Collaborative Research: Spectral and Connectivity Analysis of Non-Stationary Spatio-Temporal Data

合作研究：非平稳时空数据的谱和连通性分析

• 批准号: 0806106
• 负责人: Hernando Ombao
• 金额: $ 12.8万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0806106&HistoricalAwards=false
• 关键词: Collaborative; Research; Spectral; Connectivity; Analysis

The focus of this research is the development of new statistical methodologies for modeling connectivity in non-stationary spatio-temporal data. The investigators will develop four specific methods and models which will be applied to data provided by t he investigator's collaborators. First, motivated by the need for more sophisticated methods to investigate complex dependencies between two time series (e.g., brain regions), the investigators will build tools for exploring non-linear and time-evolving dependence between signals using dynamic mutual information in the spectral domain. Second, the notion of spatially-varying and temporally-evolving spectrum will be made precise via a stochastic representation of non-stationary spatio-temporal processes. An asymptotic framework for consistent estimation and inference will be developed. Third, a general spectral model for connectivity in a multi-subject experiment via a latent network model will be formulated. The empirically-driven model will incorporate items such as stimulus types, exogeneous time series, and subject-specific random effects. Finally, to complement this exploratory approach for modeling spectral data and connectivity, the investigators will build a scientifically-motivated semi-parametric state-space model of effective connectivity using multi-subject data.The overarching goal of this research is the development of new statistical methodologies for analyzing data that has both a time and space dimension. Spatio-temporal data are prevalent in many disciplines, including the environmental and soil sciences, meteorology and oceanography, neuroscience and the emerging fields of health and bioterrorism surveillance. The primary data source for the investigators is time-sequenced data of brain activity measured at many locations in the brain. These signals contain information on how the brain functions, how it responds to outside stimuli, and where synchronization of functionality occurs. The statistical models the investigators are developing help sift through this information, allowing for the detection of trends in brain functionality, and estimation of population- and individual-level differences in performance. The empirical nature of the models allows for data-driven confirmation and discovery of neuroscientific theory. The statistical models will also be predictive, aiding in the quest for personalized diagnosis and treatment of depression, anxiety, and other neurological conditions. While the statistical research is motivated by the investigators' ongoing collaboration with neuroscientists, there is a unified statistical theme applicable to many other areas of interest.

这项研究的重点是开发用于建模非平稳时空数据中连通性的新统计方法。研究人员将开发四种特定的方法和模型，这些方法和模型将应用于调查员合作者提供的数据。首先，由于需要更复杂的方法研究两个时间序列之间的复杂依赖性（例如大脑区域）的动机，研究人员将使用光谱域中的动态信息构建信号之间非线性和时间不断发展的依赖性工具。其次，通过非平稳时空过程的随机表示，将精确地形成空间变化和时间变化的频谱的概念。将开发一个用于一致估计和推理的渐近框架。第三，将制定通过潜在网络模型的多主体实验中连通性的一般光谱模型。经验驱动的模型将结合诸如刺激类型，外动时间序列和主体特定随机效应之类的项目。最后，为了补充用于建模光谱数据和连接性的探索方法，研究人员将使用多主体数据建立一个科学动机的半参数状态空间模型的有效连通性模型。这项研究的总体目标是开发具有时间和空间维度的新统计方法。时空数据在许多学科中都普遍存在，包括环境和土壤科学，气象和海洋学，神经科学以及健康和生物恐怖监视的新兴领域。研究人员的主要数据源是在大脑中许多位置测量的大脑活动的时间序列数据。这些信号包含有关大脑功能，其对外部刺激的响应方式以及功能同步发生的信息。研究人员正在通过此信息开发帮助筛选帮助的统计模型，从而可以检测大脑功能的趋势，并估计绩效的人口和个人水平差异。模型的经验性质允许数据驱动的确认和神经科学理论的发现。统计模型也将具有预测性，有助于寻求个性化诊断和治疗抑郁症，焦虑和其他神经系统疾病。虽然统计研究是由研究人员与神经科学家的持续合作进行的，但统一的统计主题适用于许多其他感兴趣的领域。