Abstract #3932
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
1
Department of Radiology, University Hospital
Zrich, Zrich, Switzerland,
2
Institute
of Biomedical Engineering, ETH Zrich, Zrich,
Switzerland,
3
Department
of Nuclear Medicine, University Hospital Zrich, Zrich,
Switzerland,
4
Medical
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.
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