Hybrid principal components analysis for region-referenced longitudinal functional EEG data
Hybrid principal components analysis for region-referenced longitudinal functional EEG data
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DOI：
10.1093/biostatistics/kxy034
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发表时间：
2020-01-01
期刊：
影响因子：
2.1
通讯作者：
Senturk, Damla
中科院分区：
文献类型：
--
作者：
Scheffler, Aaron;Telesca, Donatello;Senturk, Damla
脑电图（EEG）数据具有复杂的结构，包括区域，功能和纵向尺寸。我们的激励性例子是一个分词范例，其中典型发育（TD）儿童和自闭症谱系障碍（ASD）儿童暴露于连续的语音流。对于每个受试者，在每个电极处记录的连续EEG信号被分成一秒段，并通过快速傅立叶变换投影到频域。频谱主成分分析后，所得到的数据包括区域参考的主功率索引区域头皮位置，跨频率的功能，和纵向的一秒段。标准的EEG功率分析通常通过对分段的功率进行平均并集中于特定的频带来分解纵向和功能维度上的信息。我们提出了一种混合主成分分析区域参考纵向功能性EEG数据，它利用向量和功能主成分分析，并不崩溃的信息沿着任何三个维度的数据。建议的分解只假设弱可分性的高维协方差过程，并利用产品的一维特征向量和特征函数，从区域，功能和纵向边际协方差，代表观察到的数据，提供了一个计算上可行的非参数方法。一个混合效应的框架，提出了估计模型的组件，再加上一个自举测试组水平的推理，都面向稀疏数据的应用程序。从分词范式的数据分析导致有价值的见解之间的TD和语言和最低限度的语言与ASD儿童的群体区域差异。有限样本性质的估计框架和自助推断过程进一步研究通过广泛的模拟。
Electroencephalography (EEG) data possess a complex structure that includes regional, functional, and longitudinal dimensions. Our motivating example is a word segmentation paradigm in which typically developing (TD) children, and children with autism spectrum disorder (ASD) were exposed to a continuous speech stream. For each subject, continuous EEG signals recorded at each electrode were divided into one-second segments and projected into the frequency domain via fast Fourier transform. Following a spectral principal components analysis, the resulting data consist of region-referenced principal power indexed regionally by scalp location, functionally across frequencies, and longitudinally by one-second segments. Standard EEG power analyses often collapse information across the longitudinal and functional dimensions by averaging power across segments and concentrating on specific frequency bands. We propose a hybrid principal components analysis for region-referenced longitudinal functional EEG data, which utilizes both vector and functional principal components analyses and does not collapse information along any of the three dimensions of the data. The proposed decomposition only assumes weak separability of the higher-dimensional covariance process and utilizes a product of one dimensional eigen-vectors and eigenfunctions, obtained from the regional, functional, and longitudinal marginal covariances, to represent the observed data, providing a computationally feasible non-parametric approach. A mixed effects framework is proposed to estimate the model components coupled with a bootstrap test for group level inference, both geared towards sparse data applications. Analysis of the data from the word segmentation paradigm leads to valuable insights about group-region differences among the TD and verbal and minimally verbal children with ASD. Finite sample properties of the proposed estimation framework and bootstrap inference procedure are further studied via extensive simulations.