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.

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Research

JoVE Journal

Neuroscience

Low-stress Route Learning Using the Lashley III Maze in Mice

Many behavior tests designed to assess learning and memory in rodents, particularly mice, rely on visual cues, food and/or water deprivation, or other aversive stimuli to motivate task acquisition. As animals age, sensory modalities deteriorate. For example, many strains of mice develop hearing deficits or cataracts. Changes in the sensory systems required to guide mice during task acquisition present potential confounds in interpreting learning changes in aging animals. Moreover, the use of aversive stimuli to motivate animals to learn tasks is potentially confounding when comparing mice with differential sensitivities to stress. To minimize these types of confounding effects, we have implemented a modified version of the Lashley III maze. This maze relies on route learning, whereby mice learn to navigate a maze via repeated exposure under low stress conditions, e.g. dark phase, no food/water deprivation, until they navigate a path from the start location to a pseudo-home cage with 0 or 1 error(s) on two consecutive trials. We classify this as a low-stress behavior test because it does not rely on aversive stimuli to encourage exploration of the maze and learning of the task. The apparatus consists of a modular start box, a 4-arm maze body, and a goal box. At the end of the goal box is a pseudo-home cage that contains bedding similar to that found in the animal&#x2019;s home cage and is specific to each animal for the duration of maze testing. It has been demonstrated previously that this pseudo-home cage provides sufficient reward to motivate mice to learn to navigate the maze1. Here, we present the visualization of the Lashley III maze procedure in the context of evaluating age-related differences in learning and memory in mice along with a comparison of learning behavior in two different background strains of mice. We hope that other investigators interested in evaluating the effects of aging or stress vulnerability in mice will consider this maze an attractive alternative to behavioral tests that involve more stressful learning tasks and/or visual cues.

The Lashley III maze is a route-learning task that does not rely on aversive stimuli or visual cues. It is thus a highly attractive option for evaluating learning and memory, especially in aging mice or otherwise where stress is a consideration.

Many behavior tests designed to assess learning and memory in rodents, particularly mice, rely on visual cues, food and/or water deprivation, or other ...

Human Fear Conditioning Conducted in Full Immersion 3-Dimensional Virtual Reality

Fear conditioning is a widely used paradigm in non-human animal research to investigate the neural mechanisms underlying fear and anxiety. A major challenge in conducting conditioning studies in humans is the ability to strongly manipulate or simulate the environmental contexts that are associated with conditioned emotional behaviors. In this regard, virtual reality (VR) technology is a promising tool. Yet, adapting this technology to meet experimental constraints requires special accommodations. Here we address the methodological issues involved when conducting fear conditioning in a fully immersive 6-sided VR environment and present fear conditioning data.
In the real world, traumatic events occur in complex environments that are made up of many cues, engaging all of our sensory modalities. For example, cues that form the environmental configuration include not only visual elements, but aural, olfactory, and even tactile. In rodent studies of fear conditioning animals are fully immersed in a context that is rich with novel visual, tactile and olfactory cues. However, standard laboratory tests of fear conditioning in humans are typically conducted in a nondescript room in front of a flat or 2D computer screen and do not replicate the complexity of real world experiences. On the other hand, a major limitation of clinical studies aimed at reducing (extinguishing) fear and preventing relapse in anxiety disorders is that treatment occurs after participants have acquired a fear in an uncontrolled and largely unknown context. Thus the experimenters are left without information about the duration of exposure, the true nature of the stimulus, and associated background cues in the environment1. In the absence of this information it can be difficult to truly extinguish a fear that is both cue and context-dependent. Virtual reality environments address these issues by providing the complexity of the real world, and at the same time allowing experimenters to constrain fear conditioning and extinction parameters to yield empirical data that can suggest better treatment options and/or analyze mechanistic hypotheses.
In order to test the hypothesis that fear conditioning may be richly encoded and context specific when conducted in a fully immersive environment, we developed distinct virtual reality 3-D contexts in which participants experienced fear conditioning to virtual snakes or spiders. Auditory cues co-occurred with the CS in order to further evoke orienting responses and a feeling of "presence" in subjects 2 . Skin conductance response served as the dependent measure of fear acquisition, memory retention and extinction.

Classical fear conditioning paradigm was adapted for human participants in a fully immersive virtual reality setting. Using a discrimination paradigm, conditioned fear, cue and context memory retention, and extinction was measured with skin conductance response to dynamic virtual snakes and spiders (the conditioned stimuli) in two distinct virtual contexts.

Fear conditioning is a widely used paradigm in non-human animal research to investigate the neural mechanisms underlying fear and anxiety. A major ...

Building An Open-source Robotic Stereotaxic Instrument

This protocol includes the designs and software necessary to upgrade an existing stereotaxic instrument to a robotic (CNC) stereotaxic instrument for around $1,000 (excluding a drill), using industry standard stepper motors and CNC controlling software. Each axis has variable speed control and may be operated simultaneously or independently. The robot&#39;s flexibility and open coding system (g-code) make it capable of performing custom tasks that are not supported by commercial systems. Its applications include, but are not limited to, drilling holes, sharp edge craniotomies, skull thinning, and lowering electrodes or cannula. In order to expedite the writing of g-coding for simple surgeries, we have developed custom scripts that allow individuals to design a surgery with no knowledge of programming. However, for users to get the most out of the motorized stereotax, it would be beneficial to be knowledgeable in mathematical programming and G-Coding (simple programming for CNC machining).
The recommended drill speed is greater than 40,000 rpm. The stepper motor resolution is 1.8&#176;/Step, geared to 0.346&#176;/Step. A standard stereotax has a resolution of 2.88 &#956;m/step. The maximum recommended cutting speed is 500 &#956;m/sec. The maximum recommended jogging speed is 3,500 &#956;m/sec. The maximum recommended drill bit size is HP 2.

This protocol includes the designs and software necessary to upgrade an existing stereotaxic instrument to a robotic (computer numeric controlled; CNC) stereotaxic instrument for around $1,000 (excluding a drill).

This protocol includes the designs and software necessary to upgrade an existing stereotaxic instrument to a robotic (CNC) stereotaxic instrument for ...

In vivo Optogenetic Stimulation of the Rodent Central Nervous System

The ability to probe defined neural circuits in awake, freely-moving animals with cell-type specificity, spatial precision, and high temporal resolution has been a long sought tool for neuroscientists in the systems-level search for the neural circuitry governing complex behavioral states. Optogenetics is a cutting-edge tool that is revolutionizing the field of neuroscience and represents one of the first systematic approaches to enable causal testing regarding the relation between neural signaling events and behavior. By combining optical and genetic approaches, neural signaling can be bi-directionally controlled through expression of light-sensitive ion channels (opsins) in mammalian cells. The current protocol describes delivery of specific wavelengths of light to opsin-expressing cells in deep brain structures of awake, freely-moving rodents for neural circuit modulation. Theoretical principles of light transmission as an experimental consideration are discussed in the context of performing in vivo optogenetic stimulation. The protocol details the design and construction of both simple and complex laser configurations and describes tethering strategies to permit simultaneous stimulation of multiple animals for high-throughput behavioral testing.

In vivo Optogenetic Stimulation of the Rodent Central Nervous System

Optogenetics has become a powerful tool for use in behavioral neuroscience experiments. This protocol offers a step-by-step guide to the design and set-up of laser systems, and provides a full protocol for carrying out multiple and simultaneous in vivo optogenetic stimulations compatible with most rodent behavioral testing paradigms.

The ability to probe defined neural circuits in awake, freely-moving animals with cell-type specificity, spatial precision, and high temporal resolution ...

Immunohistochemical Visualization of Hippocampal Neuron Activity After Spatial Learning in a Mouse Model of Neurodevelopmental Disorders

Induction of phosphorylated extracellular-regulated kinase (pERK) is a reliable molecular readout of learning-dependent neuronal activation. Here, we describe a pERK immunohistochemistry protocol to study the profile of hippocampal neuron activation following exposure to a spatial learning task in a mouse model characterized by cognitive deficits of neurodevelopmental origin. Specifically, we used pERK immunostaining to study neuronal activation following Morris water maze (MWM, a classical hippocampal-dependent learning task) in Engrailed-2 knockout (En2-/-) mice, a model of autism spectrum disorders (ASD). As compared to wild-type (WT) controls, En2-/- mice showed significant spatial learning deficits in the MWM. After MWM, significant differences in the number of pERK-positive neurons were detected in specific hippocampal subfields of En2-/- mice, as compared to WT animals. Thus, our protocol can robustly detect differences in pERK-positive neurons associated to hippocampal-dependent learning impairment in a mouse model of ASD. More generally, our protocol can be applied to investigate the profile of hippocampal neuron activation in both genetic or pharmacological mouse models characterized by cognitive deficits.

We describe an immunohistochemistry protocol to study the profile of hippocampal neuron activation following exposure to a spatial learning task in a mouse model characterized by cognitive deficits of neurodevelopmental origin. This protocol can be applied to both genetic or pharmacological mouse models characterized by cognitive deficits.

Induction of phosphorylated extracellular-regulated kinase (pERK) is a reliable molecular readout of learning-dependent neuronal activation. Here, we ...

Electrophysiological Method for Recording Intracellular Voltage Responses of Drosophila Photoreceptors and Interneurons to Light Stimuli In Vivo

Voltage responses of insect photoreceptors and visual interneurons can be accurately recorded with conventional sharp microelectrodes. The method described here enables the investigator to measure long-lasting (from minutes to hours) high-quality intracellular responses from single Drosophila R1-R6 photoreceptors and Large Monopolar Cells (LMCs) to light stimuli. Because the recording system has low noise, it can be used to study variability among individual cells in the fly eye, and how their outputs reflect the physical properties of the visual environment. We outline all key steps in performing this technique. The basic steps in constructing an appropriate electrophysiology set-up for recording, such as design and selection of the experimental equipment are described. We also explain how to prepare for recording by making appropriate (sharp) recording and (blunt) reference electrodes. Details are given on how to fix an intact fly in a bespoke fly-holder, prepare a small window in its eye and insert a recording electrode through this hole with minimal damage. We explain how to localize the center of a cell's receptive field, dark- or light-adapt the studied cell, and to record its voltage responses to dynamic light stimuli. Finally, we describe the criteria for stable normal recordings, show characteristic high-quality voltage responses of individual cells to different light stimuli, and briefly define how to quantify their signaling performance. Many aspects of the method are technically challenging and require practice and patience to master. But once learned and optimized for the investigator's experimental objectives, it grants outstanding in vivo neurophysiological data.

Electrophysiological Method for Recording Intracellular Voltage Responses of Drosophila Photoreceptors and Interneurons to Light Stimuli In Vivo

Sharp microelectrodes enable accurate electrophysiological characterization of photoreceptor and visual interneuron output in living Drosophila. Here we show how to use this method to record high-quality voltage responses of individual cells to controlled light stimulation. This method is ideal for studying neural information processing in insect compound eyes.

Voltage responses of insect photoreceptors and visual interneurons can be accurately recorded with conventional sharp microelectrodes. The method ...

Assessing Spatial Learning and Memory in Small Squamate Reptiles

Clinical research has leveraged a variety of paradigms to assess cognitive decline, commonly targeting spatial learning and memory abilities. However, interest in the cognitive processes of nonmodel species, typically within an ecological context, has also become an emerging field of study. In particular, interest in the cognitive processes in reptiles is growing although experimental studies on reptilian cognition are sparse. The few reptilian studies that have experimentally tested for spatial learning and memory have used rodent paradigms modified for use in reptiles. However, ecologically important aspects of the physiology and behavior of this taxonomic group must be taken into account when testing for spatially based cognition. Here, we describe modifications of the dry land Barnes maze and associated testing protocol that can improve performance when probing for spatial learning and memory ability in small squamate reptiles. The described paradigm and procedures were successfully used with male side-blotched lizards (Uta stansburiana), demonstrating that spatial learning and memory can be assessed in this taxonomic group with an ecologically relevant apparatus and protocol.

This paper describes a modification of the Barnes maze, a standard rodent paradigm used to assess spatial memory and learning, for use in small squamate reptiles.

Clinical research has leveraged a variety of paradigms to assess cognitive decline, commonly targeting spatial learning and memory abilities. However, ...

An Objective and Reproducible Test of Olfactory Learning and Discrimination in Mice

Olfaction is the predominant sensory modality in mice and influences many important behaviors, including foraging, predator detection, mating, and parenting. Importantly, mice can be trained to associate novel odors with specific behavioral responses to provide insight into olfactory circuit function. This protocol details the procedure for training mice on a Go/No-Go operant learning task. In this approach, mice are trained on hundreds of automated trials daily for 2&#8211;4 weeks and can then be tested on novel Go/No-Go odor pairs to assess olfactory discrimination, or be used for studies on how odor learning alters the structure or function of the olfactory circuit. Additionally, the mouse olfactory bulb (OB) features ongoing integration of adult-born neurons. Interestingly, olfactory learning increases both the survival and synaptic connections of these adult-born neurons. Therefore, this protocol can be combined with other biochemical, electrophysiological, and imaging techniques to study learning and activity-dependent factors that mediate neuronal survival and plasticity.

Here, we train mice on an associative learning task to test odor discrimination. This protocol also allows for studies on learning-induced structural changes in the brain.

Olfaction is the predominant sensory modality in mice and influences many important behaviors, including foraging, predator detection, mating, and ...

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

Serial sectioning and subsequent high-resolution imaging of biological tissue using electron microscopy (EM) allow for the segmentation and reconstruction of high-resolution imaged stacks to reveal ultrastructural patterns that could not be resolved using 2D images. Indeed, the latter might lead to a misinterpretation of morphologies, like in the case of mitochondria; the use of 3D models is, therefore, more and more common and applied to the formulation of morphology-based functional hypotheses. To date, the use of 3D models generated from light or electron image stacks makes qualitative, visual assessments, as well as quantification, more convenient to be performed directly in 3D. As these models are often extremely complex, a virtual reality environment is also important to be set up to overcome occlusion and to take full advantage of the 3D structure. Here, a step-by-step guide from image segmentation to reconstruction and analysis is described in detail.

The described pipeline is designed for the segmentation of electron microscopy datasets larger than gigabytes, to extract whole-cell morphologies. Once the cells are reconstructed in 3D, customized software designed around individual needs can be used to perform a qualitative and quantitative analysis directly in 3D, also using virtual reality to overcome view occlusion.

Serial sectioning and subsequent high-resolution imaging of biological tissue using electron microscopy (EM) allow for the segmentation and reconstruction ...

Quantitative Measurement of Intrathecally Synthesized Proteins in Mice

Cerebrospinal fluid (CSF), a fluid found in the brain and the spinal cord, is of great importance to both basic and clinical science. The analysis of the CSF protein composition delivers crucial information in basic neuroscience research as well as neurological diseases. One caveat is that proteins measured in CSF may derive from both intrathecal synthesis and transudation from serum, and protein analysis of CSF can only determine the sum of these two components. To discriminate between protein transudation from the blood and intrathecally produced proteins in animal models as well as in humans, CSF protein profiling measurements using conventional protein analysis tools must include the calculation of the albumin CSF/serum quotient (Qalbumin), a marker of the integrity of the blood-brain interface (BBI), and the protein index (Qprotein/Qalbumin), an estimate of intrathecal protein synthesis. This protocol illustrates the entire procedure, from CSF and blood collection to quotients and indices calculations, for the quantitative measurement of intrathecal protein synthesis and BBI impairment in mouse models of neurological disorders.

Elevated spinal fluid protein levels can either be the result of diffusion of plasma protein across an altered blood-brain barrier or intrathecal synthesis. An optimized testing protocol is presented in this article that helps to discriminate both cases and provides quantitative measurements of intrathecally synthesized proteins.

Cerebrospinal fluid (CSF), a fluid found in the brain and the spinal cord, is of great importance to both basic and clinical science. The analysis of the ...