Statistical Methods for Discrete-Valued High-Dimensional Time Series with Applications to Neuroscience

离散值高维时间序列的统计方法及其在神经科学中的应用

基本信息

批准号：
1722246
负责人：
Ali Shojaie
金额：
$ 30万
依托单位：
University of Washington
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-01 至 2021-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1722246&HistoricalAwards=false
关键词：
Statistical Methods Discrete Valued Dimensional

项目摘要

The advent of high-dimensional time series from neuroscience, including EEG/MEG, fMRI and spike train data, has sparked a new interest in the analysis of multivariate time series data, particularly, to decipher the dynamics of brain connectivity networks. Despite significant recent progress, the vast majority of existing approaches for analyzing high-dimensional time series focus on real-valued time series from Gaussian noise and perturbation models. However, emerging applications in neuroscience involve discrete-valued time series, such as point processes and categorical observations. This project aims to develop flexible and scalable statistical machine learning methods and efficient software tools for inferring brain connectivity networks using discrete-valued high-dimensional time series data from neuroscience. Large-scale brain connectivity networks often involve complex nonlinear and multi-scale interactions that are usually unknown in practice. Applications of parametric models in such settings may not provide an accurate window into the brain's dynamics, especially if the model assumptions are violated. This research bridges this gap by developing scalable statistical machine learning methods and theory for flexible nonparametric analysis of high-dimensional discrete-valued time series. In particular, this project will develop (i) clustering and variable screening methods for high-dimensional point processes, (ii) an efficient and general nonparametric estimation framework for network discovery from a general class of point processes, and (iii) a novel regularized estimation framework with provable identifiablity guarantees for network reconstruction from high-dimensional categorical time series. Theoretical properties of these methods will be investigated, and efficient open-source software tools will be developed to facilitate the application of the methods by the scientific community. Together, these tools provide a comprehensive framework for analysis of high-dimensional discrete values time series arising in various neuroscience applications, and will advance the current state of statistical machine learning methods for the analysis of high-dimensional time series. The PIs also plan to release the software developed as open source and build a user community around the language by ensuring that interested researchers are able to contribute to the codebase of the software developed. This will allow a wider growth of the project. This aspect is of special interest to the software cluster in the Office of Advanced Cyberinfrastructure, which has provided co-funding for this award.

高维时间序列的出现，包括EEG/MEG，fMRI和SPIKE Train数据，引发了对分析多变量时间序列数据的新兴趣，尤其是破译大脑连接网络的动态。尽管最近取得了重大进展，但分析高维时间序列的绝大多数现有方法都集中在高斯噪声和扰动模型中的真实价值时间序列上。但是，神经科学中的新兴应用涉及离散值的时间序列，例如点过程和分类观察。该项目旨在开发灵活且可扩展的机器学习方法和有效的软件工具，用于使用神经科学的离散高维高度时间序列数据来推断大脑连接网络。大型大脑连接网络通常涉及复杂的非线性和多尺度相互作用，这些相互作用通常在实践中未知。在这种设置中，参数模型的应用可能无法为大脑的动态提供准确的窗口，尤其是在违反模型假设的情况下。这项研究通过开发可扩展的统计机器学习方法和理论来弥合这一差距，用于对高维离散值时间序列的灵活非参数分析。特别是，该项目将开发（i）用于高维点过程的聚类和可变筛选方法，（ii）从一系列点过程中进行网络发现的有效且一般的非参数估计框架，以及（iii）一个新颖的正则化估计框架，具有可验证的可预测识别识别网络构造的高度识别类别类别。将研究这些方法的理论属性，并将开发有效的开源软件工具来促进科学界的方法的应用。这些工具共同提供了一个全面的框架，用于分析在各种神经科学应用中产生的高维离散值时间序列，并将推进统计机器学习方法的当前状态，以分析高维时间序列。 PIS还计划通过确保感兴趣的研究人员能够为开发的软件的代码库做出贡献，从而释放作为开源的软件开发的软件，并围绕该语言建立用户社区。这将允许该项目的更大增长。这方面是高级网络基础设施办公室的软件集群特别感兴趣的，该策略已为该奖项提供了共同资助。