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Quantitative sleep EEG biomarkers in Rett Syndrome: Sleep as a window to understand synaptic dysfunction.
No full textBackground: 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
Referendum costituzionale 2016: il fallimento dell'agenda Renzi
Full text linkIl 4 dicembre si è svolto il cruciale referendum confermativo sulla riforma costituzionale Renzi-Boschi cui il capo dell’esecutivo aveva indissolubilmente legato la propria esperienza di governo. Come noto, l’esito elettorale è stato davvero netto. Sostanzialmente un 60 a 40 per respingere la riforma costituzionale. Le conseguenze politiche immediate di questo sono ormai storia, con le dimissioni del governo Renzi e la nascita dell’esecutivo guidato da Gentiloni. In questo articolo analizziamo in profondità i risultati elettorali registrati il 4 dicembre, inserendoli nel contesto politico della legislatura in corso e tentando di spiegare come questi si siano venuti determinando
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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