Comparison of two exploratory data analysis methods for fMRI: fuzzy clustering vs. principal component analysis
| Autor: | Lawrence Ryner, Ray L. Somorjai, Mark Jarmasz, Wolfgang Richter, Richard Baumgartner, Randy Summers |
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| Rok vydání: | 2000 |
| Předmět: |
Electronic Data Processing
Fuzzy clustering medicine.diagnostic_test Artificial neural network Phantoms Imaging Noise (signal processing) Computer science Biomedical Engineering Biophysics Brain computer.software_genre Magnetic Resonance Imaging Independent component analysis Communication noise Exploratory data analysis Principal component analysis medicine Cluster Analysis Humans Radiology Nuclear Medicine and imaging Data mining Functional magnetic resonance imaging computer |
| Zdroj: | Magnetic Resonance Imaging. 18:89-94 |
| ISSN: | 0730-725X |
| DOI: | 10.1016/s0730-725x(99)00102-2 |
| Popis: | Exploratory data-driven methods such as Fuzzy clustering analysis (FCA) and Principal component analysis (PCA) may be considered as hypothesis-generating procedures that are complementary to the hypothesis-led statistical inferential methods in functional magnetic resonance imaging (fMRI). Here, a comparison between FCA and PCA is presented in a systematic fMRI study, with MR data acquired under the null condition, i.e., no activation, with different noise contributions and simulated, varying “activation.” The contrast-to-noise (CNR) ratio ranged between 1‐10. We found that if fMRI data are corrupted by scanner noise only, FCA and PCA show comparable performance. In the presence of other sources of signal variation (e.g., physiological noise ), FCA outperforms PCA in the entire CNR range of interest in fMRI, particularly for low CNR values. The comparison method that we introduced may be used to assess other exploratory approaches such as independent component analysis or neural network-based techniques. Crown Copyright r 2000. Published by Elsevier Science Inc. |
| Databáze: | OpenAIRE |
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