Testing Stationarity of Brain Functional Connectivity Using Change-Point Detection in fMRI Data

Mengyu Dai, Zhengwu Zhang, Anuj Srivastava; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2016, pp. 19-27

This paper studies two questions: (1) Does the functional connectivity (FC) in a human brain remain stationary during performance of a task? (2) If it is non-stationary, how can one evaluate and estimate dynamic FC? The framework presented here relies on pre-segmented brain regions to represent instantaneous FC as symmetric, positive-definite matrices (SPDMs), with entries denoting covariances of fMRI signals across regions. The time series of such SPDMs is tested for change point detection using two important ideas: (1) a convenient Riemannian structure on the space of SPDMs for calculating geodesic distances and sample statistics, and (2) a graph-based approach, for testing similarity of distributions, that uses pairwise distances and a minimal spanning tree. This hypothesis test results in a temporal segmentation of observation interval into parts with stationary connectivity and an estimation of graph displaying FC during each such interval.