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

本研究计画将发展新颖的统计模型与推论方法，以分析具有复杂时序与相依结构的多领域行为资料与时间序列。 这项研究有可能促进人类和动物行为的神经基础的知识。 神经科学研究通常涉及分析和整合来自不同领域的数据，例如行为和神经衍生数据。 本项目的重点是开发用于研究来自功能性磁共振成像（fMRI）和局部场电位的时间数据的统计方法，例如神经源性脑信号。 这些方法也适用于其他类型的大脑信号，如脑电图和脑磁图。 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于直接测试决策和大脑反应之间的关联。 本课题开发的统计学工具是通用的，可用于社会学（网络建模）、环境科学、语言学、信号处理等收集具有复杂结构的时间数据的领域，开发功能磁共振成像数据的激活和连接的贝叶斯状态空间模型。 这些模型将用于同时估计大脑局部区域的血液动力学行为，并估计网络中大脑区域之间的相互依赖性，同时考虑受试者之间的差异和实验条件之间的差异。 贝叶斯状态空间模型和相关的推理工具，然后将扩展到考虑行为实验的背景下，神经源性脑信号和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。为了应对因模型复杂性增加和海量数据而导致的推理的高计算需求，这些方法将使用并行计算来实现。

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.