A deep-learning-based threshold-free method for automated analysis of rodent behavior in the forced swim test and tail suspension test

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 timeconsuming and subjective. New method: 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. Comparison with existing method(s): 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.