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瘦素缺乏导致的行为变化——使用EthoVision和DeepLabCut的比较分析。

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

作者信息

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

机构信息

Rudolf-Zenker-Institute for Experimental Surgery, Rostock University Medical Center, Rostock, Germany.

Institute of Experimental Epileptology and Cognition Research, University Medical Center Bonn, Bonn, Germany.

出版信息

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

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.

摘要

引言

肥胖啮齿动物,如瘦素缺乏(ob/ob)小鼠,表现出显著的行为变化,因此是评估肥胖导致的精神障碍的理想模型。为此,8周、24周和40周龄的雌性和雄性ob/ob小鼠接受了两项常见的行为测试,即旷场试验和高架十字迷宫试验,以研究性别和年龄依赖性的行为改变。这些测试的准确性通常取决于观察者,观察者可能会主观影响数据。

方法

为避免这种偏差,使用视频系统对小鼠进行跟踪。通过比较使用EthoVision(EV)和DeepLabCut(DLC)这两种软件对视频文件进行进一步分析。在DLC中,深度学习应用程序构成了未来在行为研究中使用人工智能的基础,在减少动物数量方面也是如此。

结果

在未发现性别差异且部分年龄相关差异也未发现后,比较发现两种软件得出的结果几乎相同,因此在基本结果方面相似,尤其是在速度和总移动距离的测定方面。此外,我们观察到与EV相比,DLC还有其他优势,因为它能够以自动化方式解释更复杂的行为,如竖毛和倾斜。

讨论

基于两种软件的可比结果,我们的研究可以作为一个起点,通过使用DLC来优化并可能预测未来肥胖临床前研究中的行为观察,从而研究行为改变。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c25/10079875/c3a144eea949/fnins-17-1052079-g001.jpg

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