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

## TL;DR

This paper develops nonparametric change point detection methods for covariance stationarity in functional time series, specifically applied to resting state fMRI data to identify deviations in brain connectivity.

## Contribution

It introduces new tools for detecting covariance changes in functional time series, combining dimension reduction with full-structure tests, tailored for fMRI data analysis.

## Key findings

- Methods effectively detect change points in simulated data.
- Application to fMRI data reveals deviations in brain connectivity.
- Full-structure tests outperform dimension-reduction methods in certain scenarios.

## Abstract

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.