Discovering Change-Point Patterns in Dynamic Functional Brain Connectivity of a Population

  • Mengyu Dai
  • Zhengwu Zhang
  • Anuj Srivastava
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10265)


This paper seeks to discover common change-point patterns, associated with functional connectivity (FC) in human brain, across multiple subjects. FC, represented as a covariance or a correlation matrix, relates to the similarity of fMRI responses across different brain regions, when a brain is simply resting or performing a task under an external stimulus. While the dynamical nature of FC is well accepted, this paper develops a formal statistical test for finding change-points in times series associated with FC observed over time. It represents instantaneous 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 detected change-points. Furthermore, it uses a temporal alignment of the test statistic, viewed as a real-valued function over time, to remove temporal variability and to discover common change-point patterns across subjects, tasks, and regions. This method is illustrated using HCP database for multiple subjects and tasks.


Functional connectivity Change-point Riemmanian metric Covariance estimation Function alignment 



This research was supported in part by NSF grants DMS 1621787 and CCF 1617397 to AS. ZZ was partially supported by NSF grant DMS-1127914 to SAMSI. Data were provided in part by the HCP, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657).


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of StatisticsFlorida State UniversityTallahasseeUSA
  2. 2.Statistical and Applied Mathematical Sciences InstituteResearch Triangle ParkUSA
  3. 3.Department of Statistical ScienceDuke UniversityDurhamUSA

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