Mathematics in Independent Component Analysis

252 Chapter 19. LNCS 3195:977-984, 2004
3D Spatial Analysis of fMRI Data
on a Word Perception Task
Ingo R. Keck 1 , Fabian J. Theis 1 , Peter Gruber 1 ,ElmarW.Lang 1 ,
Karsten Specht 2 , and Carlos G. Puntonet 3
1 Institute of Biophysics, Neuro- and Bioinformatics Group
University of Regensburg, D-93040 Regensburg, Germany
{Ingo.Keck,elmar.lang}@biologie.uni-regensburg.de
2 Institute of Medicine, Research Center Jülich, D-52425 Jülich, Germany
k.specht@fz-juelich.de
3 Departamento de Arquitectura y Tecnologia de Computadores
Universidad de Granada/ESII, E-1807 Granada, Spain
carlos@atc.ugr.es
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.
1 Introduction
Since the early 90s [1, 2], functional magnetic resonance imaging (fMRI) based
on the blood oxygen level dependent contrast (BOLD) developed into one of
the main technologies in human brain research. Its high spatial and temporal
resolution combined with its non-invasive nature makes it to an important tool
to discover functional areas in the human brain work and their interactions.
However, its low signal to noise ratio (SNR) and the high number of activities
in the passive brain require sophisticated analysis methods which can be divided
into two classes:
– model based approaches like the general linear model which require prior
knowledge of the time course of the activations,
– model free approaches like blind source separation (BSS) which try to separate
the recorded activation into different classes according to statistical
specifications without prior knowledge of the activation.
C.G. Puntonet and A. Prieto (Eds.): ICA 2004, LNCS 3195, pp. 977–984, 2004.
c○ Springer-Verlag Berlin Heidelberg 2004