Yang Xia , Xuechun Meng , Yuxing Ning , Hongqi Li , Yue Wu , Jian Zhang , Ling Liu , Zhaohuan Huang , Ji Liu
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By quantifying event numbers and their temporal distribution, we were able to determine mouse’s mobile state across time-line.</div></div><div><h3>Results</h3><div>The EC-FST results showed perfect correlation with manual scoring, suggesting that the proposed method is reliable for analyzing forced swim test. We further tested the power of the EC-FST for detecting depressive-like behavior in mouse depression models,including lipopolysaccharide (LPS) injection and chronic restraint stress (CRS). Depressive-model mice exhibited significantly fewer motion events and lower event frequency than controls, aligning with manual scoring.</div></div><div><h3>Comparison with existing methods</h3><div>Unlike traditional threshold-based approaches, EC-FST provides an automated, unbiased, and reproducible analysis of FST behavior, eliminating the subjectivity of manual scoring.</div></div><div><h3>Conclusion</h3><div>Leveraging AI-driven event cameras, we established a robust pipeline for analyzing mouse behavior in the FST, offering greater efficiency and reproducibility for depression research.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"424 ","pages":"Article 110585"},"PeriodicalIF":2.3000,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"EC-FST: A novel pipeline for automatically analyzing mouse forced swim test\",\"authors\":\"Yang Xia , Xuechun Meng , Yuxing Ning , Hongqi Li , Yue Wu , Jian Zhang , Ling Liu , Zhaohuan Huang , Ji Liu\",\"doi\":\"10.1016/j.jneumeth.2025.110585\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><div>The mouse forced swim test (FST) is widely used to evaluate the efficacy of potential anti-depressant drugs. Traditional methods for analyzing forced swim test results rely on manually setting the threshold for immobility, which is time-consuming and barely reproducible.</div></div><div><h3>New method</h3><div>In the present study, we introduced a novel pipeline (EC-FST) by extracting the feature of mouse status instead of calculating immobility time. First, we utilized event camera, a powerful AI tool for dynamic object-tracking framework, to capture the mobile events from mouse forced swim test. By quantifying event numbers and their temporal distribution, we were able to determine mouse’s mobile state across time-line.</div></div><div><h3>Results</h3><div>The EC-FST results showed perfect correlation with manual scoring, suggesting that the proposed method is reliable for analyzing forced swim test. We further tested the power of the EC-FST for detecting depressive-like behavior in mouse depression models,including lipopolysaccharide (LPS) injection and chronic restraint stress (CRS). Depressive-model mice exhibited significantly fewer motion events and lower event frequency than controls, aligning with manual scoring.</div></div><div><h3>Comparison with existing methods</h3><div>Unlike traditional threshold-based approaches, EC-FST provides an automated, unbiased, and reproducible analysis of FST behavior, eliminating the subjectivity of manual scoring.</div></div><div><h3>Conclusion</h3><div>Leveraging AI-driven event cameras, we established a robust pipeline for analyzing mouse behavior in the FST, offering greater efficiency and reproducibility for depression research.</div></div>\",\"PeriodicalId\":16415,\"journal\":{\"name\":\"Journal of Neuroscience Methods\",\"volume\":\"424 \",\"pages\":\"Article 110585\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2025-09-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Neuroscience Methods\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0165027025002298\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Neuroscience Methods","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0165027025002298","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
EC-FST: A novel pipeline for automatically analyzing mouse forced swim test
Background
The mouse forced swim test (FST) is widely used to evaluate the efficacy of potential anti-depressant drugs. Traditional methods for analyzing forced swim test results rely on manually setting the threshold for immobility, which is time-consuming and barely reproducible.
New method
In the present study, we introduced a novel pipeline (EC-FST) by extracting the feature of mouse status instead of calculating immobility time. First, we utilized event camera, a powerful AI tool for dynamic object-tracking framework, to capture the mobile events from mouse forced swim test. By quantifying event numbers and their temporal distribution, we were able to determine mouse’s mobile state across time-line.
Results
The EC-FST results showed perfect correlation with manual scoring, suggesting that the proposed method is reliable for analyzing forced swim test. We further tested the power of the EC-FST for detecting depressive-like behavior in mouse depression models,including lipopolysaccharide (LPS) injection and chronic restraint stress (CRS). Depressive-model mice exhibited significantly fewer motion events and lower event frequency than controls, aligning with manual scoring.
Comparison with existing methods
Unlike traditional threshold-based approaches, EC-FST provides an automated, unbiased, and reproducible analysis of FST behavior, eliminating the subjectivity of manual scoring.
Conclusion
Leveraging AI-driven event cameras, we established a robust pipeline for analyzing mouse behavior in the FST, offering greater efficiency and reproducibility for depression research.
期刊介绍:
The Journal of Neuroscience Methods publishes papers that describe new methods that are specifically for neuroscience research conducted in invertebrates, vertebrates or in man. Major methodological improvements or important refinements of established neuroscience methods are also considered for publication. The Journal''s Scope includes all aspects of contemporary neuroscience research, including anatomical, behavioural, biochemical, cellular, computational, molecular, invasive and non-invasive imaging, optogenetic, and physiological research investigations.