Fig. 6
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 and manual scoring. Comparison between the manual and the ML algorithm scoring following drug treatment. When the ML algorithm was compared with manual scoring, no statistically significant differences were detected between the two methods for any behaviour assessed in the FST (Immobility: F(1,42) = 0.31, p = 0.579; Swimming: F(1,42) = 1.5, p = 0.227; Climbing: F(1,42) = 1.64, p = 0.208). Consistent with well-established literature on the effects of FLX (n = 8) and DMI (n = 8) in the FST, both methods successfully identified: (A) a decrease in immobility time (F(2,42) = 19.42, p < 0.0001) in rats treated with antidepressants compared to the vehicle group (Manual: vehicle vs. DMI, p < 0.0001; vehicle vs. FLX, p = 0.0003. ML: vehicle vs. DMI, p = 0.007; vehicle vs. FLX, p = 0.003) (B) the SSRI FLX preferentially increased swimming behaviour (F(2,42) = 13.34, p < 0.0001. Manual: vehicle vs. FLX, p = 0.002. ML: vehicle vs. FLX, p = 0.018); and (C) the TCA DMI preferentially increased climbing behaviour (F(2,42) = 13.02, p < 0.0001. Manual: vehicle vs. DMI, p = 0.0003. ML: vehicle vs. DMI, p = 0.008). Two-way ANOVA followed by Dunnett’s multiple-comparisons test was used. Data are presented as mean ± s.e.m. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001 vs. vehicle treatment.
