Statistical analysis
REGRESSION, POSITIVE
All statistical analyses were performed with IBM SPSS Statistics (Version: 22.0, IBM Corp, 2020). The level of significance was set at Intervention-induced changes in peak torques and neurometabolite ratios were calculated as the percentage of pre-to-post differences relative to the pre measures: i.e., (∆) = 100 × [(Post − Pre) / Pre]. Positive values of ∆ indicated an increase in peak torque or an increase in neurometabolite ratio compared to baseline levels. Finally, bivariate correlation analyses (Pearson’s correlation) were performed between changes in PT (∆PT) with changes in neurometabolites’ ratio (∆tNAA/tCr, ∆tCho/tCr, ∆mIns/tCr, and ∆Glx/tCr) for both experimental and control group. Data visualization for the bivariate correlation analyses was done by plotting regression plots from Python seaborn library (Python, v3.9.12). A correction for multiple comparisons by false discovery rate (FDR) analysis (Benjamini and Hochberg adjustment) was carried out for all correlation analyses. In the FDR correction, each 