Statistics
REGRESSION
All data were assessed for normality by visual inspection of histograms, Q-Q plots, and with the Shapiro–Wilk test. Means and standard deviations were reported if variables were found to be normally distributed and medians and inter-quartile ranges if the data distributions were assessed to be skewed. To investigate differences in demographic variables between the groups, we used the independent-samples In the primary analysis, we used a linear mixed model using maximum likelihood estimation with random intercept per subject to assess group and time effects of iTBS on SDNN (or HF-HRV in the subsample of 37 patients).For the correlation between HRV metrics and symptom change, Pearson correlation coefficients were computed to assess correlations between delta values of symptoms and delta values of SDNN. The symptom ratings from the assessment 2 weeks after baseline were used, except for eight participants, where the ratings 4 weeks after baseline were used (due to slightly different time frames for the participants also participating in the brain imaging study mentioned above).To assess the predictive value of baseline SDNN on symptom change, we used a linear regression model with delta values of symptoms as the dependent variable and baseline SDNN as the independent variable. Here, we only analyzed the active iTBS data from both the 25 participants receiving active iTBS at first and the data from the participants from the sham group who decided to enter an open phase with active iTBS (