Independent Component Analysis (ICA) of functional QSM
PINAR SENAY ZBAY 1,2 , Cristina Rossi 1 , Geoffrey Warnock 3 , Felix Kuhn 3 , Burak Akin 4 , Klaas Paul Prssmann 2 , and Daniel Nanz 1
Department of Radiology, University Hospital
Zrich, Zrich, Switzerland,
of Biomedical Engineering, ETH Zrich, Zrich,
of Nuclear Medicine, University Hospital Zrich, Zrich,
Physics, University Medical Center, Freiburg, Germany
ICA has been widely used in task-based-fMRI in order to
separate independent signal components, without
supplying -priori knowledge of the paradigm. The aim of
this work was to identify and characterize signal
components that capture neuronal activation in
quantitative susceptibility data (QSM) acquired under
visual-stimulation. The effect of temporal-filtering on
activation maps, signal time-course and corresponding
power-spectra were investigated and results compared
with those from traditional BOLD analysis. There was a
strong correlation between BOLD and filtered QSM data.
ICA of QSM data seems promising for an accurate
localization of neuronal activation and a better
understanding of the underlying mechanisms.
This abstract and the presentation materials are available to members only;
a login is required.