STATISTICAL ANALYSIS OF EVENT RELATED POTENTIALS

事件相关电位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，但对ERP的研究 严重受限于缺乏统计方法来解决 企业资源计划数据的复杂性和多变性。在这个项目中，我们将开发 事件相关电位波形分解分析的一种新的统计方法 可变性的来源，以及对 实验条件和疾病状态。我们将评估新的 方法使用模拟的事件相关电位以及许多动物和人类的事件相关电位数据集， 包括从精神分裂症和中风患者那里获得的ERP，这些患者也 应用结构磁共振成像(MRI)对其进行研究。 新的统计方法将结合使用 具有非线性混合效应模型的小波变换。小波分析 按时间和频率分解事件相关事件。我们已经验证了我们的 小波模型在猫听觉诱发模拟数据中的应用 电位和人类P300电位。分离的小波分析 叠加组件，产生逼真的条件效果和 地形，即使在困难的情况下主成分 分析失败。 我们的非线性混合效应模型将提供节俭的 表示个体之间的可变性(人类受试者或 实验动物)和单一试验(对单一刺激的反应 演示)。它们将产生有效的显著性检验和信心。 间隔，将熟悉的线性统计过程扩展到 复杂的非线性时间序列。 该项目的具体目标是开发、评估和应用 遵循统计学方法。 1.单通道小波模型将分离叠加 单通道平均ERPS中的成分和成品率显著性检验 用于条件对每个分量的幅度和潜伏期的影响。 2.地形小波模型将对单通道小波进行扩展 模型到多通道数据，并提供估计的地形 每个组件。地形的正则化将允许分析 来自密集电极阵列的事件相关电位。 3.特定于试验的小波模型将扩展单通道 同时包括个体间试验和试验间试验的小波模型 可变性，允许估计ERP之间的关系 以及ERP组件和试验特定变量之间的关系 作为反应时间和主观强度。

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.