Fig. 7
From: Machine learning-based model for behavioural analysis in rodents applied to the forced swim test
Measurement of the effect of the drugs on FST behaviour measured with ML algorithm. The ML algorithm accurately categorizes the effect of different classes of antidepressants in the FST. According to the literature, our ML model assigned the TCA AMI (n = 5) high levels of climbing, whereas, for the SSRI PRX (n = 5), the model detected increased swimming. In the case of VLX (n = 5), which, compared to TCAs and SSRIs, has a more balanced noradrenergic/serotonergic profile, our model indicated a corresponding balance between swimming/climbing performance. (A) Immobility (F(3,16) = 4.48, p = 0.018. vehicle vs. AMI, p = 0.014; vehicle vs. PRX, p = 0.656; vehicle vs. VLX, p = 0.044); (B) Swimming (F(3,16) = 5.93, p = 0.006. vehicle vs. AMI, p = 0.97; vehicle vs. PRX, p = 0.027; vehicle vs. VLX, p = 0.011); (C) Climbing (F(3,16) = 10.55, p = 0.0005. vehicle vs. AMI, p = 0.023; vehicle vs. PRX, p = 0.075; vehicle vs. VLX, p = 0.541). One-way ANOVA followed by Dunnett’s multiple-comparisons test was used. Data are presented as mean \(\pm\) s.e.m. * p < 0.005 vs. the vehicle treatment.
