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Proceedings Paper

Model-free functional MRI analysis using transformation-based methods
Author(s): Thomas Dan Otto; Anke Meyer-Baese; Monica Hurdal; DeWitt Sunmers; Axel Wismuller; Dorothee Auer
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Paper Abstract

This paper presents new model-free fMRI methods based on independent component analysis. Commonly used methods in analyzing fMRI data, such as the student's t-test and cross correlation analyis, are model-based approaches. Although these methods are easy to implement and are effective in analyizing data with simple paradigms, they are not applicable in situations in which pattern of neural response are complicated and when fMRI response is unknown. In this paper we evaluate and compare three different neural algorithms for estimating spatial ICA on fMRI data: the Informax approach, the FastICA approach, and a new topographic ICA approach. A comparison of these new methods with principal component analysis and cross correlation analysis is done in a systematic fMRI study determining the spatial and temporal extent of task-related activation. Both topographic ICA and FastICA outperform principal component analysis and Infomax neural network and standard correlation analysis when applied to fMRI studies. The applicability of the new algorithms is demonstrated on experimental data.

Paper Details

Date Published: 1 April 2003
PDF: 12 pages
Proc. SPIE 5102, Independent Component Analyses, Wavelets, and Neural Networks, (1 April 2003); doi: 10.1117/12.487368
Show Author Affiliations
Thomas Dan Otto, Florida State Univ. (United States)
Anke Meyer-Baese, Florida State Univ. (United States)
Monica Hurdal, Florida State Univ. (United States)
DeWitt Sunmers, Florida State Univ. (United States)
Axel Wismuller, Ludwig-Maximilians Univ. (Germany)
Dorothee Auer, Max Planck Institute of Psychiatry (Germany)

Published in SPIE Proceedings Vol. 5102:
Independent Component Analyses, Wavelets, and Neural Networks
Anthony J. Bell; Mladen V. Wickerhauser; Harold H. Szu, Editor(s)

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