Independent component analysis of fMRI data: Detection of the working memory network

Giovanni Nico, Damiano Vispo, Paolo Taurisano, Giuseppe Blasi, Teresa Popolizio, Alessandro Bertolino

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

Abstract

A method is presented to process functional Magnetic Resonance Imaging (fMRI) data to detect the activation of the working memory network in patients affected by schizophrenia and normal controls. The method is based on the Independent Component Analysis (ICA) and allows to decompose the fMRI signal into statistically independent patterns which could correspond to different brain activation patterns.

Original languageEnglish
Title of host publicationMeMeA 2011 - 2011 IEEE International Symposium on Medical Measurements and Applications, Proceedings
DOIs
Publication statusPublished - 2011
Event2011 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2011 - Bari, Italy
Duration: May 30 2011May 31 2011

Other

Other2011 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2011
CountryItaly
CityBari
Period5/30/115/31/11

Keywords

  • functional MRI (fMRI)
  • Independent Component Analysis (ICA)
  • Magnetic Resonance Imaging (MRI)

ASJC Scopus subject areas

  • Biomedical Engineering

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  • Cite this

    Nico, G., Vispo, D., Taurisano, P., Blasi, G., Popolizio, T., & Bertolino, A. (2011). Independent component analysis of fMRI data: Detection of the working memory network. In MeMeA 2011 - 2011 IEEE International Symposium on Medical Measurements and Applications, Proceedings [5966709] https://doi.org/10.1109/MeMeA.2011.5966709