Name: a flexible platform for monitoring cerebellum-dependent sensory associative learning
Uploaded: 2022-01-19T13:00:00
Description: Watch this Scientific Journal Video about A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning at JoVE.com

This work is supported by grants from the National Institutes of Mental Health NRSA F32 MH120887-03 (to G.J.B.) and R01 NS045193 and R01 MH115750 (to S.S-H.W.). We thank Drs. Bas Koekkoek and Henk-Jan Boele for helpful discussions for optimizing the DEC setup and Drs. Yue Wang and Xiaoying Chen for helpful discussions for optimizing the DTSC setup.

The animal protocols described here have been approved by the Animal Care and Use Committees of Princeton University.
1. Setting up the SBC
<ol>
	<li>Connect the camera serial interface (CSI) cable to the Raspberry NoIR V2 camera and the camera port on the SBC.</li>
	<li>Download the operating system for the SBC onto the host computer. Write the operating system image to a micro secure digital (microSD) card. 
	NOTE: Detailed instructions for these procedures for a Rasp...

<ol>
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The platform with associated protocols outlined here can be used to reliably track animal behavior in two sensory associative learning tasks. Each task depends on intact communication through the climbing fiber pathway. In the design described here, we incorporate elements to facilitate learning and recording/perturbation of cerebellar response. These include a wheel to allow for free locomotion11,18 as well as head fixation. The wheel allows mouse subjects to lo...

Pavlovian conditioning of sub-second association between stimuli to elicit a conditioned response has long been used to probe cerebellar-dependent learning. For example, in classical delay eyeblink conditioning (DEC), animals learn to make a well-timed protective blink in response to a neutral conditional stimulus (CS; e.g., a flash of light or auditory tone) when it is paired repeatedly with an unconditional stimulus (US; e.g., a puff of air applied to the cornea) which always elicits a reflex blink, and which comes at or near the end of the CS. The learned response is referred to as a conditioned response (CR), while the reflex response is referred to as the unconditioned response (UR). In rabbits, cerebellum-specific lesions disrupt this form of learning1,2,3,4. Further, Purkinje cell complex spikes, driven by their climbing fiber inputs5, provide a necessary6,7 and sufficient8,9 signal for the acquisition of properly-timed CRs.
More recently, climbing fiber-dependent associative learning paradigms have been developed for head-fixed mice. DEC was the first associative learning paradigm to be adapted to this configuration10,11. DEC in head-fixed mice has been used to identify cerebellar regions11,12,13,14,15,16,17 and circuit elements11,12,13,14,15,18,19 that are required for task acquisition and extinction. This approach has also been used to demonstrate how the cellular-level physiological representation of task parameters evolves with learning13,15,16.
In addition to eyeblink, the delayed startle tactile conditioning (DTSC) paradigm was recently developed as a novel associative learning task for head-fixed mice20. Conceptually similar to DEC, DTSC involves the presentation of a neutral CS with a US, a tap to the face sufficient in intensity to engage a startle reflex21,22 as the UR. In the DTSC paradigm, both the UR and CR are read out as backward locomotion on a wheel. DTSC has now been used to uncover how associative learning alters cerebellar activity and patterns of gene expression20.
In this work, a method was developed for flexibly applying DEC or DTSC in a single platform. The stimulus and platform attributes are schematized in Figure 1. The design incorporates the capacity to track animal behavior with a camera as well as a rotary encoder to track mouse locomotion on a wheel. All aspects of data logging and trial structure are controlled by paired microcontrollers (Arduino) and a single-board computer (SBC; Raspberry Pi). These devices can be accessed through a provided graphical user interface. Here, we present a workflow for setup, experiment preparation and execution, and a customized analysis pipeline for data visualization.

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			<td>&#34;B&#34; Quick Base For C&#38;B METABOND - 10 mL bottle</td>
			<td>Parkell</td>
			<td>S398</td>
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			<td>Parkell</td>
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			<td>&#8206;ED_YT05_U_100Pcs</td>
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			<td>SanDisk</td>
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			<td>B00ZC6B8VM</td>
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			<td>Servocity</td>
			<td>585444</td>
			<td>4.5&#34; aluminum channel</td>
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			<td>Towallmark</td>
			<td>SODIAL</td>
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			<td>4PCS Breadboards Kit Include 2PCS 830 Point 2PCS 400 Point Solderless Breadboards for Proto Shield Distribution Connecting Blocks</td>
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			<td>&#8206;TL-SG105</td>
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			<td>5 V 2.5 A Raspberry Pi 3 B+ Power Supply/Adapter</td>
			<td>Canakit</td>
			<td>&#8206;DCAR-RSP-2A5</td>
			<td>Power supply for Raspberry Pi 3B+</td>
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			<td>5-0 ETHILON BLACK 1 x 18&#34; C-3</td>
			<td>Ethicon</td>
			<td>668G</td>
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			<td>6 mm Shaft Encoder 2000 PPR Pushpull Line Driver Universal Output Line Driver Output 5-26 V dc Supply</td>
			<td>Calt</td>
			<td>&#160;B01EWER68I</td>
			<td>Rotary encoder</td>
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			<td>&#216;1/2&#34; Optical Post, SS, 8-32 Setscrew, 1/4&#34;-20 Tap, L = 1&#34;, 5 Pack</td>
			<td>Thorlabs</td>
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			<td>&#216;1/2&#34; Optical Post, SS, 8-32 Setscrew, 1/4&#34;-20 Tap, L = 2&#34;, 5 Pack</td>
			<td>Thorlabs</td>
			<td>TR2-P5</td>
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			<td>&#216;1/2&#34; Optical Post, SS, 8-32 Setscrew, 1/4&#34;-20 Tap, L = 4&#34;, 5 Pack</td>
			<td>Thorlabs</td>
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			<td>&#216;1/2&#34; Optical Post, SS, 8-32 Setscrew, 1/4&#34;-20 Tap, L = 6&#34;, 5 Pack</td>
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			<td>Arduino</td>
			<td>A000066</td>
			<td>Arduino Uno (microcontroller board)</td>
		</tr>
		<tr>
			<td>Arduino Due</td>
			<td>Arduino</td>
			<td>&#8206;A000062</td>
			<td>Arduino Due (microcontroller board)</td>
		</tr>
		<tr>
			<td>Bench Power Supply, Single, Adjustable, 3 Output, 0 V, 24 V, 0 A, 2 A</td>
			<td>Tenma</td>
			<td>72-8335A</td>
			<td>Power supply</td>
		</tr>
		<tr>
			<td>Clear Scratch- and UV-Resistant Cast Acrylic Sheet, 12&#34; x 24&#34; x 1/8&#34;</td>
			<td>McMaster Carr</td>
			<td>8560K257</td>
			<td>Acrylic sheet</td>
		</tr>
		<tr>
			<td>CNC Stepper Motor Driver 1.0&#8211;4.2 A 20&#8211;50 V DC 1/128 Micro-Step Resolutions for Nema 17 and 23 Stepper Motor</td>
			<td>Stepper Online</td>
			<td>B06Y5VPSFN</td>
			<td>Stepper motor driver</td>
		</tr>
		<tr>
			<td>Compact Compressed Air Regulator, Inline Relieving, Brass Housing, 1/4 NPT</td>
			<td>McMaster Carr</td>
			<td>6763K13</td>
			<td>Air source regulator</td>
		</tr>
		<tr>
			<td>Cotton Swab</td>
			<td>Puritan</td>
			<td>806-WC</td>
			<td>Cotton swab</td>
		</tr>
		<tr>
			<td>Dell 1908FP 19&#34; Flat Panel Monitor - 1908FPC</td>
			<td>Dell</td>
			<td>1908FPC</td>
			<td>Computer monitor</td>
		</tr>
		<tr>
			<td>Flex Cable for Raspberry Pi Camera</td>
			<td>Adafruit</td>
			<td>2144</td>
			<td>camera serial interface cable</td>
		</tr>
		<tr>
			<td>High Torque Nema 17 Bipolar Stepper Motor 92 oz&#183;in/65 N&#183;cm 2.1 A Extruder Motor</td>
			<td>Stepper Online</td>
			<td>17HS24-2104S</td>
			<td>Stepper motor</td>
		</tr>
		<tr>
			<td>Isoflurane</td>
			<td>Henry Schein</td>
			<td>66794001725</td>
			<td>Isoflurane</td>
		</tr>
		<tr>
			<td>Krazy Maximum Bond Permanent Glue, 0.18 oz.</td>
			<td>Krazy Glue</td>
			<td>KG483</td>
			<td>Cyanoacrylate glue</td>
		</tr>
		<tr>
			<td>Lidocaine HCl</td>
			<td>VetOne</td>
			<td>510212</td>
			<td>Lidocaine</td>
		</tr>
		<tr>
			<td>Low-Strength Steel Hex Nut, Grade 2, Zinc-Plated, 1/4&#34;-20 Thread Size</td>
			<td>McMaster Carr</td>
			<td>90473A029</td>
			<td>Nuts</td>
		</tr>
		<tr>
			<td>M3 x 50 mm Partially Threaded Hex Key Socket Cap Head Screws 10 pcs</td>
			<td>Uxcell</td>
			<td>A16040100ux1380</td>
			<td>M3 bolt</td>
		</tr>
		<tr>
			<td>NEMA 17 Stepper Motor Mount</td>
			<td>ACTOBOTICS</td>
			<td>555152</td>
			<td>Stepper motor mount</td>
		</tr>
		<tr>
			<td>Official Raspberry Pi Power Supply 5.1 V 3 A with USB C - 1.5 m long</td>
			<td>Adafruit</td>
			<td>4298</td>
			<td>Power supply for Raspberry Pi 4B</td>
		</tr>
		<tr>
			<td>Optixcare Dog &#38; Cat Eye Lube Lubricating Gel, 0.70-oz tube</td>
			<td>Optixcare</td>
			<td>142422</td>
			<td>Opthalimic ointment</td>
		</tr>
		<tr>
			<td>Precision Stainless Steel Ball Bearing, Shielded, Trade No. R188-2Z, 13000 rpm Maximum Speed</td>
			<td>McMaster-Carr</td>
			<td>3759T57</td>
			<td>Bearing</td>
		</tr>
		<tr>
			<td>Premium Female/Female Jumper Wires - 40 x 6&#34;</td>
			<td>Adafruit</td>
			<td>266</td>
			<td>Wires</td>
		</tr>
		<tr>
			<td>Premium Female/Male &#39;Extension&#39; Jumper Wires - 40 x 6&#34; (150 mm)</td>
			<td>Adafruit</td>
			<td>826</td>
			<td>Wires</td>
		</tr>
		<tr>
			<td>Premium Male/Male Jumper Wires - 40 x 6&#34;</td>
			<td>Adafruit</td>
			<td>758</td>
			<td>Wires</td>
		</tr>
		<tr>
			<td>Radiopaque L-Powder for C&#38;B METABOND - 5 g</td>
			<td>Parkell</td>
			<td>S396</td>
			<td>Dental cement powder</td>
		</tr>
		<tr>
			<td>Raspberry Pi (3B+ or 4B)</td>
			<td>Adafruit</td>
			<td>3775 or 4295</td>
			<td>Raspberry Pi</td>
		</tr>
		<tr>
			<td>Raspberry Pi NoIR Camera Module V2 - 8MP 1080P30</td>
			<td>Raspberry Pi Foundation</td>
			<td>RPI3-NOIR-V2</td>
			<td>Raspberry NoIR V2 camera</td>
		</tr>
		<tr>
			<td>Right-Angle Bracket, 1/4&#34; (M6) Counterbored Slot, 8-32 Taps</td>
			<td>Thorlabs</td>
			<td>AB90E</td>
			<td>Right-angle bracket</td>
		</tr>
		<tr>
			<td>Right-Angle Clamp for &#216;1/2&#34; Posts, 3/16&#34; Hex</td>
			<td>Thorlabs</td>
			<td>RA90</td>
			<td>Right-angle optical post clamp</td>
		</tr>
		<tr>
			<td>Right-Angle End Clamp for &#216;1/2&#34; Posts, 1/4&#34;-20 Stud and 3/16&#34; Hex</td>
			<td>Thorlabs</td>
			<td>RA180</td>
			<td>Right-angle end clamp</td>
		</tr>
		<tr>
			<td>RJ45 Cat-6 Ethernet Patch Internet Cable</td>
			<td>Amazon</td>
			<td>&#8206;CAT6-7FT-5P-BLUE</td>
			<td>Ethernet cable</td>
		</tr>
		<tr>
			<td>Rotating Clamp for &#216;1/2&#34; Posts, 360&#176; Continuously Adjustable, 3/16&#34; Hex</td>
			<td>Thorlabs</td>
			<td>SWC</td>
			<td>Rotating optical post clamps</td>
		</tr>
		<tr>
			<td>Spike &#38; Hold Driver-0.1 TO 5 MS</td>
			<td>The Lee Co.</td>
			<td>IECX0501350A</td>
			<td>Solenoid valve driver</td>
		</tr>
		<tr>
			<td>Swivel Base Adapter</td>
			<td>Thorlabs</td>
			<td>UPHA</td>
			<td>Post holder adapter</td>
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		<tr>
			<td>USB 2.0 A-Male to Micro B Cable, 6 feet</td>
			<td>Amazon</td>
			<td>&#8206;7T9MV4</td>
			<td>USB2 type A to USB2 micro cable</td>
		</tr>
		<tr>
			<td>USB 2.0 Printer Cable - A-Male to B-Male, 6 Feet (1.8 m)</td>
			<td>Amazon</td>
			<td>B072L34SZS</td>
			<td>USB2 type B to USB2 type A cable</td>
		</tr>
		<tr>
			<td>VHS-M/SP-12 V</td>
			<td>The Lee Co.</td>
			<td>INKX0514900A</td>
			<td>Solenoid valve</td>
		</tr>
		<tr>
			<td>Zinc-Plated Steel 1/4&#34; washer, OD 1.000&#34;</td>
			<td>McMaster Carr</td>
			<td>91090A108</td>
			<td>Washers</td>
		</tr>
	</tbody>
</table>

a flexible platform for monitoring cerebellum-dependent sensory associative learning

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

Workflow for DEC experiments and analysis 
Proper experimental parameter selection is important for successful delay eyeblink conditioning (DEC) training. For the data presented here, the GUI was used to choose a CS duration of 350 ms and a US duration of 50 ms. This pairing results in an inter-stimulus interval of 300 ms: long enough to prevent low-amplitude CR production10 and short enough to avoid getting into the regime of poor learning or trace conditioning, a process th...

Watch this Scientific Journal Video about A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning at JoVE.com

A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning

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

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

Sensory associative learning paradigms have revealed a great deal about the function of the cerebellum and its relationship to the rest of the brain, and behavior. Using the mouse as a model organism, we present a method with sufficient detail for laboratories around the world to implement these powerful methods effectively and inexpensively. Our protocol will allow researchers to set up multiple cerebellum-dependent behaviors.The core functions of our research platform can be readily modified to fit a particular research question. To begin, connect the camera serial interface cable to the camera, and the camera port on the SBC. Then download the operating system for the SBC onto the host computer, and create a file called ssh in the microSD card.Eject the microSD card from the host machine. Insert it into the SBC microSD card slot, and power the SBC. Next, to prepare the SBC to accept a wired connection to the host, open a terminal, type the command ifconfig"and record the Ethernet IP address of the SBC.Then go to the Interface tab of the Raspberry Pi Configuration setting, and enable the options for camera, SSH, and VNC. For establishing the wired connection, connect an Ethernet cable to the Ethernet port on the SBC and a host computer, and attach the other end of these cables to an Ethernet switch. Then, using a virtual network computing client, such as VNC, access the desktop using the SBC IP address and the default authentication.Next, download the required software and necessary Python libraries to the SBC. To allow direct control over the micro-controller, download the micro-controller integrated development environment, or IDE software. Then open an SBC terminal window, navigate to the Downloads directory, and install the IDE.After opening the micro-controller IDE, select Tools, followed by Manage Libraries, and install the Encoder library from Paul Stoffregen. Finally, insert a thumb drive into a USB port on the SBC, and enter the commands for mounting the USB external storage device. After connecting the SBC to the programming port of the micro-controller, open the download sketch with the micro-controller IDE, and upload it to the micro-controller.Next, download and install the appropriate version of the Arduino IDE on the host computer. Then download the DTSC_US. ino sketch to the host computer.Connect the USB A to USB B wire to the host computer and micro-controller. Open the sketch, and upload it to the micro-controller. Attach wires to the micro-controller's breadboard, LEDs, rotary encoder, stepper motor with a driver, and solenoid valve with driver.Then wire one channel of a power supply to the positive-V and GND pins of the stepper motor driver. After turning on the power supply, set the attached channel voltage to 25 volts. Next, wire the positive lead of a power supply to the solenoid valve driver hold-voltage pin, and the other positive lead to the spike-voltage pin.After turning on the power supply, set the channel connected to spike voltage to 12 volts, and the channel connected to the hold voltage to 2.5 volts. Then connect an air source regulated to a pressure of 20 pounds per square inch to the solenoid valve. Next, to make the running wheel, cut a three-inch wheel from a foam roller, and drill a quarter-inch hole in the exact wheel center.Then insert a quarter-inch shaft into the wheel, and fix it in place using clamping hubs. Affix the rotary encoder to a 4.5-inch aluminum channel. Then stabilize the aluminum channel on the aluminum breadboard.After attaching the wheel to the rotary encoder, stabilize the free side of the wheel shaft with a bearing inserted in a right-angle end clamp, installed on a breadboard-mounted optical post. Next, position the head restraints using optical posts and right-angle post clamps. Then position the conditional stimulus LED and the solenoid valve outlet for the DEC unconditional stimulus around the assembled wheel.Next, mount the stepper motor used for the DTSC unconditional stimulus and Pi Camera on an optical post. Place the infrared light array on the same side as the Pi Camera, slightly above and directly facing where the animal's face will be positioned. Make a tactile stimulus for delayed tactile startle conditioning by taping foam to the edge of a piece of acrylic.Mount to a quarter-inch shaft using a clamping hub, then attach the tactile stimulus to the stepper motor shaft. To implant a head plate, anesthetize the mouse, then make an incision with a scalpel along the midline of the scalp, from the back edge of the eyes to the skull. Spread the incision open, and clamp both sides with hemostats to hold it open.Using cyanoacrylate glue, attach the head plate to the skull. Then apply a mix of dental cement powder, solvent, and catalyst to all areas of exposed bone. Suture the skin closed, behind and in front of the head plate.Then inject postoperative analgesia, while allowing the mouse to recover for at least five days. To prepare the mice for behavior sessions, allow them to habituate to the platform by mounting them in the head restraint. Before the session, ensure that the solenoid valve outlet is centered on the target eye, positioned less than one centimeter away, and the tactile stimulus is centered on the mouse's nose, positioned approximately 1.5 centimeters away.For DTSC session preparation, start the GUI from an SBC terminal. Run a test session of three trials, and ensure that the logged data that prints to the terminal show a deflection of greater than 20, but less than 100 steps. To run a session, mount a mouse to the head restraint and start the GUI from the SBC terminal.To save camera recordings, hit the Stream button prior to starting the session. Input identifying information for the animal into the Animal ID field, and hit the Set button. Then input the desired experiment parameters, and hit the Upload to Arduino"button.Finally, hit Start Session to begin the session. DEC training results from a recording session with acceptable lighting are shown here. The acceptable illumination conditions resulted in a good contrast between the eye and periocular fur.The performance of a single mouse trained for eight sessions showed behavioral traces, with no conditioned response in the untrained mouse, and robust conditioned responses once the mouse is trained. A sample trial video shows a trained mouse successfully closing its eye in response to the LED conditional stimulus, while the untrained mouse does not blink until the unconditional stimulus. The conditioned response increases in size and frequency through behavioral sessions performed across days.Suboptimal lighting conditions severely limit the quality of data acquired. When the contrast between the eye and surrounding fur is low, slight changes in the image can significantly alter the recorded shape of the unconditioned response over a single session, and decrease the signal-to-noise ratio for detecting eyelid position. DTSC training results for a mouse trained for five sessions are presented here.A sample trial video shows a trained mouse successfully backing the wheel in response to the LED conditional stimulus, while an untrained mouse fails to move the wheel until the tactile unconditional stimulus is applied. The frequency and amplitude of the conditioned response increase as training proceeds. In a cohort of animals trained with an unconditional stimulus that produced low-amplitude unconditioned responses, no animal learned to consistently produce conditioned responses after four days of training.For successful behavior, the animal's comfort while running on the rig is critical. It is important to ensure that the wheel turns freely and evenly prior to habituating animals to the rig. Using this flexible platform, we have successfully imaged and perturbed the activity of Purkinje neurons, the output cells of the cerebellum, during learning in head-fixed animals.

a-flexible-platform-for-monitoring-cerebellum-dependent-sensory

Research

JoVE Journal

Neuroscience

C. elegans Positive Butanone Learning, Short-term, and Long-term Associative Memory Assays

The memory of experiences and learned information is critical for organisms to make choices that aid their survival. C. elegans navigates its environment through neuron-specific detection of food and chemical odors1, 2, and can associate nutritive states with chemical odors3, temperature4, and the pathogenicity of a food source5.
Here, we describe assays of C. elegans associative learning and short- and long-term associative memory. We modified an aversive olfactory learning paradigm6 to instead produce a positive response; the assay involves starving ~400 worms, then feeding the worms in the presence of the AWC neuron-sensed volatile chemoattractant butanone at a concentration that elicits a low chemotactic index (similar to Toroyama et al.7). A standard population chemotaxis assay1 tests the worms' attraction to the odorant immediately or minutes to hours after conditioning.
After conditioning, wild-type animals' chemotaxis to butanone increases ~0.6 Chemotaxis Index units, its "Learning Index". Associative learning is dependent on the presence of both food and butanone during training. Pairing food and butanone for a single conditioning period ("massed training") produces short-term associative memory that lasts ~2 hours. Multiple conditioning periods with rest periods between ("spaced training") yields long-term associative memory (&lt;40 hours), and is dependent on the cAMP Response Element Binding protein (CREB),6 a transcription factor required for long-term memory across species.8
Our protocol also includes image analysis methods for quick and accurate determination of chemotaxis indices. High-contrast images of animals on chemotaxis assay plates are captured and analyzed by worm counting software in MatLab. The software corrects for uneven background using a morphological tophat transformation.9 Otsu's method is then used to determine a threshold to separate worms from the background.10 Very small particles are removed automatically and larger non-worm regions (plate edges or agar punches) are removed by manual selection. The software then estimates the size of single worm by ignoring regions that are above a specified maximum size and taking the median size of the remaining regions. The number of worms is then estimated by dividing the total area identified as occupied by worms by the estimated size of a single worm.
We have found that learning and short- and long-term memory can be distinguished, and that these processes share similar key molecules with higher organisms.6,8 Our assays can quickly test novel candidate genes or molecules that affect learning and short- or long-term memory in C. elegans that are relevant across species.

C. elegans Positive Butanone Learning, Short-term, and Long-term Associative Memory Assays

Here we describe methods to test C. elegans associative learning and short- and long-term associative memory. These population assays employ the worms abilities to chemotax toward volatile odorants, and form positive associations upon pairing food with the chemoattractant butanone. Increasing the number of conditioning periods induces long-term memory.

The memory of experiences and learned information is critical for organisms to make choices that aid their survival. C. elegans navigates its environment ...

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

Signaling of information in the vertebrate central nervous system is often carried by populations of neurons rather than individual neurons. Also propagation of suprathreshold spiking activity involves populations of neurons. Empirical studies addressing cortical function directly thus require recordings from populations of neurons with high resolution. Here we describe an optical method and a deconvolution algorithm to record neural activity from up to 100 neurons with single-cell and single-spike resolution. This method relies on detection of the transient increases in intracellular somatic calcium concentration associated with suprathreshold electrical spikes (action potentials) in cortical neurons. High temporal resolution of the optical recordings is achieved by a fast random-access scanning technique using acousto-optical deflectors (AODs)1. Two-photon excitation of the calcium-sensitive dye results in high spatial resolution in opaque brain tissue2. Reconstruction of spikes from the fluorescence calcium recordings is achieved by a maximum-likelihood method. Simultaneous electrophysiological and optical recordings indicate that our method reliably detects spikes (&gt;97% spike detection efficiency), has a low rate of false positive spike detection (&lt; 0.003 spikes/sec), and a high temporal precision (about 3 msec) 3. This optical method of spike detection can be used to record neural activity in vitro and in anesthetized animals in vivo3,4.

Understanding the function of the vertebrate central nervous system requires recordings from many neurons because cortical function arises on the level of populations of neurons. Here we describe an optical method to record suprathreshold neural activity with single-cell and single-spike resolution, dithered random-access scanning. This method records somatic fluorescence calcium signals from up to 100 neurons with high temporal resolution. A maximum-likelihood algorithm deconvolves the underlying suprathreshold neural activity from the somatic fluorescence calcium signals. This method reliably detects spikes with high detection efficiency and a low rate of false positives and can be used to study neural populations in vitro and in vivo.

Signaling of information in the vertebrate central nervous system is often carried by populations of neurons rather than individual neurons. Also ...

Appetitive Associative Olfactory Learning in Drosophila Larvae

In the following we describe the methodological details of appetitive associative olfactory learning in Drosophila larvae. The setup, in combination with genetic interference, provides a handle to analyze the neuronal and molecular fundamentals of specifically associative learning in a simple larval brain.
Organisms can use past experience to adjust present behavior. Such acquisition of behavioral potential can be defined as learning, and the physical bases of these potentials as memory traces1-4. Neuroscientists try to understand how these processes are organized in terms of molecular and neuronal changes in the brain by using a variety of methods in model organisms ranging from insects to vertebrates5,6. For such endeavors it is helpful to use model systems that are simple and experimentally accessible. The Drosophila larva has turned out to satisfy these demands based on the availability of robust behavioral assays, the existence of a variety of transgenic techniques and the elementary organization of the nervous system comprising only about 10,000 neurons (albeit with some concessions: cognitive limitations, few behavioral options, and richness of experience questionable)7-10.
Drosophila larvae can form associations between odors and appetitive gustatory reinforcement like sugar11-14. In a standard assay, established in the lab of B. Gerber, animals receive a two-odor reciprocal training: A first group of larvae is exposed to an odor A together with a gustatory reinforcer (sugar reward) and is subsequently exposed to an odor B without reinforcement 9. Meanwhile a second group of larvae receives reciprocal training while experiencing odor A without reinforcement and subsequently being exposed to odor B with reinforcement (sugar reward). In the following both groups are tested for their preference between the two odors. Relatively higher preferences for the rewarded odor reflect associative learning - presented as a performance index (PI). The conclusion regarding the associative nature of the performance index is compelling, because apart from the contingency between odors and tastants, other parameters, such as odor and reward exposure, passage of time and handling do not differ between the two groups9.

Appetitive Associative Olfactory Learning in Drosophila Larvae

Drosophila larvae are able to associate odor stimuli with gustatory reward. Here we describe a simple behavioral paradigm that allows the analysis of appetitive associative olfactory learning.

In the following we describe the methodological  details of appetitive associative olfactory learning in Drosophila larvae. The setup, in combination with ...

Production and Isolation of Axons from Sensory Neurons for Biochemical Analysis Using Porous Filters

Neuronal axons use specific mechanisms to mediate extension, maintain integrity, and induce degeneration. An appropriate balance of these events is required to shape functional neuronal circuits. The protocol described here explains how to use cell culture inserts bearing a porous membrane (filter) to obtain large amounts of pure axonal preparations suitable for examination by conventional biochemical or immunocytochemical techniques. The functionality of these filter inserts will be demonstrated with models of developmental pruning and Wallerian degeneration, using explants of embryonic dorsal root ganglion. Axonal integrity and function is compromised in a wide variety of neurodegenerative pathologies. Indeed, it is now clear that axonal dysfunction appears much earlier in the course of the disease than neuronal soma loss in several neurodegenerative diseases, indicating that axonal-specific processes are primarily targeted in these disorders. By obtaining pure axonal samples for analysis by molecular and biochemical techniques, this technique has the potential to shed new light into mechanisms regulating the physiology and pathophysiology of axons. This in turn will have an impact in our understanding of the processes that drive degenerative diseases of the nervous system.

This article describes a neuronal culture system that can be used to obtain large quantities of pure axonal samples for biochemical and immunocytochemical analyses. This technique will allow an improved understanding of normal axonal physiology and signaling pathways leading to neurodegeneration.

Neuronal axons use specific mechanisms to mediate extension, maintain integrity, and induce degeneration. An appropriate balance of these events is ...

Olfactory Behaviors Assayed by Computer Tracking Of Drosophila in a Four-quadrant Olfactometer

A key challenge in neurobiology is to understand how neural circuits function to guide appropriate animal behaviors. Drosophila melanogaster is an excellent model system for such investigations due to its complex behaviors, powerful genetic techniques, and compact nervous system. Laboratory behavioral assays have long been used with Drosophila to simulate properties of the natural environment and study the neural mechanisms underlying the corresponding behaviors (e.g. phototaxis, chemotaxis, sensory learning and memory)1-3. With the recent availability of large collections of transgenic Drosophila lines that label specific neural subsets, behavioral assays have taken on a prominent role to link neurons with behaviors4-11. Versatile and reproducible paradigms, together with the underlying computational routines for data analysis, are indispensable for rapid tests of candidate fly lines with various genotypes. Particularly useful are setups that are flexible in the number of animals tested, duration of experiments and nature of presented stimuli. The assay of choice should also generate reproducible data that is easy to acquire and analyze. Here, we present a detailed description of a system and protocol for assaying behavioral responses of Drosophila flies in a large four-field arena. The setup is used here to assay responses of flies to a single olfactory stimulus; however, the same setup may be modified to test multiple olfactory, visual or optogenetic stimuli, or a combination of these. The olfactometer setup records the activity of fly populations responding to odors, and computational analytical methods are applied to quantify fly behaviors. The collected data are analyzed to get a quick read-out of an experimental run, which is essential for efficient data collection and the optimization of experimental conditions.

Olfactory Behaviors Assayed by Computer Tracking Of Drosophila in a Four-quadrant Olfactometer

We describe here a behavioral setup and data analysis method for assaying olfactory responses of up to 100 vinegar flies (Drosophila melanogaster). This system may be used with single or multiple olfactory stimuli, and adaptable for optogenetic activation or silencing of neuronal subsets.

A key challenge in neurobiology is to understand how neural circuits function to guide appropriate animal behaviors. Drosophila melanogaster is an ...

Fabrication of High Contact-Density, Flat-Interface Nerve Electrodes for Recording and Stimulation Applications

Many attempts have been made to manufacture multi-contact nerve cuff electrodes that are safe, robust and reliable for long term neuroprosthetic applications. This protocol describes a fabrication technique of a modified cylindrical nerve cuff electrode to meet these criteria. Minimum computer-aided design and manufacturing (CAD and CAM) skills are necessary to consistently produce cuffs with high precision (contact placement 0.51 &#177; 0.04 mm) and various cuff sizes. The precision in spatially distributing the contacts and the ability to retain a predefined geometry accomplished with this design are two criteria essential to optimize the cuff&#39;s interface for selective recording and stimulation. The presented design also maximizes the flexibility in the longitudinal direction while maintaining sufficient rigidity in the transverse direction to reshape the nerve by using materials with different elasticities. The expansion of the cuff&#39;s cross sectional area as a result of increasing the pressure inside the cuff was observed to be 25% at 67 mm Hg. This test demonstrates the flexibility of the cuff and its response to nerve swelling post-implant. The stability of the contacts&#39; interface and recording quality were also examined with contacts&#39; impedance and signal-to-noise ratio metrics from a chronically implanted cuff (7.5 months), and observed to be 2.55 &#177; 0.25 k&#8486; and 5.10 &#177; 0.81 dB respectively.

This article provides a detailed description on the fabrication process of a high contact-density flat interface nerve electrode (FINE). This electrode is optimized for recording and stimulating neural activity selectively within peripheral nerves.

Many attempts have been made to manufacture multi-contact nerve cuff electrodes that are safe, robust and reliable for long term neuroprosthetic ...

Drosophila Courtship Conditioning As a Measure of Learning and Memory

Many insights into the molecular mechanisms underlying learning and memory have been elucidated through the use of simple behavioral assays in model organisms such as the fruit fly, Drosophila melanogaster. Drosophila is useful for understanding the basic neurobiology underlying cognitive deficits resulting from mutations in genes associated with human cognitive disorders, such as intellectual disability (ID) and autism. This work describes a methodology for testing learning and memory using a classic paradigm in Drosophila known as courtship conditioning. Male flies court females using a distinct pattern of easily recognizable behaviors. Premated females are not receptive to mating and will reject the male&#39;s copulation attempts. In response to this rejection, male flies reduce their courtship behavior. This learned reduction in courtship behavior is measured over time, serving as an indicator of learning and memory. The basic numerical output of this assay is the courtship index (CI), which is defined as the percentage of time that a male spends courting during a 10 min interval. The learning index (LI) is the relative reduction of CI in flies that have been exposed to a premated female compared to na&#239;ve flies with no previous social encounters. For the statistical comparison of LIs between genotypes, a randomization test with bootstrapping is used. To illustrate how the assay can be used to address the role of a gene relating to learning and memory, the pan-neuronal knockdown of Dihydroxyacetone phosphate acyltransferase (Dhap-at) was characterized here. The human ortholog of Dhap-at, glyceronephosphate O-acyltransferase (GNPT), is involved in rhizomelic chondrodysplasia punctata type 2, an autosomal-recessive syndrome characterized by severe ID. Using the courtship conditioning assay, it was determined that Dhap-at is required for long-term memory, but not for short-term memory. This result serves as a basis for further investigation of the underlying molecular mechanisms.

Drosophila Courtship Conditioning As a Measure of Learning and Memory

This protocol describes a Drosophila learning and memory assay called courtship conditioning. This classic assay is based on a reduction of male courtship behavior after sexual rejection by a non-receptive premated female. This natural form of behavioral plasticity can be used to test learning, short-term memory, and long-term memory.

Many insights into the molecular mechanisms underlying learning and memory have been elucidated through the use of simple behavioral assays in model ...

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Microstate and omega complexity are two reference-free electroencephalography (EEG) measures that can represent the temporal and spatial complexities of EEG data and have been widely used to investigate the neural mechanisms in some brain disorders. The goal of this article is to describe the protocol underlying EEG microstate and omega complexity analyses step by step. The main advantage of these two measures is that they could eliminate the reference-dependent problem inherent to traditional spectrum analysis. In addition, microstate analysis makes good use of high time resolution of resting-state EEG, and the four obtained microstate classes could match the corresponding resting-state networks respectively. The omega complexity characterizes the spatial complexity of the whole brain or specific brain regions, which has obvious advantage compared with traditional complexity measures focusing on the signal complexity in a single channel. These two EEG measures could complement each other to investigate the brain complexity from the temporal and spatial domain respectively.

This article describes the protocol underlying electroencephalography (EEG) microstate analysis and omega complexity analysis, which are two reference-free EEG measures and highly valuable to explore the neural mechanisms of brain disorders.

Microstate and omega complexity are two reference-free electroencephalography (EEG) measures that can represent the temporal and spatial complexities of ...

A High-content Assay for Monitoring AMPA Receptor Trafficking

Postsynaptic trafficking of receptors to and from the cell surface is an important mechanism by which neurons modulate their responsiveness to different stimuli. The &#945;-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors, which are responsible for fast excitatory synaptic transmission in neurons, are trafficked to and from the postsynaptic surface to dynamically alter neuronal excitability. AMPA receptor trafficking is essential for synaptic plasticity and can be disrupted in neurological disease. However, prevalent approaches for quantifying receptor trafficking ignore entire receptor pools, are overly time- and labor-intensive, or potentially disrupt normal trafficking mechanisms and therefore complicate the interpretation of resulting data. We present a high-content assay for the quantification of both surface and internal AMPA receptor populations in cultured primary hippocampal neurons using dual fluorescent immunolabeling and a near-infrared fluorescent 96-well microplate scanner. This approach facilitates the rapid screening of bulk internalized and surface receptor densities while minimizing sample material. However, our method has limitations in obtaining single-cell resolution or conducting live cell imaging. Finally, this protocol may be amenable to other receptors and different cell types, provided proper adjustments and optimization.

Neuronal excitability can be modulated through a dynamic process of endo- and exocytosis of excitatory ionotropic glutamate receptors. Described here is an accessible, high-content assay for quantifying surface and internal receptor population pools.

Postsynaptic trafficking of receptors to and from the cell surface is an important mechanism by which neurons modulate their responsiveness to different ...

Chronic Implantation of Multiple Flexible Polymer Electrode Arrays

Simultaneous recordings from large populations of individual neurons across distributed brain regions over months to years will enable new avenues of scientific and clinical development. The use of flexible polymer electrode arrays can support long-lasting recording, but the same mechanical properties that allow for longevity of recording make multiple insertions and integration into a chronic implant a challenge. Here is a methodology by which multiple polymer electrode arrays can be targeted to a relatively spatially unconstrained set of brain areas.
The method utilizes thin-film polymer devices, selected for their biocompatibility and capability to achieve long-term and stable&#160;electrophysiologic recording interfaces. The resultant implant allows accurate and flexible targeting of anatomically distant regions, physical stability for months, and robustness to electrical noise. The methodology supports up to sixteen serially inserted devices across eight different anatomic targets. As previously demonstrated, the methodology is capable of recording from 1024 channels. Of these, the 512 channels in this demonstration used for single neuron recording yielded 375 single units distributed across six recording sites. Importantly, this method also can record single units for at least 160 days.
This implantation strategy, including temporarily bracing each device with a retractable silicon insertion shuttle, involves tethering of devices at their target depths to a skull-adhered plastic base piece that is custom-designed for each set of recording targets, and stabilization/protection of the devices within a silicone-filled, custom-designed plastic case. Also covered is the preparation of devices for implantation, and design principles that should guide adaptation to different combinations of brain areas or array designs.

Described below is a method for implantation of multiple polymer electrode arrays across anatomically distant brain regions for chronic electrophysiological recording in freely moving rats. Preparation and surgical implantation are described in detail, with emphasis on design principles to guide adaptation of these methods for use in other species.

Simultaneous recordings from large populations of individual neurons across distributed brain regions over months to years will enable new avenues of ...