背景:强迫游泳试验(FST)和悬尾试验(TST)被广泛用于评估动物的抑郁样行为。不动时间在FST和TST中都被用作重要参数。分析FST和TST的传统方法依赖于手动设置固定阈值,这是耗时和主观的。
方法:我们提出了一种无阈值的方法,用于在这些测试中使用双流活动分析网络(DSAAN)对小鼠进行自动分析。具体来说,该网络使用有限数量的视频帧提取鼠标的空间信息,并将其与从差分特征图中提取的时间信息相结合,以确定鼠标的状态。要做到这一点,我们开发了MouseFSTST数据集,其中包括FST和TST的带注释的视频记录。
结果:通过使用DSAAN方法,我们在TST和FST的92.51%和88.70%的准确度下确定了不动状态,分别。DSAAN预测的不动时间与手动评分很好地相关,这表明了该方法的可靠性。重要的是,DSAAN仅使用94张注释图像,FST和TST的准确率均超过80%,这表明,即使是非常有限的训练数据集也可以在我们的模型中产生良好的性能。
结论:与DBscorer和EthoVisionXT相比,我们的方法在MouseFSTST数据集上表现出最高的Pearson相关系数和手动注释结果。
结论:我们建立了一个强大的工具来分析抑郁样行为,而与阈值无关,它能够将用户从耗时的手动分析中解放出来。
BACKGROUND: The forced swim test (FST) and tail suspension test (TST) are widely used to assess depressive-like behaviors in animals. Immobility time is used as an important parameter in both FST and TST. Traditional methods for analyzing FST and TST rely on manually setting the threshold for immobility, which is time-consuming and subjective.
METHODS: We proposed a threshold-free method for automated analysis of mice in these tests using a Dual-Stream Activity Analysis Network (DSAAN). Specifically, this network extracted spatial information of mice using a limited number of video frames and combined it with temporal information extracted from differential feature maps to determine the mouse\'s state. To do so, we developed the Mouse FSTST dataset, which consisted of annotated video recordings of FST and TST.
RESULTS: By using DSAAN methods, we identify immobility states at accuracies of 92.51 % and 88.70 % for the TST and FST, respectively. The predicted immobility time from DSAAN is nicely correlated with a manual score, which indicates the reliability of the proposed method. Importantly, the DSAAN achieved over 80 % accuracy for both FST and TST by utilizing only 94 annotated images, suggesting that even a very limited training dataset can yield good performance in our model.
CONCLUSIONS: Compared with DBscorer and EthoVision XT, our method exhibits the highest Pearson correlation coefficient with manual annotation results on the Mouse FSTST dataset.
CONCLUSIONS: We established a powerful tool for analyzing depressive-like behavior independent of threshold, which is capable of freeing users from time-consuming manual analysis.