多变量时间序列分析在神经科学数据中的应用：一些挑战和机遇。

Multivariate time series analysis of neuroscience data: some challenges and opportunities.

机构信息

Department of Statistics, Texas A&M University, College Station, TX 77843-3143, United States.

VA HSR&D Center for Chronic Diseases Outcome Research and Department of Medicine, University of Minnesota, Minneapolis, MN 55455-0460, United States.

出版信息

Curr Opin Neurobiol. 2016 Apr;37:12-15. doi: 10.1016/j.conb.2015.12.006. Epub 2016 Jan 2.

DOI:10.1016/j.conb.2015.12.006

PMID:26752736

Abstract

Neuroimaging data may be viewed as high-dimensional multivariate time series, and analyzed using techniques from regression analysis, time series analysis and spatiotemporal analysis. We discuss issues related to data quality, model specification, estimation, interpretation, dimensionality and causality. Some recent research areas addressing aspects of some recurring challenges are introduced.

摘要

神经影像学数据可以被视为高维多元时间序列，并使用回归分析、时间序列分析和时空分析技术进行分析。我们讨论了与数据质量、模型规范、估计、解释、维度和因果关系相关的问题。介绍了一些最近的研究领域，这些领域涉及一些反复出现的挑战的某些方面。