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.

中文翻译：

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功能网络映射揭示了Rett综合征对IGF1治疗的国家依赖性反应。

Rett综合征（RTT）是与基因MeCP2突变相关的神经发育障碍，它参与皮层网络的发育和功能。RTT的临床表现通常很严重，包括发育衰退和明显的神经功能缺损。胰岛素样生长因子1（IGF1）改善了动物模型中与RTT相关的表型，并改善了早期人体试验中的某些临床表现。然而，目前尚不清楚IGF1治疗是否对符合MeCP2的皮质电生理有影响在网络形成中的作用，以及这些电生理变化是否与临床反应有关。我们对18例经典RTT患者进行了临床评估和静息状态脑电图（EEG）记录，其中9例接受了IGF1治疗。在接受治疗的患者中，我们将那些在治疗后表现出改善的患者（有反应者）与没有表现出任何改变的患者（无反应者）区分开来。对所有在基线和治疗后12个月均患有RTT的患者进行临床评估。使用基于电极间相干性度量的统计建模技术得出网络度量。我们发现治疗组和时间点之间存在显着的相互作用，表明IGF1对网络测量有影响。我们还发现了响应者状态和时间点的重大影响，表明网络测量的这些变化与对治疗的临床反应有关。此外，我们发现网络特征的基线可变性，并且使用这些措施的机器学习模型应用于治疗前数据，在这个小型患者组中以100％的准确性（100％的敏感性和100％的特异性）预测了治疗反应。这些结果突出了网络病理学在RTT中的重要性，并为网络测量作为疾病亚型表征工具和临床试验生物标志物的潜力提供了初步证据。在这些小型患者组中，将用于这些数据的机器学习模型应用于预处理数据，以100％的准确性（100％的敏感性和100％的特异性）预测治疗反应。这些结果突出了网络病理学在RTT中的重要性，并为网络测量作为疾病亚型表征工具和临床试验生物标志物的潜力提供了初步证据。在这些小型患者组中，将用于这些数据的机器学习模型应用于预处理数据，以100％的准确性（100％的敏感性和100％的特异性）预测治疗反应。这些结果突出了网络病理学在RTT中的重要性，并为网络测量作为疾病亚型表征工具和临床试验生物标志物的潜力提供了初步证据。