Classification of fMRI Time Series in a Low-Dimensional Subspace With a Spatial Prior
Meyer, F.G.; Xilin Shen
Medical Imaging, IEEE Transactions on
Volume 27, Issue 1, Jan. 2008 Page(s):87 - 98
Digital Object Identifier 10.1109/TMI.2007.903251
Summary:We propose a new method for detecting activation in functional magnetic resonance imaging (fMRI) data. We project the fMRI time series on a low-dimensional subspace spanned by wavelet packets in order to create projections that are as non-Gaussian as possible. Our approach achieves two goals: it reduces the dimensionality of the problem by explicitly constructing a sparse approximation to the dataset and it also creates meaningful clusters allowing the separation of the activated regions from the clutter formed by the background time series. We use a mixture of Gaussian densities to model the distribution of the wavelet packet coefficients. We expect activated areas that are connected, and impose a spatial prior in the form of a Markov random field. Our approach was validated with in vivo data and realistic synthetic data, where it outperformed a linear model equipped with the knowledge of the true hemodynamic response.
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