Hasenstab Kyle, Sugar Catherine, Telesca Donatello, Jeste Shafali, Şentürk Damla
Department of Statistics, University of California, Los Angeles, CA, USA.
Department of Biostatistics, University of California, Los Angeles, CA, USA.
Biostatistics. 2016 Jul;17(3):484-98. doi: 10.1093/biostatistics/kxw002. Epub 2016 Feb 4.
Motivated by a study on visual implicit learning in young children with Autism Spectrum Disorder (ASD), we propose a robust functional clustering (RFC) algorithm to identify subgroups within electroencephalography (EEG) data. The proposed RFC is an iterative algorithm based on functional principal component analysis, where cluster membership is updated via predictions of the functional trajectories obtained through a non-parametric random effects model. We consider functional data resulting from event-related potential (ERP) waveforms representing EEG time-locked to stimuli over the course of an implicit learning experiment, after applying a previously proposed meta-preprocessing step. This meta-preprocessing is designed to increase the low signal-to-noise ratio in the raw data and to mitigate the longitudinal changes in the ERP waveforms which characterize the nature and speed of learning. The resulting functional ERP components (peak amplitudes and latencies) inherently exhibit covariance heterogeneity due to low data quality over some stimuli inducing the averaging of different numbers of waveforms in sliding windows of the meta-preprocessing step. The proposed RFC algorithm incorporates this known covariance heterogeneity into the clustering algorithm, improving cluster quality, as illustrated in the data application and extensive simulation studies. ASD is a heterogeneous syndrome and identifying subgroups within ASD children is of interest for understanding the diverse nature of this complex disorder. Applications to the implicit learning paradigm identify subgroups within ASD and typically developing children with diverse learning patterns over the course of the experiment, which may inform clinical stratification of ASD.
受一项关于自闭症谱系障碍（ASD）幼儿视觉内隐学习研究的启发，我们提出了一种鲁棒功能聚类（RFC）算法，用于识别脑电图（EEG）数据中的亚组。所提出的RFC是一种基于功能主成分分析的迭代算法，其中通过非参数随机效应模型获得的功能轨迹预测来更新聚类成员。在应用先前提出的元预处理步骤后，我们考虑由事件相关电位（ERP）波形产生的功能数据，这些波形表示在隐式学习实验过程中与刺激时间锁定的脑电图。这种元预处理旨在提高原始数据中的低信噪比，并减轻ERP波形中的纵向变化，这些变化表征了学习的性质和速度。由于在元预处理步骤的滑动窗口中，某些刺激的数据质量较低，导致不同数量的波形平均，从而使得到的功能性ERP成分（峰值幅度和潜伏期）固有地表现出协方差异质性。所提出的RFC算法将这种已知的协方差异质性纳入聚类算法中，提高了聚类质量，如数据应用和广泛的模拟研究所示。ASD是一种异质性综合征，识别ASD儿童中的亚组对于理解这种复杂疾病的多样性本质具有重要意义。应用于内隐学习范式可识别ASD儿童和典型发育儿童在实验过程中具有不同学习模式的亚组，这可能为ASD的临床分层提供信息。
