Evaluating stationarity via change-point alternatives with applications to fMRI data

Abstract

Functional magnetic 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.

Published in at http://dx.doi.org/10.1214/12-AOAS565 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

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Cited by in corpus (14)

• Tests for separability in nonparametric covariance operators of random surfaces
• Multi-dimensional Functional Principal Component Analysis
• A test of weak separability for multi-way functional data, with application to brain connectivity studies
• A linear time method for the detection of point and collective anomalies
• Detecting Multiple Change Points Using Adaptive Regression Splines with Application to Neural Recordings
• Break Point Detection for Functional Covariance
• CovNet: Covariance Networks for Functional Data on Multidimensional Domains
• Detecting changes in slope with an $L_0$ penalty
• Functional Time Series
• Segmentation and Estimation of Change-point Models: False Positive Control and Confidence Regions
• Improving Power by Conditioning on Less in Post-selection Inference for Changepoints
• Stable and predictive functional domain selection with application to brain images
• Estimation of high-dimensional change-points under a group sparsity structure
• Detecting and dating structural breaks in functional data without dimension reduction