  Functional magnetic resonance imaging (fMRI) data provides information
concerning activity in the brain and in particular the interactions between
brain regions. Resting state fMRI data is widely used for inferring
connectivities in the brain which are not due to external factors. As such
analyzes strongly rely on stationarity, change point procedures can be applied
in order to detect possible deviations from this crucial assumption. In this
paper, we model fMRI data as functional time series and develop tools for the
detection of deviations from covariance stationarity via change point
alternatives. We propose a nonparametric procedure which is based on dimension
reduction techniques. However, as the projection of the functional time series
on a finite and rather low-dimensional subspace involves the risk of missing
changes which are orthogonal to the projection space, we also consider two test
statistics which take the full functional structure into account. The proposed
methods are compared in a simulation study and applied to more than 100 resting
state fMRI data sets.
