Fig. A1.
Workflow diagram. The diagram illustrates main steps of image post-processing and analysis. It starts and proceeds in two directions aimed at extraction of parcelled and high-dimensional measurements using Freesurfer software (blue box). After this part had completed, the extracted measures underwent steps for outlier detection, and the resulted output was used in further Random Forest classification runs (in R programming language — gray box). We additionally tuned our models using recursive feature elimination and mtry-parameter adjustment (which defines the number of predictors randomly sampled at each node of the classifier). Finally, feature importance vectors from the best models were either mapped into the brain space (for the high-dimensional data) or plotted (for the parcelled input).