Fast Dependent Components for fMRI Analysis

Reference:

Eerika Savia, Arto Klami, and Samuel Kaski. Fast dependent components for fMRI analysis. In Proceedings of ICASSP 09, the International Conference on Acoustics, Speech, and Signal Processing, pages 1737–1740. IEEE, 2009.

Abstract:

Canonical correlation analysis (CCA) can be used to find correlating projections of two datasets with co-occurring samples. Instead of correlation, we would typically want to find more general dependencies, measured by mutual information. Variants of CCA based on non-parametric estimation of mutual information have been proposed previously; they outperform traditional CCA for non-Gaussian data but require infeasible amounts of computation for already quite modest sample sizes. We introduce a novel variant that uses a semiparametric estimate leading to a considerably faster algorithm. We apply the method on searching for statistical dependencies between multi-sensory stimuli and functional magnetic resonance imaging (fMRI) of brain activity – in contrast to using regression on either of them.

Suggested BibTeX entry:

@inproceedings{Savia09icassp,
    author = {Eerika Savia and Arto Klami and Samuel Kaski},
    booktitle = {Proceedings of ICASSP 09, the International Conference on Acoustics, Speech, and Signal Processing},
    pages = {1737--1740},
    publisher = {IEEE},
    title = {Fast Dependent Components for f{MRI} Analysis},
    year = {2009},
}