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新的统计方法，用于对非平稳时空数据的连通性进行建模。研究人员将开发四种具体方法和模型，这些方法和模型将应用于研究人员合作者提供的数据。首先，由于需要更复杂的方法来研究两个时间序列（例如大脑区域）之间的复杂依赖性，研究人员将构建工具，利用谱域中的动态互信息来探索信号之间的非线性和时间演化依赖性。其次，空间变化和时间演化频谱的概念将通过非平稳时空过程的随机表示而变得精确。将开发一个用于一致估计和推理的渐近框架。第三，将通过潜在网络模型制定多受试者实验中连接性的通用谱模型。经验驱动的模型将包含刺激类型、外生时间序列和特定于主题的随机效应等项目。最后，为了补充这种光谱数据和连通性建模的探索性方法，研究人员将使用多主题数据构建有效连通性的科学驱动的半参数状态空间模型。这项研究的首要目标是开发新的统计方法来分析具有时间和空间维度的数据。时空数据在许多学科中都很普遍，包括环境和土壤科学、气象学和海洋学、神经科学以及新兴的健康和生物恐怖主义监测领域。研究人员的主要数据源是在大脑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如何运作、如何响应外部刺激以及功能同步发生的位置的信息。研究人员正在开发的统计模型有助于筛选这些信息，从而可以检测大脑功能的趋势，并估计群体和个体水平的表现差异。模型的经验性质允许数据驱动的神经科学理论的确认和发现。统计模型也将具有预测性，有助于寻求抑郁症、焦虑症和其他神经系统疾病的个性化诊断和治疗。虽然统计研究是由研究人员与神经科学家持续合作推动的，但有一个统一的统计主题适用于许多其他感兴趣的领域。