Manual scoring represents a significant source of variability across experiments in analyzing animal behavior in preclinical neuroscience. While automated commercial tools exist, they typically contain prebuilt animal-tracking models that can underperform in different experimental situations based on camera quality, background contrast, and occluding objects like wires. In recent years several open-source tools have emerged, allowing users to train deep-learning models to identify the location of user-specified key points, such as body parts, in their video datasets. These tools can outperform commercial solutions in terms of accuracy but require programming knowledge to transform their coordinate outputs into traditionally reported scores.  
Here we developed an accessible and standardized methodology for automated scoring common behavioral paradigms such as the open field, y-maze and elevated-plus maze. We incorporate standard registration techniques in other disciplines, such as neuroimaging to remove superficial changes that commonly arise in low-cost behavioral setups and shared experimental spaces, such as variation in camera height, position, and angle relative to the behavioral arena. By removing these differences, our tool further reduces the manual labor of drawing analysis zones, which is also a source of inconsistency and error.
Ultimately, our tool acts as a wrapper around powerful open-source deep learning software to make it more accessible for scoring common behavioral paradigms, improving the consistency of behavioral definitions, and, thus, interexperimental reproducibility.

A standardized and automated machine learning pipeline for the analysis of mouse behavioral paradigms

Hippocampal-dependent spatial learning in rodents has previously been in tested using a variety of methods. More recently, the active place avoidance (APA) task has been developed as an alternative to more traditional approaches. In a relatively short period of time the APA task has been demonstrated to be very versatile and lends itself to be used longitudinally and repeatedly for the same cohorts of animals. Here we provide a comprehensive protocol for how the APA task should be conducted, describe the data collection and analysis process, and highlight the key parameters that can be altered which allow for repeat testing.

We thank the Queensland Brain Institute (QBI) Animal Behaviour Facility for the development and maintenance of the apparatus described in this manuscript.

All animal procedures were approved by The University of Queensland Animal Ethics Committee under the guidelines of the National Health and Medical Research Council of Australia (approval number: QBI/189/15).
1. APA room setup
NOTE: The APA apparatus comprises an elevated arena with a metal grid floor enclosed by a 32 cm-high transparent circular boundary. The metal bars are evenly spaced (0.5 cm apart) and have 0.3 cm diameter.
<ol>
	<li>Ensure the APA apparatus is within the camera frame mounted on the ceiling. Track the mouse using commercially available animal tracking software.</li>
	<li>The APA arena generally rotates at 1 rpm speed, and a pre-designated 60&#176; stationary shock zone is set within the rotating arena. When the mouse enters the shock zone, deliver a mild foot shock of 0.5 mA (60 Hz, 500 ms).</li>
	<li>Ensure that the location of the shock zone remains constant during testing and is set electronically within the experimental setup. The rotating arena carries the mouse into the shock zone unless the mouse actively moves to avoid it.</li>
	<li>Place four novel visual cues on four different room walls at the same height as the rotating platform, typically 30-50 cm away from the arena. Ensure the cues are neutral colors, such as black-and-white symbols or shapes printed on A3 paper and laminated for easy cleaning (Figure 1A).</li>
	<li>Ensure that the light intensity of the room is between 30-70 Lux. Increasing light intensity induces anxiety-like behavior and reduces exploration.</li>
	<li>Before starting, open the Tracker program and select the APA task.</li>
	<li>In the Options for Tracker 2D, select the Experiment tab. Here, ensure that Place Avoidance- One Frame- Position Only is selected. This will allow configuring the parameters required. Save the configuration file and adjust as required.</li>
	<li>In the Experiment tab set the experiment duration in the Experiment Time box. A typical experiment duration is 600 s or 10 min.</li>
	<li>Make sure the Enable Timer is selected. Change the shock parameters in the Timer region as discussed above.</li>
	<li>Enter the common experimental details in the space provided in the Room Frame region of the Experiment tab. For example, ensure that the Default Output File name is populated with the date, a simple experimental identifier, and the day of testing. Finish the name with an underscore &#34;_&#34; to allow the addition of a unique mouse ID during the experiment.</li>
	<li>In the Room Frame region is also the Targets tab. Click the Edit button to provide the ability to ensure the entire arena is included in the region of interest. Then, select Arc to provide the adjustable parameters for the size and location of the shock target zone (Figure 1B).</li>
	<li>Open the Tracking tab to adjust the parameters to ensure successful mice tracking. The contrast box has either Dark or Light options to allow for both dark (e.g., C57Bl/6) or light (e.g., BALB/c) mice. This creates an effective contrast between the background and the mice. When using albino strains of mice, place a piece of black plastic under the arena to allow for this contrast to be achieved (Figure 2).</li>
	<li>Set the mice size and area ranges in this region. Set these parameters to effectively recognize the mouse when in the arena. Alternatively, set these after pressing the From Calibrator button.</li>
	<li>Select the From Calibrator button to ensure the arena is completely in the region of interest mask.
	<ol>
		<li>Start the arena in this tab to make sure that when the arena rotates, the arena remains in the mask. This tab is also critical to select the appropriate contrast threshold. Move the red line in the Threshold pane to adjust the contrast threshold. 
		NOTE: Figure 3A shows an optimal threshold selection, as evidenced by a solid orange region and a blue &#34;X&#34; where the mouse is located. A poor threshold is shown in Figure 3B and shows only speckled orange and no &#34;X&#34;.</li>
	</ol>
	</li>
	<li>Use the Devices tab and set the rotation direction and speed of the arena using the velocity button. Select both positive and negative velocities, representing clockwise and anti-clockwise rotations. Set the shock intensity in the Current Source section. The most common setting for mice is 1 rpm rotation and a 0.5 mA shock.</li>
	<li>Alter how or when to deliver the shocks within the Current Source tab.
	<ol>
		<li>Ensure the Current mode is selected to Track Dependent. This will provide an electric shock when the mouse moves into the shock zone.</li>
		<li>Select Time to give shocks at a time interval set by the user. Use previously recorded tracks to shock a mouse by selecting From File. This is to provide a yoked control mouse subjected to an identical number of shocks at the same duration and intensity independent of spatial learning. 
		NOTE: The File Output and Window tabs allow data and video files to be saved in a specific directory. The From Image button within the File Output tab also allows the region of interest to capture the entire arena to be selected.</li>
	</ol>
	</li>
	<li>Retreat behind the curtain and begin the trial. The experimenter&#39;s presence close to the arena and any unnecessary noise may affect animal performance.</li>
	<li>Ensure that any noise and odor are limited during the trial, which can provide the mouse another cue, affecting their performance. Examples to minimise this include ensuring a closed clinical waste bin, using rooms removed from noisy lab spaces, and thoroughly cleaning the equipment between mice. Researchers may consider using the white noise generator to mask unrelated external noises.</li>
	<li>Let the home cage bedding remain the same throughout the behavioral testing period, as this may provide new stimulation and affect the behavior.</li>
	<li>To avoid diurnal variations, conduct testing at a consistent time each day.</li>
</ol>
2. Habituation to experimenter handling
<ol>
	<li>Handle each mouse daily for 30 s to 1 min for at least 2-3 days before the testing. Animal handling significantly reduces stress and anxiety-related behavior during testing.</li>
	<li>Use the same lab coat and avoid wearing strong deodorants, colognes, or perfume during the habituation and testing.</li>
</ol>
3. Habituation to the APA arena (1 day)
<ol>
	<li>Bring the mouse into the anteroom or testing room for habituation. Leave the mouse to habituate for a minimum of 30 min. Set the light intensity in the anteroom or testing room before mice are brought in to habituate.</li>
	<li>Set up the Tracker software.
	<ol>
		<li>Create an experiment-specific folder. Depending on the experimental paradigm, have separate folders for each day or trial. Set up experiment configurations as described above and save these configurations to use in the future.</li>
		<li>Before starting a trial, open the saved configuration by clicking the File tab, then click the Save symbol, add a unique mouse ID in the newly opened window, and run the trial by pressing the Play tab.</li>
	</ol>
	</li>
	<li>Habituate the mouse to the APA apparatus by exposing it to the rotating arena for 5 min without delivering shocks.</li>
	<li>Remove the mouse from the home cage by lifting it from the tail&#39;s base and gently placing it onto the gloved hand. Transport the mouse to the APA apparatus and place it away from the shock zone, facing the wall.</li>
	<li>Retreat behind the curtain and begin the trial.</li>
	<li>At the end of the testing, remove the mouse and return to the home cage.</li>
	<li>Collect all urine and scat, and clean the grid thoroughly with 80% (v/v) ethanol.</li>
	<li>Repeat steps 3.4-3.7 for all mice.</li>
</ol>
4. Acquisition training using APA (1-6 days)
<ol>
	<li>Set the room lighting to identical conditions as on the habituation day.</li>
	<li>Bring the mouse into the anteroom or testing room and allow it to habituate for a minimum of 30 min.</li>
	<li>Set up the Tracker software as described above.</li>
	<li>Set the duration of the trial.</li>
	<li>Ensure the current source is on and set (i.e., 0.5 mA).</li>
	<li>Place the mouse on the arena away from the shock zone and facing the wall.</li>
	<li>Retreat behind the curtain and begin the trial by pressing the Play button. Monitor the mouse on the computer screen and intervene if required. For example, the mouse is not receiving shocks or appears overly stressed, as evidenced by excessive jumping or vocalizing.</li>
	<li>At the end of the testing, remove the mouse and return to the home cage. 
	&#8203;NOTE: Make sure mice are receiving and reacting to the shocks. Mice respond to the shock by rearing back and vocalizing. If this is not the case, they may not be receiving the shock. This could be due to scat on the grid or due to inadequate tracking. Therefore, cleaning the grid after each trial and optimizing the mouse tracking, as discussed above, is essential.</li>
</ol>
5. Reversal acquisition training (Optional, 1-6 days)
<ol>
	<li>In the reversal task, reposition the shock zone to a new location, generally 180&#176; from the previous position. Assess the mouse&#39;s ability to flexibly learn a novel shock zone location. The room cues are typically not changed during reversal learning.</li>
	<li>Repeat steps 3.4-3.7 for all mice.</li>
</ol>
6. Probe trial (Optional, 1 day)
<ol>
	<li>In the probe trial, measure the time to the first entrance and/or the maximum time avoiding the shock zone. 
	NOTE: This indicates memory consolidation after the acquisition phase. A well-trained mouse will avoid entering the shock zone for a prolonged period (&#62;60 secs), showing evidence of spatial learning.</li>
	<li>Set the room light intensity as on the acquisition training day.</li>
	<li>Habituate the mouse in the testing room or anteroom for 30 min.</li>
	<li>Set up the Tracker software.</li>
	<li>Set the trial duration to the same time as the testing period previously conducted (for example, 10 min or 30 min, depending on trial parameters).</li>
	<li>Do not deliver shocks for this trial.</li>
	<li>Place the mouse on the opposite side of the aversive shock zone, facing the wall.</li>
	<li>Start the trial and retreat behind the curtain.</li>
	<li>Make sure the mouse is tracked efficiently.</li>
	<li>Monitor the mouse on the computer screen and stop the trial when it enters the shock zone. Some researchers prefer to continue the trial for 5 min to see if the mouse continues to return to the shock zone.</li>
	<li>Gently pick up the mouse and return to the home cage.</li>
	<li>Ensure all urine and scat are collected, and the grid is thoroughly cleaned with 80% (v/v) ethanol.</li>
</ol>
7. Track analysis
NOTE: The performance of the task can be achieved via different tracking software. Below is how the included software is used to determine the performance during the APA task. In this instance, the data is analyzed using the Track Analysis program.
<ol>
	<li>To analyze the data, open the Track Analysis program and select Avoidance from the dropdown menu in the main window.</li>
	<li>Click on Add Task to upload the data files saved during the acquisition phase in a new window. In Group Name, create a group to analyze, e.g., Day 1 or time of analysis.</li>
	<li>Click the Output Directory to select the location to save the analyzed data.</li>
	<li>Add the files to be analyzed by clicking on the Add Files tab and selecting the files from the local drive.</li>
	<li>Set the time to be analyzed by clicking the Set Time tab. This provides the ability to define the period which will be analyzed, i.e., 0 to 600 s. Alternatively, analyze the data in bins, i.e., 60 s.</li>
	<li>Once all tracks are added, click the Analysis tab and select Run Analysis to analyze the data. The analysis will produce several folders. The data for analysis will be in the TBLfiles folder. Open these data files in a spreadsheet and use them for further analysis, i.e., pairwise comparison or repeated measures ANOVA. 
	NOTE: The analysis will also produce other folders, including PS files that will have a single-page description of the mice during testing, showing a trace map and where the shocks were received.</li>
</ol>

<ol><li>Cimadevilla, J. M., Fenton, A. A., Bures, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Functional+inactivation+of+dorsal+hippocampus+impairs+active+place+avoidance+in+rats%5BTitle%5D)+AND+%22Neurosci+Lett%22%5BJournal%5D)">Functional inactivation of dorsal hippocampus impairs active place avoidance in rats.</a> Neurosci Lett. 285 (1), 53-56 (2000).</li>
<li>Willis, E. F., Bartlett, P. F., Vukovic, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Protocol+for+short-+and+longer-term+spatial+learning+and+memory+in+mice%5BTitle%5D)+AND+%22Front+Behav+Neurosci%22%5BJournal%5D)">Protocol for short- and longer-term spatial learning and memory in mice.</a> Front Behav Neurosci. 11, 197(2017).</li>
<li>Blackmore, D. G., Brici, D., Walker, T. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Protocol+for+three+alternative+paradigms+to+test+spatial+learning+and+memory+in+mice%5BTitle%5D)+AND+%22STAR+Protoc%22%5BJournal%5D)">Protocol for three alternative paradigms to test spatial learning and memory in mice.</a> STAR Protoc. 3 (3), 101500(2022).</li>
<li>Pastalkova, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Storage+of+spatial+information+by+the+maintenance+mechanism+of+LTP%5BTitle%5D)+AND+%22Science%22%5BJournal%5D)">Storage of spatial information by the maintenance mechanism of LTP.</a> Science. 313 (5790), 1141-1144 (2006).</li>
<li>Stuchlik, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Place+avoidance+tasks+as+tools+in+the+behavioral+neuroscience+of+learning+and+memory%5BTitle%5D)+AND+%22Physiol+Res%22%5BJournal%5D)">Place avoidance tasks as tools in the behavioral neuroscience of learning and memory.</a> Physiol Res. 62 (Suppl 1), S1-S19 (2013).</li>
<li>Vukovic, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Immature+doublecortin-positive+hippocampal+neurons+are+important+for+learning+but+not+for+remembering%5BTitle%5D)+AND+%22J+Neurosci%22%5BJournal%5D)">Immature doublecortin-positive hippocampal neurons are important for learning but not for remembering.</a> J Neurosci. 33 (15), 6603-6613 (2013).</li>
<li>Blackmore, D. G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((An+exercise+%26%2334%3Bsweet+spot%26%2334%3B+reverses+cognitive+deficits+of+aging+by+growth-hormone-induced+neurogenesis%5BTitle%5D)+AND+%22iScience%22%5BJournal%5D)">An exercise &#34;sweet spot&#34; reverses cognitive deficits of aging by growth-hormone-induced neurogenesis.</a> iScience. 24 (11), 103275(2021).</li>
<li>Codd, L. N., Blackmore, D. G., Vukovic, J., Bartlett, P. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Exercise+reverses+learning+deficits+induced+by+hippocampal+injury+by+promoting+neurogenesis%5BTitle%5D)+AND+%22Sci+Rep%22%5BJournal%5D)">Exercise reverses learning deficits induced by hippocampal injury by promoting neurogenesis.</a> Sci Rep. 10 (1), 19269(2020).</li>
<li>Harrison, F. E., Hosseini, A. H., McDonald, M. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Endogenous+anxiety+and+stress+responses+in+water+maze+and+Barnes+maze+spatial+memory+tasks%5BTitle%5D)+AND+%22Behav+Brain+Res%22%5BJournal%5D)">Endogenous anxiety and stress responses in water maze and Barnes maze spatial memory tasks.</a> Behav Brain Res. 198 (1), 247-251 (2009).</li>
<li>van Praag, H., Shubert, T., Zhao, C., Gage, F. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Exercise+enhances+learning+and+hippocampal+neurogenesis+in+aged+mice%5BTitle%5D)+AND+%22J+Neurosci%22%5BJournal%5D)">Exercise enhances learning and hippocampal neurogenesis in aged mice.</a> J Neurosci. 25 (38), 8680-8685 (2005).</li>
<li>Zhou, X. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Neurogenic-dependent+changes+in+hippocampal+circuitry+underlie+the+procognitive+effect+of+exercise+in+aging+mice%5BTitle%5D)+AND+%22iScience%22%5BJournal%5D)">Neurogenic-dependent changes in hippocampal circuitry underlie the procognitive effect of exercise in aging mice.</a> iScience. 24 (12), 103450(2021).</li>
<li>Leinenga, G., Gotz, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Scanning+ultrasound+removes+amyloid-%CE%B2+and+restores+memory+in+an+Alzheimer%27s+disease+mouse+model%5BTitle%5D)+AND+%22Sci+Transl+Med%22%5BJournal%5D)">Scanning ultrasound removes amyloid-β and restores memory in an Alzheimer's disease mouse model.</a> Sci Transl Med. 7 (278), 278ra33(2015).</li>
<li>Willis, E. F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Repopulating+microglia+promote+brain+repair+in+an+IL-6-dependent+manner%5BTitle%5D)+AND+%22Cell%22%5BJournal%5D)">Repopulating microglia promote brain repair in an IL-6-dependent manner.</a> Cell. 180 (5), 833-846 (2020).</li>
<li>Lesburgueres, E., Sparks, F. T., O'Reilly, K. C., Fenton, A. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Active+place+avoidance+is+no+more+stressful+than+unreinforced+exploration+of+a+familiar+environment%5BTitle%5D)+AND+%22Hippocampus%22%5BJournal%5D)">Active place avoidance is no more stressful than unreinforced exploration of a familiar environment.</a> Hippocampus. 26 (12), 1481-1485 (2016).</li>
<li>Crawley, J. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Behavioral+phenotyping+strategies+for+mutant+mice%5BTitle%5D)+AND+%22Neuron%22%5BJournal%5D)">Behavioral phenotyping strategies for mutant mice.</a> Neuron. 57 (6), 809-818 (2008).</li>
</ol>

The authors have nothing to disclose.

In conclusion, the active place avoidance test is an effective spatial learning task that can be used on a variety of mouse strains and experimental conditions. The APA task overcomes limitations associated with other spatial learning paradigms14, such as the MWM, which is stressful for the mice as measured by cortisol levels9. The MWM is also unsuitable for aged mice, where they have been reported to float during the task10. Although other spatial learning tests, such as the Barnes maze and novel object location test, are less stressful, they are limited by how often repeat testing can be conducted on the same cohort of mice. Therefore, the main advantage of the APA task is that it can be used multiple times as several parameters can be adjusted to maintain novelty. Indeed, we have used the APA task up to 5 times on the same cohort of mice to examine the effect of hippocampal ablation and the subsequent effect of exercise8. In each instance, the parameters, including the arena rotation, shock zone, and spatial cues, were altered between tests. This was effective in ensuring that the mice used spatial navigation cues to re-learn the task as evidenced by the control animals starting with a high number of shocks and then decreasing during subsequent test days for each testing period8. Typically, at the end of a 5-day test paradigm, we consider that any animal that received more than 10 shocks on the final day or has a maximum avoidance of less than 60 s has not learned the paradigm.
Beyond the ability to easily modify settings to allow multiple rounds of spatial testing, the APA task ensures that mice must use spatial navigation to effectively avoid the shock zone. For example, animals must use external cues to locate and avoid entering the stationary shock zone by navigating away from it5. As the arena is rotating, animals are not able to use an idiothetic approach for navigation, nor can they use exteroceptive cues such as odor because these cues rotate with the arena while the shock zone and spatial cues remain stationary5.
It is also important to ensure that mice are appropriately habituated to the researcher and APA arena. The intensity of foot shock also needs to be optimized, as both too low and too high shock intensity can compromise mice&#39;s ability to learn and perform the task5. The shock intensity is typically set to 0.5 mA and should not exceed 0.7 mA. For animals that have increased anxiety-like behavior, consider reducing both light intensity and foot shock intensity. Increased anxiety during the APA task can present as either excessive jumping, uncontrolled running within the arena, or prolonged freezing. The protocol described here used a shock intensity of 0.5 mA, the same intensity that has previously been used with BALB/c, which is known to have higher anxiety-like behavior15.
Here, we describe the animal tracking software supplied by the company that provided the active place avoidance rig used. Alternative video tracking software is also suitable for analyzing behavioral performance. These programs can also accurately measure and analyze mouse performance during APA tasks. These programs allow the creation of several zones and locations within the APA arena to assess behavior. The arena setting for an APA consists of one triangular shock zone, where the number of entrances, time to first enter, and time spent in the shock zone are measured. Additional zones can also be added within the arena. For example, we can add a central zone or a zone opposite the shock zone to measure the time spent, and distance traveled in those zones as an animal strategy to avoid the aversive zone. These programs track the mouse center of mass, which is then saved and displayed above the reference frame for visual inspection (Figure 6A,B). Finally, it is also possible to create a density heatmap for individual and group performance (Figure 6C).
When conducting the APA task, there are potential issues that need to be addressed. On occasion, mice will need to be excluded from the analysis due to non-responsiveness to the shock zone. As always, exclusion should only be considered when they meet pre-defined outlier conditions, for example, falling outside 2 standard deviations from the mean. Complex behavioral tasks such as the APA typically require high N values of animals. We suggest conducting a power analysis to calculate the appropriate sample size before conducting APA. This will depend on the strain used and treatment groups. From experience, we find that an n value of 10 or more for each group provides sufficient power when conducting APA experiments. The main issue with this task is ensuring high-quality tracking of the mouse during the task. The habituation stage of the task should be used to confirm that this is occurring. Mice not responding to a shock is often due to scat between the grid bars. It is, therefore, essential to clean the rig after every animal and remove any scat or urine. This will also reduce the stress for the animals that follow. The APA task typically involves a 5-day paradigm, which may present some limitations for studies involving interventions effective less than 5 days; however, short-term memory or spatial learning acquisition can still be assessed for such studies using the 30 min, single-session approach.
In summary, this article provides a detailed description of how to set up and use the active place avoidance paradigm to test the spatial learning of mice. The ability to change conditions so that multiple mouse strains of varying color can be tested is a distinct advantage over other, more traditional spatial tests such as the MWM. Further, the modification of multiple parameters allows for repeat testing so that the changes in spatial learning can be accurately compared during various experimental paradigms or during physiological aging. In a short period of time, the APA test has been demonstrated to be an accurate and effective alternative for hippocampus-dependent spatial learning. In the future, the APA task can be used as a reliable method for assessing therapeutic or exercise interventions on cognitive and spatial behavior in both wild-type and transgenic mice.

Active place avoidance (APA) is an effective tool to test hippocampus-dependent spatial learning in rodents1,2,3,4. During the APA task, the animal is placed on a rotating arena and is required to use visual cues to orientate themselves and avoid an aversive shock zone5. The rotation of the arena ensures that the mouse is unable to use an idiothetic approach for navigation, nor can scent marks be used, as these cues rotate on the platform while the shock zone remains stationary5. Altering the speed and direction of the arena, as well as the location of the shock zone and visual cues, allows for the re-testing of mice multiple times6,7,8. The APA offers several distinct advantages compared to the Morris water maze (MWM), one of the most widely used spatial learning tests. Importantly, mice have an aversion to swimming and find the MWM task extremely stressful9. Further, aged mice have been reported to float during the MWM task10, making it unsuitable as a spatial learning task in many instances. Further, as the MWM task requires a hidden, submerged platform for the mice to locate during testing. This necessitates the water to be opaque, which is typically achieved via the addition of white paint. Tracking and analysis of animals during behavioral tasks requires sufficient contrast between subject and surrounds, excluding certain mouse strains such as Swiss or BALB/c from being tested in the MWM. In the APA task, this issue is circumvented through the addition of black plastic under the grid.
Multiple APA paradigms have been designed to test spatial learning, demonstrating its utility as an effective behavioral tool. For example, acquisition, retention, and consolidation of spatial learning is typically accomplished by daily testing of animals that can range from 3-5 days6,7,11,12. Memory and learning are quantified by comparing the number of shocks received each acquisition day. Time to first entrance and the maximum time avoiding the shock zone are also important parameters that can be used to determine changes in learning ability during the task. Alternatively, spatial working memory can be tested by conducting a single, 30 min APA session2,13 where spatial learning is measured as within-session changes by comparing performance, such as shock number, in 5 min bins.
In this article, we describe the APA task and highlight the key features that must be considered when conducting this spatial learning test.

<table><tbody><tr><td>Constant Current Source CS02</td><td>BioSignal Group</td><td>N/A</td><td>Acton, Massachusetts, United States</td></tr><tr><td>Control Box</td><td>BioSignal Group</td><td>N/A</td><td>Acton, Massachusetts, United States</td></tr><tr><td>Ethovision</td><td>Noldus</td><td>version 16</td><td>Wageningen, Netherlands</td></tr><tr><td>Shock Scrambler</td><td>BioSignal Group</td><td>N/A</td><td>Acton, Massachusetts, United States</td></tr><tr><td>Track Analysis</td><td>BioSignal Group</td><td>version 2.2</td><td>Acton, Massachusetts, United States</td></tr><tr><td>Tracker Programme</td><td>BioSignal Group</td><td>version: 2.36</td><td>Acton, Massachusetts, United States</td></tr></tbody></table>

Hippocampus-dependent spatial learning in rodents has been tested using a variety of methods. These include the Morris water maze (MWM), Y-maze, and novel object location (NOL) tasks. More recently, the active place avoidance (APA) task has been developed as an alternative to these more traditional approaches. In the APA task, mice must use spatial cues placed around a rotating arena to avoid a stationary shock zone. Due to the multiple parameters that can be adjusted, the APA task has been demonstrated to be a very versatile approach. It lends itself to being used longitudinally and repeatedly for the same cohort of mice. Here, we provide a detailed protocol to successfully conduct the APA task. We also highlight alternative APA approaches that can be used to examine different components of spatial learning. We describe the data collection and analysis processes. Critical steps during the APA task are discussed to increase the likelihood of successfully conducting the test. The APA task has several advantages over more traditional spatial navigation tests. It is appropriate to use with aged mice or those with disease phenotypes such as Alzheimer's disease. The complexity of the task can be easily altered, allowing a wide range of mouse strains to be tested. Further, the APA task is suitable for testing animals that have undergone surgery or experimental interventions that may have affected motor or neural function, such as stroke or traumatic brain injury.

Mice with intact spatial learning ability will show a decrease in the number of shocks during successive acquisition trials (Figure 4A). Similarly, the maximum time avoiding the shock zone will increase as the mouse learns to successfully navigate away from the shock zone (Figure 4B). However, mice that are unable to learn an effective avoidance strategy will show a constant number of shocks for each acquisition trial (Figure 4A). Often, mice that fail to identify the shock zone will receive multiple shocks during each entrance into the zone. Trace maps are useful to provide examples of mice that learn to avoid the shock zone (Figure 4C) and those that are unable to avoid the shock zone (Figure 4D). In both instances, these trace maps represent the final day of acquisition. The mouse in Figure 4C received only 2 shocks, as represented by the two circles. Also, note that the trace map shows the mouse spending most of the time on the opposite side of the shock zone which is represented by the red wedge. Conversely, the mouse in Figure 4D received more shocks, and the trace map reveals a disordered pattern. Examples of mice that are unable to successfully learn to avoid the shock zone are those that have reduced hippocampal neurogenesis either due to older age, as shown by the 18-month-old mice (Figure 4A,B- modified from Blackmore et al., 20217), chemical ablation of immature neurons6 or hippocampal lesions (see Codd et al., 2020)8.
It is important to distinguish between an unsuccessful trial due to the mouse failing to learn as opposed to a failure in the set-up of the equipment. The two most common causes for poor results due to equipment failure are poor tracking of the mouse (Figure 5A) or the mouse not receiving a shock. Poor tracking can prevent the mouse from receiving a shock when it is in the shock zone. Alternatively, poor tracking can inaccurately induce a shock when the mouse is not in the zone. In both instances, this will prevent the mouse from developing an effective avoidance strategy. Poor tracking can be solved by adjusting the threshold in the "From Calibrator" tab. Poor tracking is typically defined as more than 1000 bad frames during a 10-min period and occurs very rarely. Poor tracking can become an issue with aged mice, where alopecia can develop. When receiving a shock, the mouse will react either by tensing or, on occasion, vocalizing. The mouse will typically move, if even slightly, and can be seen on the live tracking software. When the mouse stays perfectly still within the shock zone, a clear line of shocks will be shown (Figure 5B). This may be due to the shock box not being turned on or scat stuck between the bars, reducing the amplitude of the shock being delivered to the animal.
<img alt="Static equilibrium study setup with balancing platform and target measurement system." class="xfigimg" src="/files/ftp_upload/65935/65935fig01.jpg" data-altupdatedat="1762425657000" data-alt="Figure 1"> 
Figure 1: APA apparatus, behavior room, and shock zone setting. (A) An example of the testing arena and room setup. APA apparatus is elevated and placed in the center of the room, surrounded by novel visual cues. Black and white visual cues are used at the same height as the platform. (B). The Target function within the Experiment tab allows the masking of the entire arena and creates a location of the shock zone. A shock zone, represented by the red wedge, has been created at 270° in this example. <a href="https://www.jove.com/files/ftp_upload/65935/65935fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Optical table setup with grid pattern for static experiments and equipment positioning analysis." class="xfigimg" src="/files/ftp_upload/65935/65935fig02.jpg" data-altupdatedat="1762425657000" data-alt="Figure 2"> 
Figure 2: APA setup for albino mouse strains. The APA arena can be set up for albino strains of mice, such as BALB/c, by selecting the Light option in the Tracking tab and creating a black arena background. An albino mouse on a black background achieves high contrast and offers better mouse tracking. <a href="https://www.jove.com/files/ftp_upload/65935/65935fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Microscopy image analysis with histogram; particle detection; A/B comparison; threshold calibration." class="xfigimg" src="/files/ftp_upload/65935/65935fig03.jpg" data-altupdatedat="1762425657000" data-alt="Figure 3"> 
Figure 3: Adjusting the threshold for mouse tracking is essential. The threshold must be appropriately adjusted to ensure good animal tracking during the trial. The threshold is adjusted by moving the red line in the threshold pane in the From Calibrator tab. (A) An example of a good threshold selection with a solid orange region and a blue X on the object. (B) A poor threshold with speckled orange. Poor tracking leads to the loss of an animal in the arena or prevents the mouse from receiving a shock when it is in the shock zone. <a href="https://www.jove.com/files/ftp_upload/65935/65935fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Behavior analysis graphs; shock avoidance, time max-avoid; 10 week vs 18 month; activity track." class="xfigimg" src="/files/ftp_upload/65935/65935fig04.jpg" data-altupdatedat="1762425657000" data-alt="Figure 4"> 
Figure 4: Comparison of performance between young (10 weeks) and older (18 months) mice on a 5-day learning paradigm and Trace maps. (A) The 10-week-old mice received significantly fewer shocks compared to the 18-month-old mice during 5 days of testing; note that the difference in the number of shocks received was minimal on the first day of testing between the groups, but young mice with intact memory learned to avoid the entry into shock zone more quickly than the older group. (B) Maximum time avoidance was computed as the maximum time spent avoiding the shock during the 10 min trial. The younger mice rapidly learned to avoid entry into the shock zone compared with older mice, suggesting the young mice are learning effectively. (C) The mouse in this trace map received only two shocks, as represented by the two circles in this acquisition trial. This mouse also spent more time in the arena opposite the shock zone, which is represented by the red wedge. (D) This mouse received more shocks and spent more time close to the shock zone, suggesting that spatial learning was not achieved in this mouse. Two-way, repeat measure ANOVA with Bonferroni post hoc tests were used to test significance. ****p&lt;0.0001. Panels A and B were modified from Blackmore et al.7. <a href="https://www.jove.com/files/ftp_upload/65935/65935fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Dual-image diagram of optical path analysis with laser alignment setup, showing angles and reflections." class="xfigimg" src="/files/ftp_upload/65935/65935fig05.jpg" data-altupdatedat="1762425657000" data-alt="Figure 5"> 
Figure 5: Trace maps provide important information for each mouse during each trial. (A) Note the straight lines that are present in this example of tracking. This is due to the tracking software incorrectly identifying a mouse during the task. (B) An example of good tracking during the trial. <a href="https://www.jove.com/files/ftp_upload/65935/65935fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Static equilibrium diagram; displacement analysis with vector mapping; mechanical motion study." class="xfigimg" src="/files/ftp_upload/65935/65935fig06.jpg" data-altupdatedat="1762425657000" data-alt="Figure 6"> 
Figure 6: Track visualization and heatmap on different animal tracking programs. Both (A) Program 1 and (B) Program 2 detect the animal's location and movement to create track plots to visually inspect if the animal learns the task or the effect of experimental treatment. Both programs show identical track plots from an animal who learned the task efficiently. (C) A heatmap can also be created, which facilitates the identification of hotspots and clustering of the data points. <a href="https://www.jove.com/files/ftp_upload/65935/65935fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about The Active Place Avoidance APA Test, an Effective, Versatile and Repeatable Spatial Learning Task for Mice at JoVE.com

Author Spotlight: Investigating the Impact of Aging on Hippocampal-Dependent Spatial Learning

Here, we present a protocol for the active place avoidance test, a hippocampus-dependent spatial learning paradigm designed for rodents. Altering key parameters allows for re-testing of animals before and after treatments or over time.

the-active-place-avoidance-apa-test-an-effective-versatile-repeatable

The Active Place Avoidance (APA) Test, an Effective, Versatile and Repeatable Spatial Learning Task for Mice

Hippocampus-dependent spatial learning in rodents has been tested using a variety of methods. These include the Morris water maze (MWM), Y-maze, and novel ...

We want to examine how hippocampal dependent spatial learning is altered with aging. We then examine how interventions such as physical exercise alter these cognitive outcomes. This behavioral paradigm allows us to readily examine these in the same cohort of mice.Multiple cognitive tests are available to test spatial learning in mice, most of which have been established for decades. These include the Morris Water Maze, the Y Maze, and the Barnes Maze. Each of these have limitations, especially in relation to longitudinal testing.The main experimental challenge is to test animals in a longitudinal manner, making it difficult to accurately determine the effect of exercise interventions. Most spatial learning tests are either easily learned or are very stressful or inappropriate for diseased models. By altering some key components of the active place avoidance, such as the rotational direction of the arena or the location of the shock zone.This test is able to be used multiple times. By changing the color of the arena floor, we are able to test mice with different colors such as C57 black 6 or Swiss mice. To begin position the active place avoidance or APA apparatus within the camera frame mounted on the ceiling.Open the animal tracking software to track the mouse. From the experiment tab, set the trial duration to 10 minutes, then specify the shock duration to 500 milliseconds and maintain the shock zone at 270 degrees from the target's edit tab. Place four neutral colored visual cues on different walls of the room at the same height as the rotating platform and 30 to 50 centimeters away from their arena.Adjust the light intensity of the room between 30 to 70 lux. Open the tracking tab from the tracking software, then click the from calibrator tab and set the mouse size and area ranges accordingly to ensure the mouse is recognized when in the arena. While in the from calibrator tab, adjust the contrast threshold by moving the red line and the threshold pane to ensure accurate tracking of the animals.Finally start the arena and ensure that as it rotates, it remains within the mask. Next, within the devices tab, select the current source and set the current mode to track dependent to modify how or when the foot shocks are delivered, set the shock level to 0.5 milliampers. Habituate the mouse in the room for 30 minutes.Initiate the tracker software. Create an output directory using the file output tab to save the trial data. Then create an experiment specific folder, and depending on the experimental model, create separate folders for each day or trial.Save the experiment data within the output folder. Before starting a trial, click the save button and in the newly opened window, add a unique mouse ID.Next, using a glove covered hand carefully lift the mouse from the base of its tail from the home cage. Place the mouse on the rotating arena of the APA apparatus for habitation without delivering shocks.Retreat behind the curtain and on the tracker software, start the experimental trial. After five minutes, remove the mouse from the APA apparatus and return it to its home cage. Collect all waste and thoroughly clean the grid using 80%ethanol.Next, from the experiment tab, set the trial duration to 10 minutes. Then specify the shock duration to 500 milliseconds and maintain the shock zone at 270 degrees from the target's edit tab, place the mouse on the arena of the APA apparatus as demonstrated previously. Retreat behind the curtain.Click play to start the trial and monitor the mouse's activity. After the trial, remove the mouse from the APA arena and return it to its home cage. To analyze the mouse's APA task data, open the track analysis program.And from the dropdown menu, select avoidance. Click on add task to upload the saved data files. Then in group name, create a group to analyze the data.Click the output directory to select the location to save the analyzed data. Next, click add files and select the files for analysis from the local drive. Click set time and specify the analysis period between zero to 600 seconds.After adding all tracks, click on the analysis tab and select run analysis. Open the data saved in the TBL files folder in a spreadsheet. Finally, save the spreadsheet for statistical analysis.The mice with intact spatial learning ability exhibit a decrease in the number of shocks during successive acquisition trials. Additionally, these mice show an increase in the maximum time spent avoiding the shock zone. Trace maps showed that mice with intact spatial learning ability received only two shocks and predominantly spent time on the opposite side of the shock zone.In contrast, the mice with learning disabilities received more shocks, and the trace map reveals a disordered pattern.

The Active place avoidance (APA) test as an effective, versatile and repeatable spatial learning task

In social behavior research using rodents, there is a growing demand for evaluating more natural interactive behaviors under freely moving conditions. Accurate pose tracking of multiple animals is essential for this purpose. However, current markerless multi-animal pose tracking tools face a significant challenge: tracking accuracy tends to decline under conditions of occlusion and crowding. This problem becomes especially pronounced when the animals are visually indistinguishable. To overcome this issue, we developed virtual marker tracking (vmTracking), a method that improves the accuracy of multi-animal pose tracking under such challenging conditions by maintaining individual identity across frames using virtual markers. vmTracking can also be applied to existing markerless multi-animal video data by incorporating additional processing steps that add individual identity labels into standard tracking workflows. Here, we describe both the method for assigning virtual markers and the protocol for tracking animals in the resulting labeled videos. The high-accuracy multi-animal tracking enabled by vmTracking facilitates the analysis of social interactions under semi-natural conditions.

This work was supported by the Japan Society for the Promotion of Science (JSPS) (JP24K15711 and JP21H04247 to HA, and JP23H00502 and JP21H05296 to ST) and Core Research for Evolutional Science and Technology (CREST) under the Japan Science and Technology Agency (JST) (JPMJCR23P2 to ST).

All experimental procedures were approved by the Doshisha University Animal Care and Use Committee (Approval No. A23068). C57BL/6J mice (see Table of Materials) were housed in groups of 2-3 per cage under controlled conditions of 24-26 &#176;C, a 12 h light/dark cycle, with ad libitum access to food and water. All behavioral recordings were collected during the light phase. The overall workflow of the vmTracking process, along with snapshots from an example, is shown in Figure 1.
NOTE: vmTracking is not a new software or algorithm but a protocol that uses existing DLC functions to maximize multi-animal tracking accuracy. Consequently, a substantial portion of the following steps describes specific graphical user interface (GUI) operations within DLC to ensure reproducibility.
1. Preparation of markerless multi-animal video data
NOTE: Experimental parameters can be adjusted according to the specific study.
<ol>
	<li>Place three C57BL/6J mice in a circular open field with a white floor and transparent walls (31 cm diameter &#215; 17 cm height; see Table of Materials), and allow them to move freely.</li>
	<li>Using a top-down camera view, record the animals for approximately 60 min at a resolution of 460 &#215; 460 pixels and a frame rate of 30 fps (see Table of Materials).</li>
</ol>
2. Creation of a virtual marker video
<ol>
	<li>Perform standard markerless pose tracking using maDLC in DLC version 2.2.3 (Figure 2; see Table of Materials). 
	NOTE: In the steps below, DLC GUI tab menu names and parameter settings are shown in boldto clarify operations in DLC. All procedures are performed using default settings unless otherwise specified. For detailed instructions on using DLC, refer to the official documentation10.
	<ol>
		<li>Create a multi-animal project in Manage Project (Supplementary Figure 1) for markerless video tracking. Modify the individuals and body parts fields in the config.yaml file according to the analysis requirements. In this protocol, individual1 to individual3 and bodypart1 to bodypart6 are used. All other settings are optional. 
		NOTE: The parameters numframes2pick (number of extracted frames) and dotsize (marker size) may be adjusted based on the video conditions and the experimenter&#39;s workload. Other settings were not systematically tested under all possible conditions; however, successful tracking has been confirmed with default configurations.</li>
		<li>Extract frames from the video in Extract Frames (Supplementary Figure 1) for subsequent labeling and training. Set all other parameters to their default values.</li>
		<li>Label each extracted frame in Label Data (Supplementary Figure 1), assigning the appropriate body parts to each visible individual without maintaining consistent IDs between frames.</li>
		<li>Create a training dataset from the extracted frames in Create Training Dataset&#160;(Supplementary Figure 1). In this protocol, set the network to dlcrnet_ms5. Set all other parameters to their default values.</li>
		<li>Start training the network in Train&#160;(Supplementary Figure 1). In this protocol, the Maximum iterations parameter is set to 200,000 for training. Set all other parameters to their default values.</li>
		<li>Evaluate the trained network in Evaluate&#160;(Supplementary Figure 1). Set all other parameters to their default values.</li>
		<li>Run pose estimation on the selected videos in Analyze videos&#160;(Supplementary Figure 1), generating .h5 coordinate data and other output files in the videos folder inside the project folder. Set all other parameters to their default values. 
		NOTE: If the training dataset is insufficient, analysis may fail to generate .h5 coordinate files. In that case, restart from step 2.1.2.</li>
		<li>Manual verification by the experimenter.
		<ol>
			<li>Create a labeled tracking video in Create videos&#160;(Supplementary Figure 1). Select the Create video with animal ID colored? option in the DLC GUI, and generate a labeled tracking video (Figure 2A, maDLC). Set all other parameters to their default values.</li>
			<li>Inspect the labeled videos (Figure 2A, maDLC) to check for ID switches and missing predictions. If noticeable errors are observed, perform retraining (see step 2.1.8.3). If retraining is not performed, proceed to step 2.2. 
			&#8203;NOTE: Retraining is not mandatory, but it improves accuracy and reduces workload in later steps.</li>
			<li>For retraining, extract and annotate outlier frames in Extract/Refine Outliers (Supplementary Figure 1). After annotation is completed, click the Merge dataset button to automatically proceed to Create Training Dataset, and then repeat from step 2.1.4 onward.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Correct the maDLC results to create a virtual marker video (Figure 3).
	<ol>
		<li>Using the custom GUI11 (Figure 3B), retain only the selected keypoints for virtual markers and replace all others with NaN in the .h5 file. This reduction in keypoints decreases the workload for subsequent correction steps (Figure 3C,E). In this protocol, bodyparts 2 and 4 are used. 
		&#8203;NOTE: Selecting two keypoints that efficiently define the body-axis vector is recommended. Performing the initial maDLC tracking using only the keypoints intended as virtual markers may fail to generate the .h5 file in step 2.1.4. Therefore, performing the initial tracking with a larger set of keypoints is recommended.</li>
		<li>Using Refine tracklets&#160;(Supplementary Figure 1), correct the .h5 file from the previous step, containing only the selected keypoints. Correct the keypoints so that each individual retains a consistent ID throughout the video (Figure 3E,F). Save the file; the .h5 file in the videos folder will be updated automatically.</li>
		<li>Create a labeled video with ID color labels from the corrected data in Create videos (Supplementary Figure 1, Figure 3A, Virtual Marker). In DLC 2.2, an existing labeled video in the videos folder will prevent a new one from being created; remove, rename, or move the file before proceeding. Here, set the marker size to 2 in the configuration file, using the smallest size that allows identification without obscuring the animal in this dataset.</li>
		<li>Rename the created video to a short filename to prevent errors during DLC processing when re-tracking in the next step with saDLC.</li>
	</ol>
	</li>
</ol>
3. Perform pose tracking of virtual marker video using saDLC (Figure 4)
NOTE: All procedures are performed using default DLC GUI settings unless otherwise specified.
<ol>
	<li>Create a new saDLC project in Manage Project (Supplementary Figure 2) for virtual marker video tracking. In this protocol, list all combinations of individuals and body parts in the bodyparts field of the config.yaml file (e.g., individual1-bodypart1, individual2-bodypart1, &#8230;, individual3-bodypart6). All other settings are optional. 
	NOTE: As in step 2.1.1, the parameters numframes2pick and dotsize may be adjusted according to video conditions and the experimenter&#39;s workload. Successful tracking has been confirmed with default configurations.</li>
	<li>Extract frames from the virtual marker video in Extract Frames&#160;(Supplementary Figure 2). Set all other parameters to their default values.</li>
	<li>Label each extracted frame in Label Data&#160;(Supplementary Figure 2), using the virtual markers to assign individual IDs so that both IDs and bodyparts match across frames (Figure 3B-D). When an individual&#39;s ID is known and a body part is partially hidden, estimate its position and place the label accordingly. If individual IDs cannot be uniquely determined (e.g., virtual markers for &#8805;2 individuals are missing in the frame), do not label those body parts.</li>
	<li>Create a training dataset from the extracted frames in Create Training Dataset&#160;(Supplementary Figure 2). In this protocol, set the network to efficientnet-b0. Set all other parameters to their default values.</li>
	<li>Start training the network in Train&#160;(Supplementary Figure 2). In this protocol, set the Maximum iterations parameter to 200,000 for training. Set all other parameters to their default values.</li>
	<li>Evaluate the trained network in Evaluate (Supplementary Figure 2). Set all other parameters to their default values.</li>
	<li>Run pose estimation on the selected videos in Analyze videos&#160;(Supplementary Figure 2), generating .h5 coordinate data and other output files in the videos folder. Set all other parameters to their default values.</li>
	<li>Manual verification by the experimenter.
	<ol>
		<li>Create a labeled tracking video in Create videos&#160;(Supplementary Figure 2). Set all other parameters to their default values.</li>
		<li>Inspect the labeled videos (Figure 4A, vmTracking) to check for tracking inconsistencies. Retrain until the experimenter is satisfied with the results. If retraining is not required, the procedure is complete. 
		NOTE: After confirming satisfactory tracking results, you may create a presentation-quality video without virtual markers (Figure 5), output the tracking results on the original markerless video instead of the virtual marker video in Create videos&#160;(Supplementary Figure 2). Replace the virtual marker video in the videos folder with the markerless video, renaming it to match the virtual marker video filename used earlier in the analysis. For DLC 2.2, ensure no labeled videos are present in the folder before creating the new one (Figure 5B,C).</li>
		<li>For retraining, extract and annotate outlier frames in Extract/Refine Outliers&#160;(Supplementary Figure 2). After annotation is completed, click the Merge dataset button to automatically proceed to Create Training Dataset, and then repeat from step 3.1.4 onward.</li>
	</ol>
	</li>
</ol>

<ol><li>Smith, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Lab+mice+go+wild%3A+making+experiments+more+natural+in+order+to+decode+the+brain%5BTitle%5D)+AND+%22Nature%22%5BJournal%5D)">Lab mice go wild: making experiments more natural in order to decode the brain.</a> Nature. 618 (7965), 448-450 (2023).</li>
<li>Rein, B., Ma, K., Yan, Z. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+standardized+social+preference+protocol+for+measuring+social+deficits+in+mouse+models+of+autism%5BTitle%5D)+AND+%22Nat+Protoc%22%5BJournal%5D)">A standardized social preference protocol for measuring social deficits in mouse models of autism.</a> Nat Protoc. 15 (10), 3464-3477 (2020).</li>
<li>Jabarin, R., Netser, S., Wagner, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Beyond+the+three-chamber+test%3A+toward+a+multimodal+and+objective+assessment+of+social+behavior+in+rodents%5BTitle%5D)+AND+%22Mol+Autism%22%5BJournal%5D)">Beyond the three-chamber test: toward a multimodal and objective assessment of social behavior in rodents.</a> Mol Autism. 13 (1), 41(2022).</li>
<li>Lauer, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Multi-animal+pose+estimation%2C+identification+and+tracking+with+DeepLabCut%5BTitle%5D)+AND+%22Nat+Methods%22%5BJournal%5D)">Multi-animal pose estimation, identification and tracking with DeepLabCut.</a> Nat Methods. 19 (4), 496-504 (2022).</li>
<li>Pereira, T. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((SLEAP%3A+A+deep+learning+system+for+multi-animal+pose+tracking%5BTitle%5D)+AND+%22Nat+Methods%22%5BJournal%5D)">SLEAP: A deep learning system for multi-animal pose tracking.</a> Nat Methods. 19 (4), 486-495 (2022).</li>
<li>Bordes, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Automatically+annotated+motion+tracking+identifies+a+distinct+social+behavioral+profile+following+chronic+social+defeat+stress%5BTitle%5D)+AND+%22Nat+Commun%22%5BJournal%5D)">Automatically annotated motion tracking identifies a distinct social behavioral profile following chronic social defeat stress.</a> Nat Commun. 14 (1), 4319(2023).</li>
<li>Mathis, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((DeepLabCut%3A+markerless+pose+estimation+of+user-defined+body+parts+with+deep+learning%5BTitle%5D)+AND+%22Nat+Neurosci%22%5BJournal%5D)">DeepLabCut: markerless pose estimation of user-defined body parts with deep learning.</a> Nat Neurosci. 21 (9), 1281-1289 (2018).</li>
<li>Bolaños, F., LeDue, J. M., Murphy, T. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Cost+effective+raspberry+pi-based+radio+frequency+identification+tagging+of+mice+suitable+for+automated+in+vivo+imaging%5BTitle%5D)+AND+%22J+Neurosci+Methods%22%5BJournal%5D)">Cost effective raspberry pi-based radio frequency identification tagging of mice suitable for automated in vivo imaging.</a> J Neurosci Methods. 276, 79-83 (2017).</li>
<li>Azechi, H., Takahashi, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((vmTracking+enables+highly+accurate+multi-animal+pose+tracking+in+crowded+environments%5BTitle%5D)+AND+%22PLOS+Biol%22%5BJournal%5D)">vmTracking enables highly accurate multi-animal pose tracking in crowded environments.</a> PLOS Biol. 23 (2), e3003002(2025).</li>
<li>DeepLabCut: documentation. , DeepLabCut Team. 
 <a target="_blank" href="https://deeplabcut.github.io/DeepLabCut/README.html">https://deeplabcut.github.io/DeepLabCut/README.html</a> (2025).</li>
<li>Azechi, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((vmTracking+figure+and+code%5BTitle%5D)+AND+%22Zenodo%22%5BJournal%5D)">vmTracking figure and code.</a> Zenodo. , (2025).</li>
<li>Pereira, T. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Fast+animal+pose+estimation+using+deep+neural+networks%5BTitle%5D)+AND+%22Nat+Methods%22%5BJournal%5D)">Fast animal pose estimation using deep neural networks.</a> Nat Methods. 16 (1), 117-125 (2019).</li>
<li>Romero-Ferrero, F., Bergomi, M. G., Hinz, R. C., Heras, F. J. H., de Polavieja, G. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((idtracker.ai%3A+tracking+all+individuals+in+small+or+large+collectives+of+unmarked+animals%5BTitle%5D)+AND+%22Nat+Methods%22%5BJournal%5D)">idtracker.ai: tracking all individuals in small or large collectives of unmarked animals.</a> Nat Methods. 16 (2), 179-182 (2019).</li>
</ol>

Although there is a growing demand to study naturalistic forms of social interaction in rodents&#185;, obtaining reliable identity and pose information for multiple freely moving animals-particularly during close interactions or occlusions-remains a technical challenge. vmTracking overcomes this limitation, enabling accurate tracking under such conditions. vmTracking reliably achieves high tracking accuracy in diverse experimental conditions, including crowded environments9 where conventional multi-animal trackers4,5 often fail. This robustness stems from the combination of virtual marker creation, based on multi-animal tracking, with saDLC tracking, enabling precise identity and pose estimation. Importantly, the method maintained high performance even when virtual marker accuracy was suboptimal. This tolerance to a certain degree of imperfection in the virtual markers ensures that vmTracking remains practical for reliably tracking multiple freely moving individuals, thereby advancing studies of rodent social interactions under semi-natural conditions.
A critical step in vmTracking is correcting the multi-animal tracking results during virtual marker creation. In this process, overlooking identity switches or incorrect labels can cause saDLC to maintain these misidentifications throughout subsequent tracking. If such mistakes are later discovered, they can be corrected by revising the affected segments, regenerating the virtual marker video, and re-running saDLC tracking. In some cases, virtual markers may be placed correctly, yet saDLC may still misidentify individuals when markers on different animals come into close proximity during crowded interactions. Such errors can be mitigated by repositioning the affected virtual markers during the creation step to maximize inter-marker distances, thereby reducing the likelihood of confusion in the subsequent tracking phase.
Although our protocol uses DLC4,7 for both steps, it can also be implemented with SLEAP5,12. In our experience, SLEAP in single-animal mode is generally less stable than saDLC, with performance more dependent on the video9. For the virtual marker creation step, however, SLEAP or idtracker.ai13 can be equally effective, and the optimal choice may vary with the video. Therefore, if the initial maDLC tracking shows frequent ID switches or severe keypoint loss on visual inspection, a mixed approach is possible-for example, creating virtual markers with SLEAP or idtracker.ai and then tracking the resulting video with saDLC.
Because vmTracking involves two tracking steps, some may view it as cumbersome and question why physical markers are not simply attached from the outset, eliminating the need for virtual marker creation. However, virtual markers offer clear advantages: they can be applied post hoc to existing markerless videos, avoiding any behavioral effects or ethical concerns associated with attaching devices to animals. They can be placed anywhere, never fall off, and are unaffected by posture or close interactions, allowing them to be positioned and adjusted to suit the specific video. This flexibility enables more reliable individual identification and, consequently, improved tracking accuracy compared with physical markers. As a result, vmTracking, which uses virtual markers, enables high-accuracy tracking in studies of free-moving social interactions as well as in the re-analysis of archival video data, where physical markers cannot be applied. The high-accuracy tracking data thereby obtained are broadly useful across fields that rely on behavioral experiments, including behavioral science, psychology, and neuroscience.
vmTracking has several limitations. First, because individual identification relies on differences in marker color, the number of trackable individuals is limited by the finite range of distinguishable colors. To date, we have successfully tracked a group of ten fish9; however, increasing this number will require expanding the variety of virtual markers-for example, by altering marker shapes or exploring the use of skeleton structures as additional identifiers-although these approaches require further validation. Second, vmTracking necessarily involves an extra step of adding virtual markers to each markerless video, which can be labor-intensive for long recordings or datasets with many individuals. In addition, while this method can, in principle, be applied to various species and recording conditions, the practicality of achieving high-precision tracking may decrease when experimental parameters cannot be optimized. For example, in recordings of wild or freely moving animals under field-like conditions, where lighting, camera resolution, or recording angles are difficult to control, the manual correction required during virtual marker assignment tends to increase, potentially limiting the ease of obtaining high-quality tracking data. In the future, streamlining and automating the virtual marker assignment process will further enhance the method&#39;s applicability and usability.

Research on rodent social behavior has increasingly emphasized the study of interactions in more naturalistic social contexts1. Traditional approaches, such as the three-chamber test, have been widely used to evaluate sociability and social preference2, but these paradigms capture only simplified aspects of social interaction3. To address this need, multi-animal DeepLabCut (maDLC)4 and Social LEAP Estimates Animal Poses (SLEAP)5 have become indispensable tools for markerless pose tracking of multiple animals in free-moving conditions, providing advanced deep learning-based technology for markerless multi-animal pose tracking. However, in crowded environments where animals are in close proximity, these tools are prone to tracking errors such as prediction failures and identity (ID) switches. Achieving reliable data under such conditions often requires extensive manual correction.
Accurate pose tracking of multiple freely moving animals opens the door to investigating more complex social dynamics. For instance, detailed tracking data allow the evaluation of leader-follower relationships, approach-avoidance behaviors during movement, and other nuanced patterns of interaction that cannot be reliably assessed with conventional behavioral assays. Such advances extend the utility of multi-animal tracking beyond relatively simple descriptive measures of behavior, enabling analyses of more complex social interactions.
One approach to improve tracking accuracy is to clearly differentiate individual animals. For example, Bordes6 demonstrated simultaneous tracking of a white CD1 mouse and a black C57BL/6N mouse using single-animal DeepLabCut (saDLC)7. This finding suggests that when animals are visually distinguishable, accurate individual tracking is feasible. However, the use of physical markers for identification may alter natural behaviors. Even minimally invasive methods, such as implanting radio-frequency identification tags8, raise concerns regarding their potential effects on behavior.
Here, we present a multi-animal pose tracking method that uses virtual markers (vmTracking)9 for non-invasive individual identification. Accurate evaluation of social behavior, taking into account its social context, requires highly precise multi-animal pose tracking data. vmTracking was developed to meet this need by providing a reliable protocol-rather than a new software or algorithm-for obtaining such high-quality tracking data using existing tools such as DLC. Therefore, social behavioral analysis based on these data is beyond the scope of this protocol. vmTracking involves two main steps: adding virtual markers to markerless videos and tracking the resulting virtual marker videos. This method enables high-accuracy pose tracking without the need for physical markers, even in conditions involving three or more visually indistinguishable animals of the same strain. By allowing reliable tracking under semi-naturalistic conditions, vmTracking provides an effective tool for advancing research on complex social interactions, with potential applications across behavioral science, psychology, and neuroscience.

<table><tbody><tr><td>Acrylic pipe (clear, thickness 5 mm, inner diameter 31 cm)</td><td>Sugawarakougei Co., Ltd.</td><td>https://www.sugawarakougei.jp/</td><td>Purchased from Hazaiya (an online acrylic materials retailer)</td></tr><tr><td>Acrylic plate (white, 3 mm thickness, 31 cm diameter)</td><td>Sugawarakougei Co., Ltd.</td><td>https://www.sugawarakougei.jp/</td><td>Purchased from Hazaiya (an online acrylic materials retailer)</td></tr><tr><td>C57BL/6J mouse</td><td>Shimizu Laboratory Supplies, Co.LTD.</td><td>N/A</td><td></td></tr><tr><td>Camera</td><td>Basler</td><td>acA3088-57uc</td><td></td></tr><tr><td>DeepLabCut 2.2.3</td><td>Mathis laboratory at Swiss Federal Institute of Technology in Lausanne</td><td>https://www.mackenziemathislab.org/deeplabcut</td><td></td></tr><tr><td>Pylon Camera Software Suite (Pylon Viewer)</td><td>Basler</td><td>N/A</td><td></td></tr></tbody></table>

In social behavior research using rodents, there is a growing demand for evaluating more natural interactive behaviors under freely moving conditions. Accurate pose tracking of multiple animals is essential for this purpose. However, current markerless multi-animal pose tracking tools face a significant challenge: tracking accuracy tends to decline under conditions of occlusion and crowding. This problem becomes especially pronounced when the animals are visually indistinguishable from one another. To overcome this issue, we developed virtual marker tracking (vmTracking), a method that improves the accuracy of multi-animal pose tracking under such challenging conditions by maintaining individual identity across frames using virtual markers. vmTracking can also be applied to existing markerless multi-animal video data by incorporating additional processing steps that add individual identity labels into standard tracking workflows. Here, we describe both the method for assigning virtual markers and the protocol for tracking animals in the resulting labeled videos. High-accuracy multi-animal tracking enabled by vmTracking provides a reliable foundation for subsequent quantitative analyses of social interactions under semi-natural conditions.

Tracking accuracy was evaluated by comparison with a manually generated ground truth (GT). Twenty 5- or 10-s scenes were extracted and classified as either crowded (CR) scenes, which included overlaps such as mice crossing paths (12 scenes; total of 1,950 frames), or non-crowded (nCR) scenes without overlaps (8 scenes; total of 1,350 frames). Tracking predictions within 10 pixels of the GT position were counted as matches (Match); predictions &#8805;10 pixels away were counted as false positives (FP); and missing predictions were counted as false negatives (FN). In addition, if the predicted identity differed from that in the immediately preceding frame, it was counted as an ID switch.
Compared with maDLC (Figure 6), vmTracking showed a significant improvement in the Match in both CR and nCR scenes (Figure 6A, CR: p &#60; 0.001; Figure 6E, nCR: p &#60; 0.01). In both scene types, no FNs occurred, and the number of FNs was significantly reduced with vmTracking (Figure 6B, CR: p &#60; 0.001; Figure 6F, nCR: p &#60; 0.05). The FP (Figure 6C and 6G) and ID switch (Figure 6D,H) counts showed no changes in either scene type. A side-by-side comparison of tracking results obtained using vmTracking and conventional maDLC is presented, highlighting the enhanced accuracy and stability of vmTracking under crowded conditions (Supplementary Video 1).
We also examined the relationship between the number of annotation frames and both Match and FN for CR scenes in maDLC and vmTracking (Figure 6I). In maDLC, the Match plateaued at approximately 85% with around 400 annotation frames, with little further improvement as the number increased. Increasing the annotation frames for training did not reduce FNs. In contrast, vmTracking showed a steady increase in Match with more annotation frames, reaching approximately 95% at around 1,000 frames.
Virtual markers were classified into six categories based on their assignment patterns, and the proportion of each category was used as an index of virtual marker accuracy to examine its relationship with tracking Match (Figure 6J). Scenes with a higher proportion of correctly assigned virtual markers tended to show higher Match. However, some scenes in which the proportion of correct markers was low, either because of many incorrect assignments or many missing markers, still exhibited high Match (e.g., CR scenes 3 and 11).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/69506/69506fig1.jpg" /> 
Figure 1: Overview of the vmTracking workflow. (A) Schematic diagram of the vmTracking workflow. First, perform maDLC on the markerless video (I). Based on the resulting maDLC tracking data, correct the output so that each individual maintains a consistent ID throughout the video, and create a labeled video that displays only a selected subset of keypoints (II). Using the labels in this video as virtual markers, track the virtual marker video with saDLC (III). (B) Two examples of vmTracking are shown as snapshots from each stage of the process. The far-left image shows the full view, and the images to the right are magnified views of the areas enclosed by yellow boxes. The orange bars in the lower right corners of the images indicate the scales: 5 cm for the &#34;Markerless (Overview)&#34; image and 1 cm for all others. <a href="https://www.jove.com/files/ftp_upload/69506/69506fig1large.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/69506/69506fig2.jpg" /> 
Figure 2: Markerless multi-animal tracking workflow using maDLC. This figure is related to Protocol step 2.1. (A) Panels are adapted from Figure 1B and illustrate Process I (markerless multi-animal tracking) shown in Figure 1A. For clarity, each animal is outlined with a dashed line. The orange bar in the lower right corner of each image indicates a 1 cm scale bar. (B-D) Snapshots of frames used for labeling during maDLC execution. The orange bar in the lower right corner of each image indicates a 5 cm scale bar. (B) Example frame before labels are applied. (C) Same frame as in B, after labeling. (D) Example of another labeled frame. The identity of individuals between frames C and D is unknown, but individual identity does not need to be considered when labeling. As shown in the snapshots, it is sufficient to correctly label the posture of each animal. <a href="https://www.jove.com/files/ftp_upload/69506/69506fig2large.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/69506/69506fig3.jpg" /> 
Figure 3: Workflow for creating a virtual marker video based on maDLC results. This figure is related to Protocol step 2.2. (A) Panels are adapted from Figure 1B and illustrate Process II (virtual marker creation) shown in Figure 1A. For clarity, each animal is outlined with a dashed line. White arrows indicate the keypoints used as virtual markers (the 2nd and 4th keypoints for each ID). In this example, virtual markers are output in grayscale: purple, green, and red maDLC labels appear as black, gray, and white, respectively. In Example 1, maDLC results were output directly as virtual markers without correction. In Example 2, the purple and red IDs were swapped and corrected before output, due to an ID switch in consecutive frames. A green point in the maDLC result that should have been corrected was overlooked and output without modification. The orange bar in the lower right corner of each image indicates a 1 cm scale bar. (B) Python-based GUI for editing h5 coordinate data files to retain only specific keypoints and replace all others with NaN11. (Ba) Initial menu window on program launch. Select the file to edit via the Select h5 file button. (Bb) Window after selecting the h5 file. Check the keypoints to replace with NaN (displayed as individual name-body part name), and specify either start-end times (with frame rate) or frame numbers for NaN replacement. Clicking Process Data saves the edited file in the same folder and creates a backup of the original. (C) Consecutive frames from a scene in which an ID switch occurred. (D) Example of correcting all tracking points from B. (E) Snapshot showing the result from B with only the 2nd and 4th keypoints retained. (F) Example of correcting the result in E. Compared with correcting all keypoints (C &#8594; D), limiting correction to two points (C &#8594; E &#8594; F) reduces the number of required edits. The orange bar in the lower right corner of each image C-F indicates a 5 cm scale bar. <a href="https://www.jove.com/files/ftp_upload/69506/69506fig3large.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/69506/69506fig4.jpg" /> 
Figure 4: Workflow for tracking the virtual marker video. This figure is related to Protocol step 3. (A) Panels are adapted from Figure 1B and illustrate Process III (tracking of the virtual marker video) shown in Figure 1A. Each individual is enclosed in a dotted outline for clarity of the tracking target. In this example, individuals with black, gray, and white virtual markers are tracked as purple, green, and red labels, respectively. In Example 1, the gray virtual marker is absent, yet vmTracking maintains correct identity tracking. In Example 2, the gray marker appears on two individuals, but tracking remains correct. The orange bar in the lower right corner of each image indicates a 1 cm scale bar. (B-D) Snapshots from frames used for labeling during vmTracking. In this step, saDLC is used, with each keypoint defined to include both individual and body part information. Labeling specifies which body part of which individual is represented. The orange bar in the lower right corner of each image indicates a 5 cm scale bar. (B) Snapshot of a frame before labeling. (C) Snapshot of the frame in B after labeling. (D) Snapshot of a different frame from C after labeling. Even in different frames (e.g., C and D), labeling maintains individual identity using the virtual marker as a cue, as indicated by consistent label colors. <a href="https://www.jove.com/files/ftp_upload/69506/69506fig4large.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/69506/69506fig5.jpg" /> 
Figure 5: Workflow for merging tracking results with a markerless video to produce a tracking video without virtual markers. This is an optional step related to the NOTE in Step 3.8.2. (A) When tracking results are applied to the virtual marker video, the resulting tracking video retains the virtual markers (vmTrackingvm+). Yellow arrows indicate the virtual markers visible in the output. Applying the tracking results to the original markerless video instead produces a tracking video without virtual markers (vmTrackingvm-), suitable for presentation or other purposes. The orange bar in the lower right corner of each image indicates a 1 cm scale bar. (B,C) File management in the &#34;videos&#34; folder of the project directory when applying vmTracking results to a markerless video. The orange box schematically illustrates the folder contents. (B) The &#34;videos&#34; folder after generating a vmTracking tracking video by outputting the tracking results onto the virtual marker video (schematically shown as &#39;4. Labels on virtual marker video&#39;). (C) Preparing the &#34;videos&#34; folder for outputting vmTracking results onto the markerless video. Move or rename the &#39;1. Virtual marker video&#39; and &#39;4. Labels on virtual marker video&#39; files from panel B so they are excluded from the folder. The issue is not the files themselves, but that their original filenames interfere with processing. Then, place the &#39;5. Markerless video&#39; in the folder, renaming it to match the original virtual marker video filename (e.g., VMvideo.MP4). In this state, re-running Create videos produces a tracking video with the results applied to the markerless video (&#39;6. Labels on markerless video&#39;). This figure has been modified from Azechi &#38; Takahashi, 2025, PLOS Biology (CC BY 4.0)7. <a href="https://www.jove.com/files/ftp_upload/69506/69506fig5large.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/69506/69506fig6.jpg" /> 
Figure 6: Evaluation of tracking accuracy with vmTracking. (A-H) Comparison between maDLC and vmTracking for the proportions of Matches. (A), false negatives (B), false positives (C), and ID switches (D) in 12 crowded (CR) scenes, and Matches (E), false negatives (F), false positives (G), and ID switches (H) in 8 non-crowded (nCR) scenes. Plots represent the measured values for each scene, and bars represent the mean values calculated from them. Statistical comparisons were conducted using the Wilcoxon signed-rank test. (I) Relationship between the number of annotated frames in CR and the proportions of Matches and false negatives. The green box on the right shows a magnified view of the area enclosed by the green dotted lines in the left panel. Plots represent the mean values. (J) Relationship between virtual marker accuracy and tracking Matches in CR and nCR scenes. Virtual marker accuracy was categorized for each pair of markers into six types: both points correctly assigned to the correct individual, one correct and one missing, one correct and one incorrect, both incorrect, one incorrect and one missing, and both missing. For each scene, the proportions of these categories are shown as stacked bars, with the corresponding tracking Match for that scene indicated above each bar. Scenes are arranged in ascending order of tracking Match from left to right, separately for CR and nCR scenes. CR scenes: n = 12; nCR scenes: n = 8. ***p &#60; 0.001, **p &#60; 0.01, *p &#60; 0.05. This figure has been modified from Azechi and Takahashi (2025)9. <a href="https://www.jove.com/files/ftp_upload/69506/69506fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplementary Figure 1: Snapshot of the main window in multi-animal DeepLabCut, showing the location of each operation tab. The snapshots illustrate, with arrows, the tabs used in each step of the vmTracking procedure. Images were captured from the DeepLabCut GUI (version 2.2.3). <a href="https://www.jove.com/files/ftp_upload/69506/Supplementary Figure 1.tiff" target="_blank">Please click here to download this figure.</a>
Supplementary Figure 2: Snapshot of the main window in single-animal DeepLabCut, showing the location of each operation tab. The snapshots illustrate, with arrows, the tabs used in each step of the vmTracking procedure. Images were captured from the DeepLabCut GUI (version 2.2.3). <a href="https://www.jove.com/files/ftp_upload/69506/Supplementary Figure 2.tiff" target="_blank">Please click here to download this figure.</a>
Supplementary Video 1: Comparison of multi-animal DeepLabCut and vmTracking in tracking three mice. This comparative video shows the performance of multi-animal DeepLabCut (maDLC; left) and vmTracking (right) during the tracking of three mice. The vmTracking video was generated by replacing the virtual marker video with the original markerless video, following the procedure described in the NOTE of Step 3.8.2, to present the tracking results without virtual markers. For each tracking method, individual identities are indicated using color labels of the same color family to facilitate comparison. Throughout the video, vmTracking shows fewer instances where body parts belonging to different individuals are detected, compared with maDLC. Notably, around 15-17 s, maDLC misidentifies the green-labeled and red-labeled individuals, resulting in a switch of their assigned identities. <a href="https://www.jove.com/files/ftp_upload/69506/Supplementary Video 1.mp4" target="_blank">Please click here to download this file.</a>

This protocol introduces vmTracking, a method that enables high-accuracy pose tracking in videos of multiple freely moving, markerless animals, even under crowded conditions where animals are densely grouped. This approach provides reliable data for analyzing social interactions that occur in semi-natural environments where animals can move freely.

utilizing-vmtracking-to-improve-accuracy-multi-animal-pose-estimation

Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies

In social behavior research using rodents, there is a growing demand for evaluating more natural interactive behaviors under freely moving conditions. ...

Rabab Sayed

Ahmed Seif Kamel

Behavioral testing in rodents is used to evaluate traits such as sensory-motor function, depressive-like behavior, anxiety-like behavior, social interaction, memory, and many others. While behavioral tests are a vital part of neurological assessments, they also belong to the group of the most challenging experiments.&nbsp;Neurological disorders pose a large global burden on the healthcare system. Although neurological conditions such as Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS) may present with specific clinical patterns, patients often show vague symptom complexes. The vague pathophysiology of these neurological disorders and their multifaceted consequences urge the need for the design and development of novel behavioral tests to overcome this problem.&nbsp;Overall, this collection aims to highlight the recent advancements and modifications in rodent behavioral testing. We believe that sharing this knowledge will assist the scientific community in developing reliable and validated methods to investigate multiple facets of animal models of neurological disorders.

JoVE Neuroscience is a multidisciplinary section devoted to investigations of the structure, function, physiology, and pathophysiology of the brain and nervous system. Included methodologies range from molecular and cellular level studies to full central and peripheral neural systems. Potential treatment platforms and surgical techniques for neurological diseases and disorders are also presented in this section.

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Articles

The Active Place Avoidance (APA) Test, an Effective, Versatile and Repeatable Spatial Learning Task for Mice

Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies

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