Stratification of responses to tDCS intervention in a healthy paediatric population based on resting-state EEG profiles

Transcranial Direct Current Stimulation (tDCS) is a non-invasive neuromodulation technique with a wide variety of applications in both the clinical and cognitive psychology domains. As increasingly acknowledged, its effectiveness is subject dependent, which may lead to timely and costly treatments with ineffective results if this variability is not taken into account. We propose the usage of electroencephalography (EEG) for the analysis and prediction of individual responses to tDCS. In this context the application of machine learning can be of enormous help. We analysed resting-state EEG activity to identify subgroups of participants with an homogeneous electrophysiological profile and their response to different tDCS interventions. The study described herein, which focuses on healthy controls, was conducted within a clinical trial for the development of treatments based on tDCS for age-matched children diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD). We have studied a randomized, double-blind, sham-controlled tDCS intervention in 56 healthy children and adolescents aged 10-17, applied in 2 parallel groups over 2 target regions, namely left Dorsolateral Prefrontal Cortex (lDLPFC) and right Inferior Frontal Gyrus (rIFG). Cognitive behavioural tasks were used to both activate particular brain areas during the stimulation and to assess the impact of the intervention afterwards. We have implemented an unsupervised learning approach to stratify participants based on their resting-state EEG spectral features before the tDCS application. We have then applied a correlational analysis to identify EEG profiles associated with tDCS subject response to the specific stimulation sites and the presence or not of concurrent tasks during the intervention. In the results we found specific digital electrophysiological profiles that can be associated to a positive response, whereas subjects with other profiles respond negatively or do not respond to the intervention. Findings suggest that unsupervised machine learning procedures, when associated with proper visualization features, can be successfully used to interpret and eventually to predict responses of individuals to tDCS treatment.