Discussion
ADHD
GROUP B
Our work provides a novel approach to study the effects of multichannel anodal tDCS treatment in healthy children and adolescents with different specific brain profiles. This is of interest given the limitations of existent treatments in pediatric clinical populations, e.g. ADHD and ASD, for which new treatments with no side effectsBy grouping experimental subjects in clusters of typical versus non-typical digital EEG phenotypes, a differential response can be found in subjects with a non-typical electrophysiological profile. EEG profiles (summarized in Fig. It can be observed that Group B (i.e. receiving the stimulation over the rIFG brain area) is the only one with responders (both positive and negative), and Group A does not have any significant correlations supported with significant Wilcoxon-test results. This suggests an advantage in terms of post-tDCS task performance in stimulating the rIFG over the more traditional approach of lDLPFC stimulation, even when participant stratification is applied. In almost all groups of participants, the behavioral task showing changes is the Flanker task, when the applied stimulation montage targets enhancing the activity of the rIFG. This result is consistent with the current understanding of the role of the rIFG, associated with response inhibition and behavioral controlFuture work could be done in order to establish a relationship between the electrophysiological profiles and functional outcomes. A hypothesis would be based on the transfer of stimulation effects from the stimulated working memory network components involved in the Flanker task to the working memory network components involved in the N-Back taskIn summary, we can state that the effect of brain stimulation (and its parameters including location and presence/absence of concurrent task during the protocol), measured by different behavioral measures, depends on the individual digital EEG phenotype of the subjects undergoing the stimulation intervention. Such a digital phenotype can be derived by applying the unsupervised clustering approach that we have developed. The robustness of our overall methodology was validated in 3 independent tests. First, the feature space of all participants was visualized showing that the features of all trials of pre-treatment EEG for each subject lie close to each other. Therefore, their baseline neurophysiological activity was similar over the different trials, which were conducted one week apart. Second, the k-hold-out methodology applied to the clustering pipeline demonstrates the robustness of the clustering algorithms in our specific dataset. This validation let us ensure to a certain extent the generalization capability of the clustering for generating similar digital EEG phenotypes on unseen data. Finally, the M-hold-out validatioin with Some limitations arise from the fact that the dataset is relatively small compared to the large amount of features analysed, resulting in a very high dimensional space for the employed clustering validity measures. This hinders to a certain extent the full automation of the methodology. In order to reduce the feature space dimensionality, Principal Feature AnalysisSummary of EEG prototypes. Topoplots and PSD for Clusters that present a significant response in Spectral Algorithm. Cluster 1 (blue PSD) has a negative response to rIFG stimulation with Flanker concurrent task, measured with Flanker Accuracy. Cluster 2 (orange PSD) has a positive response to rIFG stimulation with no concurrent task, measured with N-Back Accuracy. Cluster 3 (green PSD) has a positive response to rIFG stimulation with Flanker concurrent task, measured with Flanker RT; and a negative response to rIFG stimulation with no concurrent task, measured with Flanker RT. Cluster 4 (red PSD) has a positive response to rIFG stimulation with no concurrent task, measured with Flanker RT; and a negative response to rIFG stimulation with Flanker concurrent task, measured with Flanker RT.