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

fYear

2011

fDate

Aug. 30 2011-Sept. 3 2011

Firstpage

8122

Lastpage

8125

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE

Conference_Location

Boston, MA

ISSN

1557-170X

Print_ISBN

978-1-4244-4121-1

Electronic_ISBN

1557-170X

Type

conf

DOI

10.1109/IEMBS.2011.6092003

Filename

6092003