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

Biao Cai, Gemeng Zhang, Aiying Zhang, Julia M. Stephen, Tony W Wilson, Vince D. Calhoun, Yuping Wang

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

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 from fMRI 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 rs-fMRI 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.

Original languageEnglish (US)
Article number8528484
Pages (from-to)1852-1862
Number of pages11
JournalIEEE Transactions on Biomedical Engineering
Volume66
Issue number7
DOIs
StatePublished - Jul 2019

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Brain
Magnetic Resonance Imaging
Health
Experiments

Keywords

  • Dynamic functional connectivity
  • brain development
  • resting state fMRI
  • time-varying graphical lasso

ASJC Scopus subject areas

  • Biomedical Engineering

Cite this

Capturing Dynamic Connectivity from Resting State fMRI Using Time-Varying Graphical Lasso. / Cai, Biao; Zhang, Gemeng; Zhang, Aiying; Stephen, Julia M.; Wilson, Tony W; Calhoun, Vince D.; Wang, Yuping.

In: IEEE Transactions on Biomedical Engineering, Vol. 66, No. 7, 8528484, 07.2019, p. 1852-1862.

Research output: Contribution to journalArticle

Cai, Biao ; Zhang, Gemeng ; Zhang, Aiying ; Stephen, Julia M. ; Wilson, Tony W ; Calhoun, Vince D. ; Wang, Yuping. / Capturing Dynamic Connectivity from Resting State fMRI Using Time-Varying Graphical Lasso. In: IEEE Transactions on Biomedical Engineering. 2019 ; Vol. 66, No. 7. pp. 1852-1862.
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