Rett syndrome (RTT) is a devastating neurodevelopmental disorder that has no cure. Patients show regression of acquired skills, motor, and speech impairment, cardio-respiratory distress, microcephaly, and stereotyped hand movements. The majority of RTT patients display mutations in the gene that codes for the Methyl-CpG binding protein 2 (MeCP2), which is involved in the development of the central nervous system, especially synaptic and circuit maturation. Thus, agents that promote brain development and synaptic function are good candidates for ameliorating the symptoms of RTT. In particular, insulin-like growth factor 1 (IGF1) and its active peptide (1–3) IGF1 cross the Blood Brain Barrier, and therefore are ideal treatments for RTT Indeed, both (1–3) IGF1 and IGF1 treatment significantly ameliorates RTT symptoms in a mouse model of the disease In a previous study, we established that IGF1 is safe and well tolerated on Rett patients. In this open label clinical case study, we assess the safety and tolerability of IGF1 administration in two cycles of the treatment. Before and after each cycle, we monitored the clinical and blood parameters, autonomic function, and social and cognitive abilities, and we found that IGF1 was well tolerated each time and did not induce any side effect, nor it interfered with the other treatments that the patient was undergoing. We noticed a moderate improvement in the cognitive, social, and autonomic abilities of the patient after each cycle but the benefits were not retained between the two cycles, consistent with the pre-clinical observation that treatments for RTT should be administered through life. We find that repeated IGF1 treatment is safe and well tolerated in Rett patients but observed effects are not retained between cycles. These results have applications to other pathologies considering that IGF1 has been shown to be effective in other disorders of the autism spectrum.

BackgroundRett Syndrome (RTT) is a complex neurodevelopmental disorder, frequently associated with epilepsy. Despite increasing recognition of the clinical heterogeneity of RTT and its variants (e.g Classical, Hanefeld and PSV(Preserved Speech Variant)), the link between causative mutations and observed clinical phenotypes remains unclear. Quantitative analysis of electroencephalogram (EEG) recordings may further elucidate important differences between the different clinical and genetic forms of RTT.MethodsUsing a large cohort (n = 42) of RTT patients, we analysed the electrophysiological profiles of RTT variants (genetic and clinical) in addition to epilepsy status (no epilepsy/treatment-responsive epilepsy/treatment-resistant epilepsy). The distribution of spectral power and inter-electrode coherence measures were derived from continuous resting-state EEG recordings.ResultsRTT genetic variants (MeCP2/CDLK5) were characterised by significant differences in network architecture on comparing first principal components of inter-electrode coherence across all frequency bands (p < 0.0001). Greater coherence in occipital and temporal pairs were seen in MeCP2 vs CDLK5 variants, the main drivers in between group differences. Similarly, clinical phenotypes (Classical RTT/Hanefeld/PSV) demonstrated significant differences in network architecture (p < 0.0001). Right tempero-parietal connectivity was found to differ between groups (p = 0.04), with greatest coherence in the Classical RTT phenotype. PSV demonstrated a significant difference in left-sided parieto-occipital coherence (p = 0.026). Whilst overall power decreased over time, there were no difference in asymmetry and inter-electrode coherence profiles over time. There was a significant difference in asymmetry in the overall power spectra between epilepsy groups (p = 0.04) in addition to occipital asymmetry across all frequency bands. Significant differences in network architecture were also seen across epilepsy groups (p = 0.044).ConclusionsGenetic and clinical variants of RTT are characterised by discrete patterns of inter-electrode coherence and network architecture which remain stable over time. Further, hemispheric distribution of spectral power and measures of network dysfunction are associated with epilepsy status and treatment responsiveness. These findings support the role of discrete EEG profiles as non-invasive biomarkers in RTT and its genetic/clinical variants.Electronic supplementary materialThe online version of this article (10.1186/s12887-018-1304-7) contains supplementary material, which is available to authorized users.

Deficits in emotional reactivity are frequently reported in Disruptive Behavior Disorders (DBDs). A deficit in prosocial emotions, namely the callous unemotional traits (CU), may be a mediator of emotional reactivity. Our aim is to investigate subjective emotional reactivity towards visual stimuli with different affective valence in youths with DBDs and healthy controls. The clinical sample included 62 youths with DBDs (51 males, 8 to 16 years, mean 11.3±2.1 years), the control group 53 subjects (36 males, 8 to 16 years, mean 10.8±1.5 years). The groups were compared using the Child Behavior Checklist (CBCL), the Inventory of Callous-Unemotional Traits (ICU), and the International Affective Picture System (IAPS), which explores the affective (pleasant/unpleasant emotional reaction) and arousal (low/high intensity of emotion) dimensions. The DBD group presented higher scores in externalizing and internalizing CBCL scores, and in ICU callous and indifferent subscales. At the IAPS, DBD patients differed from controls in the affective valence of the images, rating less unpleasant neutral and negative images. The CU traits were the only predictor of emotional reactivity in the DBD sample. A less aversive way to interpret neutral and negative stimuli may explain why DBD patients are less responsive to negative reinforcements.

Rett Syndrome (RTT) is a neurodevelopmental disorder associated with mutations in the gene MeCP2, which is involved in the development and function of cortical networks. The clinical presentation of RTT is generally severe and includes developmental regression and marked neurologic impairment. Insulin-Like growth factor 1 (IGF1) ameliorates RTT-relevant phenotypes in animal models and improves some clinical manifestations in early human trials. However, it remains unclear whether IGF1 treatment has an impact on cortical electrophysiology in line with MeCP2’s role in network formation, and whether these electrophysiological changes are related to clinical response. We performed clinical assessments and resting-state electroencephalogram (EEG) recordings in eighteen patients with classic RTT, nine of whom were treated with IGF1. Among the treated patients, we distinguished those who showed improvements after treatment (responders) from those who did not show any changes (nonresponders). Clinical assessments were carried out for all individuals with RTT at baseline and 12 months after treatment. Network measures were derived using statistical modelling techniques based on interelectrode coherence measures. We found significant interaction between treatment groups and timepoints, indicating an effect of IGF1 on network measures. We also found a significant effect of responder status and timepoint, indicating that these changes in network measures are associated with clinical response to treatment. Further, we found baseline variability in network characteristics, and a machine learning model using these measures applied to pretreatment data predicted treatment response with 100% accuracy (100% sensitivity and 100% specificity) in this small patient group. These results highlight the importance of network pathology in RTT, as well as providing preliminary evidence for the potential of network measures as tools for the characterisation of disease subtypes and as biomarkers for clinical trials.

No abstract