PREVIOUS SEMINAR - 3rd July 2020
Speaker: Claudia Kirch (Otto-von-Guericke University)
Title: Functional change point detection for 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, the statistical analyzes depends crucially on stationarity which could easily be violated. We introduce tests for the detection of deviations from this assumption by making use of change point alternatives, where changes in the mean as well as covariance structure of functional time series are considered. Because of the very high-dimensionality of the data an approach based on a general covariance structure is not feasible, such that computations will be conducted by making use of a multidimensional separable functional covariance structure. 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. This is joint work with Christina Stoehr (Ruhr-Universität Bochum) and John Aston (University of Cambridge).