Auditory event-related potential differentiates girls with Rett syndrome from their typically-developing peers with high accuracy: Machine learning study

Abstract

Rett Syndrome (RTT) is a rare neurodevelopmental disorder caused by mutation in the MECP2 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.