Translator Disclaimer
12 April 2004 Data partitioning and independent component analysis techniques applied to fMRI
Author Affiliations +
Exploratory data-driven methods such as data partitioning techniques and independent component analysis (ICA) are considered to be hypothesis-generating procedures, and are complementary to the hypothesis-led statistical inferential methods in functional magnetic resonance imaging (fMRI). In this paper, we present a comparison between data partitioning techniques and ICA in a systematic fMRI study. The comparative results were evaluated by (1) task-related activation maps and (2) associated time-courses. For the fMRI data, a comparative quantitative evaluation between the three clustering techniques, SOM, “neural gas” network, and fuzzy clustering based on deterministic annealing, and the three ICA methods, FastICA, Infomax and topographic ICA was performed. The ICA methods proved to extract features better than the clustering methods but are limited to the linear mixture assumption. The data partitioning techniques outperform ICA in terms of classification results but requires a longer processing time than the ICA methods.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Axel Wismueller M.D., Anke Meyer-Base, Oliver Lange, Thomas Dan Otto, and Dorothee Auer "Data partitioning and independent component analysis techniques applied to fMRI", Proc. SPIE 5439, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks II, (12 April 2004);

Back to Top