Estimation of dynamic sparse connectivity patterns from resting state fMRI

Biao Cai, Pascal Zille, Julia M. Stephen, Tony W Wilson, Vince D. Calhoun, Yu Ping Wang

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

Functional connectivity (FC) estimated from functional magnetic resonance imaging (fMRI) time series, especially during resting state periods, provides a powerful tool to assess human brain functional architecture in health, disease, and developmental states. Recently, the focus of connectivity analysis has shifted toward the subnetworks of the brain, which reveals co-activating patterns over time. Most prior works produced a dense set of high-dimensional vectors, which are hard to interpret. In addition, their estimations to a large extent were based on an implicit assumption of spatial and temporal stationarity throughout the fMRI scanning session. In this paper, we propose an approach called dynamic sparse connectivity patterns (dSCPs), which takes advantage of both matrix factorization and time-varying fMRI time series to improve the estimation power of FC. The feasibility of analyzing dynamic FC with our model is first validated through simulated experiments. Then, we use our framework to measure the difference between young adults and children with real fMRI data set from the Philadelphia Neurodevelopmental Cohort (PNC). The results from the PNC data set showed significant FC differences between young adults and children in four different states. For instance, young adults had reduced connectivity between the default mode network and other subnetworks, as well as hyperconnectivity within the visual system in states 1 and 3, and hypoconnectivity in state 2. Meanwhile, they exhibited temporal correlation patterns that changed over time within functional subnetworks. In addition, the dSCPs model indicated that older people tend to spend more time within a relatively connected FC pattern. Overall, the proposed method provides a valid means to assess dynamic FC, which could facilitate the study of brain networks.

Original languageEnglish (US)
Pages (from-to)1224-1234
Number of pages11
JournalIEEE Transactions on Medical Imaging
Volume37
Issue number5
DOIs
StatePublished - May 2018

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Magnetic Resonance Imaging
Brain
Time series
Young Adult
Factorization
Health
Scanning
Experiments

Keywords

  • Sparse model
  • brain development
  • dynamic functional connectivity
  • resting state fMRI

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

Estimation of dynamic sparse connectivity patterns from resting state fMRI. / Cai, Biao; Zille, Pascal; Stephen, Julia M.; Wilson, Tony W; Calhoun, Vince D.; Wang, Yu Ping.

In: IEEE Transactions on Medical Imaging, Vol. 37, No. 5, 05.2018, p. 1224-1234.

Research output: Contribution to journalArticle

Cai, Biao ; Zille, Pascal ; Stephen, Julia M. ; Wilson, Tony W ; Calhoun, Vince D. ; Wang, Yu Ping. / Estimation of dynamic sparse connectivity patterns from resting state fMRI. In: IEEE Transactions on Medical Imaging. 2018 ; Vol. 37, No. 5. pp. 1224-1234.
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