# Dynamic connectivity detection: an algorithm for determining functional connectivity change points in fMRI data

## TL;DR

This paper introduces a new algorithm for detecting changes in brain connectivity over time using fMRI data, which is faster and more efficient for large datasets.

## Contribution

The paper introduces the DCD algorithm, which improves upon existing methods by being faster and handling high-dimensional data better.

## Key findings

- DCD outperforms DCR in handling large numbers of brain regions.
- The algorithm effectively detects change points in simulated and real fMRI data.
- DCD requires less user input and computational resources.

## Abstract

Recently there has been an increased interest in using fMRI data to study the dynamic nature of brain connectivity. In this setting, the activity in a set of regions of interest (ROIs) is often modeled using a multivariate Gaussian distribution, with a mean vector and covariance matrix that are allowed to vary as the experiment progresses, representing changing brain states. In this work, we introduce the Dynamic Connectivity Detection (DCD) algorithm, which is a data-driven technique to detect temporal change points in functional connectivity, and estimate a graph between ROIs for data within each segment defined by the change points. DCD builds upon the framework of the recently developed Dynamic Connectivity Regression (DCR) algorithm, which has proven efficient at detecting changes in connectivity for problems consisting of a small to medium (< 50) number of regions, but which runs into computational problems as the number of regions becomes large (>100). The newly proposed DCD method is faster, requires less user input, and is better able to handle high-dimensional data. It overcomes the shortcomings of DCR by adopting a simplified sparse matrix estimation approach and a different hypothesis testing procedure to determine change points. The application of DCD to simulated data, as well as fMRI data, illustrates the efficacy of the proposed method.