Multilevel hybrid principal components analysis for region-referenced functional electroencephalography data.
Multilevel hybrid principal components analysis for region-referenced functional electroencephalography data.
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DOI：
10.1002/sim.9445
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发表时间：
2022-08-30
影响因子：
2
通讯作者：
Senturk, Damla
中科院分区：
文献类型：
--
作者：
Campos, Emilie;Wolfe Scheffler, Aaron;Telesca, Donatello;Sugar, Catherine;DiStefano, Charlotte;Jeste, Shafali;Levin, April R.;Naples, Adam;Webb, Sara J.;Shic, Frederick;Dawson, Geraldine;Faja, Susan;McPartland, James C.;Senturk, Damla
关键词：
脑电图 (EEG) 实验产生区域参考功能数据，代表在头皮上收集的时域或频域中的大脑信号。数据通常还具有多级结构，其中包含在多个实验条件或访问中收集的高维观察结果。常见的分析方法通过折叠功能和区域维度来降低数据复杂性，其中事件相关电位（ERP）特征或频带功率针对预先指定的头皮区域。这种做法可能无法描绘整个 ERP 信号或整个头皮的功率谱密度 (PSD) 的更全面的差异。基于高维协方差过程的弱可分离性，所提出的多级混合主成分分析（M-HPCA）利用向量和函数主成分分析的降维工具将总变异分解为受试者间和受试者内方差。通过计算高效的最小化最大化 (MM) 算法与引导程序相结合，在混合效应建模框架中估计生成的模型组件。两项针对自闭症患者的研究展示了 M-HPCA 的多种应用。第一项研究中，在音频奇怪范式中比较了 ERP 对匹配与不匹配条件的响应，而第二项研究则比较了访问期间 PSD 的短期可靠性。通过广泛的模拟研究了所提出方法的有限样本属性。
Electroencephalography (EEG) experiments produce region-referenced functional data representing brain signals in the time or the frequency domain collected across the scalp. The data typically also have a multilevel structure with high-dimensional observations collected across multiple experimental conditions or visits. Common analysis approaches reduce the data complexity by collapsing the functional and regional dimensions, where event-related potential (ERP) features or band power are targeted in a pre-specified scalp region. This practice can fail to portray more comprehensive differences in the entire ERP signal or the power spectral density (PSD) across the scalp. Building on the weak separability of the high-dimensional covariance process, the proposed multilevel hybrid principal components analysis (M-HPCA) utilizes dimension reduction tools from both vector and functional principal components analysis to decompose the total variation into between- and within-subject variance. The resulting model components are estimated in a mixed effects modeling framework via a computationally efficient minorization-maximization (MM) algorithm coupled with bootstrap. The diverse array of applications of M-HPCA is showcased with two studies of individuals with autism. While ERP responses to match vs. mismatch conditions are compared in an audio odd-ball paradigm in the first study, short-term reliability of the PSD across visits is compared in the second. Finite sample properties of the proposed methodology are studied in extensive simulations.
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影响因子：
3.7
作者：
Crainiceanu CM;Staicu AM;Di CZ
通讯作者：
Di CZ
影响因子：
3.5
作者：
Levin, April R.;Naples, Adam J.;Senturk, Damla
通讯作者：
Senturk, Damla
DOI：
10.1002/sta4.89
发表时间：
2015
期刊：
Stat (International Statistical Institute)
影响因子：
--
作者：
Park SY;Staicu AM
通讯作者：
Staicu AM
影响因子：
4.7
作者：
DiStefano, Charlotte;Senturk, Damla;Jeste, Shafali Spurling
通讯作者：
Jeste, Shafali Spurling
影响因子：
2.1
作者：
Scheffler, Aaron;Telesca, Donatello;Senturk, Damla
通讯作者：
Senturk, Damla