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Classification of fMRI Time Series in a Low-Dimensional Subspace With a Spatial Prior

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2 Author(s)
Meyer, F.G. ; Univ. of Colorado at Boulder, Boulder ; Xilin Shen

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.

Published in:

Medical Imaging, IEEE Transactions on  (Volume:27 ,  Issue: 1 )