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慢性社交挫败应激后多维行为改变的计算分析。

Computational Analysis of Multidimensional Behavioral Alterations After Chronic Social Defeat Stress.

机构信息

Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York.

PsychoGenics Inc., Paramus, New Jersey.

出版信息

Biol Psychiatry. 2021 May 1;89(9):920-928. doi: 10.1016/j.biopsych.2020.10.010. Epub 2020 Oct 24.

DOI:10.1016/j.biopsych.2020.10.010
PMID:33309017
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8052271/
Abstract

BACKGROUND

The study of depression in humans depends on animal models that attempt to mimic specific features of the human syndrome. Most studies focus on one or a few behavioral domains, with time and practical considerations prohibiting a comprehensive evaluation. Although machine learning has enabled unbiased analysis of behavior in animals, this has not yet been applied to animal models of psychiatric disease.

METHODS

We performed chronic social defeat stress (CSDS) in mice and evaluated behavior with PsychoGenics' SmartCube, a high-throughput unbiased automated phenotyping platform that collects >2000 behavioral features based on machine learning. We evaluated group differences at several times post-CSDS and after administration of the antidepressant medication imipramine.

RESULTS

SmartCube analysis after CSDS successfully separated control and defeated-susceptible mice, and defeated-resilient mice more resembled control mice. We observed a potentiation of CSDS effects over time. Treatment of susceptible mice with imipramine induced a 40.2% recovery of the defeated-susceptible phenotype as assessed by SmartCube.

CONCLUSIONS

High-throughput analysis can simultaneously evaluate multiple behavioral alterations in an animal model for the study of depression, which provides a more unbiased and holistic approach to evaluating group differences after CSDS and perhaps can be applied to other mouse models of psychiatric disease.

摘要

背景

人类抑郁症的研究依赖于试图模拟人类综合征特定特征的动物模型。大多数研究集中在一个或几个行为领域,由于时间和实际考虑,无法进行全面评估。尽管机器学习使对动物行为进行无偏分析成为可能,但尚未将其应用于精神疾病的动物模型。

方法

我们在小鼠中进行慢性社交挫败应激(CSDS),并使用 PsychoGenics 的 SmartCube 进行行为评估,这是一种高通量、无偏自动化表型平台,可基于机器学习收集>2000 种行为特征。我们在 CSDS 后和抗抑郁药物丙咪嗪给药后几个时间点评估组间差异。

结果

CSDS 后的 SmartCube 分析成功分离了对照组和易败组以及不易败组的小鼠,而且不易败组的小鼠更类似于对照组的小鼠。我们观察到 CSDS 效应随时间增强。易败组的小鼠用丙咪嗪治疗后,SmartCube 评估的易败表型恢复了 40.2%。

结论

高通量分析可以同时评估抑郁症研究动物模型中的多种行为改变,为 CSDS 后评估组间差异提供了更无偏和整体的方法,并且可能适用于其他精神疾病的小鼠模型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/edb0/8052271/a753aace4b9a/nihms-1640534-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/edb0/8052271/6814c7f4c695/nihms-1640534-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/edb0/8052271/157806874e94/nihms-1640534-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/edb0/8052271/dc8646b91f56/nihms-1640534-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/edb0/8052271/a753aace4b9a/nihms-1640534-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/edb0/8052271/6814c7f4c695/nihms-1640534-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/edb0/8052271/157806874e94/nihms-1640534-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/edb0/8052271/dc8646b91f56/nihms-1640534-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/edb0/8052271/a753aace4b9a/nihms-1640534-f0004.jpg

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