2.8 Statistical analysis
depression, MADRS-S, AFF
REGRESSION
All statistical analysis was performed using Statistical and Machine Learning toolboxes in MATLAB 2021b. At the optimal working point within the search interval, our whole-brain model simulated BOLD signals with FC patterns similar to the empirical resting state data. Based on the similarity between each individual’s empirical and simulated FC, we assigned patients to one of the identified depression models/subtypes.We further indicated that there is no evidence that these two subtypes are allocated to treatment groups in a biased manner using a Pearson’s chi squared test. Yet to remove any potential bias driven by having different numbers of individuals associated with subtypes across two treatment groups, we used bootstrap method for hypothesis testing. Thereby, we equalized the number of individuals assigned to each subtype in every treatment group using resampling with replacement as implemented in the bootstrapping method.At baseline, we fitted separate logistic regression models (LoM) for MADRS-S total, BPRS AFF and CAINS. Hence, in total, three LoMs were fitted alongside with 1000-bootstrap resampling to assess the effect of depression subtype on the individual items of the three depression symptom ratings (i.e., MADRS-S total, BPRS AFF and CAINS), In the follow-up session, subsequent to the treatment (i.e., either active or sham iTBS over dmPFC), the behavioral measures consisting of MADRS-S total, BPRS AFF, CAINS and TMT were collected and contrasted to values at the baseline to generate the change score across two sessions. The main effects of treatment (active or sham), depression subtypes and their interaction on behavioral change scores (follow-up session contrasted to the baseline session) were assessed using a linear regression model (LiM) and 1000-bootstrap resampling of the coefficients (In the follow-up analysis, we used a nested ANOVA model to determine if there was any difference between the depression subtypes nested in the treatment, explaining the change in the individual symptom ratings. Age and sex were controlled in the nested ANOVA model. We complemented our analysis with a subsequent post-hoc 1000-resampling bootstrap test to determine which depression subtype showed more improvement after receiving the active treatment, with regard to individual items of MADRS-S, BPRS AFF and CAINS. It should be further noted that the change score of each individual item was quantified as symptom improvement for active treatment contrasted against the median improvement of sham treatment.