Figure 6 | Scientific Reports

Figure 6

From: Stress and behavioral correlates in the head-fixed method: stress measurements, habituation dynamics, locomotion, and motor-skill learning in mice

Figure 6

Motor control over the container movement and correlation with the blood corticosterone. (a–h) Detailed velocity analysis based on the bouts of activity (for details see “Methods”) during the 25-day head-fixed habituation protocol; solid orange lines correspond to the grand averages (all days and all animals together). (a, b) Data analyzed with one-way RM ANOVA followed by Bonferroni’s post hoc tests. (a) Statistically significant increase in the average velocity of individual bouts during the habituation protocol: F(2.669, 18.69) = 3.825, p = 0.0307 for the ANOVA, and *p = 0.0425 for the post hoc day 1 (D1) vs. D25 comparison. (b) Stable average duration of a bout throughout the habituation protocol with minor statistically non-significant fluctuations: F(3.301, 23.10) = 1.538, p = 0.2293 for the ANOVA. (c) Bouts of activity dynamics, changes in the proportion between the 4 different categories of bouts with passing time (for details see “Methods”). (dg) Correlation analysis between the blood corticosterone concentration and changes in bout proportions: Spearman’s in (d, f, g), and Pearson’s in (e). Corticosterone level measured every 5 days during the 25-day head-fixed habituation protocol. Bouts presented as averages for the head-fixed sessions in-between blood sampling (e.g. D2–D5, D6–D10, etc.). Higher corticosterone level corresponded to more of the long & slow movements (r = 0.3666, *p = 0.0200) and to less of the short & fast movements (r = − 0.3238, *p = 0.0415). However, no correlation was observed in comparisons with other types of bouts: long & fast (r = − 0.2004, p = 0.2151); short & slow (r = − 0.1208, p = 0.4577). (h) Statistically significant changes in the proportion of the long bouts with training time and no changes in the short bouts, one-way RM ANOVA: long & fast, F(2.990, 20.93) = 3.408, *p = 0.0366; long & slow, F(4.138, 28.97) = 5.920, **p = 0.0012; short & fast, F(3.085, 21.59) = 2.248, p = 0.1103; short & slow, F(4.352, 30.46) = 1.627, p = 0.1893; n = 8 for all datasets.

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