Figure 3.

Detailed analysis pipeline. Initial data from all cohorts is split into training and test sets according to splitting strategies (Splitting by Age/Sex and Splitting by Site) after removing subjects with more than 75% missing data and data imputation steps. The corresponding training folds are then residualized directly to remove ICV, age and sex related effects and fed to the classification algorithms. In case of harmonization by ComBat, the residualization step takes place after the harmonization step is conducted. If training folds were harmonized by ComBat, the test fold was harmonized as well by using ComBat estimates from the training folds. Next, the test fold was residualized by using estimates obtained from the training folds. We estimated classification performance on the residualized test fold. This routine was performed iteratively for each combination of training and test folds.