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Proceedings Article

Model-free functional MRI analysis using transformation-based methods

[+] Author Affiliations
Thomas D. Otto, Anke Meyer-Baese, Monica Hurdal, DeWitt Sunmers

Florida State Univ. (USA)

Axel Wismuller

Ludwig-Maximilians Univ. (Germany)

Dorothee Auer

Max Planck Institute of Psychiatry (Germany)

Proc. SPIE 5102, Independent Component Analyses, Wavelets, and Neural Networks, 156 (April 4, 2003); doi:10.1117/12.487368
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From Conference Volume 5102

  • Independent Component Analyses, Wavelets, and Neural Networks
  • Anthony J. Bell; Mladen V. Wickerhauser; Harold H. Szu
  • Orlando, FL | April 21, 2003

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.

© (2003) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Citation

Thomas D. Otto ; Anke Meyer-Baese ; Monica Hurdal ; DeWitt Sunmers ; Axel Wismuller, et al.
"Model-free functional MRI analysis using transformation-based methods", Proc. SPIE 5102, Independent Component Analyses, Wavelets, and Neural Networks, 156 (April 4, 2003); doi:10.1117/12.487368; http://dx.doi.org/10.1117/12.487368


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