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><tbody><tr><td>&lt;strong&gt;Electronics&lt;/strong&gt;</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>&lt;strong&gt;Connectors, screws, and miscellaneous items&lt;/strong&gt;</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&quot; + 1/8&quot; + 3/16&quot;</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 4400 mAh</td><td>Adafruit</td><td>354</td><td>Any 3.7 V Lithium battery with a JST connector will work&amp;nbsp;</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&quot; 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&quot; 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&quot; 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&quot; 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>2 Pin JST M F Connector 200 mm 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 (16 mm, Red)</td><td>SparkFun</td><td>COM-11971</td><td>Any push button or switch will work</td></tr><tr><td>Resistor Kit - 1/4 W</td><td>SparkFun</td><td>COM-10969</td><td>Any 1/4 W 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 ...

feeding-experimentation-device-fed-construction-validation-an-open

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

A Novel Procedure for Evaluating the Reinforcing Properties of Tastants in Laboratory Rats: Operant Intraoral Self-administration

This paper describes a novel method for studying the bio-behavioral basis of addiction to food. This method combines the surgical component of taste reactivity with the behavioral aspects of operant self-administration of drugs. Under very brief general anaesthesia, rats are implanted with an intraoral (IO) cannula that allows delivery of test solutions directly in the oral cavity. Animals are then tested in operant self-administration chambers whereby they can press a lever to receive IO infusions of test solutions. IO self-administration has several advantages over experimental procedures that involve drinking a solution from a spout&#160;or operant responding for solid pellets or solutions delivered in a receptacle. Here, we show that IO self-administration can be employed to study self-administration of high fructose corn syrup (HFCS). Rats were first tested for self-administration on a progressive ratio (PR) schedule, which assesses the maximum amount of operant behavior that will be emitted for different concentrations of HFCS (i.e.&#160;8%, 25%, and 50%). Following this test, rats self-administered these concentrations on a continuous schedule of reinforcement (i.e.&#160;one infusion for each lever press) for 10 consecutive days (1 session/day; each lasting 3 hr), and then they were retested on the PR schedule. On the continuous reinforcement schedule, rats took fewer infusions of higher concentrations, although the lowest concentration of HFCS (8%) maintained more variable self-administration. Furthermore, the PR tests revealed that 8% had lower reinforcing value than 25% and 50%. These results indicate that IO self-administration can be employed to study acquisition and maintenance of responding for sweet solutions. The sensitivity of the operant response to differences in concentration and schedule of reinforcement makes IO self-administration an ideal procedure to investigate the neurobiology of voluntary intake of sweets.

The current study evaluates a novel procedure for assessing the reinforcing effects of palatable solutions in laboratory rats: intraoral self-administration. To this end, operant responding (i.e.&#160;lever pressing) for intraoral infusions of sweet solutions at different concentrations was measured on continuous and progressive ratios schedules of reinforcement.

This paper describes a novel method for studying the bio-behavioral basis of addiction to food. This method combines the surgical component of taste ...

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, ...

Assessing Activity-based Anorexia in Mice

Rodents develop activity-based anorexia (ABA) when exposed to a restricted feeding schedule and allowed free access to a running wheel. These conditions lead to a life-threatening reduction in body weight. However, rodents exposed to only one of these conditions ultimately adapt to re-establish normal body weight. Although increased running coupled with reduction in voluntary food intake appear paradoxical under ABA conditions, ABA behavior is observed across numerous mammalian species. 
The ABA paradigm provides an animal model for anorexia nervosa (AN), an eating disorder with severe dysregulation of appetite-behavior. Subjects are singly housed with free access to a running wheel. Each day, the subject is offered food for a limited amount of time. During the course of the experiment, a subject's body weight decreases from high activity and low caloric intake. The duration of the study varies based on how long food is offered daily, the type of food offered, the strain of mouse, if drugs are being tested, and environmental factors.
A lack of effective pharmacological treatments for AN patients, their low quality of life, high cost of treatment, and their high mortality rate indicate the urgency to further research AN. We provide a basic outline for performing ABA experiments with mice, offering a method to investigate AN-like behavior in order to develop novel therapies. This protocol is optimized for use in Balb/cJ mice, but can easily be manipulated for other strains, providing great flexibility in working with different questions, especially related to genetic factors of ABA.

Mice individually housed with a running wheel while given limited access to food develop reductions in food consumption and increase activity on the running wheel. This experimental phenomenon is called activity-based anorexia. This paradigm provides an experimental tool for studying the neurobiology and behaviors underlying aspects of anorexia nervosa.

Rodents develop activity-based anorexia (ABA) when exposed to a restricted feeding schedule and allowed free access to a running wheel. These conditions ...

Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method

The vast majority of dietary and eating behavior assessment methods are based on self-reports. They are burdensome and also prone to measurement errors. Recent technological innovations allow for the development of more accurate and precise dietary and eating behavior assessment tools that require less effort for both the user and the researcher. Therefore, a new sensor-based device to assess food intake and eating behavior was developed. The device is a regular dining tray equipped with a video camera and three separate built-in weighing stations. The weighing stations measure the weight of the bowl, plate, and drinking cup continuously over the course of a meal. The video camera positioned to the face records eating behavior characteristics (chews, bites), which are analyzed using artificial intelligence (AI)-based automatic facial expression software. The tray weight and the video data are transported at real-time to a personal computer (PC) using a wireless receiver. The outcomes of interest, such as the amount eaten, eating rate and bite size, can be calculated by subtracting the data of these measures at the timepoints of interest. The information obtained by the current version of the tray can be used for research purposes, an upgraded version of the device would also facilitate the provision of more personalized advice on dietary intake and eating behavior. Contrary to the conventional dietary assessment methods, this dietary assessment device measures food intake directly within a meal and is not dependent on memory or the portion size estimation. Ultimately, this device is therefore suited for daily main meal food intake and eating behavior measures. In the future, this technology based dietary assessment method can be linked to health applications or smart watches to obtain a complete overview of exercise, energy intake, and eating behavior.

This protocol shows and explains a new technology-based dietary assessment method. The method consists of a dining tray with multiple built-in weighing scales and a video camera. The device is unique in the sense that it incorporates automated measures of food and drink intake and eating behavior over the course of a meal.

The vast majority of dietary and eating behavior assessment methods are based on self-reports. They are burdensome and also prone to measurement errors. ...

High-Throughput Assay to Measure Caenorhabditis elegans Food Intake and Lifespan

Quantifying Food Intake in Caenorhabditis elegans by Measuring Bacterial Clearance

Feeding is an essential biological process for an organism&#39;s growth, reproduction, and survival. This assay aims to measure the food intake of Caenorhabditis elegans&#160;(C. elegans), an important parameter when studying the genetics of aging or metabolism. In most species, feeding is determined by measuring the difference between the amount of food provided and the amount left after a given time interval. The method presented here uses the same strategy to determine the feeding of C. elegans. It measures the amount of bacteria, the food source of C. elegans, cleared within 72 h. This method uses 96-well microtiter plates and has allowed the screening of hundreds of drugs for their ability to modulate food intake at a speed and depth not possible in other animal models. The strength of this assay is that it allows to measure feeding and lifespan simultaneously and directly measures the disappearance of food and, thus, is based on the same principles used for other organisms, facilitating species-to-species comparison.

Author Spotlight: Exploring the Relationship Between Lipotoxicity and HFpEF 

This protocol describes an assay to quantify Caenorhabditis elegans feeding rate based on measuring the clearance of bacteria in liquid culture.

Feeding is an essential biological process for an organism&#39;s growth, reproduction, and survival. This assay aims to measure the food intake of ...

Investigating Binge Eating Disorder with a Palatable Food Access Mouse Model

Intermittent Binge-Intake Model in Mice

Obesity affects one in eight individuals globally. Overeating, especially high-calorie foods, plays a significant role in obesity. Binge eating disorder (BED) is a complex condition caused by a combination of biological, psychological, and social factors. It involves excessive consumption of high-calorie foods in a short period and is often linked to anxiety and cravings.
Here, we present a protocol employing continuous and intermittent access to a new highly palatable food (HPF) -- M&amp;M&#39;s -- to develop a mouse model for investigating binge eating disorder (BED). The HPF, chosen for its high fat and sugar content, represents an optimal food source to study due to its strong palatability, which makes it particularly suited for examining compulsive eating behaviors. Using C57BL/6 mice, we provided continuous or intermittent access to M&amp;M&#39;s while allowing unrestricted access to standard chow and water. By the 8th day, the mice in the intermittent access group exhibited pronounced binge-eating behaviors, which persisted through the 26th day. These mice also consumed significantly more calories -- predominantly from the confectionary -- than those in the continuous access and control groups. The contrast between continuous and intermittent access underscores the critical role of feeding schedules in promoting the overconsumption of palatable foods.
Furthermore, behavioral assessments revealed intermittent access to the HPF-induced anxiety-like behaviors, highlighting the psychological impact of access patterns on both eating behavior and emotional states. By incorporating these two distinct feeding paradigms, this study offers valuable insights into how the availability of highly palatable foods can exacerbate binge-eating tendencies. This model provides a more realistic approach to studying binge-eating behaviors and their metabolic consequences. It highlights the importance of standardized models in biomedical research. Our findings offer insights into the physiological and neural mechanisms underlying BED, which could pave the way for developing effective therapeutic interventions for BED and obesity.

Author Spotlight: Accessible M&amp;M-Based Mouse Model for Investigating Binge Eating Disorder - Insights into Eating Behaviors, Anxiety, and Neural Mechanisms

A binge-eating model was established in mice using intermittent access to M&amp;M's (a highly palatable food made of fat and sugar). Mice with intermittent access to this high-fat, high-sugar food showed stress-like behaviors, increased caloric intake, and a preference for that food over standard chow compared to mice with continuous or no access to the food.

Obesity affects one in eight individuals globally. Overeating, especially high-calorie foods, plays a significant role in obesity. Binge eating disorder ...

Designing a Flexible, Cost-Effective Olfactometer for Rodent Olfactory Experiments

Constructing an Olfactometer for Rodent Olfactory Behavior Studies

The use of olfactometers to study rodent behavior and brain activity during olfactory tasks is crucial for understanding brain circuits. These sophisticated devices allow researchers to precisely control and deliver odor stimuli, enabling the investigation of complex olfactory processes in rodents. Although commercially available olfactometers are convenient, they present challenges when technical issues arise, often requiring costly assistance and potentially disrupting research timelines. This article details the construction of a custom olfactometer specifically designed for mouse olfactory behavior experiments, providing a comprehensive list of parts and step-by-step instructions. The olfactometer is controlled through MATLAB, offering a user-friendly interface for researchers. Importantly, the open-source code allows users to modify and adapt the system, tailoring behavioral tasks to meet specific experimental needs. Building a customized olfactometer empowers users with the knowledge and capability to perform custom experimental design and troubleshooting independently, saving both time and resources. This approach not only enhances research flexibility but also fosters a deeper understanding of the equipment's functionality, ultimately leading to more robust and reliable olfactory studies in rodents.

Constructing an Olfactometer for Rodent Olfactory Behavior Studies Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling

This protocol describes the construction of an olfactometer for go/no-go olfactory behavior experiments. Step-by-step instructions, along with images, are provided to ensure the successful construction of the olfactometer. Information for troubleshooting issues encountered during the process is also included.

The use of olfactometers to study rodent behavior and brain activity during olfactory tasks is crucial for understanding brain circuits. These ...

'Boden Food Plate': Novel Interactive Web-based Method for the Assessment of Dietary Intake

Different methods can be used in research to assess dietary intake, many of which are still paper-based. Written estimated food diaries are often utilized in clinical trials, despite being a burden for both study participants and researchers. This method requires participant literacy, it is time consuming, labor intensive, and can easily lead to under-reporting. With advancements in technology, there is a growing interest in electronic diaries that automate the dietary assessment process. These are focused on improving accuracy, reducing both time and cost and providing users with a visual and more enjoyable experience. The methodology presented here aimed to validate the &#39;Boden Food Plate&#39;, a novel web-based platform for self-recording of food and drink items, compared to a traditional estimated food diary. The application was also rated on a satisfaction scale by study participants using a paper-based questionnaire. Sixty-seven participants completed the dietary measures on both the three-day electronic and paper food diaries. For the analysis, only dietary data completed at both study time points (baseline and week six) was utilized. Despite small mean differences between dietary data collection methods, Bland Altman analysis showed fairly wide 95% limits of agreement between the electronic platform and the written estimated food diary and there were few cases which did not fall within the 95% confidence intervals. Overall, participants found the electronic food diary to be more fun than the paper method and as easy to use as hard copy diaries. The new platform has potential as a self-recording tool for the collection of dietary data, particularly when utilized in clinical trial settings. However, further validation studies are needed to improve the validity of this novel electronic dietary data collection tool.

The &#39;Boden Food Plate&#39; is an electronic food diary designed to be an interactive and fun method to collect dietary intake using visual depictions. The purpose of this study was to validate the web-based application against a traditional three-day estimated food diary method.

Different methods can be used in research to assess dietary intake, many of which are still paper-based. Written estimated food diaries are often utilized ...

Medicine

Palatable Western-style Cafeteria Diet as a Reliable Method for Modeling Diet-induced Obesity in Rodents

Obesity is rapidly increasing in incidence in developed and developing countries and is known to induce or exacerbate many diseases.&#160;The health burden of obesity and its comorbid conditions highlight the need for better understanding of its pathogenesis, yet ethical constraints limit studies in humans. To this end externally valid models of obesity in laboratory animals are essential for the understanding of being overweight and obesity.&#160;While many species have been used to model the range of changes that accompany obesity in humans, rodents are most commonly used. Our laboratory has developed a western-style cafeteria diet that consistently leads to considerable weight gain and markers of metabolic disease in rodents. The diet exposes rodents to a variety of highly palatable foods to induce hyperphagia, modeling the modern western food environment. This diet rapidly induces weight gain and body fat accumulation in rats allowing for the study of effects of overeating and obesity. While the cafeteria diet may not provide the same control over macronutrient and micronutrient profile as purified high-fat or high-fat, high-sugar diets, the cafeteria diet typically induces a more severe metabolic phenotype than that observed with purified diets and is more in line with metabolic disturbances observed in the overweight and obese human population.

This protocol describes the use of a highly palatable, western-style cafeteria diet to model overeating and obesity in rodents. Here, we provide a detailed outline of food selection, preparation and measurement, and explain methodological factors that assist in generating a robust and reproducible phenotype.

Obesity is rapidly increasing in incidence in developed and developing countries and is known to induce or exacerbate many diseases.&#160;The health ...

Sucrose Preference and Novelty-Induced Hypophagia Tests in Rats using an Automated Food Intake Monitoring System

The prevalence and incidence of depressive disorders are rising worldwide, affecting about 322 million individuals, underlining the need for behavioral studies in animal models. In this protocol, to study depression-like and anhedonic behavior in rats, the established sucrose preference and novelty-induced hypophagia tests are combined with an automated food and liquid intake monitoring system. Prior to testing, in the sucrose preference paradigm, male rats are trained for at least 2 days to consume a sucrose solution in addition to tap water. During the test, rats are again exposed to water and sucrose solution. Consumption is registered every second by the automated system. The ratio of sucrose to total water intake (sucrose preference ratio) is a surrogate parameter for anhedonia. In the novelty-induced hypophagia test, male rats undergo a training period in which they are exposed to a palatable snack. During training, rodents show a stable baseline snack intake. On test day, the animals are transferred from home cages into a fresh, empty cage representing a novel unknown environment with access to the known palatable snack. The automated system records the total intake and its underlying microstructure (e.g., latency to approaching the snack), providing insight into anhedonic and anxious behaviors. The combination of these paradigms with an automated measuring system provides more detailed information, along with higher accuracy by reducing measuring errors. However, the tests use surrogate parameters and only depict depression and anhedonia in an indirect manner.

Presented here is a protocol to study depression-like and anhedonic behavior in rats. It combines two well-established behavioral methods, the sucrose preference and novelty-induced hypophagia tests, with an automated food and liquid intake monitoring system, to indirectly investigate rodent behavior using surrogate parameters.

The prevalence and incidence of depressive disorders are rising worldwide, affecting about 322 million individuals, underlining the need for behavioral ...

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

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