Quantitative sleep EEG biomarkers in Rett Syndrome: Sleep as a window to understand synaptic dysfunction.

Background: Rett Syndrome (RTT) is a neurodevelopmental disorder primarily caused by mutations in the MECP2 gene, characterized by sleep disturbances in about 80% of affected individuals. Interestingly, evidence from various neurological disorders has linked dysregulation of thalamocortical connectivity and synaptic dysfunction, respectively, to abnormalities in sleep spindles and slow waves. In this study we aim to investigate the role of quantitative sleep EEG analysis in understanding the neural circuit abnormalities of RTT. Specifically, we plan to compare these insights with literature data from MECP2-deficient animal models (1). By examining spindle density along with various slow-waves parameters and related markers of sleep homeostasis, our goal is to link macroscale electrophysiological observations to microscale neuronal malfunctions. Methods: In our study, we enrolled 14 females with typical Rett Syndrome (RTT) and MECP2 mutations, alongside age-matched controls. Employing overnight polysomnography complemented by quantitative EEG analysis, our focus was on evaluating spindle density and key slow-wave parameters—namely power, slope, and amplitude—utilizing custom MATLAB scripts. For clinical insights, we applied Spearman's correlation analysis, while non-parametric t-tests (Wilcoxon) facilitated the comparison between the patients with RTT and controls. Results: Two main outcomes emerged from this study: on one hand, our semi-automated analysis revealed a significant reduction in the spindle density (number of spindles per minute) in patients with RTT compared to controls. On the other hand, parameters of slow waves exhibited a reduced nocturnal decrease in patients with RTT relative to controls. This last finding may reflect a disruption of sleep dependant synaptic homeostasis that could underpin abrupt cortical synaptic plasticity. Conclusions: The study confirms a marked decrease in spindle density and altered slow wave parameters in RTT patients (2,3), markers that have been linked to sleep-dependent synaptic dysfunctions. The scenario becomes highly interesting due to its complete coherence with the numerous animal models available (1). Further research on quantitative sleep EEG biomarkers could provide valuable prognostic and therapeutic insights for RTT. Key Points: 1. Spindle density (number of spindles per minute) is significantly reduced in patients with RTT compared to controls. 2. Slow waves parameters seem to exhibit a reduced nocturnal decrease in patients with RTT relative to controls. 3. These findings ultimately converge on the assumption of altered synaptic plasticity in individuals affected by RTT. 4. These results pave the way for the prognostic and therapeutic implications of sleep EEG biomarkers. References: 1. Johnston M, Blue ME, Naidu S. Recent advances in understanding synaptic abnormalities in Rett syndrome. F1000Research. 2015;4:F1000 Faculty Rev-1490. 2. Pretl M, Challamel MJ, Nevsímalová S. Rett’s syndrome--spindle activity analysis in NREM sleep. Suppl Clin Neurophysiol. 2000;53:375–7. 3. Ammanuel S, Chan WC, Adler DA, Lakshamanan BM, Gupta SS, Ewen JB, et al. Heightened Delta Power during Slow-Wave-Sleep in Patients with Rett Syndrome Associated with Poor Sleep Efficiency. PloS One. 2015;10(10):e0138113.