) This is a second example of a plot of a single animal&#39;s 3-min grid run. This animal was extremely active for the entire 3-min testing period. The red O&#39;s and X&#39;s denote where the turn detection algorithm detected a left (O) or a right (X) turn. (D) This is an example of the output in the data file for the two runs seen in panels B and C. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57917/57917fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/57917/57917fig2.jpg" /> 
Figure 2: Example of a ladder data reviewer screen. (A) This panel shows the ladder data reviewer screen, complete with a screenshot demonstrating the ladder test set-up. This screen is where the user will confirm detected slips and failures. This example shows a positively detected right front paw slip. (B) This is an example of the output in the data file for the ladder run that was examined in panel A. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57917/57917fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/57917/57917fig3.jpg" /> 
Figure 3: Example of BVAS analysis screens. (A) This panel shows the top menu of the BVAS. Note the four buttons along the bottom and the Email Settings drop-down menu in the top left corner. (B) This is an example of a file selection screen, in this case for the ladder video analysis. (C) This is an example of a debug analysis viewer for a grid test video. This demonstrates the detection algorithm during the analysis of a grid test. (D) This panel shows an example of a debug analysis viewer for a ladder test video. This demonstrates the detection algorithm during the analysis of a ladder test. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57917/57917fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/57917/57917fig4.jpg" /> 
Figure 4: Example of expert and novice testing results. These panels show the results of a manual and a BVAS expert and novice testing for (A) the number of gridlines crossed on the open field grid test and (B) the time to cross on the ladder test. For both tests, the results showed that expert users had a lower variance than the novice users for the manual analysis, while using the BVAS resulted in zero variance, regardless of the user expertise. The standard deviation is reported. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57917/57917fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/57917/57917fig5.jpg" /> 
Figure 5: Representative open field grid test results. These panels show representative open field grid test results for a non-implanted control animal for (A) the maximum velocity achieved, (B) the total distance traveled, (C) the total number of gridlines crossed, and (D) the percentage of right turns made by the animal. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57917/57917fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/57917/57917fig6.jpg" /> 
Figure 6: Representative ladder test results. These panels show representative ladder test results for a non-implanted control animal for (A) the time to cross the ladder, and (B) the number of right- and left-front-paw slips. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57917/57917fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software at JoVE.com

A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software

The current study aimed to automate the quantification of motor deficits in rats. The initial evaluation model assesses motor loss resulting from an intracortical microelectrode implantation in the motor cortex. We report on the development and use of a tracking algorithm using easily adaptable, simple, and readily available coding software.

We have recently demonstrated that implanting intracortical microelectrodes in the motor corteces of rats&#160;results in immediate and lasting motor ...

a-novel-single-animal-motor-function-tracking-system-using-simple

Nanyang Technological University

Research

JoVE Journal

Behavior

6.5K Views.  Louis Stokes Cleveland Department of Veterans Affairs Medical Center. The current study aimed to automate the quantification of motor deficits in rats. The initial evaluation model assesses motor loss resulting from an intracortical microelectrode implantation in the motor cortex. We report on the development and use of a tracking algorithm using easily adaptable, simple, and readily available coding software.

Video: A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software

Mouse Short- and Long-term Locomotor Activity Analyzed by Video Tracking Software

Locomotor activity (LMA) is a simple and easily performed measurement of behavior in mice and other rodents. Improvements in video tracking software (VTS) have allowed it to be coupled to LMA testing, dramatically improving specificity and sensitivity when compared to the line crossings method with manual scoring. In addition, VTS enables high-throughput experimentation. While similar to automated video tracking used for the open field test (OFT), LMA testing is unique in that it allows mice to remain in their home cage and does not utilize the anxiogenic stimulus of bright lighting during the active phase of the light-dark cycle. Traditionally, LMA has been used for short periods of time (mins), while longer movement studies (hrs-days) have often used implanted transmitters and biotelemetry. With the option of real-time tracking, long-, like short-term LMA testing, can now be conducted using videography. Long-term LMA testing requires a specialized, but easily constructed, cage so that food and water (which is usually positioned on the cage top) does not obstruct videography. Importantly, videography and VTS allows for the quantification of parameters, such as path of mouse movement, that are difficult or unfeasible to measure with line crossing and/or biotelemetry. In sum, LMA testing coupled to VTS affords a more complete description of mouse movement and the ability to examine locomotion over an extended period of time.

Locomotor activity (LMA) is a simple and easily performed measurement of behavior in mice. Coupling of video tracking software (VTS) and LMA allows for the improvement of specificity and sensitivity, especially when compared with the manual, line crossing method of LMA analysis. Additionally VTS allows long-term tracking of mouse LMA.

Locomotor  activity (LMA) is a simple and easily performed measurement of behavior in mice  and other rodents. Improvements in video tracking software ...

The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task

This protocol describes a modified serial reaction time (SRT) task used to study implicit motor sequence learning. Unlike the classic SRT task that involves finger-pressing movements while sitting, the modified SRT task requires participants to step with both feet while maintaining a standing posture. This stepping task necessitates whole body actions that impose postural challenges. The foot-stepping task complements the classic SRT task in several ways. The foot-stepping SRT task is a better proxy for the daily activities that require ongoing postural control, and thus may help us better understand sequence learning in real-life situations. In addition, response time serves as an indicator of sequence learning in the classic SRT task, but it is unclear whether response time, reaction time (RT) representing mental process, or movement time (MT) reflecting the movement itself, is a key player in motor sequence learning. The foot-stepping SRT task allows researchers to disentangle response time into RT and MT, which may clarify how motor planning and movement execution are involved in sequence learning. Lastly, postural control and cognition are interactively related, but little is known about how postural control interacts with learning motor sequences. With a motion capture system, the movement of the whole body (e.g., the center of mass (COM)) can be recorded. Such measures allow us to reveal the dynamic processes underlying discrete responses measured by RT and MT, and may aid in elucidating the relationship between postural control and the explicit and implicit processes involved in sequence learning. Details of the experimental set-up, procedure, and data processing are described. The representative data are adopted from one of our previous studies. Results are related to response time, RT, and MT, as well as the relationship between the anticipatory postural response and the explicit processes involved in implicit motor sequence learning.

We introduce the foot-stepping serial reaction time (SRT) task. This modified SRT task, complementing the classic SRT task that involves only finger-pressing movement, better approximates daily sequenced activities and allows researchers to study the dynamic processes underlying discrete response measures and disentangle the explicit process operating in implicit sequence learning.

This protocol describes a modified serial reaction time (SRT) task used to study implicit motor sequence learning. Unlike the classic SRT task that ...

A Simple and Low-cost Assay for Measuring Ambulation in Mouse Models of Muscular Dystrophy

Measuring functional outcomes in the treatment of muscular dystrophy is an essential aspect of preclinical testing. The assessment of voluntary ambulation in mouse models is a non-invasive and reproducible activity assay that is directly analogous to measures of patient ambulation such as the 6-minute walk test and related mobility scores. Many common methods for testing mouse ambulation speed and distance are based on the open field test, where an animal&#39;s free movement within an arena is measured over time. One major downside to this approach is that commercial software and equipment for high-resolution motion tracking is expensive and may require transferring mice to specialized facilities for testing. Here, we describe a low-cost, video-based system for measuring mouse ambulation that utilizes free and open-source software. Using this protocol, we demonstrate that voluntary ambulation in the dystrophin-null mdx mouse model for Duchenne muscular dystrophy (DMD) is decreased relative to wild-type mouse activity. In mdx mice expressing the utrophin transgene, these activity deficits are not observed and the total distance traveled is indistinguishable from wild-type mice. This method is effective for measuring changes in voluntary ambulation associated with dystrophic pathology, and provides a versatile platform that can be readily adapted to diverse research settings.

This protocol describes a flexible, low-cost system for measuring mouse ambulation in an open field activity assay. We show that a 6-minute ambulation assay based on this system detects a decrease in voluntary movement in mdx mice, and accurately distinguishes improvement in a muscle-specific rescue of these animals.

Measuring functional outcomes in the treatment of muscular dystrophy is an essential aspect of preclinical testing. The assessment of voluntary ambulation ...

Elevated Plus Maze Test Combined with Video Tracking Software to Investigate the Anxiolytic Effect of Exogenous Ketogenic Supplements

The overall goal of this study is to describe the methodology of the elevated plus maze (EPM) test in combination with a video tracking software. The purpose of the method is to document the effect of various potential anxiolytic treatments on laboratory rodent models. The EPM test is based on the rodents&#39; proclivity toward protected, enclosed dark spaces and unconditioned fear of open spaces and heights, and their innate intense motivation to explore novel environments. The EPM test is a widely used behavioral test for investigating the anxiolytic or anxiogenic responses of rodents given drugs that are known to affect behavior. Observation demonstrating a decreased proportion of time spent on closed arms, an increased proportion of time spent on open arms, a reduced number of entries to closed arms, and an elevated number of entries to open arms measured by the EPM test may reflect reduced anxiety levels. Using this method, the effect of exogenous ketone supplements on anxiety-related behavior is tested in Sprague Dawley (SPD) rats. Exogenous ketone supplements are chronically fed to the rats for 83 days or subchronically and acutely orally gavaged, daily for 7 days, before conducting the EPM test. Behavioral data collection is performed using the SMART video tracking system by a blinded observer at the end of the treatments. The main findings indicate that the EPM test is an effective method to detect the ketone supplement-induced anxiolytic effect and can be considered a sensitive measure to assess changes in anxiety behavior associated with drug- or metabolic-based therapies.

Here, we present a protocol to investigate changes in the anxiety level of rodent animal models. The elevated plus maze (EPM) test, used together with a video tracking software, provides a reliable method to document the effect of various potential anxiolytic treatments in preclinical laboratory scenarios.

The overall goal of this study is to describe the methodology of the elevated plus maze (EPM) test in combination with a video tracking software. The ...

3D Kinematic Gait Analysis for Preclinical Studies in Rodents

The utility of three-dimensional (3D) kinematic motion analysis systems is limited in rodents. Part of the reason for this inadequacy is the use of complex algorithms and mathematical modeling that accompany 3D data collection and analysis procedures. This work provides a simple, user-friendly, step-by-step detailed methodology for 3D kinematic gait analysis during treadmill locomotion in healthy and neurotraumatic rats using a six-camera motion capture system. Also provided are details on 1) calibration of the system in an experimental set-up customized for quadrupedal locomotion, 2) data collection for treadmill locomotion in adult rats using markers positioned on all four limbs, 3) options available for video tracking and processing, and 4) basic 3D kinematic data generation and visualization and quantification of data using the built-in data collection software. Finally, it is suggested that the utility of this motion capture system be expanded to studying a variety of motor behaviors before and after neurotrauma.

Presented here is a protocol to collect and analyze three-dimensional kinematics of quadrupedal locomotion in rodents for preclinical studies.

The utility of three-dimensional (3D) kinematic motion analysis systems is limited in rodents. Part of the reason for this inadequacy is the use of ...

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

Long-term behavioral tracking can capture and quantify natural animal behaviors, including those occurring infrequently. Behaviors such as exploration and social interactions can be best studied by observing unrestrained, freely behaving animals. Weakly electric fish (WEF) display readily observable exploratory and social behaviors by emitting electric organ discharge (EOD). Here, we describe three effective techniques to synchronously measure the EOD, body position, and posture of a free-swimming WEF for an extended period of time. First, we describe the construction of an experimental tank inside of an isolation chamber designed to block external sources of sensory stimuli such as light, sound, and vibration. The aquarium was partitioned to accommodate four test specimens, and automated gates remotely control the animals&#39; access to the central arena. Second, we describe a precise and reliable real-time EOD timing measurement method from freely swimming WEF. Signal distortions caused by the animal&#39;s body movements are corrected by spatial averaging and temporal processing stages. Third, we describe an underwater near-infrared imaging setup to observe unperturbed nocturnal animal behaviors. Infrared light pulses were used to synchronize the timing between the video and the physiological signal over a long recording duration. Our automated tracking software measures the animal&#39;s body position and posture reliably in an aquatic scene. In combination, these techniques enable long term observation of spontaneous behavior of freely swimming weakly electric fish in a reliable and precise manner. We believe our method can be similarly applied to the study of other aquatic animals by relating their physiological signals with exploratory or social behaviors.

We describe a set of techniques for studying spontaneous behavior of freely swimming weakly electric fish over an extended period of time, by synchronously measuring the animal&#39;s electric organ discharge timing, body position and posture both accurately and reliably in a specially designed aquarium tank inside a sensory isolation chamber.

Long-term behavioral tracking can capture and quantify natural animal behaviors, including those occurring infrequently. Behaviors such as exploration and ...

Neuroscience

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

Operant conditioning chambers are used to perform a wide range of behavioral tests in the field of neuroscience. The recorded data is typically based on the triggering of lever and nose-poke sensors present inside the chambers. While this provides a detailed view of when and how animals perform certain responses, it cannot be used to evaluate behaviors that do not trigger any sensors. As such, assessing how animals position themselves and move inside the chamber is rarely possible. To obtain this information, researchers generally have to record and analyze videos. Manufacturers of operant conditioning chambers can typically supply their customers with high-quality camera setups. However, these can be very costly and do not necessarily fit chambers from other manufacturers or other behavioral test setups. The current protocol describes how to build an inexpensive and versatile video camera using hobby electronics components. It further describes how to use the image analysis software package DeepLabCut to track the status of a strong light signal, as well as the position of a rat, in videos gathered from an operant conditioning chamber. The former is a great aid when selecting short segments of interest in videos that cover entire test sessions, and the latter enables analysis of parameters that cannot be obtained from the data logs produced by the operant chambers.

This protocol describes how to build a small and versatile video camera, and how to use videos obtained from it to train a neural network to track the position of an animal inside operant conditioning chambers. This is a valuable complement to standard analyses of data logs obtained from operant conditioning tests.

Operant conditioning chambers are used to perform a wide range of behavioral tests in the field of neuroscience. The recorded data is typically based on ...

A Simple Technique to Assay Locomotor Activity in Drosophila

Drosophila melanogaster is an ideal model organism for studying various diseases due to its abundance of advanced genetic manipulation techniques and diverse behavioral features. Identifying behavioral deficiency in animal models is a crucial measure of disease severity, for example, in neurodegenerative diseases where patients often experience impairments in motor function. However, with the availability of various systems to track and assess motor deficits in fly models, such as drug-treated or transgenic individuals, an economical and user-friendly system for precise evaluation from multiple angles is still lacking. A method based on the AnimalTracker application programming interface (API) is developed here, which is compatible with the Fiji image processing program, to systematically evaluate the movement activities of both adult and larval individuals from recorded video, thus allowing for the analysis of their tracking behavior. This method requires only a high-definition camera and a computer peripheral hardware integration to record and analyze behavior, making it an affordable and effective approach for screening fly models with transgenic or environmental behavioral deficiencies. Examples of behavioral tests using pharmacologically treated flies are given to show how the techniques can detect behavioral changes in both adult flies and larvae in a highly repeatable manner.

A Simple Technique to Assay Locomotor Activity in Drosophila

The present protocol assesses the locomotor activity of Drosophila by tracking and analyzing the movement of flies in a hand-made arena using open-source software Fiji, compatible with plugins to segment pixels of each frame based on high-definition video recording to calculate parameters of speed, distance, etc.

Drosophila melanogaster is an ideal model organism for studying various diseases due to its abundance of advanced genetic manipulation techniques and ...

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents

Predatory pursuit behavior involves a series of important physiological processes, such as locomotion, learning, and decision-making that are critical to the success of an animal in capturing prey. However, there are few methods and systems for studying predatory pursuit behavior in the laboratory, especially in mice, a commonly used mammalian model. The main factors limiting this research are the uncontrollability of live prey (e.g., crickets) and the challenge of harmonizing experimental standards. The goal of this study was to develop an interactive platform to detect and quantify predatory pursuit behaviors in mice on a robotic bait. The platform uses computer vision to monitor the relative positions of the mouse and robotic bait in real time to program the motion patterns of the robotic bait, and the interactive two-dimensional sliders magnetically control the movement of the robotic bait to achieve a closed-loop system. The robotic bait is able to evade approaching hungry mice in real-time, and its escape speed and direction can be adjusted to mimic the predatory pursuit process in different contexts. After a short period of unsupervised training (less than two weeks), the mice were able to perform the predation task with a relatively high efficiency (less than 15 s). By recording kinematic parameters such as speed and trajectories of the robotic bait and the mice, we were able to quantify the pursuit process under different conditions. In conclusion, this method provides a new paradigm for the study of predatory behavior and can be used to further investigate the dynamics and neural mechanisms of predatory pursuit behavior.

Here, we present a protocol to detect and quantify predatory pursuit behavior in a mouse model. This platform provides a new research paradigm for studying the dynamics and neural mechanisms of predatory pursuit behavior in mice and will provide a standardized platform for studying pursuit behavior.

Predatory pursuit behavior involves a series of important physiological processes, such as locomotion, learning, and decision-making that are critical to ...

A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning

Climbing fiber inputs to Purkinje cells provide instructive signals critical for cerebellum-dependent associative learning. Studying these signals in head-fixed mice facilitates the use of imaging, electrophysiological, and optogenetic methods. Here, a low-cost behavioral platform (~$1000) was developed that allows tracking of associative learning in head-fixed mice that locomote freely on a running wheel. The platform incorporates two common associative learning paradigms: eyeblink conditioning and delayed tactile startle conditioning. Behavior is tracked using a camera and the wheel movement by a detector. We describe the components and setup and provide a detailed protocol for training and data analysis. This platform allows the incorporation of optogenetic stimulation and fluorescence imaging. The design allows a single host computer to control multiple platforms for training multiple animals simultaneously.

We have developed a single platform to track animal behavior during two climbing fiber-dependent associative learning tasks. The low-cost design allows integration with optogenetic or imaging experiments directed towards climbing fiber-associated cerebellar activity.

Climbing fiber inputs to Purkinje cells provide instructive signals critical for cerebellum-dependent associative learning. Studying these signals in ...

A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software

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