Statistical analysis
| Location | Dataset | Data structure | Type of test | Power |
|---|---|---|---|---|
| a | Fig. 2P | Non-normal distribution (unequal variances) | Kruskal–Wallis, Mann–Whitney U | H(11) = 41.5, p = 0.000; ipsilateral hemisphere: d2 vs. d7: p = 0.021; d2 vs. d14: p = 0.021; d2 vs. d28: p = 0.021; d2 vs. d56: p = 0.021; d2 vs. d112: p = 0.021; d7 vs. d14: p = 0.021; d7 vs. d28: p = 0.021; d7 vs. d56: p = 0.083; d7 vs. 112: p = 0.083; d14 vs. d28: p = 1.00; d14 vs. d56: p = 0.043; d14 vs. d112: p = 0.021; d28 vs. d56: p = 0.083; d28 vs. d112: p = 0.043; d56 s. d112: p = 0.773; ipsilateral vs. contralateral: d2: p = 0.773; d7: p = 0.021; d14: p = 0.021; d28: p = 0.021; d56: p = 0.021; d112: p = 0.021 |
| b | Fig. 5B | Non-normal distribution | Kruskal–Wallis, Mann–Whitney U | d1: H(2) = 2.26, p = 0.323; d3: H(2) = 4.93, p = 0.085; d7: H(2) = 1.29, p = 0.524; d10: H(2) = 15.5, p = 0.000; NT vs. (+)-Nal: p = 0.006; Veh vs. (+)-Nal: p = 0.000; NT vs. Veh: p = 0.651; d14: H(2) = 12.3, p = 0.002; NT vs. (+)-Nal: p = 0.012; Veh vs. (+)-Nal: p = 0.001; NT vs. Veh: p = 0.485 |
| c | Fig. 5C | Non-normal distribution | Kruskal–Wallis, Mann–Whitney U | d1: H(2) = 3.25, p = 0.197; d3: H(2) = 6.92, p = 0.032; NT vs. (+)-Nal: p = 0.017; Veh vs. (+)-Nal: p = 0.059; NT vs. Veh: p = 0.393; d7: H(2) = 3.26, p = 0.196; d10: H(2) = 15.4, p = 0.000; NT vs. (+)-Nal: p = 0.002; Veh vs. (+)-Nal: p = 0.002; NT vs. Veh: p = 0.203; d14: H(2) = 19.1, p = 0.000; NT vs. (+)-Nal: p = 0.000; Veh vs. (+)-Nal: p = 0.000; NT vs. Veh: p = 0.209 |
| d | Fig. 5D | Non-normal distribution | Kruskal–Wallis, Mann–Whitney U | H(4) = 15.1, p = 0.004; Veh vs. 8 × 10−4: p = 0.206; Veh vs. 8 × 10−3: p = 0.966; Veh vs. 8 × 10−2: p = 0.002; Veh vs. 8 × 10−1: p = 0.059; 8 × 10−4 vs. 8 × 10−3: p = 0.552; 8 × 10−4 vs. 8 × 10−2: p = 0.001; 8 × 10−4 vs. 8 × 10−1: p = 0.019; 8 × 10−3 vs. 8 × 10−2: p = 0.031; 8 × 10−3 vs. 8 × 10−1: p = 0.142; 8 × 10−2 vs. 8 × 10−1: p = 0.488 |
| e | Fig. 5E | Non-normal distribution | Kruskal–Wallis, Mann–Whitney U | H(4) = 6.38, p = 0.041; Veh vs. 8 × 10−4: p = 0.690; Veh vs. 8 × 10−3: p = 0.487; Veh vs. 8 × 10−2: p = 0.016; Veh vs. 8 × 10−1: p = 0.038; 8 × 10−4 vs. 8 × 10−3: p = 0.314; 8 × 10−4 vs. 8 × 10−2: p = 0.006; 8 × 10−4 vs. 8 × 10−1: p = 0.025; 8 × 10−3 vs. 8 × 10−2: p = 0.144; 8 × 10−3 vs. 8 × 10−1: p = 0.208; 8 × 10−2 vs. 8 × 10−1: p = 0.730 |
| f | Fig. 5F | Normal distribution | One-way ANOVA, Bonferroni | F(2,42) = 0.054; NT vs. (+)-Nal: p = 0.064; Veh vs. (+)-Nal: p = 0.242; NT vs. Veh: p = 1.00 |
| g | Fig. 5G | Non-normal distribution (unequal variances) | Kruskal–Wallis, Mann–Whitney U | H(2) = 6.82, p = 0.033; NT vs. (+)-Nal: p = 0.004; Veh vs. (+)-Nal: p = 0.243; NT vs. Veh: p = 0.313 |
| h | Fig. 5H | Non-normal distribution | Mann–Whitney U | d1: p = 0.663; d3: p = 0.663; d7: p = 0.963; d10: p = 0.001; d14: p = 0.000 |
| i | Fig. 5I | Non-normal distribution | Mann–Whitney U | d1: p = 0.159; d3: p = 0.401; d7: p = 0.565; d10: p = 0.002; d14: p = 0.005 |
| j | Fig. 5J | Normal distribution | t test | d7: t(16) = 0.47, p = 0.647; d14: t(16) = 0.06, p = 0.953 |
| k | Fig. 5K | Normal distribution | One-way ANOVA, Bonferroni | d7: F(2,59) = 5.27, p = 0.008; NT vs. (+)-Nal: p = 0.574; Veh vs. (+)-Nal: p = 0.113; NT vs. Veh: p = 0.009; d14: F(2,62) = 1.79, p = 0.175 |
| l | Fig. 6A | Normal distribution | t test | t(26) = 2.51, p = 0.019 |
| m | Fig. 6B | Non-normal distribution (unequal variances) | Kruskal–Wallis, Mann–Whitney U | H(2) = 11.4, p = 0.003; Naïve vs. Ctrl: p = 0.002; Naïve vs. (+)-Nal: p = 0.157; Ctrl vs. (+)-Nal: p = 0.036 |
| n | Fig. 7A | Normal distribution | One-way ANOVA, Bonferroni | F(2,31) = 8.63, p = 0.001; Naïve vs. Ctrl: p = 0.003; Naïve vs. (+)-Nal: p = 1.00; Ctrl vs. (+)-Nal: p = 0.013 |
| o | Fig. 7B | Non-normal distribution (unequal variances) | Kruskal–Wallis, Mann–Whitney U | H(2) = 5.77, p = 0.056 |
| p | Fig. 7C | Non-normal distribution (unequal variances) | Kruskal–Wallis, Mann–Whitney U | H(2) = 17.6, p = 0.000; Naïve vs. Ctrl: p = 0.000; Naïve vs. (+)-Nal: p = 0.001; Ctrl vs. (+)-Nal: p = 0.027 |
| q | Fig. 8B | Normal distribution | t test | t(13) = 0.89, p = 0.389 |
| r | Fig. 8D | Non-normal distribution | Mann–Whitney U | d2: p = 0.861; d6: p = 0.825; d10: p = 0.279; d16: p = 0.066 |
| s | Fig. 8F | Non-normal distribution | Mann–Whitney U | d1: p = 0.800; d3: p = 0.861; d7: p = 0.516; d10: p = 0.391; d14: p = 0.694 |
| t | Fig. 8G | Normal distribution | Two-way RM ANOVA | F(4,52) = 0.29, p = 0.88 |
| u | Fig. 9A | Non-normal distribution (unequal variances) | Mann–Whitney U | p = 0.006 |
| v | Fig. 9B | Normal distribution | One-way ANOVA, Dunnett | F(6,54) = 2.74, p = 0.022; 20µM (–)-Nal: p = 0.669; 50µM (–)-Nal: p = 0.020; 100µM (–)-Nal: p = 0.218; 20µM (+)-Nal: p = 0.492; 50µM (+)-Nal: p = 0.637; 100µM (+)-Nal: p = 0.006 |
(+)-Nal, (+)-naloxone; (–)-Nal, (–)-naloxone; Ctrl, control; d, post-stroke day; NT, no treatment; RM, repeated measures; Veh, vehicle.