Correlation between clusters and behavioral data
handedness
After clustering, we correlated the membership function and the behavioral data of the tasks after tDCS of each participant using a Partial Spearman Correlation. This type of correlation allows to take into account spurious factors that may affect the EEG features. The correlation was corrected for age, sex, IQ and handedness. The False Discovery Rate (FDR) multiple comparisons correction was applied in order to avoid random significant values of the correlation measure using the Benjamini and Hochberg step-up procedure (BH)The purpose of the correlational analysis is to label the clusters of homogeneous EEG digital phenotypes that we obtained either as positive, negative or non-responders to the tDCS intervention depending on their behavioral response. As behavioral response we used the accuracy and reaction times in the Flanker task, N-Back and CPT tasks performed after the tDCS stimulation. In order to obtain a unique behavioural measure to conduct the correlation, we computed the difference in performance between the active and sham tDCS conditions for each task and behavioural metric (accuracy and reaction time). This performance difference was computed independently for trials with a concurrent task during tDCS and trials without a concurrent task.From combining the 2 stimulation sites (Groups A/B) with the 2 task concurrency conditions (concurrent/non-concurrent) we obtained 4 different experimental conditions and thus for each clustering algorithm 4 correlations were computed. Hence we correlated the performance difference between active and sham tDCS of each experimental condition and the average membership to the cluster of each subject. The rationale for computing these 4 correlations is to identify the concrete condition, i.e. stimulation site A/B and task concurrence, in which significant results are found and predict later the response of the subjects who fall into the same cluster. If there is a significant correlation (results are considered statistically significant if the corrected correlation p-value is less than 0.05), we can establish a relationship between the digital EEG profile and the behavioral performance difference between the active and sham conditions. Clusters of positive responders are defined as showing positive accuracy correlations and negative RT correlations, i.e. positive responders show higher accuracy and smaller RTs following active tDCS than following sham tDCS. On the contrary, clusters of negative responders, are defined as presenting a negative correlation with Accuracy and positive correlation with RT measures.