This work was supported by the Intramural Research Program of the National Institutes of Health (NIH), The National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). We thank the NIH Section on Instrumentation and the NIH Library for assistance with 3D printing.

NOTE: This protocol is written for components specifically named in the Table of Materials. While similar functionality can be achieved using other hardware, FED was programmed for the Arduino Pro microcontroller (henceforth termed: microcontroller) and listed accessories. Other microcontrollers may work equally well, but will require the user to modify the code to support them. Offline data analysis was coded using the Python programming language.
1. Preparation and Software Installation
<ol>
	<li>Procure electronic components needed to construct FED (see Table 1 andFed Github BoM.xlxs at: https://github.com/KravitzLab/FED/tree/master/doc). 
	NOTE: Alternative suppliers may be used for many parts on this table, provided they have equivalent specifications.</li>
	<li>Print all 3D designed components (Figure 1, available at: https://github.com/KravitzLab/FED/wiki/3D-Printed-Components). 3D printers with a 200 micron resolution should be capable of printing FED.</li>
	<li>Download and install the Integrated Development Environment (IDE) platform to program the microcontroller.</li>
	<li>Download and install additional libraries to enable functionality of motor shield and data logger (available at: https://github.com/KravitzLab/fed/tree/master/fed-arduino).</li>
	<li>Procure tools needed for assembly (e.g. a soldering iron, heat gun, solder, wire strippers, needle-nosed pliers, and both flat-head and screwdrivers).</li>
</ol>
2. Soldering Electrical Components
NOTE: Use heat shrink tubing to protect all soldered joints. Prior to soldering connections, slide a piece of shrink wrap tubing (~2 cm) tubing around one of the wires. After soldering the connection, center the tubing on the connection point and use a heat gun to heat shrink the tubing.
<ol>
	<li>Preparing connectors (Figure 2A)
	<ol>
		<li>Prepare four 2-pin JST connector pairs and label both male and female sides &#34;A&#34;, &#34;B&#34;, &#34;C&#34;, and &#34;D&#34;, respectively. Remove the red wire from both sides of connector pair &#34;D&#34;.</li>
		<li>Prepare one 3-pin JST connector pair and label both male and female sides &#34;E&#34;.</li>
	</ol>
	</li>
	<li>Microcontroller and stackable shields (Figure 2B)
	<ol>
		<li>Solder female stackable headers with sockets on the top side of the microcontroller. Clip protruding wire from headers on the bottom of the microcontroller.</li>
		<li>Solder female stackable headers with sockets on the top side of the SD data logging shield. Leave protruding wires at bottom of the shield.</li>
		<li>Solder male headers onto the motor shield with pins protruding from the bottom.</li>
		<li>Place a coin cell battery into the slot of SD shield to provide power to the real-time clock module.</li>
	</ol>
	</li>
	<li>External power button (Figure 2C) 
	NOTE: A latching metal pushbutton has five connections: power, ground, normally closed (NC1), normally open (NO1), and common (C1).
	<ol>
		<li>Solder the 2-pin male connector &#34;A&#34; to C1 (use red wire) and ground (use black wire). Heat-shrink all connections.</li>
		<li>Solder the 2-pin male connector &#34;B&#34; to + (use red wire) and NO1 (use black wire). Heat-shrink all connections.</li>
	</ol>
	</li>
	<li>Photointerrupter (Figure 2D)
	<ol>
		<li>Solder photointerrupter (the black part) to breakout board.</li>
		<li>Solder a 4.7K resistor to the front of the breakout board.</li>
		<li>Solder the male 3-pin connector &#34;E&#34; to the back of the breakout board: red wire to PWR, green wire to GND, and white wire to SGL.</li>
		<li>Trim loose wires on back of photointerrupter break out board.</li>
	</ol>
	</li>
	<li>Boost board (Figure 2E)
	<ol>
		<li>Solder the 2-pin female connector &#34;A&#34; to 5V and Ground pins on the boost board.</li>
		<li>Solder the black wire from male connector &#34;D&#34; to the additional GND pin on the boost board.</li>
	</ol>
	</li>
	<li>BNC output cable (optional: Figure 2F)
	<ol>
		<li>Solder the 2-pin connector &#34;C&#34; to the terminals of a BNC cable (red wire to central pin, black wire to outside pin). 
		NOTE: For assembly, the 2-pin connector must fit through the nut on the BNC plug. We use a smaller connector, or shave down the JST connector with a razor blade to make it fit.</li>
	</ol>
	</li>
	<li>Motor Shield (Figure 2G)
	<ol>
		<li>Twist the red and black wires of the female connector &#34;B&#34; together and solder to Vin.</li>
		<li>Solder the black wire of the female connector &#34;C&#34; to the ground pin next to ARef, and the red wire of this connector to pin 3.</li>
		<li>Solder the black wire of the female connector &#34;D&#34; to the ground pin next to Vin.</li>
		<li>Solder the green wire of the female connector &#34;E&#34; to the ground pin next to 5V, the red wire of this connector to 5V, and the white wire of this connector to pin 2.</li>
	</ol>
	</li>
</ol>
3. Software Upload
<ol>
	<li>Connect the FTDI breakout board to the programming pins of the microcontroller, and then connect FTDI breakout board to computer via micro USB cable.</li>
	<li>Open the IDE (integrated development environment) program.</li>
	<li>Select the correct microcontroller board for software upload through Tools &#62; Board dropdown menu.</li>
	<li>Select ATMega 328 (5V, 16mHz) through the Tools &#62; Processor menu.</li>
	<li>Select the port that the microcontroller is connected to through Tools &#62; Port &#62; COM# (will vary depending on which port is currently in use).</li>
	<li>Click the &#34;upload&#34; button to upload the FED sketch to the board (available at: https://github.com/KravitzLab/fed/tree/master/fed-arduino).</li>
</ol>
4. Hardware Assembly
<ol>
	<li>Stepper motor and motor shield (Figures 1C and 3A and 3B)
	<ol>
		<li>Secure the 5V stepper motor onto the 3D printed motor mount with two #6 x &#188;&#34; sheetmetal screws (Figures 1C and 3A).</li>
		<li>Insert rotating disk into motor mount and push down to securely attach to stepper motor shaft (Figure 3B).</li>
		<li>Twist on 3D printed food silo onto the motor mount making sure the pellet leveler arm is over the hole in the motor mount.</li>
		<li>Twist on connected pieces from above (steps 4.1.1 - 4.1.3) to the top of the printed base, with the stepper motor positioned towards the back of the base and the hole positioned in the front.</li>
		<li>Cut the 5-pin connector from the stepper motor wires and strip ~2 mm from the end of each wire.</li>
		<li>Connect wires from stepper motor to the terminal block connectors on the motor shield: red to ground, orange and pink to one motor port (e.g., M1), and blue and yellow to the other motor port (e.g., M2).</li>
	</ol>
	</li>
	<li>External power button
	<ol>
		<li>Remove the nut from the power button and insert the power button into the hole in the right side of the base. Secure button in place with hex nut.</li>
	</ol>
	</li>
	<li>Photointerrupter (Figure 3C)
	<ol>
		<li>Place the photointerrupter into its 3D printed housing. 
		NOTE: use a heat gun to heat up the housing if the photointerrupter does not seat all the way in.</li>
		<li>String the 3-pin male connector &#34;E&#34; from the photointerrupter (PWR, GND, and SGL) through the front middle hole of the 3D printed base.</li>
		<li>Secure the housing into the FED base with two 1&#34; nylon screws and corresponding nuts.</li>
	</ol>
	</li>
	<li>BNC output cable (optional)
	<ol>
		<li>Insert BNC connector into hole on the left side of the FED base. Secure in place with nut.</li>
		<li>If BNC connector is not used, plug hole with 3D printed plug.</li>
	</ol>
	</li>
	<li>Battery and boost board (Figure 3D)
	<ol>
		<li>Connect 3.7 V battery pack to the DC/DC boost converter module via the JST 2-pin connection. The blue LED on the Boost board will illuminate if battery is charged.</li>
	</ol>
	</li>
	<li>Mounting boards inside of housing (Figure 3E)
	<ol>
		<li>Mount microcontroller inside of the base with FTDI connections facing the power switch, using #4 x &#188;&#34; steel sheet metal screws.</li>
		<li>Stack motor shield and data logging shield on top of the microcontroler.</li>
		<li>Screw the Boost board into the case using #2 x &#188;&#34; steel sheet metal screws. Mount Boost with the micro-SD slot pointing down. FED can be charged through this port without opening the case.</li>
		<li>Connect the five connectors, &#34;A&#34; male to &#34;A&#34; female, &#34;B&#34; male to &#34;B&#34; female, etc.</li>
		<li>Place the battery inside the 3D printed base and close by sliding the back cover.</li>
		<li>Slide on the 3D printed face plate.</li>
	</ol>
	</li>
</ol>
5. Validation and Data Acquisition
NOTE: Prior to powering on a FED system, ensure an SD card is inserted on the SD shield, otherwise FED will not dispense pellets. Additionally, ensure power jumper on the motor shield (just above the power block) is in place.
<ol>
	<li>Power on FED system with the power pushbutton and test device functionality.
	<ol>
		<li>Fill food silo with 20 mg food pellets before powering on. 
		NOTE: The power switch should light up, as should LEDs on the microcontroller, SD shield, and motor shield. If there is no pellet in the well, one should dispense.</li>
		<li>Manually remove 5 - 10 pellets from the food well and confirm that replacement pellets are dispensed.</li>
	</ol>
	</li>
	<li>Remove the SD card and verify that data was logged properly. Data should be acquired in a comma-separated value (.CSV) that is named according to the variable FILENAME in the code.</li>
	<li>Place FED unit inside experimental setting, power on, and ensure that a pellet is dispensed into the food well.</li>
	<li>Over the course of data acquisition, check FED daily to verify that it is working properly by confirming that the LED light on the power switch is on (this indicates that the battery has enough charge) and a pellet is sitting in the food well (indicates that there are no problems with pellet dispensing).</li>
	<li>After data acquisition, retrieve SD card and access .csv file. 
	NOTE: Analysis scripts for meals and patterns of feeding are available at: https://github.com/KravitzLab/fed.</li>
</ol>

<ol>
	<li>Ellacott, K. L., Morton, G. J., Woods, S. C., Tso, P., Schwartz, M. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Assessment+of+feeding+behavior+in+laboratory+mice.">Assessment of feeding behavior in laboratory mice.</a> Cell Metab. 12 (1), 10-17 (2010).</li><li>Betley, J. N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neurons+for+hunger+and+thirst+transmit+a+negative-valence+teaching+signal.">Neurons for hunger and thirst transmit a negative-valence teaching signal.</a> Nature. 521 (7551), 180-185 (2015).</li><li>van den Heuvel, J. K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neuropeptide+Y+activity+in+the+nucleus+accumbens+modulates+feeding+behavior+and+neuronal+activity.">Neuropeptide Y activity in the nucleus accumbens modulates feeding behavior and neuronal activity.</a> Biol Psychiatry. 77 (7), 633-641 (2015).</li><li>Cone, J. J., Roitman, J. D., Roitman, M. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ghrelin+regulates+phasic+dopamine+and+nucleus+accumbens+signaling+evoked+by+food-predictive+stimuli.">Ghrelin regulates phasic dopamine and nucleus accumbens signaling evoked by food-predictive stimuli.</a> J Neurochem. 133 (6), 844-856 (2015).</li><li>Ulman, E. A., Compton, D., Kochanek, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Measuring+food+and+water+intake+in+rats+and+mice.">Measuring food and water intake in rats and mice.</a> ALN Mag. , 17-20 (2008).</li><li>Nguyen, K. P., O'Neal, T. J., Bolonduro, O. A., White, E., Kravitz, A. V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Feeding+Experimentation+Device+(FED):+A+flexible+open-source+device+for+measuring+feeding+behavior.">Feeding Experimentation Device (FED): A flexible open-source device for measuring feeding behavior.</a> J Neurosci Methods. 267, 108-114 (2016).</li><li>Aguiar, P., Mendonca, L., Galhardo, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=OpenControl:+a+free+opensource+software+for+video+tracking+and+automated+control+of+behavioral+mazes.">OpenControl: a free opensource software for video tracking and automated control of behavioral mazes.</a> J Neurosci Methods. 166 (1), 66-72 (2007).</li><li>Devarakonda, K., Nguyen, K. P., Kravitz, A. V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ROBucket:+A+low+cost+operant+chamber+based+on+the+Arduino+microcontroller.">ROBucket: A low cost operant chamber based on the Arduino microcontroller.</a> Behav Res Methods. 48 (2), 503-509 (2016).</li><li>Hoffman, A. M., Song, J., Tuttle, E. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ELOPTA:+a+novel+microcontroller-based+operant+device.">ELOPTA: a novel microcontroller-based operant device.</a> Behav Res Methods. 39 (4), 776-782 (2007).</li><li>Crall, J. D., Gravish, N., Mountcastle, A. M., Combes, S. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=BEEtag:+A+Low-Cost,+Image-Based+Tracking+System+for+the+Study+of+Animal+Behavior+and+Locomotion.">BEEtag: A Low-Cost, Image-Based Tracking System for the Study of Animal Behavior and Locomotion.</a> PLoS One. 10 (9), (2015).</li></ol>

Authors declare no conflict of interests, financial or otherwise.

The Feeding Experimentation Device (FED) is a flexible food intake monitoring system. Here, we describe detailed instructions on fabricating and troubleshooting the device, including the assembly of 3D printed hardware, soldering of electrical components, and uploading of sketches onto the microcontrollers. Though it is important to follow all steps outlined in the protocol carefully, there are critical steps that deserve extra attention in each section to ensure a successful end product. The 3D printed rotating disk should fit snugly onto the stepper motor shaft and be able to rotate with minimum resistance from neighboring parts. When soldering pin connections, be sure that the solder joint is secure without having excess solder. Ensure that all additional libraries installed must be located in the correct directory before uploading the script onto the microcontroller. The process of assembling FED involves tasks that are easily achievable, even with no prior experience in 3D printing or electronics.
There are few limitations with FED. First, each FED uses an internal SD card to log and store data. SD cards can be a cumbersome means to track and store data from many FEDs. We are exploring wireless data transfer to alleviate this issue, but the current iteration of FED relies on local storage on SD cards. Second, ~ 5% of the time FED dispenses two pellets instead of one. While pellet jams have not occurred in our hands with enough frequency to count, this possibility should be strictly monitored as we cannot rule them out completely. Users should never leave mice with FED as their only food source without checking FED&#39;s functionality daily. Despite these limitations, we believe FED&#39;s error rate is acceptable for most research studies. That said, in future work we will seek to decrease these errors further. Finally, the tolerance of 3D printing can vary depending on the specific 3D printer and material. As such, the 3D design files we provide may need to be tweaked for printing on other models of 3D printer, or with other materials. As one helpful tip, we have found that a heat gun can be used after printing to soften 3D printed parts for minor adjustments.
A single FED can be assembled for approximately $350. Of this price, we estimate ~$200 for commercial 3D printing out of PLA material, and ~$150 for purchasing the electronic components. 3D printing costs vary greatly depending on the quality and material. It is possible to reduce this cost considerably if a user prints their own parts. FED could also be printed or machined out of more durable materials such as stainless steel or aluminum, although this would be costlier.
We chose to build FED with a microcontroller on a programmable circuit board with an open-source platform. For this, we chose the open source microcontroller (see Table of Materials) as it has a low power draw. However, our code should work on any board that has sufficient input/output pins. Changing microcontroller boards will likely require minor changes to the code. We provide all design files and code online, and our design is open-source to provide researchers with flexibility to modify FED to suit their needs, including the use of other microcontroller boards.
There is a growing trend towards open-source hardware for scientific research. Various groups have made open-source devices for neuroscience research, including OpenControl, a software for video tracking animals during behavioral tasks7; ROBucket, an Arduino-based operant chamber for liquid outcome delivery8; ELOPTA, a PICmicro-based operant device for pellet outcome delivery9; and BEEtag, an image-based tracking system for bumblebees10. FED complements these with its unique form factor and low cost. A key aim for FED was to make it open-source. Open-source hardware allows researchers to perform minor tweaks to designs (for example to modify the dimensions), but is particularly powerful when users perform major modifications to expand the functionality of the hardware. For example, it is fairly easy to modify the design of FED for external mounting on a cage, which both protects FED and increases floor space within the cage (Figure 5A). Additionally, we designed FED to have room for nose-pokes on each side of the feeding well for operant training, and have successfully implemented these with minor design changes (Figure 5B). We will post such developments on our website (https://github.com/KravitzLab/fed), where we also look forward to hosting other such improvements from the research community.

With the rise of global obesity over the latter part of the 20th century, there is renewed attention on the mechanisms underlying feeding1,2,3,4. Typically, food intake is weighed manually5, or with commercially-available feeding systems. Commercial systems are excellent, but provide limited flexibility in modifying their designs or code. Here, we describe the Feeding Experimentation Device (FED): an open-source feeding system for measuring food intake with fine temporal resolution and minimal human interference6. FED is battery powered and fully contained within a 3D printed case that can fit inside of standard colony rack caging or other scientific equipment.
In its steady state, FED operates in a low-power mode with a food pellet resting in its food well. The presence of the pellet is monitored via an infrared beam. When a mouse removes a pellet, a photointerrupter sensor sends a signal to the microcontroller and the time-stamp is logged on the onboard secure digital (SD) card. Concurrently, a transistor-transistor logic (TTL) output provides a real-time output of pellet retrieval. Immediately following this event, the motor rotates to dispense another pellet, and the system returns to its low power mode. Due to its open-source nature, FED can be modified and improved to fit specific research needs. For example, the code can be easily altered to limit feeding to specific times of the day, or to stop dispensing when a number of pellets has been reached, without requiring human interference.
Here, we outline the step-by-step instructions for the construction, validation, and use of FED for measuring food intake in mice. We provide a list of all components to construct a system. Importantly, no prior experience in electronics is needed to construct FED.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Electronics</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Adafruit Motor/Stepper/Servo Shield for Arduino v2 Kit - v2.3</td>
			<td>Adafruit</td>
			<td>1438</td>
			<td>Use of other motor shields has not been tested and will require changes to the code</td>
		</tr>
		<tr>
			<td>Adafruit Assembled Data Logging shield for Arduino</td>
			<td>Adafruit</td>
			<td>1141</td>
			<td>Use of other data logging shields has not been tested and will require changes to the code</td>
		</tr>
		<tr>
			<td>PowerBoost 500 Charger</td>
			<td>Adafruit</td>
			<td>1944</td>
			<td>Other voltge regulator boards have not been tested, but should work if they have similar specifications</td>
		</tr>
		<tr>
			<td>FTDI Friend + extras - v1.0</td>
			<td>Adafruit</td>
			<td>284</td>
			<td>Any FTDI-USB connection will work</td>
		</tr>
		<tr>
			<td>Small Reduction Stepper Motor - 5VDC 32-Step 1/16 Gearing</td>
			<td>Adafruit</td>
			<td>858</td>
			<td>Use of other stepper motors has not been tested</td>
		</tr>
		<tr>
			<td>Arduino Pro 328 - 5V/16MHz</td>
			<td>SparkFun</td>
			<td>DEV-10915</td>
			<td>Other Arduino boards should work, although may require changes to the code</td>
		</tr>
		<tr>
			<td>Photo Interrupter - GP1A57HRJ00F</td>
			<td>SparkFun</td>
			<td>SEN-09299</td>
			<td>Other photointerrupters will work, but may require changes to the 3D design</td>
		</tr>
		<tr>
			<td>SparkFun Photo Interrupter Breakout Board - GP1A57HRJ00F</td>
			<td>SparkFun</td>
			<td>BOB-09322</td>
			<td>Other photointerrupters will work, but may require changes to the 3D design</td>
		</tr>
		<tr>
			<td>Connectors, screws, and miscellaneous items</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Shield stacking headers for Arduino (R3 Compatible)</td>
			<td>Adafruit</td>
			<td>85</td>
			<td>Any stacking header that says Arduiono R3 compatible will work</td>
		</tr>
		<tr>
			<td>Multi-Colored Heat Shrink Pack - 3/32&#34; + 1/8&#34; + 3/16&#34;</td>
			<td>Adafruit</td>
			<td>1649</td>
			<td>Any heatshrink will work</td>
		</tr>
		<tr>
			<td>Hook-up Wire Spool Set - 22AWG Solid Core - 6x25ft</td>
			<td>Adafruit</td>
			<td>1311</td>
			<td>Any wire will work</td>
		</tr>
		<tr>
			<td>Lithium Ion Battery Pack - 3.7V 4400mAh</td>
			<td>Adafruit</td>
			<td>354</td>
			<td>Any 3.7V Lithium battery with a JST connector will work&#160;</td>
		</tr>
		<tr>
			<td>SD/MicroSD Memory Card (8GB SDHC)</td>
			<td>Adafruit</td>
			<td>1294</td>
			<td>Any SD card will work</td>
		</tr>
		<tr>
			<td>50 Ohm BNC Bulkhead Jack (3/8&#34; D-Hole)</td>
			<td>L-com</td>
			<td>BAC70A</td>
			<td>Any BNC bulkhead will work</td>
		</tr>
		<tr>
			<td>Type 316 Stainless Steel Pan Head Phillips Sheet metal screw, No 6 size, 1/4&#34; Length</td>
			<td>McMaster-Carr</td>
			<td>90184A120</td>
			<td>Any screws of this specification will work</td>
		</tr>
		<tr>
			<td>Type 316 Stainless Steel Pan Head Phillips Sheet metal screw, No 2 size, 1/4&#34; Length</td>
			<td>McMaster-Carr</td>
			<td>91735A102</td>
			<td>Any screws of this specification will work</td>
		</tr>
		<tr>
			<td>Nylon 100 Degree Flat Head Slotted Machine Screw, 4-40 Thread, 1&#34; Length</td>
			<td>McMaster-Carr</td>
			<td>90241A253</td>
			<td>Any screws of this specification will work</td>
		</tr>
		<tr>
			<td>Nylon Hex Nut, 4-40 Thread Size</td>
			<td>McMaster-Carr</td>
			<td>94812A200</td>
			<td>Any nut of this specification will work</td>
		</tr>
		<tr>
			<td>2Pin JST M F Connector 200mm 22AWG Wire Cable</td>
			<td>NewEgg</td>
			<td>9SIA27C3FY2876</td>
			<td>Any 2 pin connector will work for this connection</td>
		</tr>
		<tr>
			<td>Metal Pushbutton - Latching (16mm, Red)</td>
			<td>SparkFun</td>
			<td>COM-11971</td>
			<td>Any push button or switch will work</td>
		</tr>
		<tr>
			<td>Resistor Kit - 1/4W</td>
			<td>SparkFun</td>
			<td>COM-10969</td>
			<td>Any 1/4W resistors will work</td>
		</tr>
	</tbody>
</table>

feeding experimentation device (fed): construction and validation of an open-source device for measuring food intake in rodents

Food intake measurements are essential for many research studies. Here, we provide a detailed description of a novel solution for measuring food intake in mice: the Feeding Experimentation Device (FED). FED is an open-source system that was designed to facilitate flexibility in food intake studies. Due to its compact and battery powered design, FED can be placed within standard home cages or other experimental equipment. Food intake measurements can also be synchronized with other equipment in real-time via FED's transistor-transistor logic (TTL) digital output, or in post-acquisition processing as FED timestamps every event with a real-time clock. When in use, a food pellet sits within FED's food well where it is monitored via an infrared beam. When the pellet is removed by the mouse, FED logs the timestamp onto its internal secure digital (SD) card and dispenses another pellet. FED can run for up to 5 days before it is necessary to charge the battery and refill the pellet hopper, minimizing human interference in data collection. Assembly of FED requires minimal engineering background, and off-the-shelf materials and electronics were prioritized in its construction. We also provide scripts for analysis of food intake and meal patterns. Finally, FED is open-source and all design and construction files are online, to facilitate modifications and improvements by other researchers.

Validation tests involving the use of animals were reviewed and approved by the Animal Care and Use Committee at the National Institute of Diabetes and Digestive and Kidney Diseases. To demonstrate the use of FED for measuring home cage feeding, adult female C57BL/6 mice (n = 4) were individually housed with ad libitum access to water and standard laboratory chow under a 12/12 h light/dark cycle (lights on at 05:00). After a one week habituation period, the food hopper was removed and replaced with a FED for five days of validation testing. At the end of the testing period, FEDs were removed from cages and feeding data from each SD card was analyzed using custom, freely-available scripts and a spreadsheet program. As shown in Figure 4A, pellet retrieval for individual mice (top panel; rasters indicate single pellet retrieval events) and average pellet retrieval across all mice in 30 min bins (bottom panel; line indicates mean &#177; SEM) show continuous feeding across the validation testing period, with clearly visualized circadian rhythmicity. To quantify the accuracy of FED&#39;s data logging capacity, each FED system was given 1000 pellets for the validation testing period and the remaining pellets were manually counted and compared to logged data on the SD card. FED logged 95.35 &#177; 1.25% of pellets that were dispensed to the SD card (Figure 4B).
<img alt="Figure 1" src="/files/ftp_upload/55098/55098fig1.jpg" /> 
Figure 1: 3D Designed FED Components. (A) Measurements of assembled FED. (B) Exploded view of 3D printed components in an assembled FED. (C) Exploded view of complete pellet hopper assembly (left), assembly of stepper motor to rotating disk (middle), and assembly of food silo to rotating disk (right). (D) Design of food well with arms to secure the photointerrupter for pellet detection. (E) Photograph of fully assembled FED. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55098/55098fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/55098/55098fig2.jpg" /> 
Figure 2: Wiring of FED Electrical Components. Wiring and assembly of (A) Connectors (B) Microcontroller, Data logging/SD shield, Motor shield, (C) Power button, (D) Photointerrupter, (E) Boost board, (F) BNC output cable, (G) Wiring of motor shield. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55098/55098fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/55098/55098fig3.jpg" /> 
Figure 3: Assembly of FED Hardware. (A) Mounting stepper motor (left) into motor mount and attaching pellet disk (right). (B) Wiring stepper motor to motor shield. (C) Inserting photointerrupter into 3D printed housing. (D) Attaching battery to Boost board. (E) Boards assembled inside FED housing. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55098/55098fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" src="/files/ftp_upload/55098/55098fig4.jpg" /> 
Figure 4: Representative Food Intake Data Collected via FED. (A) Food intake data collected via FED and visualized using open-source analysis scripts. Top panel: individual rasters indicate single pellet retrieval events, with rows containing individual mice. Bottom panel: line indicates mean &#177; SEM of food intake across mice (n = 4). (B) Accuracy of individual FED units in logging dispensed pellets. Colors correspond with rasters in Figure 3A. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55098/55098fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" src="/files/ftp_upload/55098/55098fig5.jpg" /> 
Figure 5: Modified uses for FED. (A) Externally mounted FED reduces floor space in rodent cages. (B) Changing the front-face configuration allows for the addition of two nose poke ports for operant training. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55098/55098fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplemental File 1: Hardware Schematic. Graphic schematic showing the electronic connections of FED. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55098/SDFED_redPI.png">Please click here to download this file.</a>
Supplemental File 2: Sample Output Code. Sample .CSV file showing output code from FED. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55098/SampleFEDoutputfile.csv">Please click here to download this file.</a>
Supplemental Files 3-6: Analysis Scripts. Four analysis scripts are provided for analyzing FED data: eating_rate.py, meals.py, meal_bars.py, and plotmice.py. Input parameters and description is provided in the commented sections at the top of each script. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55098/eating_rate.py.txt">Please click here to download this file.</a>

Watch this Scientific Journal Video about Feeding Experimentation Device FED: Construction and Validation of an Open-source Device for Measuring Food Intake in Rodents at JoVE.com

Feeding Experimentation Device FED: Construction and Validation of an Open-source Device for Measuring Food Intake in Rodents

Feeding Experimentation Device (FED) is an open-source device for measuring food intake in mice. FED can also synchronize food intake measurements with other techniques via a real-time digital output. Here, we provide a step-by-step tutorial for the construction, validation, and usage of FED.

Feeding Experimentation Device (FED): Construction and Validation of an Open-source Device for Measuring Food Intake in Rodents

Food intake measurements are essential for many research studies. Here, we provide a detailed description of a novel solution for measuring food intake in ...

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Nanyang Technological University

Research

JoVE Journal

Behavior

8.1K Views.  National Insttitute of Diabetes and Digestive and Kidney Diseases. Feeding Experimentation Device (FED) is an open-source device for measuring food intake in mice. FED can also synchronize food intake measurements with other techniques via a real-time digital output. Here, we provide a step-by-step tutorial for the construction, validation, and usage of FED.

Video: Feeding Experimentation Device FED: Construction and Validation of an Open-source Device for Measuring Food Intake in Rodents

Use of the Operant Orofacial Pain Assessment Device (OPAD) to Measure Changes in Nociceptive Behavior

We present an operant system for the detection of pain in awake, conscious rodents. The Orofacial Pain Assessment Device (OPAD) assesses pain behaviors in a more clinically relevant way by not relying on reflex-based measures of nociception. Food fasted, hairless (or shaved) rodents are placed into a Plexiglas chamber which has two Peltier-based thermodes that can be programmed to any temperature between 7 &deg;C and 60 &deg;C. The rodent is trained to make contact with these in order to access a reward bottle. During a session, a number of behavioral pain outcomes are automatically recorded and saved. These measures include the number of reward bottle activations (licks) and facial contact stimuli (face contacts), but custom measures like the lick/face ratio (total number of licks per session/total number of contacts) can also be created. The stimulus temperature can be set to a single temperature or multiple temperatures within a session. The OPAD is a high-throughput, easy to use operant assay which will lead to better translation of pain research in the future as it includes cortical input instead of relying on spinal reflex-based nociceptive assays.

Use of the Operant Orofacial Pain Assessment Device OPAD to Measure Changes in Nociceptive Behavior

We present a user-friendly, high-throughput operant system for the evaluation of pain behaviors in awake, conscious rodents. The Orofacial Pain Assessment Device (OPAD) can assess pain through a reward/conflict paradigm thus providing a more humane way of testing. This protocol will yield more clinically relevant and translational data from rodents.

We present an operant  system for the detection of pain in awake, conscious rodents. The Orofacial  Pain Assessment Device (OPAD) assesses pain behaviors ...

A Procedure to Study the Effect of Prolonged Food Restriction on Heroin Seeking in Abstinent Rats

In human drug addicts, exposure to drug-associated cues or environments that were previously associated with drug taking can trigger relapse during abstinence. Moreover, various environmental challenges can exacerbate this effect, as well as increase ongoing drug intake.
The procedure we describe here highlights the impact of a common environmental challenge, food restriction, on drug craving that is expressed as an augmentation of drug seeking in abstinent rats.
Rats are implanted with chronic intravenous i.v. catheters, and then trained to press a lever for i.v. heroin over a period of 10-12 days. Following the heroin self-administration phase the rats are removed from the operant conditioning chambers and housed in the animal care facility for a period of at least 14 days. While one group is maintained under unrestricted access to food (sated group), a second group (FDR group) is exposed to a mild food restriction regimen that results in their body weights maintained at 90% of their nonrestricted body weight. On day 14 of food restriction the rats are transferred back to the drug-training environment, and a drug-seeking test is run under extinction conditions (i.e.&#160;lever presses do not result in heroin delivery).
The procedure presented here results in a highly robust augmentation of heroin seeking on test day in the food restricted rats. In addition, compared to the acute food deprivation manipulations we have used before, the current procedure is a more clinically relevant model for the impact of caloric restriction on drug seeking. Moreover, it might be closer to the human condition as the rats are not required to go through an extinction-training phase before the drug-seeking test, which is an integral component of the popular reinstatement procedure.

A procedure that allows a demonstration of robust augmentation of drug seeking in food-restricted rats is described. Following heroin self-administration training, rats go through an abstinence period, in a drug-free environment, during which they are mildly food restricted. Drug seeking is then tested in the drug-associated environment.

In human drug addicts, exposure to drug-associated cues or environments that were previously associated with drug taking can trigger relapse during ...

Studying Food Reward and Motivation in Humans

A key challenge in studying reward processing in humans is to go beyond subjective self-report measures and quantify different aspects of reward such as hedonics, motivation, and goal value in more objective ways. This is particularly relevant for the understanding of overeating and obesity as well as their potential treatments. In this paper are described a set of measures of food-related motivation using handgrip force as a motivational measure. These methods can be used to examine changes in food related motivation with metabolic (satiety) and pharmacological manipulations and can be used to evaluate interventions targeted at overeating and obesity. However to understand food-related decision making in the complex food environment it is essential to be able to ascertain the reward goal values that guide the decisions and behavioral choices that people make. These values are hidden but it is possible to ascertain them more objectively using metrics such as the willingness to pay and a method for this is described. Both these sets of methods provide quantitative measures of motivation and goal value that can be compared within and between individuals.

This article describes a set of methods for the measurement of food related motivation and food related goal values in humans.

A key challenge in studying reward processing in humans is to go beyond subjective self-report measures and quantify different aspects of reward such as ...

Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device (ALDM) Test Systems

Fagot &#38; Paleressompoulle1 and Fagot &#38; Bonte2 have published an automated learning device (ALDM) for the study of cognitive abilities of monkeys maintained in semi-free ranging conditions. Data accumulated during the last five years have consistently demonstrated the efficiency of this protocol to investigate individual/physical cognition in monkeys, and have further shown that this procedure reduces stress level during animal testing3. This paper demonstrates that networks of ALDM can also be used to investigate different facets of social cognition and in-group expressed behaviors in monkeys, and describes three illustrative protocols developed for that purpose. The first study demonstrates how ethological assessments of social behavior and computerized assessments of cognitive performance could be integrated to investigate the effects of socially exhibited moods on the cognitive performance of individuals. The second study shows that batteries of ALDM running in parallel can provide unique information on the influence of the presence of others on task performance. Finally, the last study shows that networks of ALDM test units can also be used to study issues related to social transmission and cultural evolution. Combined together, these three studies demonstrate clearly that ALDM testing is a highly promising experimental tool for bridging the gap in the animal literature between research on individual cognition and research on social cognition.

Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device ALDM Test Systems

Fagot &#38; Paleressompoulle1 have published an automated learning device (ALDM) aimed at testing individual cognitive abilities in semi-free ranging monkeys. The main goal of our protocol is to use a network of ALDM test units to study social cognition in non-human primates.

Fagot &#38; Paleressompoulle1 and Fagot &#38; Bonte2 have published an automated learning device (ALDM) for the study of cognitive abilities of monkeys ...

Combining Quantitative Food-intake Assays and Forcibly Activating Neurons to Study Appetite in Drosophila

Food consumption is under the tight control of the brain, which integrates the physiological status, palatability, and nutritional contents of the food, and issues commands to start or stop feeding. Deciphering the processes underlying the decision-making of timely and moderate feeding carries major implications in our understanding of physiological and psychological disorders related to feeding control. Simple, quantitative, and robust methods are required to measure the food ingestion of animals after experimental manipulation, such as forcibly increasing the activities of certain target neurons. Here, we introduced dye-labeling-based feeding assays to facilitate the neurogenetic study of feeding control in adult fruit flies. We review available feeding assays, and then describe our methods step-by-step from setup to analysis, which combine thermogenetic and optogenetic manipulation of neurons controlling feeding motivation with dye-labeled food intake assay. We also discuss the advantages and limitations of our methods, compared with other feeding assays, to help readers choose an appropriate assay.

Combining Quantitative Food-intake Assays and Forcibly Activating Neurons to Study Appetite in Drosophila

Quantitative food-intake assays with dyed food provide a robust and high-throughput means to evaluate feeding motivation. Combining the food consumption assay with thermogenetic and optogenetic screens is a powerful approach to investigate the neural circuits underlying appetite in adult Drosophila melanogaster.

Food consumption is under the tight control of the brain, which integrates the physiological status, palatability, and nutritional contents of the food, ...

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in  Free Moving Rodents

Many neurological and psychiatric patients demonstrate difficulties and/or deficits in decision making. Rodent models are helpful to produce a deeper understanding of the neurobiological causes underlying the decision-making problems. A cost-benefit based T-maze task is used for measuring decision making in which rodents choose between a high reward arm (HRA) and a low reward arm (LRA). There are two paradigms of the T-maze decision-making task, one in which the cost is a time delay and the other in which it is physical effort. Both paradigms require a tedious and labor-intensive management of experimental animals, multiple doors, pellet reward, and arm choice recordings. In the current work, we invented an apparatus based on traditional T-maze with full automation for pellet delivery, door management and choice recordings. This automated setup can be used for the evaluation of both delay- and effort-based decision making in rodents. With the protocol described here, our lab investigated the decision-making phenotypes of multiple genetically modified mice. In the representative data, we showed that the mice with ablated medial habenular showed aversions of both delay and effort and tended to choose the immediate and effortless reward. This protocol helps to decrease the variability caused by experimenter intervention and to enhance experiment efficiency. In addition, chronic silicon probe or microelectrode recording, fiber-optic imaging and/or manipulation of neural activity can be easily applied during the decision-making task using the setup described here.

This article introduces an automated T-maze apparatus that we invented, and a protocol based on this apparatus for analyzing delay-based decision making and effort-based decision making in free moving rodents.

Many neurological and psychiatric patients demonstrate difficulties and/or deficits in decision making. Rodent models are helpful to produce a deeper ...

A Vibrotactile Feedback Device for Seated Balance Assessment and Training

Postural perturbations, motion tracking, and sensory feedback are modern techniques used to challenge, assess, and train upright sitting, respectively. The goal of the developed protocol is to construct and operate a sitting platform that can be passively destabilized while an inertial measurement unit quantifies its motion and vibrating elements deliver tactile feedback to the user. Interchangeable seat attachments alter the stability level of the device to safely challenge sitting balance. A built-in microcontroller allows fine-tuning of the feedback parameters to augment sensory function. Posturographic measures, typical of balance assessment protocols, summarize the motion signals acquired during timed balance trials. No dynamic sitting protocol to date provides variable challenge, quantification, and sensory feedback free of laboratory constraints. Our results demonstrate that non-disabled users of the device exhibit significant changes in posturographic measures when balance difficulty is altered or vibrational feedback provided. The portable, versatile device has potential applications in rehabilitation (following skeletal, muscular, or neurological injury), training (for sports or spatial awareness), entertainment (via virtual or augmented reality), and research (of sitting-related disorders).

A sitting platform has been developed and assembled that passively destabilizes sitting posture in humans. During the user&#39;s stabilizing task, an inertial measurement unit records the device&#39;s motion, and vibrating elements deliver performance-based feedback to the seat. The portable, versatile device may be used in rehabilitation, assessment, and training paradigms.

Postural perturbations, motion tracking, and sensory feedback are modern techniques used to challenge, assess, and train upright sitting, respectively. ...

In Vivo Protocol of Controlled Subconcussive Head Impacts for the Validation of Field Study Data

Subconcussive hits pose a threat to neuronal health as they have shown to induce neuronal structural damage and functional impairment without causing outward symptomology and appear to be a key contributor to an irreversible neurodegenerative disease, chronic traumatic encephalopathy (CTE). In addition, athletes can incur more than 1,000 of these hits per season. The subconcussive soccer heading model (SSHM) is a relevant, reproducible, and leading method of isolating and examining the effects of these subconcussive head impacts. By controlling variables such as ball traveling speed, the frequency of impacts, interval, ball placement to the head, as well as by measuring head impact magnitude, the SSHM provides the scientific community with a superior avenue of investigating the acute subconcussive effects on neuronal health. In this paper, we demonstrate the utility of SSHM in studying a time-course expression of neurofilament-light polypeptide (NF-L) in plasma in a repeated measures fashion. NF-L is an axonal injury marker that has previously been shown to be elevated in boxers and football players following subconcussive head trauma. Thirty-four adult aged soccer players were recruited and randomly assigned to either a soccer heading (n = 18) or kicking (n = 16) group. The heading group executed 10 headers with soccer balls projected at a velocity of 25 mph over 10 min. The kicking group followed the same protocol with 10 kicks. Plasma samples were obtained before and at 0 h, 2 h, and 24 h after heading/kicking and assessed for NF-L expressions. The heading group showed a gradual increase in plasma NF-L expression and peaked at 24 h after the heading protocol, whereas the kicking group remained consistent across the time points. These results confirmed the NF-L data from clinical field studies, encouraging the use of SSHM to validate clinical subconcussion data.

The subconcussive soccer heading model is a safe and concise methodological approach to isolate and measure the effects of subconcussive head impacts.

Subconcussive hits pose a threat to neuronal health as they have shown to induce neuronal structural damage and functional impairment without causing ...

Providing Meaningful Environmental Enrichment and Measuring Saliva Cortisol in Pigs Housed on Slatted Flooring

As pork is the most consumed meat worldwide, the welfare of animals in the swine industry has increasingly become a major public concern, which imposes a substantial pressure coming from customers, legislators and other stakeholders, to make management changes to improve the well-being of these animals. Several studies have demonstrated that providing environmental enrichment to pigs allows them to express their natural behavior, such as rooting and exploring, as well as nesting prior to farrowing, and is associated with reduced stress and improved production and welfare. However, many considerations should be taken into account when providing environmental enrichment, such as the type of floor, drainage and sewage systems, the pigs&#39; stage in life, the material, as well as its hanging method, height and location within the pen. The objectives of this paper are (1) to give methodologic information on how to provide a relatively simple and practical meaningful environmental enrichment for pigs which are housed on slatted floors during the different stages of their life, and (2) to demonstrate how to collect saliva samples for the measurement of cortisol concentrations, as a biomarker for acute stress. Protocols include information regarding the use of jute, cotton ropes, straw in racks, as well as chewable silicone sticks devices as environmental enrichment in pens of farrowing and lactation, weaners and finishers. In addition, the use of cotton rope for a non-invasive saliva samples collection for cortisol concentrations analysis is detailed. The protocols provided are relevant for professionals aiming to improve and monitor animal welfare, in both research and industrial swine farming.

This protocol demonstrates how to provide practical meaningful environmental enrichment for pigs which are housed on slatted flooring during the different stages of their lives, and how to collect saliva samples in a non-invasive manner for the measurement of cortisol concentrations, as a biomarker for acute stress.

As pork is the most consumed meat worldwide, the welfare of animals in the swine industry has increasingly become a major public concern, which imposes a ...

An Improved Assay and Tools for Measuring Mechanical Nociception in Drosophila Larvae

Published assays for mechanical nociception in Drosophila have led to variable assessments of behavior. Here, we fabricated, for use with Drosophila larvae, customized metal nickel-titanium alloy (nitinol) filaments. These mechanical probes are similar to the von Frey filaments used in vertebrates to measure mechanical nociception. Here, we demonstrate how to make and calibrate these mechanical probes and how to generate a full behavioral dose-response from subthreshold (innocuous or non-noxious range) to suprathreshold (low to high noxious range) stimuli. To demonstrate the utility of the probes, we investigated tissue damage-induced hypersensitivity in Drosophila larvae. Mechanical allodynia (hypersensitivity to a normally innocuous mechanical stimulus) and hyperalgesia (exaggerated responsiveness to a noxious mechanical stimulus) have not yet been established in Drosophila larvae. Using mechanical probes that are normally innocuous or probes that typically elicit an aversive behavior, we found that Drosophila larvae develop mechanical hypersensitization (both allodynia and hyperalgesia) after tissue damage. Thus, the mechanical probes and assay that we illustrate here will likely be important tools to dissect the fundamental molecular/genetic mechanisms of mechanical hypersensitivity.

An Improved Assay and Tools for Measuring Mechanical Nociception in Drosophila Larvae

The goal of this protocol is to show how to perform an improved assay for mechanical nociception in Drosophila larvae. We use the assay here to demonstrate that mechanical hypersensitivity (allodynia and hyperalgesia) exists in Drosophila larvae.

Published assays for mechanical nociception in Drosophila have led to variable assessments of behavior. Here, we fabricated, for use with Drosophila ...