Rett Syndrome (RTT) is a rare neurodevelopmental disorder caused by mutation in the gene. No cures are still available, but several clinical trials are ongoing. Here we examine neurophysiological correlates of auditory processing for ability to differentiate patients with RTT from typically developing (TD) peers applying standard machine learning (ML) methods and pipelines. Capitalized on the available event-related potential (ERP) data recorded in response to tone presented at different rates (stimulus onset asynchrony 900, 1800 and 3600 ms) from 24 patients with RTT and 27 their TD peer. We considered the most common ML models that are widely used for classification tasks. These include both linear models (logistic regression, support-vector machine with linear kernel) and tree-based nonlinear models (random forest, gradient boosting). Based on these methods we were able to differentiate RTT from TD children with high accuracy (with up to 0.94 ROC-AUC score), which was evidently higher at the fastest presentation rate. Importance analysis and perturbation importance pointed out that the most important feature for classification is P2-N2 peak-to-peak amplitude, consistently across the approaches and blocks with different presentation rate. The results suggest the unique pattern of ERP characteristics for RTT and points to features of importance. The results might be relevant for establishing outcome measures for clinical trials.

中文翻译：

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听觉事件相关电位可将患有雷特综合征的女孩与发育正常的同龄人区分开来，准确性很高：机器学习研究

雷特综合症（RTT）是一种由基因突变引起的罕见神经发育障碍。目前尚无治愈方法，但多项临床试验正在进行中。在这里，我们研究了听觉处理的神经生理学相关性，以便应用标准机器学习 (ML) 方法和流程将 RTT 患者与典型发育 (TD) 患者区分开来。利用 24 名 RTT 患者和 27 名 TD 患者以不同速率（刺激开始异步 900、1800 和 3600 毫秒）呈现的音调记录的可用事件相关电位 (ERP) 数据。我们考虑了广泛用于分类任务的最常见的机器学习模型。其中包括线性模型（逻辑回归、具有线性内核的支持向量机）和基于树的非线性模型（随机森林、梯度提升）。基于这些方法，我们能够以高精度区分 RTT 和 TD 儿童（ROC-AUC 分数高达 0.94），在最快呈现率下明显更高。重要性分析和扰动重要性指出，分类最重要的特征是 P2-N2 峰峰值幅度，在不同呈现率的方法和块中保持一致。结果表明了 RTT 的 ERP 特征的独特模式，并指出了重要的特征。结果可能与建立临床试验的结果测量相关。