A Spectral Graphical Model Approach for Learning Brain Connectivity Network of Children's Narrative Comprehension

Xiaodong Lin, Xiangxiang Meng, Prasanna Karunanayaka, Scott K. Holland

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Narrative comprehension is a fundamental cognitive skill that involves the coordination of different functional brain regions. We develop a spectral graphical model with model averaging to study the connectivity networks underlying these brain regions using fMRI data collected from a story comprehension task. Based on the spectral density matrices in the frequency domain, this model captures the temporal dependency of the entire fMRI time series between brain regions. A Bayesian model averaging procedure is then applied to select the best directional links that constitute the brain network. Using this model, brain networks of three distinct age groups are constructed to assess the dynamic change of network connectivity with respect to age.

Original languageEnglish (US)
Pages (from-to)389-400
Number of pages12
JournalBrain Connectivity
Volume1
Issue number5
DOIs
StatePublished - Dec 1 2011

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Learning
Brain
Magnetic Resonance Imaging
Age Groups

All Science Journal Classification (ASJC) codes

  • Neuroscience(all)

Cite this

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A Spectral Graphical Model Approach for Learning Brain Connectivity Network of Children's Narrative Comprehension. / Lin, Xiaodong; Meng, Xiangxiang; Karunanayaka, Prasanna; Holland, Scott K.

In: Brain Connectivity, Vol. 1, No. 5, 01.12.2011, p. 389-400.

Research output: Contribution to journalArticle

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