EVALUATING STATIONARITY VIA CHANGE-POINT ALTERNATIVES WITH APPLICATIONS TO FMRI DATA

• 成果类型: Article
• 署名作者: Aston, John A. D.; Kirch, Claudia
• 署名单位: University of Warwick; Helmholtz Association; Karlsruhe Institute of Technology
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/12-AOAS565
• 发表日期: 2012
• 页码: 1906-1948
• 关键词: principal-component analysis; statistical-analysis; brain; SPACE; connectivity; separability

摘要

Functionalmagnetic resonance imaging (fMRI) is now a well-established technique for studying the brain. However, in many situations, such as when data are acquired in a resting state, it is difficult to know whether the data are truly stationary or if level shifts have occurred. To this end, change-point detection in sequences of functional data is examined where the functional observations are dependent and where the distributions of change-points from multiple subjects are required. Of particular interest is the case where the change-point is an epidemic change-a change occurs and then the observations return to baseline at a later time. The case where the covariance can be decomposed as a tensor product is considered with particular attention to the power analysis for detection. This is of interest in the application to fMRI, where the estimation of a full covariance structure for the three-dimensional image is not computationally feasible. Using the developed methods, a large study of resting state fMRI data is conducted to determine whether the subjects undertaking the resting scan have nonstationarities present in their time courses. It is found that a sizeable proportion of the subjects studied are not stationary. The change-point distribution for those subjects is empirically determined, as well as its theoretical properties examined.