This work was supported by grants from the Swedish Brain Foundation, the Swedish Parkinson Foundation, and the Swedish Government Funds for Clinical Research (M.A.C.), as well as the Wenner-Gren foundations (M.A.C, E.K.H.C), &#197;hl&#233;n foundation (M.A.C) and the foundation Blanceflor Boncompagni Ludovisi, n&#233;e Bildt (S.F).

All procedures that include animal handling have been approved by the Malm&#246;-Lund Ethical committee for animal research.
1. Building the video camera
NOTE: A list of the components needed for building the camera is provided in the Table of Materials. Also refer to Figure 1, Figure 2, Figure 3, Figure 4, Figur...

<ol>
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H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Response-initiated+imaging+of+operant+behavior+using+a+digital+camera.">Response-initiated imaging of operant behavior using a digital camera.</a> Journal of the Experimental Analysis of Behavior. 77 (3), 283-300 (2002).</li><li>Med Associates Inc. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Video+monitor:+Video+monitor+SOF-842+user's+manual.">Video monitor: Video monitor SOF-842 user's manual.</a> Med Associates Inc. , 26-30 (2004).</li><li>. Coulbourn Instruments Available from: <a target="_blank" href="https://www.coulbourn.com/product_p/h39-16.htm">https://www.coulbourn.com/product_p/h39-16.htm</a> (2020)</li><li>Mathis, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=DeepLabCut:+markerless+pose+estimation+of+user-defined+body+parts+with+deep+learning.">DeepLabCut: markerless pose estimation of user-defined body parts with deep learning.</a> Nature Neuroscience. 21 (9), 1281-1289 (2018).</li><li>Nath, T., Mathis, A., Chen, A. C., Patel, A., Bethge, M., Mathis, M. 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Raspberry Pi foundation Available from: <a target="_blank" href="https://thepi.io/how-to-install-raspbian-on-the-raspberry-pi/">https://thepi.io/how-to-install-raspbian-on-the-raspberry-pi/</a> (2020)</li><li>. Pi-supply Available from: <a target="_blank" href="https://learn.pi-supply.com/make/bright-pi-quickstart-faq/">https://learn.pi-supply.com/make/bright-pi-quickstart-faq/</a> (2018)</li><li>. Python Available from: <a target="_blank" href="https://wiki.python.org/moin/BeginnersGuide/NonProgrammers">https://wiki.python.org/moin/BeginnersGuide/NonProgrammers</a> (2020)</li><li>. MathWorks Available from: <a target="_blank" href="https://mathworks.com/academia/highschool/">https://mathworks.com/academia/highschool/</a> (2020)</li><li>. Cran.R-Project.org Available from: <a target="_blank" href="https://cran.r-project.org/manuals.html">https://cran.r-project.org/manuals.html</a> (2020)</li><li>Liu, Y., Tian, C., Huang, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Critical assessment of correction methods for fisheye lens distortion. The international archives of the photogrammetry, remote sensing and spatial information sciences. , (2016).</li><li>Pereira, T. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fast+animal+pose+estimation+using+deep+neural+networks.">Fast animal pose estimation using deep neural networks.</a> Nature Methods. 16 (1), 117-125 (2019).</li><li>Graving, J. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=DeepPoseKit,+a+software+toolkit+for+fast+and+robust+animal+pose+estimation+using+deep+learning.">DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning.</a> Elife. 8 (47994), (2019).</li><li>Geuther, B. Q., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Robust+mouse+tracking+in+complex+environments+using+neural+networks.">Robust mouse tracking in complex environments using neural networks.</a> Communications Biology. 2 (124), (2019).</li></ol>

While materials and resources from The Raspberry Pi foundation has been used and cited in this manuscript, the foundation was not actively involved in the preparation or use of equipment and data in this manuscript. The same is true for Pi-Supply. The authors have nothing to disclose.

This protocol describes how to build an inexpensive and flexible video camera that can be used to record videos from operant conditioning chambers and other behavioral test setups. It further demonstrates how to use DeepLabCut to track a strong light signal within these videos, and how that can be used to aid in identifying brief video segments of interest in video files that cover full test sessions. Finally, it describes how to use the tracking of a rat&#8217;s head to complement the analysis of behaviors during operan...

In the field of behavioral neuroscience, researchers commonly use operant conditioning chambers to assess a wide range of different cognitive and psychiatric features in rodents. While there are several different manufacturers of such systems, they typically share certain attributes and have an almost standardized design1,2,3. The chambers are generally square- or rectangle-shaped, with one wall that can be opened for placing animals inside, and one or two of the remaining walls containing components such as levers, nose-poke openings, reward trays, response wheels and lights of various kinds1,2,3. The lights and sensors present in the chambers are used to both control the test protocol and track the animals&#8217; behaviors1,2,3,4,5. The typical operant conditioning systems allow for a very detailed analysis of how the animals interact with the different operanda and openings present in the chambers. In general, any occasions where sensors are triggered can be recorded by the system, and from this data users can obtain detailed log files describing what the animal did during specific steps of the test4,5. While this provides an extensive representation of an animal&#8217;s performance, it can only be used to describe behaviors that directly trigger one or more sensors4,5. As such, aspects related to how the animal positions itself and moves inside the chamber during different phases of the test are not well described6,7,8,9,10. This is unfortunate, as such information can be valuable for fully understanding the animal&#8217;s behavior. For example, it can be used to clarify why certain animals perform poorly on a given test6, to describe the strategies that animals might develop to handle difficult tasks6,7,8,9,10, or to appreciate the true complexity of supposedly simple behaviors11,12. To obtain such articulate information, researchers commonly turn to manual analysis of videos6,7,8,9,10,11.
When recording videos from operant conditioning chambers, the choice of camera is critical. The chambers are commonly located in isolation cubicles, with protocols frequently making use of steps where no visible light is shining3,6,7,8,9. Therefore, the use of infra-red (IR) illumination in combination with an IR-sensitive camera is necessary, as it allows visibility even in complete darkness. Further, the space available for placing a camera inside the isolation cubicle is often very limited, meaning that one benefits strongly from having small cameras that use lenses with a wide field of view (e.g., fish-eye lenses)9. While manufacturers of operant conditioning systems can often supply high-quality camera setups to their customers, these systems can be expensive and do not necessarily fit chambers from other manufacturers or setups for other behavioral tests. However, a notable benefit over using stand-alone video cameras is that these setups can often interface directly with the operant conditioning systems13,14. Through this, they can be set up to only record specific events rather than full test sessions, which can greatly aid in the analysis that follows.
The current protocol describes how to build an inexpensive and versatile video camera using hobby electronics components. The camera uses a fisheye lens, is sensitive to IR illumination and has a set of IR light emitting diodes (IR LEDs) attached to it. Moreover, it is built to have a flat and slim profile. Together, these aspects make it ideal for recording videos from most commercially available operant conditioning chambers as well as other behavioral test setups. The protocol further describes how to process videos obtained with the camera and how to use the software package DeepLabCut15,16 to aid in extracting video sequences of interest as well as tracking an animal&#8217;s movements therein. This partially circumvents the draw-back of using a stand-alone camera over the integrated solutions provided by operant manufacturers of conditioning systems, and offers a complement to manual scoring of behaviors.
Efforts have been made to write the protocol in a general format to highlight that the overall process can be adapted to videos from different operant conditioning tests. To illustrate certain key concepts, videos of rats performing the 5-choice serial reaction time test (5CSRTT)17 are used as examples.

<table>
	<tbody>
		<tr>
			<td>32 Gb micro SD card with New Our Of Box Software (NOOBS) preinstalled</td>
			<td>The Pi hut (https://thpihut.com)</td>
			<td>32GB</td>
			<td></td>
		</tr>
		<tr>
			<td>330-Ohm resistor</td>
			<td>The Pi hut (https://thpihut.com)</td>
			<td>100287</td>
			<td>This article is for a package with mixed resistors, where 330-ohm resistors are included.</td>
		</tr>
		<tr>
			<td>Camera module (Raspberry Pi NoIR camera v.2)</td>
			<td>The Pi hut (https://thpihut.com)</td>
			<td>100004</td>
			<td></td>
		</tr>
		<tr>
			<td>Camera ribbon cable (Raspberry Pi Zero camera cable stub)</td>
			<td>The Pi hut (https://thpihut.com)</td>
			<td>MMP-1294</td>
			<td>This is only needed if a Raspberry Pi zero is used. If another Raspberry Pi board is used, a suitable camera ribbon cable accompanies the camera component</td>
		</tr>
		<tr>
			<td>Colored LEDs</td>
			<td>The Pi hut (https://thpihut.com)</td>
			<td>ADA4203</td>
			<td>This article is for a package with mixed colors of LEDs. Any color can be used.</td>
		</tr>
		<tr>
			<td>Female-Female jumper cables</td>
			<td>The Pi hut (https://thpihut.com)</td>
			<td>ADA266</td>
			<td></td>
		</tr>
		<tr>
			<td>IR LED module (Bright Pi)</td>
			<td>Pi Supply (https://uk.pi-supply.com)</td>
			<td>PIS-0027</td>
			<td></td>
		</tr>
		<tr>
			<td>microcomputer motherboard (Raspberry Pi Zero board with presoldered headers)</td>
			<td>The Pi hut (https://thpihut.com)</td>
			<td>102373</td>
			<td>Other Raspberry Pi boards can also be used, although the method for automatically starting the Python script only works with Raspberry Pi zero. If using other models, the python script needs to be started manually.</td>
		</tr>
		<tr>
			<td>Push button switch</td>
			<td>The Pi hut (https://thpihut.com)</td>
			<td>ADA367</td>
			<td></td>
		</tr>
		<tr>
			<td>Raspberry Pi power supply cable</td>
			<td>The Pi hut (https://thpihut.com)</td>
			<td>102032</td>
			<td></td>
		</tr>
		<tr>
			<td>Raspberry Pi Zero case</td>
			<td>The Pi hut (https://thpihut.com)</td>
			<td>102118</td>
			<td></td>
		</tr>
		<tr>
			<td>Raspberry Pi, Mod my pi, camera stand with magnetic fish eye lens and magnetic metal ring attachment</td>
			<td>The Pi hut (https://thpihut.com)</td>
			<td>MMP-0310-KIT</td>
			<td></td>
		</tr>
	</tbody>
</table>

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.

Video camera performance
The representative results were gathered in operant conditioning chambers for rats with floor areas of 28.5 cm x 25.5 cm, and heights of 28.5 cm. With the fisheye lens attached, the camera captures the full floor area and large parts of the surrounding walls, when placed above the chamber (Figure 7A). As such, a good view can be obtained, even if the camera is placed off-center on the chamber&#8217;s top. This should hold ...

Watch this Scientific Journal Video about Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut at JoVE.com

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

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 ...

By following the methods outlined in this protocol researchers will be able to record and analyze videos of rodents performing complex behavioral tests in operant conditioning chambers. The protocol describes how to build inexpensive video camera and use it together with an open source tracking software. This is an attractive approach for labs on a budget.The method is valuable for research projects that involve operant conditioning in rodents. As video analysis can greatly improve the understanding of behaviors seen in this type of tests. Begin by attaching the metal ring around the opening of the camera stand.Then attach the camera module to the stand using the nuts and bolts that accompany the kit. Open the ribbon cable ports on the camera module and microcomputer by gently pooling on the edges of their plastic lips. Blaze the ribbon cable in the open port on the camera module so that the cables silver connectors face the circuit board.Then lock it in place by pushing in the plastic clip. Repeat the process with a port on the microcomputer. Then attach the fisheye lens to the metal ring on the camera stand.Place the microcomputer in the plastic case and insert the listed micro SD card. Then connect a monitor, keyboard and a mouse to the microcomputer and started by connecting its power supply. Open a terminal window and type sudo apt hyphen, get update.Then press the enter key. Next type sudo apt full hyphen upgrade and press enter. Under the start menu, select preferences and raspberry PI configurations.When the window opens, go to the interfaces tab and enable the camera and I2C Then click okay. Copy supplementary file one onto a USB memory stick. Then transfer it to the microcomputers home PI folder and rename it.Open a terminal window type pseudo nano slash etc, slash RC dot local and press enter. Use the keyboards arrow keys to move the cursor down to the space between fi and exit zero. Then add text to make the computer start the copied script.And the infrared LEDs, whenever it boots. Save the changes by pressing control and X followed by Y and enter. Next solder resistors and female jumper cables onto the legs of two colored LEDs.Solder female jumper cables onto two button switches. Then connect the switches colored LEDs and the listed infrared led module to the computers GPI hoop ends. When connected properly, one led will indicate that the camera is switched on and ready to be used.While the other indicates that the camera is recording a video. The button with the long cables is used to start and stop video recordings. While the button with these short cables is used to switch off the camera.Set the protocol to use the operant chambers house light as an indicator of a specific step in the protocol. Then set the protocol to record all events of interest with timestamps in relation to when this protocol step indicator becomes active. Place the camera on top of the operant chambers and started by connecting it to an electrical outlet via the power supply cable.Use the previously connected button to start and stop video recordings. When the video recordings are finished, connect the camera to a monitor, keyboard mouse and USB storage device and retrieve the video files from its desktop. Use DeeplabCuts frame grabbing function to extract 700 to 900 video frames from one or more of the recorded videos.Make sure that the video frames you select display the animal in different postures, both stationary with the head outside and inside of openings and moving in different directions. Use the labeling toolbox to manually mark the position of the rat's head in each video frame by placing a head label in a central position between the rat's ears. Also label other body parts that may be of interest.In addition, mark a position of the protocol step indicator in each video frame where it is actively shining. Next use the create training data set and train network functions to create a training data set from the labeled video frames and start the training of a neural network. When a neural network has been trained, use it to analyze the gathered videos.This will create a CSV file listing the track positions of the rat's head, other body parts of interest and the protocol step indicator in each video frame. In addition, it will create marked up video files where the track positions are displayed visually. To obtain coordinates of specific points of interest inside the operant chambers, manually mark these as previously described and retrieve the coordinates from the CSV file that is automatically stored under labeled data in the project folder.Note, in which video segments the protocol step indicator is tracked within 60 pixels of the position obtained manually in the previous section and extract the exact starting point for each period where the indicator is active. Use the points where the protocol step indicator becomes active and the timestamps recorded by the operant chambers. To determine which video segments cover specific events of the test protocol such as inter-trial intervals, responses or reward retrievals.Note the video frames they cover any events that are specific interest. Finally perform relevant in-depth analysis of the animal's position and movements, during these events. The camera's fish eye lens should allow it to capture a full view of the inside of most rodent operant conditioning chambers.By using a suitable source of infrared illumination, the camera will also allow video capture in complete darkness. A well-trained network should allow for over 90%accuracy when tracking an animal's head. Accurate tracking is clearly identifiable by markers following an animal throughout its movements and plotted paths appearing smooth.In contrast, inaccurate tracking is characterized by markers that do not reliably stay on target and by jagged plotted paths. As a result of this inaccurate tracking typically causes sudden shifts in calculated movements speeds. By tracking where an animal is located throughout a test session, one can assess how distinct movement patterns relate to performance.As an example, in the five choice serial reaction time test, head movements during the inter-trial interval, can be used to separate emission trials where the animal shows a limited interest in performing a response. From a mission trials where the animal simply fails to notice the brief light cue. In addition investigating head movements can enable the detection and characterization of different attentional strategies.When attempting these procedure, it is important that the protocol step indicator is reliable. And that the neural network is trained with a dives ert of video frames. To ensure an accurate tracking.

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JoVE Journal

Behavior

12.1K vistas.  Lund University. Siguiendo los métodos descritos en este protocolo, los investigadores podrán grabar y analizar vídeos de roedores realizando pruebas de comportamiento complejas en cámaras de acondicionamiento de operandos. El protocolo describe cómo construir una cámara de vídeo de bajo costo y utilizarla junto con un software de seguimiento de código abierto. Este es un enfoque atractivo para los laboratorios con un presupuesto ajustado.El método es valioso para proyectos de investigación q...