Assessing negative affect in mice during abstinence from alcohol drinking: Limitations and future challenges - PubMed (original) (raw)

Review

doi: 10.1016/j.alcohol.2022.02.001. Epub 2022 Feb 15.

Katherine M Holleran 2, Thomas L Kash 3, Elena M Vazey 4, Jennifer A Rinker 1, Christina L Lebonville 1, Krysten O'Hara 1, Marcelo F Lopez 5, Sara R Jones 2, Kathleen A Grant 6, Howard C Becker 5, Patrick J Mulholland 7

Affiliations

Review

Assessing negative affect in mice during abstinence from alcohol drinking: Limitations and future challenges

Solal Bloch et al. Alcohol. 2022 May.

Abstract

Alcohol use disorder (AUD) is frequently comorbid with mood disorders, and these co-occurring neuropsychiatric disorders contribute to the development and maintenance of alcohol dependence and relapse. In preclinical models, mice chronically exposed to alcohol display anxiety-like and depressive-like behaviors during acute withdrawal and protracted abstinence. However, in total, results from studies using voluntary alcohol-drinking paradigms show variable behavioral outcomes in assays measuring negative affective behaviors. Thus, the main objective of this review is to summarize the literature on the variability of negative affective behaviors in mice after chronic alcohol exposure. We compare the behavioral phenotypes that emerge during abstinence across different exposure models, including models of alcohol and stress interactions. The complicated outcomes from these studies highlight the difficulties of assessing negative affective behaviors in mouse models designed for the study of AUD. We discuss new behavioral assays, comprehensive platforms, and unbiased machine-learning algorithms as promising approaches to better understand the interaction between alcohol and negative affect in mice. New data-driven approaches in the understanding of mouse behavior hold promise for improving the identification of mechanisms, cell subtypes, and neurocircuits that mediate negative affect. In turn, improving our understanding of the neurobehavioral basis of alcohol-associated negative affect will provide a platform to test hypotheses in mouse models that aim to improve the development of more effective strategies for treating individuals with AUD and co-occurring mood disorders.

Keywords: alcohol drinking; alcohol use disorder; mouse; negative affective behaviors; new behavioral assays.

Copyright © 2022 Elsevier Inc. All rights reserved.

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Conflict of interest statement

Declaration of competing interest None.

Figures

Figure 1.

Figure 1.

Behavioral disruption during abstinence from alcohol vapor inhalation in mice. The alcohol exposure length (short: 7 d or less, intermediate: 8 d to 3 weeks, or chronic: 4+ weeks) is represented by different shading. See Table 2 for references. EPM/EZM, elevated plus maze/elevated zero maze; FS, forced swim; LDB, light-dark box; MB, marble burying; NSF, novelty-suppressed feeding; Soc/Agg, social interaction/aggression; SP, sucrose/saccharin preference.

Figure 2.

Figure 2.

Behavioral changes during early and late abstinence from A) continuous alcohol drinking, B) intermittent access to alcohol, and C) drinking-in-the-dark. The length of the alcohol drinking paradigm (short: 7 d or less, intermediate: 8 d to 3 weeks, or chronic: 4+ weeks) is represented by different shading. See Table 3 for references. EPM/EZM, elevated plus maze/elevated zero maze; FS, forced swim; LDB, light-dark box; MB, marble burying; NSF, novelty-suppressed feeding; 5CSRT, five-choice serial reaction time; Soc/Agg, social interaction/aggression; SP, sucrose/saccharin preference.

Figure 3.

Figure 3.

Machine learning approaches for pose estimation and clustering of behaviors in mice. Top: Pose estimation algorithms, such as DeepLabCut, can track mouse body parts across time. Middle: Supervised machine learning approaches (e.g., SiMBA) can identify specific behaviors when classifiers are predefined. Bottom: Unsupervised machine learning algorithms (e.g., B-SOID, VAME) are used to identify different sets of behaviors in an unbiased manner.

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