Abstract
Drinking behavior has been used in basic research to study metabolism, motivation, decision-making and different aspects of health problems, such as anhedonia and alcohol use disorders. In the majority of studies, liquid intake is measured by weighing the bottles before and after the experiment. This method does not tell much about the drinking microstructure, e.g., licking bouts and periods of preference for each liquid, which could be valuable to understand drinking behavior. To improve the data acquisition of drinking microstructure, companies have developed lick-o-meters devices that acquire timestamps when animals approach or drink from a specific sipper. Nevertheless, commercially available devices have elevated costs. Here, we present a low-cost alternative for a lick-o-meter system that allows wireless data acquisition of licking from eight cages with two sippers each. We run a three-phase validation protocol to ensure 1) proper choice of the sensor to detect licks; 2) adaptation of the device to a wireless transmission and realistic in silico tests; and 3) in vivo tests to correlate the amount of licks measured by the prototype and the bottle weight. The capacitive sensor presented appropriate recall and precision for our device. After adaptation to wireless transmission, the in silico validation demonstrated low reading and transmission errors for the device even when tested in extreme simultaneous licking conditions. Finally, a positive correlation between volume consumption and lick's count in the in vivo test was observed, showing that the prototype can be used for in vivo studies interested in rodent drinking microstructure.Significant StatementThis study presents an innovative and low-cost solution for drinking behavioral studies: a lick-o-meter system based on an open-source hardware platform with a user-friendly interface software, capable of simultaneously receiving data from eight automated cages with two drinking bottles each. The lick-o-meter brings an accessible device to acquire high-quality and detailed data. This device also has the possibility to be adaptable to new types of sensors or other neuroscience tools capable of measuring brain activity simultaneously to the behavior.