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. 2021 Mar 29:10:e66173.
doi: 10.7554/eLife.66173.

An open-source device for measuring food intake and operant behavior in rodent home-cages

Bridget A Matikainen-Ankney #  1 , Thomas Earnest #  1 , Mohamed Ali #  2   3 , Eric Casey  1 , Justin G Wang  4 , Amy K Sutton  2 , Alex A Legaria  4 , Kia M Barclay  4 , Laura B Murdaugh  5 , Makenzie R Norris  4   6 , Yu-Hsuan Chang  4 , Katrina P Nguyen  2 , Eric Lin  1 , Alex Reichenbach  7 , Rachel E Clarke  7 , Romana Stark  7 , Sineadh M Conway  6   8 , Filipe Carvalho  9 , Ream Al-Hasani  6   8 , Jordan G McCall  6   8 , Meaghan C Creed  1   4   8 , Victor Cazares  10 , Matthew W Buczynski  5 , Michael J Krashes  2 , Zane B Andrews  7 , Alexxai V Kravitz  1   4   8
Affiliations

An open-source device for measuring food intake and operant behavior in rodent home-cages

Bridget A Matikainen-Ankney et al. Elife. .

Abstract

Feeding is critical for survival, and disruption in the mechanisms that govern food intake underlies disorders such as obesity and anorexia nervosa. It is important to understand both food intake and food motivation to reveal mechanisms underlying feeding disorders. Operant behavioral testing can be used to measure the motivational component to feeding, but most food intake monitoring systems do not measure operant behavior. Here, we present a new solution for monitoring both food intake and motivation in rodent home-cages: the Feeding Experimentation Device version 3 (FED3). FED3 measures food intake and operant behavior in rodent home-cages, enabling longitudinal studies of feeding behavior with minimal experimenter intervention. It has a programmable output for synchronizing behavior with optogenetic stimulation or neural recordings. Finally, FED3 design files are open-source and freely available, allowing researchers to modify FED3 to suit their needs.

Keywords: circadian; feeding; mouse; neuroscience; open-source; operant behavior; self-stimulation.

Plain language summary

Obesity and anorexia nervosa are two health conditions related to food intake. Researchers studying these disorders in animal models need to both measure food intake and assess behavioural factors: that is, why animals seek and consume food. Measuring an animal’s food intake is usually done by weighing food containers. However, this can be inaccurate due to the small amount of food that rodents eat. As for studying feeding motivation, this can involve calculating the number of times an animal presses a lever to receive a food pellet. These tests are typically conducted in hour-long sessions in temporary testing cages, called operant boxes. Yet, these tests only measure a brief period of a rodent's life. In addition, it takes rodents time to adjust to these foreign environments, which can introduce stress and may alter their feeding behaviour. To address this, Matikainen-Ankney, Earnest, Ali et al. developed a device for monitoring food intake and feeding behaviours around the clock in rodent home cages with minimal experimenter intervention. This ‘Feeding Experimentation Device’ (FED3) features a pellet dispenser and two ‘nose-poke’ sensors to measure total food intake, as well as motivation for and learning about food rewards. The battery-powered, wire-free device fits in standard home cages, enabling long-term studies of feeding behaviour with minimal intervention from investigators and less stress on the animals. This means researchers can relate data to circadian rhythms and meal patterns, as Matikainen-Ankney did here. Moreover, the device software is open-source so researchers can customise it to suit their experimental needs. It can also be programmed to synchronise with other instruments used in animal experiments, or across labs running the same behavioural tasks for multi-site studies. Used in this way, it could help improve reproducibility and reliability of results from such studies. In summary, Matikainen-Ankney et al. have presented a new practical solution for studying food-related behaviours in mice and rats. Not only could the device be useful to researchers, it may also be suitable to use in educational settings such as teaching labs and classrooms.

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Conflict of interest statement

BM, TE, MA, EC, JW, AS, AL, KB, LM, MN, YC, KN, EL, AR, RC, RS, SC, RA, JM, MC, VC, MB, MK, ZA, AK No competing interests declared, FC Director of Open Ephys Production Site

Figures

Figure 1.
Figure 1.. Assembly of FED3.
(A) Exploded view schematic and (B) photos of FED3 with main components highlighted. (C) Assembled FED in an Allentown NextGen home-cage, top view, and side view. (D) Back view (left) of assembled and populated FED3 PCB, and side view (right) of assembled FED3 electronics.
Figure 1—figure supplement 1.
Figure 1—figure supplement 1.. FED3 circuit.
(A) Block schematic, and (B) PCB layout for FED3.
Figure 2.
Figure 2.. FED3 tracks food intake.
(A) Schematic of FED3 in free-feeding mode. (B) Pellets per hour across six consecutive days. Shaded areas indicate dark cycle. (C) Chronograms of pellets eaten in heatmap (top) and line plot. (D) Regression of the calculated weight of the pellets recorded by FED vs. the measured difference in weight of the FED between days. (E) Mice weight across time during free feeding. (F) Schematic of FED3VIZ workflow. Data is shown as means ± SEM in (B, C, E).
Figure 2—figure supplement 1.
Figure 2—figure supplement 1.. FED3 pellet delivery cute.
A specially designed release chute is curved to prevent pellets from ricocheting at the end of the chute, and to prevent jamming. 3D renderings with pellet chute highlighted in orange from front (A), top angled (B), top (C), and side (D) view.
Figure 3.
Figure 3.. Meal analysis with FED3.
(A) Inter-pellet interval histograms for dark and light cycle feeding. (B) Meals per day, (C) pellets per meal, and (D) % of pellets within meals for dark and light cycle feeding. N = 10 mice, paired t-tests.
Figure 4.
Figure 4.. FED3 reveals circadian feeding patterns.
(A) Schematic of FED3 in FR1 mode. (B) Active (blue) and inactive (orange) pokes over 6 days. n = 10 mice. (C) Average pokes (active, blue; inactive, orange) over 24 hr cycle. (D) Average pokes during light and dark cycles. Significant interaction between day/night and average pokes (F(1,792)=85.225, p<0.0001); significant effect of day/night (F(1,792)=668.700, p<0.0001); significant effect of active or inactive pokes (F(1,792)=610.824, p<0.0001). Fitted linear model for two-way ANOVA, (F(3,792)=454.917, p<0.0001.) (E) Average poke efficiency (p=0.0001, student’s t-test).
Figure 5.
Figure 5.. FR1 acquisition across seven research sites.
(A) Pellets earned over first 16 hr of exposure to FED3 at seven research sites. (B) Scatterplots and kernel density estimation plots showing pellets earned after 16 hr with FED3, effect of group (F(6,115)=6.4223, p<0.0001), significant post hoc differences between groups B and D (p=0.001), B and G (p=0.001), C and D (p=0.030), and C and G (p=0.010). (C) Poke efficiency across the session. Significant effect of time, p=0.0007, F(3,386)=5.79. (D) Active pokes across the session. Significant effect of time, p=0.0001, F(3, 393)=115.2. (E) Retrieval time across the session. Significant effect of time, p=0.0001, F(3,351)=14.02. (E) Linear models with Tukey post-tests were conducted in (C-E). (F) Poke efficiency across continuous 16 hr sessions (gray line, n = 122 mice) and across multiple days with 1 hr sessions each day (teal line, n = 11 mice). Two-way ANOVA revealed significant effect of time (p<0.0001), no significant effect of group or interaction. F(7,500)=3.974.
Figure 6.
Figure 6.. Effect of magazine training.
(A) Schematic showing paradigm for magazine trained group (MAG) vs. no magazine training (noMAG). (B) Active pokes over time during first exposure to FED3 FR1 between noMAG and MAG groups. (C) Scatterplots and kernel density estimation plots showing distribution of active poke counts at 16 hr (p=0.0001). Student’s t-test. N = 146 mice.
Figure 7.
Figure 7.. Longitudinal closed-economy feeding with FED3.
(A) Active pokes required per pellet in a 4 day closed-economy progressive ratio task with 30 min resets. (B) Inset illustrating increasing number of pokes (y-axis, and orange rasters) per pellet earned during dark cycle. (C) Mouse weights over time (n = 7 mice). (D–F) Chronograms (top) and bar/scatter plots (bottom) showing daily average pellets (D), pokes (E), and pokes per pellet (F) over time, binned by 1 hr. Shaded region in chronograms indicates dark cycle. Significant increase in active pokes, p=0.0044 (E, bottom), and pokes per pellet, p=0.0007 (F, bottom) during dark cycle. No significant difference in pellets earned during dark cycle, p=0.8622 (D, bottom).
Figure 8.
Figure 8.. Self-stimulation of dMSNs using FED3.
(A) Schematic showing Cre-dependent ChR2 injected into dorsal medial striatum with fiber optic implanted. (B) Schematic showing FED3 operant self-stim setup. (C) Mice poked significantly more on the active port (p=0.0002, Student’s t-test, n = 3 mice). (D) Cumulative inactive and active pokes plotted over time for each mouse.
Author response image 1.
Author response image 1.
Author response image 2.
Author response image 2.. FED3 as a function generator.
(A) Recorded 20 Hz Pulse train generated by FED3. (B) Peripulse rasters (top) and histogram showing change in voltage in 1ms around the onset of each pulse in a 20Hz train. Note that the observable lag in this test was zero. Y-axis is mV, x-axis in sec.
See this image and copyright information in PMC

Comment in

  • Feeding with FED3.
    Neff E. Neff E. Lab Anim (NY). 2021 May;50(5):121. doi: 10.1038/s41684-021-00771-6. Lab Anim (NY). 2021. PMID: 33911250 No abstract available.

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