# Discovering Common Change-Point Patterns in Functional Connectivity Across Subjects

## TL;DR

This paper introduces a statistical method to detect change-points in brain functional connectivity from fMRI data, identifying common patterns across subjects and tasks using Riemannian metrics and temporal alignment.

## Contribution

It develops a novel Riemannian metric-based graphical test for change-points in FC and a method to find shared patterns across subjects, advancing analysis of dynamic brain connectivity.

## Key findings

- Effective detection of change-points in FC matrices.
- Identification of common change-point patterns across subjects.
- Application to Human Connectome Project data demonstrates utility.

## Abstract

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.