EC-FST: A novel pipeline for automatically analyzing mouse forced swim test

Area of Science:

• Neuroscience
• Behavioral Science
• Pharmacology

Background:

• The mouse forced swim test (FST) is a standard preclinical model for evaluating antidepressant efficacy.
• Manual analysis of FST is subjective, time-consuming, and lacks reproducibility.

Purpose of the Study:

• To develop and validate a novel, automated pipeline for analyzing mouse behavior during the FST.
• To improve the objectivity and efficiency of FST data analysis for depression research.

Main Methods:

• Utilized event cameras and AI to capture and quantify mouse movement events during the FST.
• Developed the Event Camera-Forced Swim Test (EC-FST) pipeline to analyze mouse mobility based on event data.
• Validated EC-FST against traditional manual scoring methods.

Main Results:

• EC-FST demonstrated perfect correlation with manual scoring, confirming its reliability.
• The system accurately detected behavioral changes in mouse models of depression (LPS injection, chronic restraint stress).
• Depression model mice showed significantly fewer motion events and lower event frequency compared to controls.

Conclusions:

• The AI-driven EC-FST pipeline offers an automated, unbiased, and reproducible method for analyzing FST behavior.
• This approach enhances efficiency and reliability in preclinical depression research and antidepressant screening.
• EC-FST provides a robust tool for advancing our understanding of depression-related behaviors in mice.