Model-free functional MRI analysis using transformation-based methods

Thomas Dan Otto; Anke Meyer-Baese; Monica Hurdal; DeWitt Sunmers; Axel Wismuller; Dorothee Auer

doi:10.1117/12.487368

1 April 2003 Model-free functional MRI analysis using transformation-based methods

Thomas Dan Otto, Anke Meyer-Baese, Monica Hurdal, DeWitt Sunmers, Axel Wismuller, Dorothee Auer

Author Affiliations +

Proceedings Volume 5102, Independent Component Analyses, Wavelets, and Neural Networks; (2003) https://doi.org/10.1117/12.487368
Event: AeroSense 2003, 2003, Orlando, Florida, United States

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.

Citation Download Citation

Thomas Dan Otto, Anke Meyer-Baese, Monica Hurdal, DeWitt Sunmers, Axel Wismuller, and Dorothee Auer "Model-free functional MRI analysis using transformation-based methods", Proc. SPIE 5102, Independent Component Analyses, Wavelets, and Neural Networks, (1 April 2003); https://doi.org/10.1117/12.487368

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