Discovering common change-point patterns in functional connectivity across subjects

• Published in: Medical image analysis Vol. 58; p. 101532
• Main Authors: Dai, Mengyu, Zhang, Zhengwu, Srivastava, Anuj
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.12.2019; Elsevier BV
• Subjects: Brain; Brain mapping; Change detection; Change-points; Dynamic functional connectivity; Functional magnetic resonance imaging; Graphical methods; Graphical representations; Mathematical analysis; Mathematical functions; Matrix methods; Minimal spanning trees; Neural networks; Statistical analysis; Statistical tests; Symmetric positive definite matrix; Temporal alignment; Change-points; Minimal spanning trees; Dynamic functional connectivity; Symmetric positive definite matrix; Temporal alignment
• ISSN: 1361-8415; 1361-8423; 1361-8423
• DOI: 10.1016/j.media.2019.101532

•A graph-based change-point detection model combing a Riemannian metric on the space of symmetric positive-definite matrices.•Graphical representations of estimated FC.•Temporal alignment of functions for discovering common change-point patterns across subjects.•Experiment block designs for validation.•Comprehensive Human Connectome Project (HCP) data analysis results. [Display omitted] 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 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.