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STATISTICAL ANALYSIS OF EVENT RELATED POTENTIALS

事件相关潜力的统计分析

基本信息

批准号：
2839189
负责人：
RODERICK J. LITTLE
金额：
$ 11.14万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
财政年份：
1994
资助国家：
美国
起止时间：
1994-12-01 至 2000-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/2839189
关键词：
brain electrical activity computational neuroscience computer simulation evoked potentials human data magnetic resonance imaging model design /development neuropharmacologic agent schizophrenia statistics /biometry stroke

项目摘要

The millisecond time resolution of event-related potentials (ERPs) gives them a unique advantage in studying brain function, but ERP research is seriously limited by the lack of statistical methods to address the complexity and variability of ERP data. In this project, we will develop a new statistical approach to decomposition of ERP waveforms, analysis of the sources of variability, and estimation of the effects of experimental conditions and disease states. We will evaluate the new methods using simulated ERPs and many animal and human ERP data sets, including ERPs acquired from schizophrenic and stroke patients who also were studied using structural magnetic resonance imaging (MRI). The new statistical methods will combine time series modeling using the wavelet transform with nonlinear mixed effects models. Wavelet analysis decomposes ERPs by time and frequency. We have already validated our wavelet models in applications to simulated data, cat auditory evoked potentials, and human P300 potentials. Wavelet analysis separated superimposed components, yielding realistic condition effects and topographies, even in difficult cases in which principal components analysis failed. Our nonlinear mixed effects models will provide a parsimonious representation of the variability among individuals (human subjects or experimental animals) and single trials (responses to single stimulus presentations). They will yield valid significance tests and confidence intervals, extending familiar linear statistical procedures to complicated nonlinear time series. The specific aims of this project are to develop, evaluate, and apply the following statistical methods. 1. The Single Channel Wavelet Model will separate superimposed components in single channel average ERPs, and yield significance tests for condition effects on the amplitude and latency of each component. 2. The Topographic Wavelet Model will extend the single channel wavelet model to multichannel data, and provide estimated of the topography of each component. Regularization of the topography will allow analysis of ERPs from dense electrode arrays. 3. The Trial-Specific Wavelet Model will extend the single channel wavelet model to include both inter-individual and inter-trial variability, allowing estimation of the relationships among ERP components and between ERP components and trial-specific variables such as reaction time and subjective intensity.

事件相关电位（ERP）的毫秒时间分辨率给出了它们在研究大脑功能方面具有独特的优势，但ERP研究由于缺乏统计方法来处理 ERP数据的复杂性和可变性。在这个项目中，我们将开发一种新的ERP波形分解统计方法，的影响的估计实验条件和疾病状态。我们将评估新的方法使用模拟ERP和许多动物和人类ERP数据集，包括从精神分裂症和中风患者获得的ERP，使用结构磁共振成像（MRI）进行了研究。新的统计方法将结合联合收割机时间序列建模，小波变换与非线性混合效应模型小波分析根据时间和频率分解ERP。我们已经验证了我们的小波模型在模拟数据中的应用，猫听觉诱发人的P300电位。小波分析分离叠加组件，产生逼真的条件效果，拓扑，即使在困难的情况下，其中主成分分析失败。我们的非线性混合效应模型将提供一个简约的个体（人类受试者或实验动物）和单一试验（对单一刺激的反应介绍）。它们将产生有效的显著性检验和信心区间，扩展熟悉的线性统计程序，复杂非线性时间序列该项目的具体目标是开发、评估和应用根据统计方法。 1. 单通道小波模型将叠加的单通道平均ERP中的成分，并进行产量显著性检验条件对每个分量的振幅和潜伏期的影响。 2. 地形子波模型是对单通道子波的扩展对多通道数据进行建模，并提供每个组件。地形的规则化将允许分析来自密集电极阵列的ERP。 3. 特定于试验的小波模型将扩展单通道小波模型包括个体间和试验间可变性，允许估计ERP之间的关系 ERP组件和试验特定变量之间的差异，例如反应时间和主观强度。