Name: an open-source virtual reality system for the measurement of spatial learning in head-restrained mice
Uploaded: 2023-03-03T13:33:00
Description: Watch this Scientific Journal Video about An Open-Source Virtual Reality System for the Measurement of Spatial Learning in Head-Restrained Mice at JoVE.com

We would like to thank Noah Pettit from the Harvey lab for the discussion and suggestions while developing the protocol in this manuscript. This work was supported by a BBRF Young Investigator Award and NIMH 1R21MH122965 (G.F.T.), in addition to NINDS R56NS128177 (R.H., C.L.) and NIMH R01MH068542 (R.H.).

All procedures in this protocol were approved by the Institutional Animal Care and Use Committee of the New York State Psychiatric Institute.
NOTE: A single-board computer is used to display a VR visual environment coordinated with the running of a head-restrained mouse on a wheel. Movement information is received as serial input from an ESP32 microcontroller reading a rotary encoder coupled to the wheel axle. The VR environment is rendered using OpenGL hardware acceleration on the Raspberry P...

<ol>
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This open-source VR system for mice will only function if the serial connections are made properly between the rotary and behavior ESP32 microcontrollers and the single-board computer (step 2), which can be confirmed using the IDE serial monitor (step 2.4.5). For successful behavioral results from this protocol (step 4), the mice must be habituated to the apparatus and be comfortable running on the wheel for liquid rewards (steps 4.3-4.5). This requires sufficient (but not excessive) water restriction, as mice given ...

Spatial navigation is an ethologically important behavior by which animals encode the features of new locations into a cognitive map, which is used for finding areas of possible reward and avoiding areas of potential danger. Inextricably linked with memory, the cognitive processes underlying spatial navigation share a neural substrate in the hippocampus1 and cortex, where neural circuits in these areas integrate incoming information and form cognitive maps of environments and events for later recall2. While the discovery of place cells in the hippocampus3,4 and grid cells in the entorhinal cortex5 has shed light on how the cognitive map within the hippocampus is formed, many questions remain about how specific neural subtypes, microcircuits, and individual subregions of the hippocampus (the dentate gyrus, and cornu ammonis areas, CA3-1) interact and participate in spatial memory formation and recall.
In vivo two-photon imaging has been a useful tool in uncovering cellular and population dynamics in sensory neurophysiology6,7; however, the typical necessity for head restraint limits the utility of this method for examining mammalian spatial behavior. The advent of virtual reality (VR)8 has addressed this shortcoming by presenting immersive and realistic visuospatial environments while head-restrained mice run on a ball or treadmill to study spatial and contextual encoding in the hippocampus8,9,10 and cortex11. Furthermore, the use of VR environments with behaving mice has allowed neuroscience researchers to dissect the components of spatial behavior by precisely controlling the elements of the VR environment12 (e.g., visual flow, contextual modulation) in ways not possible in real-world experiments of spatial learning, such as the Morris water maze, Barnes maze, or hole board tasks.
Visual VR environments are typically rendered on the graphical processing unit (GPU) of a computer, which handles the load of rapidly computing the thousands of polygons necessary to model a moving 3D environment on a screen in real time. The large processing requirements generally require the use of a separate PC with a GPU that renders the visual environment to a monitor, multiple screens13, or a projector14 as the movement is recorded from a treadmill, wheel, or foam ball under the animal. The resulting apparatus for controlling, rendering, and projecting the VR environment is, therefore, relatively expensive, bulky, and cumbersome. Furthermore, many such environments in the literature have been implemented using proprietary software that is both costly and can only be run on a dedicated PC.
For these reasons, we have designed an open-source VR system to study spatial learning behaviors in head-restrained mice using a Raspberry Pi single-board computer. This Linux computer is both small and inexpensive yet contains a GPU chip for 3D rendering, allowing the integration of VR environments with the display or behavioral apparatus in varied individual setups. Furthermore, we have developed a graphical software package written in Python, &#34;HallPassVR&#34;, which utilizes the single-board computer to render a simple visuospatial environment, a virtual linear track or hallway, by recombining custom visual features selected using a graphical user interface (GUI). This is combined with microcontroller subsystems (e.g., ESP32 or Arduino) to measure locomotion and coordinate behavior, such as by the delivery of other modalities of sensory stimuli or rewards to facilitate reinforcement learning. This system provides an inexpensive, flexible, and easy-to-use alternative method for delivering visuospatial VR environments to head-restrained mice during two-photon imaging (or other techniques requiring head fixation) for studying the neural circuits underlying spatial learning behavior.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>1/4 &#34; diam aluminum rod</td>
			<td>McMaster-Carr</td>
			<td>9062K26</td>
			<td>3&#34; in length for wheel axle</td>
		</tr>
		<tr>
			<td>1/4&#34;-20 cap screws, 3/4&#34; long (x2)</td>
			<td>Amazon.com</td>
			<td>B09ZNMR41V</td>
			<td>for affixing head post holders to optical posts</td>
		</tr>
		<tr>
			<td>2&#34;x7&#34; T-slotted aluminum bar (x2)</td>
			<td>8020.net</td>
			<td>1020</td>
			<td>wheel/animal mounting frame</td>
		</tr>
		<tr>
			<td>6&#34; diam, 3&#34; wide acrylic cylinder (1/8&#34; thick)</td>
			<td>Canal Plastics</td>
			<td>33210090702</td>
			<td>Running wheel (custom width cut at canalplastics.com)</td>
		</tr>
		<tr>
			<td>8-32 x 1/2&#34; socket head screws</td>
			<td>McMaster-Carr</td>
			<td>92196A194</td>
			<td>fastening head post holder to optical post&#160;</td>
		</tr>
		<tr>
			<td>Adjustable arm (14&#34;)</td>
			<td>Amazon.com</td>
			<td>B087BZGKSL</td>
			<td>to hold/adjust lick spout</td>
		</tr>
		<tr>
			<td>Analysis code (MATLAB)</td>
			<td></td>
			<td>custom written</td>
			<td>file at github.com/GergelyTuri/HallPassVR/software/Analysis code</td>
		</tr>
		<tr>
			<td>Axle mounting flange, 1/4&#34; ID</td>
			<td>Pololu</td>
			<td>1993</td>
			<td>for mounting wheel to axle</td>
		</tr>
		<tr>
			<td>Ball bearing (5/8&#34; OD, 1/4&#34; ID, x2)</td>
			<td>McMaster-Carr</td>
			<td>57155K324</td>
			<td>for mounting wheel axle to frame</td>
		</tr>
		<tr>
			<td>Behavior ESP32 code</td>
			<td></td>
			<td>custom written</td>
			<td>file at github.com/GergelyTuri/HallPassVR/software/Arduino code/Behavior board</td>
		</tr>
		<tr>
			<td>Black opaque matte acrylic sheets (1/4&#34; thick)</td>
			<td>Canal Plastics</td>
			<td>32918353422</td>
			<td>laser cut file at github.com/GergelyTuri/HallPassVR/hardware/VR screen assembly</td>
		</tr>
		<tr>
			<td>Clear acrylic sheet (1/4&#34; thick)</td>
			<td>Canal Plastics</td>
			<td>32920770574</td>
			<td>laser cut file at github.com/GergelyTuri/HallPassVR/hardware/VR wheel assembly</td>
		</tr>
		<tr>
			<td>ESP32 devKitC v4 (x2)</td>
			<td>Amazon.com</td>
			<td>B086YS4Z3F</td>
			<td>microcontroller for behavior and rotary encoder</td>
		</tr>
		<tr>
			<td>ESP32 shield</td>
			<td>OpenMaze.org</td>
			<td>OMwSmall</td>
			<td>description at www.openmaze.org (https://claylacefield.wixsite.com/openmazehome/copy-of-om2shield). ZIP gerber files at: https://github.com/claylacefield/OpenMaze/tree/master/OM_PCBs</td>
		</tr>
		<tr>
			<td>Fasteners and brackets&#160;</td>
			<td>8020.net</td>
			<td>4138, 3382,3280</td>
			<td>for wheel frame mounts</td>
		</tr>
		<tr>
			<td>goniometers</td>
			<td>Edmund Optics</td>
			<td>66-526, 66-527</td>
			<td>optional for behavior. Fine tuning head for imaging</td>
		</tr>
		<tr>
			<td>HallPassVR python code</td>
			<td></td>
			<td>custom written</td>
			<td>file at github.com/GergelyTuri/HallPassVR/software/HallPassVR</td>
		</tr>
		<tr>
			<td>Head post holder</td>
			<td></td>
			<td>custom design</td>
			<td>3D design file at github.com/GergelyTuri/HallPassVR/hardware/VR head mount/Headpost Clamp</td>
		</tr>
		<tr>
			<td>LED projector</td>
			<td>Texas Instruments</td>
			<td>DLPDLCR230NPEVM</td>
			<td>or other small LED projector</td>
		</tr>
		<tr>
			<td>Lick spout</td>
			<td>VWR</td>
			<td>20068-638</td>
			<td>(or ~16 G metal hypodermic tubing)</td>
		</tr>
		<tr>
			<td>M 2.5 x 6 set screws</td>
			<td>McMaster-Carr</td>
			<td>92015A097</td>
			<td>securing head post&#160;</td>
		</tr>
		<tr>
			<td>Matte white diffusion paper</td>
			<td>Amazon.com</td>
			<td></td>
			<td>screen material</td>
		</tr>
		<tr>
			<td>Metal headposts</td>
			<td></td>
			<td>custom design</td>
			<td>3D design file at github.com/GergelyTuri/HallPassVR/hardware/VR head mount/head post designs</td>
		</tr>
		<tr>
			<td>Miscellenous tubing and tubing adapters (1/16&#34; ID)</td>
			<td></td>
			<td></td>
			<td>for constructing the water line</td>
		</tr>
		<tr>
			<td>Optical breadboard</td>
			<td>Thorlabs</td>
			<td>as per user&#39;s requirements</td>
			<td></td>
		</tr>
		<tr>
			<td>Optical posts, 1/2&#34; diam (2x)</td>
			<td>Thorlabs</td>
			<td>TR4</td>
			<td>for head fixation setup</td>
		</tr>
		<tr>
			<td>Processing code</td>
			<td></td>
			<td>custom written</td>
			<td>file at github.com/GergelyTuri/HallPassVR/software/Processing code</td>
		</tr>
		<tr>
			<td>Raspberry Pi 4B</td>
			<td>raspberry.com, adafruit.com</td>
			<td></td>
			<td>Single-board computer for rendering of HallPassVR envir.</td>
		</tr>
		<tr>
			<td>Right angle clamp</td>
			<td>Thorlabs</td>
			<td>RA90</td>
			<td>for head fixation setup</td>
		</tr>
		<tr>
			<td>Rotary encoder (quadrature, 256 step)</td>
			<td>DigiKey</td>
			<td>ENS1J-B28-L00256L</td>
			<td>to measure wheel rotation</td>
		</tr>
		<tr>
			<td>Rotary encoder ESP32 code</td>
			<td></td>
			<td>custom written</td>
			<td>file at github.com/GergelyTuri/HallPassVR/software/Arduino code/Rotary encoder</td>
		</tr>
		<tr>
			<td>SCIGRIP 10315 acrylic cement</td>
			<td>Amazon.com</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Shaft coupler</td>
			<td>McMaster-Carr</td>
			<td>9861T426</td>
			<td>to couple rotary encoder shaft with axle</td>
		</tr>
		<tr>
			<td>Silver mirror acrylic sheets</td>
			<td>Canal Plastics</td>
			<td>32913817934</td>
			<td>laser cut file at github.com/GergelyTuri/HallPassVR/hardware/VR screen assembly</td>
		</tr>
		<tr>
			<td>Solenoid valve</td>
			<td>Parker</td>
			<td>003-0137-900</td>
			<td>to administer water rewards</td>
		</tr>
	</tbody>
</table>

an open-source virtual reality system for the measurement of spatial learning in head-restrained mice

Head-restrained behavioral experiments in mice allow neuroscientists to observe neural circuit activity with high-resolution electrophysiological and optical imaging tools while delivering precise sensory stimuli to a behaving animal. Recently, human and rodent studies using virtual reality (VR) environments have shown VR to be an important tool for uncovering the neural mechanisms underlying spatial learning in the hippocampus and cortex, due to the extremely precise control over parameters such as spatial and contextual cues. Setting up virtual environments for rodent spatial behaviors can, however, be costly and require an extensive background in engineering and computer programming. Here, we present a simple yet powerful system based upon inexpensive, modular, open-source hardware and software that enables researchers to study spatial learning in head-restrained mice using a VR environment. This system uses coupled microcontrollers to measure locomotion and deliver behavioral stimuli while head-restrained mice run on a wheel in concert with a virtual linear track environment rendered by a graphical software package running on a single-board computer. The emphasis on distributed processing allows researchers to design flexible, modular systems to elicit and measure complex spatial behaviors in mice in order to determine the connection between neural circuit activity and spatial learning in the mammalian brain.

This open-source virtual reality behavioral setup allowed us to quantify licking behavior as a read-out of spatial learning as head-restrained mice navigated a virtual linear track environment. Seven C57BL/6 mice of both sexes at 4 months of age were placed on a restricted water schedule and first trained to lick continuously at low levels while running on the wheel for random spatial rewards (&#34;random foraging&#34;) without VR. Although their performance was initially affected when moved to the VR projection screen s...

Watch this Scientific Journal Video about An Open-Source Virtual Reality System for the Measurement of Spatial Learning in Head-Restrained Mice at JoVE.com

An Open-Source Virtual Reality System for the Measurement of Spatial Learning in Head-Restrained Mice

Here, we present a simplified open-source hardware and software setup for investigating mouse spatial learning using virtual reality (VR). This system displays a virtual linear track to a head-restrained mouse running on a wheel by utilizing a network of microcontrollers and a single-board computer running an easy-to-use Python graphical software package.

Head-restrained behavioral experiments in mice allow neuroscientists to observe neural circuit activity with high-resolution electrophysiological and ...

This open source virtual reality system is an important tool for the studying of spatial learning in the brain because it allows researchers to present a consistent set of spatial stimuli to a head-restrained mouse using a simple modular electronic setup. The advantage of this system is that it is inexpensive, easy to set up, compact, and modular, which allows for building multiple behavioral setups for training and integration with existing head-restrained behavioral setups. This system is ideal for measuring spatial learning and head-restrained mice, however, it is equally able to deliver visual virtual reality environments for experiments in other species and preparations, including human psychophysics and neuroimaging.Demonstrating this procedure will be Carla Diaz and Hannah Chung, research assistants in our laboratory. To begin, connect the wires between the rotary encoder component and the rotary ESP32. Rotary encoders generally have four wires, positive, GND, A, and B.Connect these via jumper wires to the ESP32, 3 0.3 volts, GND 25 and 26 pins.Connect the serial RX/TX wires between the rotary ESP32 and the behavior ESP32. Make a simple two wire connection between the rotary ESP32, Serial0 RX/TX and the Serial2 port of the behavior ESP32. Connect the serial RX/TX wires between the rotary ESP32 and the single board computer GPIO or direct USB connection.Make a two wire connection between the single board computer GPIO pins, 14, 15, RX/TX, and the rotary ESP32, Serial2, TX/RX pins 1716. Next, plug the rotary ESP32 USB into the single board computer USB to upload the initial rotary encoder code. Connect the 12 volt liquid solenoid valve to the ULN2803 IC output on the far left of the OMW small PCB, connect the lick port to the ESP32 touch input.Plug the USB into the single board computer's USB port to upload new programs to the behavior ESP32 for different experimental paradigms and to capture behavior data using the included processing sketch. Then plug the 12 volt DC wall adapter into the 2.1 millimeter barrel jack connector on the behavior ESP32 OMW small PCB to provide the power for the reward solenoid valve. Plug the single board computer's HDMI two output into the projector HDMI port.This will carry the graphical software environment rendered by the single board computer GPU to the projection screen. Open the terminal window in the single board computer and navigate to the Hall Pass VR folder. Run the indicated virtual reality or VR graphical user interface or GUI to open the GUI window.Select and add four elements from the list box for each of the three patterns along the track, and then click on Generate. Select Floor and Ceiling Images from the dropdown menus and set the length of the track as two meters for this example code. Name this pattern, if desired.Click the Start button and wait until the VR window starts before clicking elsewhere. The graphical software environment will appear on screen two. Run the processing sketch to acquire and plot the behavioral data movement.Open the indicated command in the processing IDE. Change the animal to your mouse number variable and set session minutes equal to the length of the behavioral session in minutes. Click on the Run button on the processing IDE.Check the processing plot window which should show the current mouse position on the virtual linear track as the wheel rotates along with the reward zones and running histograms of the licks, laps, and rewards, updated every 30 seconds. Advance the running wheel by hand to simulate the mouse running for testing or use a test mouse for the initial setup. Click on the plot window and press the Q key on the keyboard to stop acquiring behavioral data.A text file of the behavioral events and times and an image of the final plot window in PNG is saved when session minutes has elapsed or the user presses the Q key to quit. For random forging with non-operant rewards, run the graphical software GUI program with a path of arbitrary visual elements. Then upload the behavior program to the behavior ESP32 with multiple non-operant rewards to condition the mouse to run and lick.Gently place the mouse in the head fixation apparatus, adjust the lick spout to a location just anterior to the mouse's mouth, and position the mouse wheel into the center of the projection screen zone. Set the animal's name in the processing sketch and then press Run in the processing IDE to start acquiring and plotting the behavioral data. Run the mouse in 20 to 30 minute sessions until the mouse runs for at least 20 laps per session and lick for rewards presented in random locations.For random foraging with operant rewards on alternate laps, upload the behavior program with alternating operant equal to one and train the mouse until it licks for both non-operant and operant reward zones. For fully operant random foraging, upload the behavior program with four operant random reward zones and train the mouse until it licks for rewards consistently along the track. Next for spatial learning, run the graphical software program with a path of a dark hallway with a single visual cue in the center.Then upload the behavior program with a single hidden reward zone to the behavior ESP32. Let the mouse run for 30 minute sessions with a single hidden reward zone and single visual cue VR hallway and capture data during the session as described before. Download the txt data file from the processing sketch folder and analyze the behavioral data to observe emergence of spatially selective licking as an indicator of spatial learning.Spatial learning using the graphical software environment is shown here. Through progressive stages of training on random foraging, mice learned to run on the wheel and lick consistently along the track at low levels before being switched to a single hidden reward location to show spatial learning. In this study, four of the seven mice learned the hidden reward task with a single visual cue in two to four sessions, as shown by their licking near the reward zone with increasing selectivity.Furthermore, the mice exhibited both substantial within session and between session learning. Spatial licks per lap on day two showed increased licking prior to the reward zone and decreased licking elsewhere, indicating the development of spatially specific anticipatory licking. The main thing to remember when using the system is that mice will only perform well if reinforced and comfortable on the running wheel.Therefore, water restrict the animals appropriately, handle them gently, and ensure their head restrain position is optimal for running while viewing the projection screen. A neuroscience researcher can combine this open source VR system with in vivo imaging or electrophysiology to investigate neuron circuits underlying spatial learning in the brain. We think that the simplicity of this open source VR system will allow researchers to integrate the system into diverse neuro recording setups.The precise control over spatial stimuli in the VR environment will allow researchers to examine the contributions of specific neuron circuits to spatial learning.

Research

JoVE Journal

Neuroscience

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