Are posterior default-mode networks more robust than anterior default-mode networks? Evidence from resting-state fMRI data analysis

Dong Youl Kim, Jong-Hwan Lee

Research output: Contribution to journalArticle

21 Citations (Scopus)

Abstract

Intrinsic brain activity known as default-mode networks (DMNs) has been observed predominantly within the medial/superior frontal areas, anterior/posterior cingulate gyri, and precuneus using blood-oxygenation-level-dependent (BOLD) functional MRI (fMRI). Despite anecdotal evidence of distinct spatial patterns reflecting neuropsychiatric conditions in these DMNs, rigorous analysis of the characteristic traits of DMNs has been limited in previous studies. In this letter, the reproducibility and potential variability of the anterior and posterior DMNs were evaluated based on individual-level variations in effect sizes, activated areas, and causal interactions. Our results indicated that the DMNs were indeed reproducible between sessions/subjects. Region-specific traits were also observed: the posterior DMN seemed more robust to individual-level variations than the anterior DMN. The proposed analytical methods and reported findings may be useful in the development of a wide range of applications, including those involving clinical populations, which utilize the characteristic traits of DMNs.

Original languageEnglish
Pages (from-to)57-62
Number of pages6
JournalNeuroscience Letters
Volume498
Issue number1
DOIs
Publication statusPublished - 2011 Jul 1

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Keywords

  • Default-mode networks
  • Effective connectivity
  • Functional connectivity
  • Functional MRI
  • Granger causality analysis
  • Group independent component analysis
  • Reproducibility
  • Resting-state networks

ASJC Scopus subject areas

  • Neuroscience(all)

Cite this

Are posterior default-mode networks more robust than anterior default-mode networks? Evidence from resting-state fMRI data analysis. / Kim, Dong Youl; Lee, Jong-Hwan.

In: Neuroscience Letters, Vol. 498, No. 1, 01.07.2011, p. 57-62.

Research output: Contribution to journalArticle

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