Capturing Dynamic Connectivity From Resting State fMRI Using Time-Varying Graphical Lasso

被引:37
作者
Cai, Biao [1 ]
Zhang, Gemeng [1 ]
Zhang, Aiying [1 ]
Stephen, Julia M. [2 ]
Wilson, Tony W. [3 ]
Calhoun, Vince D. [2 ,4 ]
Wang, Yu-Ping [1 ]
机构
[1] Tulane Univ, Biomed Engn Dept, New Orleans, LA 70118 USA
[2] Mind Res Network, Albuquerque, NM USA
[3] Univ Nebraska Med Ctr, Dept Neurol Sci, Omaha, NE USA
[4] Univ New Mexico, Dept Elect & Comp Engn, Albuquerque, NM 87131 USA
基金
美国国家科学基金会;
关键词
Dynamic functional connectivity; resting state fMRI; time-varying graphical lasso; brain development; FUNCTIONAL CONNECTIVITY; DEFAULT MODE; DATA REVEALS; BRAIN; NETWORKS; MRI; TRAITS;
D O I
10.1109/TBME.2018.2880428
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Functional connectivity (FC) within the human brain evaluated through functional magnetic resonance imaging (fMRI) data has attracted increasing attention and has been employed to study the development of the brain or health conditions of the brain. Many different approaches have been proposed to estimate FC fromfMRI data, whereas many of them rely on an implicit assumption that functional connectivity should be static throughout the fMRI scan session. Recently, the fMRI community has realized the limitation of assuming static connectivity and dynamic approaches are more prominent in the resting state fMRI (rs-fMRI) analysis. The sliding window technique has been widely used in many studies to capture network dynamics, but has a number of limitations. In this study, we apply a time-varying graphical lasso (TVGL) model, an extension from the traditional graphical lasso, to address the challenge, which can greatly improve the estimation of FC. The performance of estimating dynamic FC is evaluated with the TVGL through both simulated experiments and real rsfMRI data from the Philadelphia Neurodevelopmental Cohort project. Improved performance is achieved over the sliding window technique. In particular, group differences and transition behaviors between young adults and children are investigated using the estimated dynamic connectivity networks, which help us to better unveil the mechanisms underlying the evolution of the brain over time.
引用
收藏
页码:1852 / 1862
页数:11
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