Unified framework for robust estimation of brain networks from fMRI using temporal and spatial correlation analyses

Research output: Contribution to journalArticle

Abstract

There is a rapidly growing interest in the neu-roimaging field to use functional magnetic resonance imaging (fMRI) to explore brain networks, i.e., how regions of the brain communicate with one another. This paper presents a general and novel statistical framework for robust and more complete estimation of brain functional connectivity from fMRI based on correlation analyses and hypothesis testing. In addition to the ability of examining the correlations with each individual seed as in the standard and existing methods, the proposed framework can detect functional interactions by simultaneously examining multiseed correlations via multiple correlation coefficients. Spatially structured noise in fMRI is also taken into account during the identification of functional interconnection networks through noncentral F hypothesis tests. The associated issues for the multiple testing and the effective degrees-of-freedom are considered as well. Furthermore, partial multiple correlations are introduced and formulated to measure any additional task-induced but not stimulus-locked relation over brain regions so that we can take the analysis of functional connectivity closer to the characterization of direct functional interactions of the brain. Evaluation for accuracy and advantages, and comparisons of the new approaches in the presented general framework are performed using both realistic synthetic data and in vivo fMRI data.

Original languageEnglish (US)
Article number4785525
Pages (from-to)1296-1307
Number of pages12
JournalIEEE transactions on medical imaging
Volume28
Issue number8
DOIs
StatePublished - Aug 2009

Keywords

  • Functional connectivity
  • Functional magnetic resonance imaging (fMRI)
  • Hypothesis testing
  • Multiple correlation
  • Multivariate statistical analysis
  • Noncentral F random fields
  • Partial correlation
  • Spatial noise modeling
  • Time series analysis

ASJC Scopus subject areas

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

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