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.

对行为和生理数据的研究通常很困难，因为此类数据通常由大维，高分辨率的非组织时间序列组成。 因此，对于复杂的时间序列数据，对统计原则和计算有效的方法的需求越来越大。 该研究项目的重点是开发一系列新型统计模型和相关方法，用于大约高分辨率的多元时间序列。 要开发的统计方法将用于将生理时间序列的非平稳特征（例如功能磁共振图像（fMRI））与行为和神经认知评估数据联系起来。 该项目将开发两种用于建模多维时间序列的方法。 第一种方法将通过局部固定的表示形式对非组织时间序列进行建模，该表示以随时间变化的光谱密度矩阵来表征该过程的光谱动力学。 第二种方法包括通过贝叶斯州空间模型捕获数据中的动态依赖性，这些模型将能够估算随时间序列的整个时间序列的相干性。该研究项目中开发的统计模型和方法将用于研究健康个体中的生理时间序列如何与神经认知分数相关。 将研究的数据包括源自大脑图像的措施以及来自fMRI的各个兴趣区域的时间序列。 还将研究记录以监测认知疲劳和工作量的行为和生理信号。 尽管该项目的重点是分析生理和行为数据，但该研究项目中将开发的模型和方法非常笼统，并且具有影响其他科学领域的潜力，因为高度结构化的多元时间序列数据通常是在计量经济学，环境，环境，地球科学和信号处理的领域中收集的。