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

本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认识。神经科学研究通常涉及分析和整合来自不同领域的数据，如行为和神经来源的数据。这个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（如神经来源的大脑信号）获得的时间数据。这些方法也适用于其他类型的脑信号，如脑电图和脑磁图。这些统计方法将整合来自不同领域的数据，可以被行为科学家用来直接测试决策和大脑反应之间的联系。本项目将开发的统计工具是通用的，可用于在社会学（网络建模）、环境科学、语言学和信号处理等其他收集具有复杂结构的时间数据的领域中推进知识。该项目将开发贝叶斯状态空间模型，用于fMRI数据的激活和连接。这些模型将用于同时估计大脑局部区域的血流动力学行为和估计网络中大脑区域之间的相互依赖性，同时考虑到不同受试者的差异和不同实验条件的差异。然后将贝叶斯状态空间模型和相关的推理工具扩展到考虑行为实验背景下神经衍生的大脑信号和行为数据之间的关联。使用电生理信号的脑连接贝叶斯状态空间模型也将得到发展。为了处理由于模型复杂性增加和海量数据导致的高计算需求，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.