Journal
ENEURO
Volume 6, Issue 5, Pages -Publisher
SOC NEUROSCIENCE
DOI: 10.1523/ENEURO.0292-19.2019
Keywords
Arduino; open source hardware; two-bottle choice
Categories
Funding
- Washington University in St. Louis
- Brain and Behavior Research Foundation (NARSAD) [27461, 27197]
- National Institutes of Health National Institute on Drug Abuse Cutting-Edge Basic Research Award [R21-DA-047127]
- Whitehall Foundation [2017-12-54]
- Rita Allen Scholar Award in Pain
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Measuring ingestive behavior of liquids in rodents is commonly used in studies of reward, metabolism, and circadian biology. Common approaches for measuring liquid intake in real time include computer-tethered lickometers or video-based systems. Additionally, liquids can be measured or weighed to determine the amount consumed without real-time sensing. Here, we built a photobeam-based sipper device that has the following advantages over traditional methods: (1) it is battery powered and fits in vivarium caging to allow home-measurements; (2) it quantifies the intake of two different liquids simultaneously for preference studies; (3) it is low cost and easily constructed, enabling high-throughput experiments; and (4) it is open source so that others can modify it to fit their experimental needs. We validated the performance of this device in three experiments. First, we calibrated our device using time-lapse video-based measurements of liquid intake and correlated sipper interactions with liquid intake. Second, we used the sipper device to measure preference for water versus chocolate milk, demonstrating its utility for two-bottle choice tasks. Third, we integrated the device with fiber photometry, establishing its utility for measuring neural activity in studies of ingestive behavior. This device requires no special equipment or caging, and is small, battery powered, and wireless, allowing it to be placed directly in rodent home cages. The total cost of fabrication is less than $100, and all design files and code are open source. Together, these factors greatly increase scalability and utility for a variety of behavioral neuroscience applications.
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