Collaborative Research: Bayesian State-Space Models for Behavioral Time Series Data

合作研究：行为时间序列数据的贝叶斯状态空间模型

• 批准号: 1461534
• 负责人: Zhaoxia Yu
• 金额: $ 16万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-06-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1461534&HistoricalAwards=false
• 关键词: Collaborative; Research; Bayesian; State; Space

This research project will develop novel statistical models and inferential methods for the analysis of multi-domain behavioral data and time series with complex temporal and dependence structures. This research has the potential to advance the knowledge on the neural underpinnings of human and animal behavior. Neuroscience studies often involve the analysis and integration of data from different domains, such as behavioral and neural-derived data. The focus of this project will be on developing statistical methods for studying temporal data derived from functional magnetic resonance imagining (fMRI) and local field potentials, such as neural-derived brain signals. These methods also are applicable to other types of brain signals, such as electroencephalograms and magnetoencephalograms. These statistical approaches will integrate data from different domains and could be used by behavioral scientists to directly test for associations between decision making and brain response. The statistical tools that will be developed in this project are general and could be used to advance knowledge in other fields that collect temporal data with complex structure, such as sociology (network modeling), environmental sciences, linguistics, and signal processing.The project will develop Bayesian state-space models for activation and connectivity in fMRI data. These models will be used to simultaneously estimate the hemodynamic behavior in local areas of the brain and to estimate inter-dependence between brain regions in a network, while taking into account variations across subjects and differences across experimental conditions. The Bayesian state-space models and related inferential tools then will be extended to consider associations between the neural-derived brain signals and behavioral data under the context of behavioral experiments. Bayesian state-space models for brain connectivity using electrophysiological signals also will be developed. To deal with high computational demands for inference resulting from increased model complexity and massive data, the methods will be implemented using parallel computing.

这项研究项目将开发新的统计模型和推理方法，用于分析具有复杂时间和依赖结构的多领域行为数据和时间序列。这项研究有可能促进对人类和动物行为的神经基础的了解。神经科学研究通常涉及分析和整合来自不同领域的数据，如行为数据和神经衍生数据。该项目的重点将是开发统计方法，用于研究从功能磁共振成像(FMRI)和局部场电位(如神经衍生的大脑信号)获得的时间数据。这些方法也适用于其他类型的脑信号，如脑电和脑磁图。这些统计方法将整合来自不同领域的数据，并可被行为科学家用来直接测试决策和大脑反应之间的关联。该项目将开发的统计工具是通用的，可以用于推进其他领域的知识，这些领域收集具有复杂结构的时间数据，如社会学(网络建模)、环境科学、语言学和信号处理。该项目将开发用于激活和连接功能磁共振数据的贝叶斯状态空间模型。这些模型将被用来同时估计大脑局部区域的血流动力学行为，并估计网络中大脑区域之间的相互依赖，同时考虑到不同受试者的差异和不同实验条件的差异。然后，贝叶斯状态空间模型和相关的推理工具将被扩展到考虑神经衍生的大脑信号和行为实验背景下的行为数据之间的关联。还将开发使用电生理信号的大脑连接的贝叶斯状态空间模型。为了处理因模型复杂性增加和海量数据而导致的高计算要求，这些方法将使用并行计算来实现。