title: Estimation of neuronal responses from fMRI data.
abstract: In this paper we describe a deconvolution technique for estimation of the neuronal signal from an observed hemodynamic responses in fMRI data. Our approach, based on the Rauch-Tung-Striebel smoother for square-root cubature Kalman filter, enables us to accurately infer the hidden states, parameters, and the input of the dynamic system. Additionally, we enhance the cubature Kalman filter with a variational Bayesian approach for adaptive estimation of the measurement noise covariance.
authors.affiliation: Dept. of Biomedical Engineering, FEEC, Brno University of Technology, Czech Republic. havlicekmartin@gmail.com
authors.forename: M
authors.initials: M
authors.lastname: Havlicek
authors.forename: J
authors.initials: J
authors.lastname: Jan
authors.forename: M
authors.initials: M
authors.lastname: Brazdil
authors.forename: V D
authors.initials: VD
authors.lastname: Calhoun
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D009010:Monte Carlo Method; D009474:Neurons / Q000502:physiology*; D059629:Signal-To-Noise Ratio; D013223:Statistics as Topic