Researchers from the Immanuel Kant Baltic Federal University in collaboration with the Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences conducted an in depth study into brain rhythm disorders in individuals affected by Rett syndrome. The findings, which focus on how brain waves change in this condition, offer promising directions for improving both early diagnosis and the monitoring of how well treatments work. The study was highlighted as part of the Priority 2030 program under Russia’s Ministry of Education and Science, emphasizing the potential practical impact on patient care and clinical decision making.
Rett syndrome is a hereditary neuropsychiatric disorder that largely manifests as severe cognitive impairment and communication challenges, predominantly affecting girls. People with Rett syndrome often experience recurrent seizures, breathing irregularities, and difficulties with speech, thought processing, and coordinated movement. At present there is no cure, and many patients live with significant, lifelong disability. Diagnosing Rett syndrome can be challenging in the earliest stages because pregnancies and the initial months of life can proceed without obvious complications, making early neurological signs easy to miss.
In the study, researchers analyzed brain activity in 61 children ranging in age from 3 to 17 years, comparing those diagnosed with Rett syndrome to a control group of healthy peers. The team found that activity in the gamma frequency band, a brain wave range linked to memory, sleep regulation, and sustained attention, showed increased intensity in the frontal, central, and temporal regions of the Rett group. This shift points to an imbalance between neuronal excitation and inhibition, a disruption that likely contributes to the cognitive and perceptual challenges observed in Rett syndrome.
Project leader Alexander Khramov, a professor at IKBFU, emphasized that the observed EEG patterns reveal noticeable functional changes accompanying the disease’s progression. He noted that the measured electroencephalography (EEG) indicators could distinguish between groups with an accuracy of about 92%, underscoring the clinical relevance of these findings for future diagnostic and therapeutic approaches. The implications extend to how clinicians might use EEG data to track disease evolution and to quantify responses to pharmacological or other interventions, potentially enabling more tailored treatment plans for each patient.