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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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