Chapter

Independent Component Analysis and Blind Signal Separation

Volume 3195 of the series Lecture Notes in Computer Science pp 977-984

3D Spatial Analysis of fMRI Data on a Word Perception Task

  • Ingo R. KeckAffiliated withInstitute of Biophysics, Neuro- and Bioinformatics Group, University of Regensburg
  • , Fabian J. TheisAffiliated withInstitute of Biophysics, Neuro- and Bioinformatics Group, University of Regensburg
  • , Peter GruberAffiliated withInstitute of Biophysics, Neuro- and Bioinformatics Group, University of Regensburg
  • , Elmar W. LangAffiliated withInstitute of Biophysics, Neuro- and Bioinformatics Group, University of Regensburg
  • , Karsten SpechtAffiliated withInstitute of Medicine, Research Center Jülich
  • , Carlos G. PuntonetAffiliated withDepartamento de Arquitectura y Tecnologia de Computadores, Universidad de Granada/ESII

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Abstract

We discuss a 3D spatial analysis of fMRI data taken during a combined word perception and motor task. The event – based experiment was part of a study to investigate the network of neurons involved in the perception of speech and the decoding of auditory speech stimuli. We show that a classical general linear model analysis using SPM does not yield reasonable results. With blind source separation (BSS) techniques using the FastICA algorithm it is possible to identify different independent components (IC) in the auditory cortex corresponding to four different stimuli. Most interesting, we could detect an IC representing a network of simultaneously active areas in the inferior frontal gyrus responsible for word perception.