A Bayesian approach for dynamic brain network Open Access

Ming, Jin (2016)

Permanent URL: https://etd.library.emory.edu/concern/etds/nc580m895?locale=en


Functional magnetic resonance imaging (fMRI) has been widely used in brain network research. Functional connectivity (FC) measures how different brain regions contact with each other. Recently there has been an increased interest in understanding the dynamic manner in the functional connectivity. Although sliding window method is still the most widely used one, because of its limitations in window size pick and interpretation, many researchers are trying to create new method. Dynamic Connectivity Regression (DCR) is a data-driven method to detect temporal change points in different brain regions. However, DCR may fail to detect some change points and is hard to detect rapid change in functional connectivity. In this paper, we introduce our Bayesian approach which combines both change point detection and Bayesian method to detect the number and positions of change points in FC simultaneously. Our method is based on the change point in precision matrix instead of the mean value of time series. Screening method like screening and ranking algorithm (SaRa) is also included in our method to increase the computation speed. Different choices of change points combinations are also provided to get an accurate estimation. Two simulation show that our method can provide a good estimation of positions when the number of change points is given. In addition, we provide an experiment data which can be used to validate our method.

Table of Contents

1. Introduction. 1

2. Methods. 3

2.1 Problem setup. 4

2.2 Change points method. 5

2.3 Screening method. 6

2.4 Dynamic Change point method. 6

2.5 Dynamic Connectivity Regression (DCR) method. 9

3. Simulation. 10

3.1 simulation design. 10

3.2 Experimental data. 11

4. Results. 12

5. Discussion. 16

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