Thomas D. Otto, Anke Meyer-Baese, Monica Hurdal, DeWitt Sumners, Dorothee Auer, Axel Wismuller
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.
Reference
Thomas D. Otto, Anke Meyer-Baese,
Monica Hurdal, De Witt Sumners, Axel Wismuller and Dorothee Auer,
Model-Free Functional MRI Analysis Using Transformation-Based Methods,
in A. J. Bell, M. V. Wickerhauser and H. H. Szu (eds),
Independent Component Analysis, Wavelets, and Neural Networks, Vol. 5102 of Proceedings of SPIE, pp. 156-167, 2003.