Evaluation of mouse behavioral responses to nutritive versus nonnutritive sugar using a deep learning-based 3D real-time pose estimation system

Abstract

Animals are able to detect the nutritional content of sugar independently of taste. When given a choice between nutritive sugar and nonnutritive sugar, animals develop a preference for nutritive sugar over nonnutritive sugar during a period of food deprivation (Buchanan et al., 2022; Dus et al., 2011; 2015; Tan et al., 2020; Tellez et al., 2016). To quantify behavioral features during an episode of licking nutritive versus nonnutritive sugar, we implemented a multi-vision, deep learning-based 3D pose estimation system, termed the AI Vision Analysis for Three-dimensional Action in Real-Time (AVATAR)(Kim et al., 2022). Using this method, we found that mice exhibit significantly different approach behavioral responses toward nutritive sugar versus nonnutritive sugar even before licking a sugar solution. Notably, the behavioral sequences during the approach toward nutritive versus nonnutritive sugar became significantly different over time. These results suggest that the nutritional value of sugar not only promotes its consumption but also elicits distinct repertoires of feeding behavior in deprived mice.

Keywords:

• Deep learning based
• 3D real-time pose estimation
• mouse behavior
• nutritive vs. nonnutritive sugar
• two-choice assay

Author Contributions

D-G. K. developed the AVATAR hardware and software algorithm platforms. J. K. performed behavioral experiments with help from W.G. and D-G. K. J.K. and W.G. contributed to the development of the hardware and analysis platform for customized lickometer. G.S.B.S conceived and supervised the project. J.K., and G.S.B.S wrote the manuscript with inputs from other authors.

Disclosure statement

The authors declare the following competing interests: D-G Kim is a co-founder of the company ACTNOVA. The other authors declare no competing interests.

Data availability statement

The datasets that support the findings of this study are available from the corresponding author upon reasonable request.

Additional information

Funding

This study was supported by grants from KAIST Advanced Institute-X (KAIX) Fellowship and ASAN Biomedical Science Fellowship to J.K, grants from the Samsung Science and Technology Foundation (SSTF-BA-1802–11), National Research Foundation of Korea (NRF-2020R1A2C2009865 and NRF-2022M3A9F3082982) to G.S.B.S. and (NRF-2019M3E5D2A01066259) to Daesoo Kim who contributed to the development of the AVATAR system, the AI-based analysis and interpretation of animal behavior, and a grant from the NRF funded by Ministry and Science and ICT (2021 NRF-M3F3A2A01037365) to G.S.B.S as a co-PI.