Mapping the connectivity with structural equation modeling in an fMRI study of shape-from-motion task

Jiancheng Zhuang, Scott Peltier, Sheng He, Stephen LaConte, Xiaoping Hu

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

In this fMRI study, we explore the connectivity among brain regions in a shape-from-motion task using the causal mapping analysis of structural equation modeling (SEM). An important distinction of our approach is that we have adapted SEM from its traditional role in confirmatory analysis to provide utility as an exploratory mapping technique. Our current approaches include (I) detecting brain regions that fit well in a hypothesized neural network model, and (II) identifying the best connectivity model at each brain region. We demonstrate that SEM effectively detects the dorsal and ventral visual pathways from the covariance structure in fMRI data, confirming previous neuroscience results. Further, our SEM mapping methodology found that the two pathways interact through specific cortical areas such as the superior lateral occipital cortex in the perception of shape from motion.

Original languageEnglish (US)
Pages (from-to)799-806
Number of pages8
JournalNeuroImage
Volume42
Issue number2
DOIs
StatePublished - Aug 15 2008

Bibliographical note

Funding Information:
This work was supported by the National Institutes of Health (Grants R01EB002009 and RO1EB000331), the Georgia Research Alliance, and the Whitaker Foundation. We thank Dr. Mary Helen Immordino-Yang for her assistance with manuscript preparation.

Keywords

  • Effective connectivity
  • Functional magnetic resonance imaging
  • Shape from motion
  • Structural equation modeling

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