Title :
Estimation of neuronal responses from fMRI data
Author :
Havlicek, M. ; Jan, J. ; Brazdil, M. ; Calhoun, V.D.
Author_Institution :
Dept. of Biomed. Eng., Brno Univ. of Technol., Brno, Czech Republic
fDate :
Aug. 30 2011-Sept. 3 2011
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.
Keywords :
Bayes methods; Kalman filters; biomedical MRI; brain; deconvolution; haemodynamics; medical signal processing; neurophysiology; Rauch-Tung-Striebel smoother; deconvolution technique; fMRI data; hemodynamic responses; measurement noise covariance; neuronal response estimation; neuronal signal estimation; square root cubature Kalman filter; variational Bayesian approach; Bayesian methods; Estimation; Hemodynamics; Kalman filters; Mathematical model; Noise; Noise measurement; Algorithms; Computer Simulation; Humans; Magnetic Resonance Imaging; Models, Neurological; Monte Carlo Method; Neurons; Signal-To-Noise Ratio; Statistics as Topic;
Conference_Titel :
Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE
Conference_Location :
Boston, MA
Print_ISBN :
978-1-4244-4121-1
Electronic_ISBN :
1557-170X
DOI :
10.1109/IEMBS.2011.6092003
