Evaluating stationarity via changepoint alternatives with applications to fMRI data
Abstract
Functional magnetic resonance imaging (fMRI) is now a wellestablished 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, changepoint detection in sequences of functional data is examined where the functional observations are dependent and where the distributions of changepoints from multiple subjects are required. Of particular interest is the case where the changepoint is an epidemic changea 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 threedimensional 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 changepoint distribution for those subjects is empirically determined, as well as its theoretical properties examined.
 Publication:

arXiv eprints
 Pub Date:
 January 2013
 arXiv:
 arXiv:1301.2894
 Bibcode:
 2013arXiv1301.2894A
 Keywords:

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