Rett Syndrome (RTT) is a devastating rare disorder associated to mutations in the gene coding for the Methyl-CpG binding protein 2 (MeCP2). In RTT the variability of the clinical presentation, and the lack of measurable parameters for patients’ classification hamper patients’ menagemeny and a discovery of suitable therapeutic interventions. There is the need to identify measurable outcomes and parameters, which will ensure appropriate characterization of the disease, and the quantitative evaluation of the severity of the disorder. Quantitative evaluation of brain activity provides improved stratification of patients by prognosis and for comparison with other patients (Keogh et al., 2018). More importantly, these parameters may distinguish sub-cohorts of patients who will respond to specific candidate drug. Our goal is to analyze human EEG data derived from RTT patients to identify measurable physiological parameters for patients’ stratification and prognosis in RTT. We have access to EEG recording from 14 patients with RTT and their clinical information including the genetic mutation and the severity of clinical parameters. The brain activity has been recorded with a 32 electrode cap, with patients in resting state awake and asleep. The recordings have a duration of one hour.
