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Bayesian Inference for Functional Dynamics Exploring in fMRI Data


Affiliations
1 Department of Computer Science, Georgia State University, Atlanta, GA 30303, United States
2 Department of Mathematics and Statistics, Georgia State University, Atlanta, GA 30303, United States
 

This paper aims to review state-of-the-art Bayesian-inference-based methods applied to functional magnetic resonance imaging (fMRI) data. Particularly, we focus on one specific long-standing challenge in the computational modeling of fMRI datasets: how to effectively explore typical functional interactions from fMRI time series and the corresponding boundaries of temporal segments. Bayesian inference is a method of statistical inference which has been shown to be a powerful tool to encode dependence relationships among the variables with uncertainty. Here we provide an introduction to a group of Bayesian-inference-based methods for fMRI data analysis, which were designed to detect magnitude or functional connectivity change points and to infer their functional interaction patterns based on corresponding temporal boundaries. We also provide a comparison of three popular Bayesian models, that is, Bayesian Magnitude Change Point Model (BMCPM), Bayesian Connectivity Change Point Model (BCCPM), and Dynamic Bayesian Variable Partition Model (DBVPM), and give a summary of their applications. We envision that more delicate Bayesian inference models will be emerging and play increasingly important roles in modeling brain functions in the years to come.
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  • Bayesian Inference for Functional Dynamics Exploring in fMRI Data

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Authors

Xuan Guo
Department of Computer Science, Georgia State University, Atlanta, GA 30303, United States
Bing Liu
Department of Mathematics and Statistics, Georgia State University, Atlanta, GA 30303, United States
Le Chen
Department of Mathematics and Statistics, Georgia State University, Atlanta, GA 30303, United States
Guantao Chen
Department of Mathematics and Statistics, Georgia State University, Atlanta, GA 30303, United States
Yi Pan
Department of Computer Science, Georgia State University, Atlanta, GA 30303, United States
Jing Zhang
Department of Mathematics and Statistics, Georgia State University, Atlanta, GA 30303, United States

Abstract


This paper aims to review state-of-the-art Bayesian-inference-based methods applied to functional magnetic resonance imaging (fMRI) data. Particularly, we focus on one specific long-standing challenge in the computational modeling of fMRI datasets: how to effectively explore typical functional interactions from fMRI time series and the corresponding boundaries of temporal segments. Bayesian inference is a method of statistical inference which has been shown to be a powerful tool to encode dependence relationships among the variables with uncertainty. Here we provide an introduction to a group of Bayesian-inference-based methods for fMRI data analysis, which were designed to detect magnitude or functional connectivity change points and to infer their functional interaction patterns based on corresponding temporal boundaries. We also provide a comparison of three popular Bayesian models, that is, Bayesian Magnitude Change Point Model (BMCPM), Bayesian Connectivity Change Point Model (BCCPM), and Dynamic Bayesian Variable Partition Model (DBVPM), and give a summary of their applications. We envision that more delicate Bayesian inference models will be emerging and play increasingly important roles in modeling brain functions in the years to come.