title: Classification of fMRI time series in a low-dimensional subspace with a spatial prior.
abstract: 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.
authors.affiliation: Department of Electrical Engineering, University of Colorado, Boulder, CO 80309, USA. francois.meyer@colorado.edu
authors.forename: F G
authors.initials: FG
authors.lastname: Meyer
authors.forename: X
authors.initials: X
authors.lastname: Shen
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D005074:Evoked Potentials, Visual / Q000502:physiology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014793:Visual Cortex / Q000033:anatomy & histology / Q000502:physiology*