fMRI activation patterns in an analytic reasoning task: Consistency with EEG source localization

Bian Li, Kalyana C. Vasanta, Michael O'Boyle, Mary C. Baker, Brian Nutter, Sunanda Mitra

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Functional magnetic resonance imaging (fMRI) is used to model brain activation patterns associated with various perceptual and cognitive processes as reflected by the hemodynamic (BOLD) response. While many sensory and motor tasks are associated with relatively simple activation patterns in localized regions, higher-order cognitive tasks may produce activity in many different brain areas involving complex neural circuitry. We applied a recently proposed probabilistic independent component analysis technique (PICA) to determine the true dimensionality of the fMRI data and used EEG localization to identify the common activated patterns (mapped as Brodmann areas) associated with a complex cognitive task like analytic reasoning. Our preliminary study suggests that a hybrid GLM/PICA analysis may reveal additional regions of activation (beyond simple GLM) that are consistent with electroencephalography (EEG) source localization patterns.

Original languageEnglish
Title of host publicationMedical Imaging 2010 - Biomedical Applications in Molecular, Structural, and Functional Imaging
DOIs
StatePublished - 2010
EventMedical Imaging 2010 - Biomedical Applications in Molecular, Structural, and Functional Imaging - San Diego, CA, United States
Duration: Feb 14 2010Feb 16 2010

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume7626
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2010 - Biomedical Applications in Molecular, Structural, and Functional Imaging
Country/TerritoryUnited States
CitySan Diego, CA
Period02/14/1002/16/10

Keywords

  • Brodmann areas
  • Cognitive tasks
  • Data fusion
  • Electroencephalography (EEG)
  • Functional Magnetic Resonance Imaging (fMRI)
  • General Linear Model(GLM)
  • Probabilistic Independent Component Analysis technique (PICA)

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