title: A sparse and spatially constrained generative regression model for fMRI data analysis.
abstract: In this study, we present an advanced Bayesian framework for the analysis of functional magnetic resonance imaging (fMRI) data that simultaneously employs both spatial and sparse properties. The basic building block of our method is the general linear regression model that constitutes a well-known probabilistic approach. By treating regression coefficients as random variables, we can apply an enhanced Gibbs distribution function that captures spatial constrains and at the same time allows sparse representation of fMRI time series. The proposed scheme is described as a maximum a posteriori approach, where the known expectation maximization algorithm is applied offering closed-form update equations for the model parameters. We have demonstrated that our method produces improved performance and functional activation detection capabilities in both simulated data and real applications.
authors.affiliation: Department of Computer Science, University of Ioannina, Ioannina 45110, Greece. voikonom@cs.uoi.gr
authors.forename: Vangelis P
authors.initials: VP
authors.lastname: Oikonomou
authors.forename: Konstantinos
authors.initials: K
authors.lastname: Blekas
authors.forename: Loukas
authors.initials: L
authors.lastname: Astrakas
mesh_terms: D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D015233:Models, Statistical; D009415:Nerve Net / Q000502:physiology*; D012044:Regression Analysis