Figure 4.

Machine-learning-based classification of mouse behaviors using SimBA achieved a detection of behaviors similar to human experimenters. (A): the scheme of the machine-learning-based classification of mouse behavior by using SimBA is shown. After the mouse pose estimation using DeepLabCut, pose features were extracted. A human experimenter defined behaviors in the video through manual classification on SimBA to define the training data. The training data were used to classify the mouse behaviors with supervised machine learning. The trained network was used to detect the behaviors of the mice in the video. We analyzed grooming (shown in (D,E)) and rearing (shown in (F,G)) behaviors in this study. (B): DLC/SimBA analysis did not detect significant changes in travel distance in each period (0–10 min: t(22) = 0.016, p > 0.1; 10–20 min: t(22) = 0.71, p > 0.1; 20–30 min: t(22) = 0.81, p > 0.1). (C): the decrease in the time spent in the center in PNE mice (n = 12) was detected by DLC/SimBA, in comparison with Con mice (n = 12; 0–10 min: t(22) = 0.58, p > 0.1; 10–20 min: t(22) = 2.55, p < 0.05; 20–30 min: t(22) = 0.62, p > 0.1). (D): the total frequency of grooming in the open-field test is shown and the frequency of grooming was decreased in PNE mice (t(2) = 4.60, p < 0.05). (E): the time spent grooming showed a decrease by PNE (t(2) = 4.48, p < 0.05). (F): the total frequency of rearing behaviors decreased in the PNE mice (t(2) = 5.85, p < 0.05). (G): the duration of rearing behavior did not change (t(2) = 1.75, p > 0.1). ns = not statistically significant, * p < 0.05, by using Student’s t-test.