Sigmaplot

Animal numbers were determined beforehand using a power calculation (Gpower). All statistical tests were performed using SPSS 17.0 (IBM SPSS, Inc., Chicago, IL, USA) and data presented as mean ± standard error of the mean (SEM) with graphs constructed using SigmaPlot (San Jose, CA, USA). The Shapiro-Wilkes test was used to ensure all data were normally distributed before parametric testing using a one-way analysis of variance (ANOVA) with a Tukey post hoc test. Statistical differences were considered significant at P values <0.05. For the quantitative real-time polymerase chain reaction (qRT)-PCR, data were compared by a Student's t-test and statistical significance set at P < 0.001.

Statistical analyses were performed using SigmaPlot and SPSS (IBM, Armonk, NY). Kolmogorov-Smirnov tests determined the normality of data distributions. A one-way ANCOVA was conducted using SPSS to determine whether differences existed between the three groups (AIS, PVI, controls) for each reaching parameter while controlling for age. Post-hoc pairwise comparisons were then conducted using Bonferroni corrections for multiple comparisons (α = 0.05). Within each group, Mann-Whitney U-tests or paired t-tests compared performance between both upper limbs and between out and back performance of each limb. Partial Spearman’s correlations controlling for age assessed the relationship of bilateral reaching parameters with clinical assessments (controlled for comparisons to 6 clinical measures, α = 0.05, p < 0.008). In the case of missing clinical data, participants were removed from the analysis.

Statistical analysis included an analysis of the variance with a t-test (Shapiro–Wilk test, equal variance test, and Mann–Whitney rank-sum test), performed using SigmaPlot version 12.0 (IBM Corp., Armonk, NY, USA). Data are presented as the means of at least three independent experiments. A p < 0.05 was considered statistically significant.

Normal distribution was examined by D’Agostino-Pearson and Shapiro-Wilk tests. Continuous variables are presented as mean ± SD. Comparisons were performed with the use of Mann-Whiney U test and unpaired t-test for not normally and normally distributed parameters, respectively. Spearman’s test was used to examine correlations. If both parameters were normally distributed, Pearson test was used, instead. The level of statistical significance was set at p<0.05. Statistical analysis was performed in SigmaPlot, SPSS v.26 (IBM, USA) and GraphPad Prism 5.00 (GraphPad Software, Inc., USA).

All of the data represent an average of at least three independent experiments or measurements. Data are presented as mean ± standard deviation. The kinetic parameters and the curves were estimated and fitted using a linear or nonlinear regression iteration procedure using SigmaPlot (V10.0; IBM Co., Armonk, NY, USA). Statistical analyses were firstly started by examination of the data using the distribution normality test (Shapiro–Wilk test) and the variance homogeneity test (Levene’s test). If normal distribution and homogeneous variance were guaranteed, further statistical analyses (Tukey’s test or Student’s t-test) were conducted using SPSS Statistics (V23.0; IBM Co., Armonk, NY, USA).

All quantitative features were analyzed with SPSS 25. P<0.05 was considered as statistically significant.
Categorical variables are shown as frequencies, and continuous variables are presented as the mean and standard deviation or median and interquartile range. The χ2 test was used to analyze the categorical variables, the t test was applied to analyze the continuous variables with a normal distribution, and the Mann−Whitney U test was used for variables with an abnormal or unknown distribution. Multivariable logistic regression analysis was used to select the independent prognostic factors. The performance of the model was assessed in the primary and validation cohorts. The discrimination of the signature was measured by the area under the curve (AUC).
The ICC was graded as follows: poor (<0.20), moderate (0.20–0.40), fair (0.40–0.60), good (0.60–0.80), or very good (0.80–1.00).
Statistical analyses were performed using SPSS software (Ver. 25, IBM, Armonk, New York), SigmaPlot (Ver. 14.0), R software package (Ver. 3.5.2, R Development Core Team: https://www.r-project.org/), and the Python scikit-learn package (Ver. 3.7, scikit-learn Ver. 0.21, http://scikit-learn.org/).