Computational modeling reveals cognitive processes in simple rodent depression tests

Simple behavioral tests like the forced swim test (FST) and tail suspension test (TST) are widely used to assess depression-like behaviors in rodents, primarily measuring immobility time. However, this approach can oversimplify behavioral readouts and obscure cognitive processes driving behavior, leaving the relationship between increased immobility and cognitive biases unclear. Here, we developed the SwimStruggleTracker (SST) to extract fine-grained behavioral trajectories and integrate computational modeling to systematically analyze behavior. Our findings show that behavior in the FST and TST follows reinforcement learning principles involving learning, consequence perception, and decision-making. Notably, the cognitive processes underlying behavior differ between the two tests, challenging the assumption that they are interchangeable for cross-validation. Regression analyses identify distinct behavior phases: early behavior is primarily influenced by learning-related factors, while later stages are more affected by consequence sensitivity. These findings suggest that traditional analyses may underestimate the role of learning and overemphasize consequence sensitivity.

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Publication Details

Journal
Cell Reports Methods
Published
2025-12-01
DOI
https://doi.org/10.1016/j.crmeth.2025.101246
Primary Topic
Zebrafish Biomedical Research Applications
Type
article
Field-Weighted Citation Impact
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article

Computational modeling reveals cognitive processes in simple rodent depression tests

Tianyu Lu, Jiaozhao Yan, Yunfeng Li, Xiang Zhang et al.
Cell Reports Methods
Zebrafish Biomedical Research Applications
article

Computational modeling reveals cognitive processes in simple rodent depression tests

Tianyu Lu, Jiaozhao Yan, Yunfeng Li, Xiang Zhang, Zhihan Li
article en

Abstract

Simple behavioral tests like the forced swim test (FST) and tail suspension test (TST) are widely used to assess depression-like behaviors in rodents, primarily measuring immobility time. However, this approach can oversimplify behavioral readouts and obscure cognitive processes driving behavior, leaving the relationship between increased immobility and cognitive biases unclear. Here, we developed the SwimStruggleTracker (SST) to extract fine-grained behavioral trajectories and integrate computational modeling to systematically analyze behavior. Our findings show that behavior in the FST and TST follows reinforcement learning principles involving learning, consequence perception, and decision-making. Notably, the cognitive processes underlying behavior differ between the two tests, challenging the assumption that they are interchangeable for cross-validation. Regression analyses identify distinct behavior phases: early behavior is primarily influenced by learning-related factors, while later stages are more affected by consequence sensitivity. These findings suggest that traditional analyses may underestimate the role of learning and overemphasize consequence sensitivity.

Cell Reports MethodsVol. 5(12)
ShanghaiTech University (CN)
Openalex Percentile: Top 60%
Zebrafish Biomedical Research Applications
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