文档介绍：FMRI Time Series Analysis with the Software
SPM99
Diplomarbeit
zur Erlangung des akademischen Grades
Magistra der Naturwissenschaften
an der
Formal- und Naturwissenschaftlichen Fakult¨at
der Universit¨at Wien
eingereicht von
Karin Wimmer
betreut von
. Kurt Hornik
Institut f¨urStatistik und Wahrscheinlichkeitstheorie
TU Wien
Wien, Februar 2003
Abstract
In recent years the technique of functional ic resonance imaging (fMRI)
has been rapidly developed and improved. Its interest is moving from the
mere technical side to wide clinical applications. Functional ic reso-
nance imaging yields images with very high resolution and contrast. But its
certainly greatest advantage is, to locate human brain activity in plete
non invasive manner, ., without the use of external contrast agents. It uses
blood, which serves as intrinsic contrast agent, due to its oxygenation state
and flow. Image signal changes are induced by the different ic suscep-
tibility of oxygenated and deoxygenated blood hemoglobin and the changing
blood flow. Those signal changes are in the range of a few percent and cannot
be seen with the naked eye. To obtain reliable information about size and
location of the activated brain regions, statistical analysis is necessary.
This thesis describes the analysis of fMRI images with the software SPM99,
where SPM stands for Statistical Parametric Mapping. Firstly, a number of
optional preprocessing steps of the software are explained, which can improve
the image quality. Then the time course of a single voxel of the images is
modeled. The general linear model and theoretical results will be summa-
rized since the statistical models used by SPM99 are all special cases of the
general linear model. Temporal filtering is introduced and critically assessed,
because it is a possibility to deal with the temporal autocorrelations of the
error terms in fMRI time series. In order to be able to report size and location