Data Partitioning and Independent Component Analysis Techniques Applied to fMRI

Wismueller, A., Meyer-Base, A., Lange, O., Otto, T. D. and Auer, D. (2004) Data Partitioning and Independent Component Analysis Techniques Applied to fMRI. In: Defense and Security, Orlando, Florida, 12-16 Apr 2004, pp. 104-115. (doi:10.1117/12.542219)

Full text not currently available from Enlighten.

Abstract

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.

Item Type:Conference Proceedings
Additional Information:Proc. of SPIE Vol. 5439.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Otto, Dr Thomas
Authors: Wismueller, A., Meyer-Base, A., Lange, O., Otto, T. D., and Auer, D.
College/School:College of Medical Veterinary and Life Sciences > Institute of Infection Immunity and Inflammation
Publisher:Society of Photo-optical Instrumentation Engineers (SPIE)
ISSN:0277-786X
ISSN (Online):1996-756X

University Staff: Request a correction | Enlighten Editors: Update this record