Leptin deficiency-caused behavioral change - A comparative analysis using EthoVision and DeepLabCut

Daniel Bühler; Nicole Power Guerra; Luisa Müller; Olaf Wolkenhauer; Martin Düffer; Brigitte Vollmar; Angela Kuhla; Markus Wolfien

doi:10.3389/fnins.2023.1052079

Leptin deficiency-caused behavioral change - A comparative analysis using EthoVision and DeepLabCut

Front Neurosci. 2023 Mar 24:17:1052079. doi: 10.3389/fnins.2023.1052079. eCollection 2023.

Authors

Daniel Bühler^{1

2

3}, Nicole Power Guerra^{1

4}, Luisa Müller^{1

5

6}, Olaf Wolkenhauer^{3

7}, Martin Düffer¹, Brigitte Vollmar^{1

5}, Angela Kuhla^{1

5}, Markus Wolfien^{3

8

9}

Affiliations

¹ Rudolf-Zenker-Institute for Experimental Surgery, Rostock University Medical Center, Rostock, Germany.
² Institute of Experimental Epileptology and Cognition Research, University Medical Center Bonn, Bonn, Germany.
³ Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany.
⁴ Clinic and Polyclinic for Otorhinolaryngology and Otolaryngology, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
⁵ Centre for Transdisciplinary Neurosciences Rostock (CTNR), Rostock University Medical Center, Rostock, Germany.
⁶ Department of Psychosomatic Medicine and Psychotherapy, Rostock University Medical Center, Rostock, Germany.
⁷ Leibniz-Institute for Food Systems Biology, Technical University of Munich, Freising, Germany.
⁸ Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
⁹ Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Dresden, Germany.

Abstract

Introduction: Obese rodents e.g., the leptin-deficient (ob/ob) mouse exhibit remarkable behavioral changes and are therefore ideal models for evaluating mental disorders resulting from obesity. In doing so, female as well as male ob/ob mice at 8, 24, and 40 weeks of age underwent two common behavioral tests, namely the Open Field test and Elevated Plus Maze, to investigate behavioral alteration in a sex- and age dependent manner. The accuracy of these tests is often dependent on the observer that can subjectively influence the data.

Methods: To avoid this bias, mice were tracked with a video system. Video files were further analyzed by the compared use of two software, namely EthoVision (EV) and DeepLabCut (DLC). In DLC a Deep Learning application forms the basis for using artificial intelligence in behavioral research in the future, also with regard to the reduction of animal numbers.

Results: After no sex and partly also no age-related differences were found, comparison revealed that both software lead to almost identical results and are therefore similar in their basic outcomes, especially in the determination of velocity and total distance movement. Moreover, we observed additional benefits of DLC compared to EV as it enabled the interpretation of more complex behavior, such as rearing and leaning, in an automated manner.

Discussion: Based on the comparable results from both software, our study can serve as a starting point for investigating behavioral alterations in preclinical studies of obesity by using DLC to optimize and probably to predict behavioral observations in the future.

Keywords: DeepLabCut; EthoVision; behavioral analysis; deep learning; obesity.

Grants and funding

This study was supported by a grant from the Deutsche Forschungsgemeinschaft, Bonn, Germany (KU 3280/1-2).