Discovering Common Change-Point Patterns in Functional Connectivity Across Subjects
This work addresses the challenge of analyzing dynamic brain connectivity patterns across multiple subjects, which is incremental in providing a graphical method for detecting and aligning change-points.
The paper tackles the problem of identifying common change-points in functional connectivity across subjects under identical stimuli by developing a formal statistical test using Riemannian metrics on connectivity matrices and temporal alignment to remove inter-subject variability, applied to Human Connectome Project data.
This paper studies change-points in human brain functional connectivity (FC) and seeks patterns that are common across multiple subjects under identical external stimulus. FC relates to the similarity of fMRI responses across different brain regions when the brain is simply resting or performing a task. While the dynamic nature of FC is well accepted, this paper develops a formal statistical test for finding {\it change-points} in times series associated with FC. It represents short-term connectivity by a symmetric positive-definite matrix, and uses a Riemannian metric on this space to develop a graphical method for detecting change-points in a time series of such matrices. It also provides a graphical representation of estimated FC for stationary subintervals in between the detected change-points. Furthermore, it uses a temporal alignment of the test statistic, viewed as a real-valued function over time, to remove inter-subject variability and to discover common change-point patterns across subjects. This method is illustrated using data from Human Connectome Project (HCP) database for multiple subjects and tasks.