Evaluation of functional network connectivity in event-related FMRI data based on ICA and time-frequency granger causality

M. Havlicek, J. Jan, V. D. Calhoun, M. Brazdil, M. Mikl

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this article we show that Adaptive Multivariate Autoregressive (AMVAR) modeling accompanied by proper preprocessing is an effective technique for evaluation of spectral Granger causality among functional brain networks identified by independent component analysis from event-related fMRI data.

Original languageEnglish (US)
Title of host publicationWorld Congress on Medical Physics and Biomedical Engineering
Subtitle of host publicationImage Processing, Biosignal Processing, Modelling and Simulation, Biomechanics
PublisherSpringer Verlag
Pages716-719
Number of pages4
Edition4
ISBN (Print)9783642038815
DOIs
StatePublished - 2009
Externally publishedYes
EventWorld Congress on Medical Physics and Biomedical Engineering: Image Processing, Biosignal Processing, Modelling and Simulation, Biomechanics - Munich, Germany
Duration: Sep 7 2009Sep 12 2009

Publication series

NameIFMBE Proceedings
Number4
Volume25
ISSN (Print)1680-0737

Other

OtherWorld Congress on Medical Physics and Biomedical Engineering: Image Processing, Biosignal Processing, Modelling and Simulation, Biomechanics
CountryGermany
CityMunich
Period9/7/099/12/09

Keywords

  • Adaptive
  • Granger
  • ICA
  • Spectral
  • fMRI

ASJC Scopus subject areas

  • Bioengineering
  • Biomedical Engineering

Fingerprint Dive into the research topics of 'Evaluation of functional network connectivity in event-related FMRI data based on ICA and time-frequency granger causality'. Together they form a unique fingerprint.

Cite this