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Functional magnetic resonance imaging (fMRI) data provides informationconcerning activity in the brain and in particular the interactions betweenbrain regions. Resting state fMRI data is widely used for inferringconnectivities in the brain which are not due to external factors. As suchanalyzes strongly rely on stationarity, change point procedures can be appliedin order to detect possible deviations from this crucial assumption. In thispaper, we model fMRI data as functional time series and develop tools for thedetection of deviations from covariance stationarity via change pointalternatives. We propose a nonparametric procedure which is based on dimensionreduction techniques. However, as the projection of the functional time serieson a finite and rather low-dimensional subspace involves the risk of missingchanges which are orthogonal to the projection space, we also consider two teststatistics which take the full functional structure into account. The proposedmethods are compared in a simulation study and applied to more than 100 restingstate fMRI data sets.

Detecting changes in the covariance structure of functional time series with application to fMRI data