文献类型：期刊文献

英文题名：An Optimized Method for Bayesian Connectivity Change Point Model

作者：Xiao, Xiuchun[1,2];Liu, Bing[3];Zhang, Jing[3];Xiao, Xueli[2];Pan, Yi[2]

机构：[1]Guangdong Ocean Univ, Coll Elect & Informat Engn, Zhanjiang, Peoples R China;[2]Georgia State Univ, Dept Comp Sci, 25 Pk Pl,Room 744, Atlanta, GA 30302 USA;[3]Georgia State Univ, Dept Math & Stat, 25 Pk Pl,1407A, Atlanta, GA 30303 USA

年份：2018

卷号：25

期号：3

起止页码：337

外文期刊名：JOURNAL OF COMPUTATIONAL BIOLOGY

收录：SCI-EXPANDED(收录号：WOS:000417270400001)、、WOS

基金：This research is supported by the Brains-Behavior Seed grant and Molecular Basis of Disease (MBD) from Georgia State University.

语种：英文

外文关键词：functional brain dynamics; Bayesian inference; optimization algorithm

外文摘要：The brain undergoes functional dynamic changes at all times. Investigating functional dynamics has been recently verified to be helpful for detecting psychological conditions and powerful for analyzing disease-related abnormalities of the brain. This article aims to detect functional dynamics. Specifically, we focus on how to effectively distinguish corresponding functional connectivity and change points from functional magnetic resonance imaging (fMRI) data. By combining Bayesian connectivity change point model (BCCPM), a modified genetic algorithm (GA) is presented to optimize the evolutionary procedure toward the most probable distributions of real change points in fMRI. We randomly initialize different binary indicator vectors to represent different distributions of change points. Each indicator vector represents an individual in GA, and together they form an initial population. Then we calculate Bayesian posterior probability and use it as the fitness of each individual. Finally, we evolve individuals of current generation toward the next higher fitness generation by a series of modified genetic operators. After several evolutionary procedures, individuals in the final generation may have outstanding fitness and the one with highest fitness can represent the most likely change point distribution in the corresponding fMRI data. Furthermore, the most probable change point distribution could be resolved. We test the optimized method for BCCPM on several synthesized data sets, and the experimental results verify that the proposed model produces higher accuracy results with lower time consumption. Also, we apply the new model to real block-designed task-based fMRI data set and excellent results are obtained.