ERUDIT-Workshop
Fuzzy Diagnostic and Therapeutic Decision Support
11–12 May 2000, Vienna, Austria
1NMR Group, Institute for Medical Physics
University of Vienna, Währingerstrasse 13, A-1090 Vienna, Austria
e-mail: ewald.moser@univie.ac.at
2Department of Radiology, University of Vienna Medical School
Währinger Gürtel 18-20, A-1090 Vienna, Austria
Abstract. Functional magnetic resonance imaging (fMRI) based on blood-oxygen level dependent (BOLD) contrast today is an established brain research method and quickly gains acceptance for complementary clinical diagnosis. However, neither the basic mechanisms and exact coupling between neuronal activation and hemodynamic response are known exactly, nor can the various artifacts be predicted or controlled. Thus, modeling functional signal changes is non-trivial and exploratory data analysis may be rather useful. In particular, identification and separation of artifacts as well as quantification of expected, i.e., stimulus correlated, and novel information on brain activity is important for both, new insights in neuroscience and future developments in functional MRI of the human brain. After an introduction on fMRI data evaluation and fuzzy clustering we present several examples where fuzzy cluster analysis (FCA) of fMRI time series helps to identify and locally separate different aspects in time series of fMRI in vivo data sets.
(In: Adlassnig, K.-P. (ed.) Fuzzy Diagnostic and Therapeutic Decision Support. Proc. of the ERUDIT Workshop, Österreichische Computer Gesellschaft, Vienna, p.111.)