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它们在研究大脑功能方面是一个独特的优势，但是ERP研究是由于缺乏解决该方法的统计方法的严重限制 ERP数据的复杂性和可变性。在这个项目中，我们将开发 ERP波形分解的一种新的统计方法，分析可变性来源以及估计的影响实验条件和疾病状态。我们将评估新的使用模拟ERP以及许多动物和人类ERP数据集的方法，包括从精神分裂症和中风患者中获得的ERP 使用结构磁共振成像（MRI）研究。新的统计方法将结合时间序列建模小波转换具有非线性混合效应模型。小波分析按时间和频率分解ERP。我们已经验证了我们的在模拟数据的应用中，小波模型，猫的听觉唤起了电势和人类P300电位。小波分析分开叠加的组件，产生逼真的状况影响，并即使在主要组成部分的困难情况下，分析失败。我们的非线性混合效果模型将提供一个简约的代表个体之间的变异性（人类受试者或实验动物）和单个试验（对单个刺激的反应演讲）。他们将产生有效的显着性测试和信心间隔，将熟悉的线性统计程序扩展到复杂的非线性时间序列。该项目的具体目的是开发，评估和应用以下统计方法。 1。单个通道小波模型将分开叠加单个通道平均ERP中的组件，并产生显着性测试为了对每个组件的幅度和潜伏期的条件影响。 2。地形小波模型将扩展单个通道小波多通道数据的模型，并提供了估计的地形每个组件。地形的正则化将允许分析来自致密电极阵列的ERP。 3。特定的小波模型将扩展单个通道小波模型包括个体间和审判可变性，允许估计ERP之间的关系组件以及ERP组件和特定于试验的变量之间作为反应时间和主观强度。