Statistical comparison of behavioral measures between clusters of positive and negative responders using the Wilcoxon Test
This step aims at testing the significance of the difference in behavioral response between each specific clusters of positive or negative responders. If any of the 4 correlations computed for each cluster showed significance (positive or negative), we then analysed the difference in behavioral response between the specific cluster members and all non-members of the cluster. Hence, a rank-sum Wilcoxon Test was calculated on the behavioral metrics within each cluster samples and the samples belonging to the remaining clusters. The FDR Benjamini and Hochberg step-up procedureIn this part of the analysis we want to assess if there are significant differences between the behavioral outcomes for each specific cluster, once the cluster has shown a correlation between the membership to that cluster and the corresponding behavioral metric. We illustrate this operation with an example: If a significant correlation is found for Cluster 1 in behavioral metric Accuracy of the Flanker task—i.e. considering the membership values of all data-points to that specific Cluster 1—then a Wilcoxon test is performed on the Flanker Accuracy active-sham difference. In this test we compare the performance of the subjects who belong to Cluster 1 versus the rest of the subjects. The objective of this step is to have an estimation of the improvement in the treatment response in case the clustering analysis is used for recruiting subjects in a future clinical trial.At this point, since we have been calculating the significant difference between behavioral response of the subject samples belonging to a cluster and the rest of the subject samples, we need to determine to which final cluster a given subject belongs to. This was done using the 