Collaborative Research: Models and Methods for Nonstationary Behavioral Time Series

合作研究：非平稳行为时间序列的模型和方法

• 批准号: 1060937
• 负责人: Hernando Ombao
• 金额: $ 15万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-15 至 2012-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1060937&HistoricalAwards=false
• 关键词: Collaborative; Research; Models; Methods; Nonstationary

The study of behavioral and physiological data often is difficult because such data typically consist of large-dimensional, high-resolution nonstationary time series. Consequently, there is an increasing need for statistically principled and computationally efficient approaches for complex time series data. This research project focuses on the development of a coherent suite of novel statistical models and related methodology for large-dimensional, high-resolution multivariate time series. The statistical methods to be developed will be used to link nonstationary features of physiological time series, such as functional magnetic resonance image (fMRI), to behavioral and neurocognitive assessment data. The project will develop two types of approaches for modeling multi-dimensional time series. The first approach will model the set of nonstationary time series via the locally stationary representation that characterizes the spectral dynamics of the process in terms of a time-varying spectral density matrix. The second approach consists of capturing dynamical dependencies in the data via Bayesian state-space models that will be able to estimate the coherency across the time series over time.The statistical models and methods developed in this research project will be used to study how physiological time series in healthy individuals are related to neurocognitive scores. Data that will be studied include measures derived from brain images as well as time series of various regions of interest derived from fMRI. Behavioral and physiological signals recorded to monitor cognitive fatigue and workload also will be studied. Even though the focus of this project is on the analysis of physiological and behavioral data, the models and methods that will be developed in this research project are very general and have the potential of impacting other scientific fields given that highly structured multivariate time series data often are collected in the areas of econometrics, environmetrics, geosciences, and signal processing.

行为和生理数据的研究通常是困难的，因为这些数据通常由大维度、高分辨率的非平稳时间序列组成。因此，对复杂时间序列数据的统计原则和计算效率方法的需求日益增加。本研究项目的重点是为大维度、高分辨率多元时间序列开发一套连贯的新型统计模型和相关方法。即将开发的统计方法将用于将生理时间序列的非平稳特征（如功能磁共振图像（fMRI））与行为和神经认知评估数据联系起来。该项目将开发两种方法来建模多维时间序列。第一种方法将通过局部平稳表示对非平稳时间序列集进行建模，该表示以时变谱密度矩阵的形式表征过程的谱动力学。第二种方法包括通过贝叶斯状态空间模型捕获数据中的动态依赖关系，该模型将能够随着时间的推移估计跨时间序列的一致性。本研究项目开发的统计模型和方法将用于研究健康个体的生理时间序列与神经认知评分之间的关系。将研究的数据包括来自大脑图像的测量数据，以及来自功能磁共振成像的不同感兴趣区域的时间序列。行为和生理信号记录监测认知疲劳和工作量也将进行研究。尽管这个项目的重点是对生理和行为数据的分析，但在这个研究项目中开发的模型和方法是非常通用的，并且具有影响其他科学领域的潜力，因为高度结构化的多变量时间序列数据通常是在计量经济学、环境计量学、地球科学和信号处理领域收集的。