Journal article
HRF Estimation in fMRI Data with an Unknown Drift Matrix by Iterative Minimization of the Kullback-Leibler Divergence
AK Seghouane, A Shah
IEEE Transactions on Medical Imaging | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | Published : 2012
Abstract
Hemodynamic response function (HRF) estimation in noisy functional magnetic resonance imaging (fMRI) plays an important role when investigating the temporal dynamic of a brain region response during activations. Nonparametric methods which allow more flexibility in the estimation by inferring the HRF at each time sample have provided improved performance in comparison to the parametric methods. In this paper, the mixed-effects model is used to derive a new algorithm for nonparametric maximum likelihood HRF estimation. In this model, the random effect is used to better account for the variability of the drift. Contrary to the usual approaches, the proposed algorithm has the benefit of conside..
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Awarded by Human Frontier Science Program
Funding Acknowledgements
Manuscript received June 08, 2011; revised August 24, 2011; accepted August 26, 2011. Date of publication September 06, 2011; date of current version February 03, 2012. NICTA is funded by the Australian Government as represented by the Department of Broadband, Communications and the Digital Economy and the Australian Research Council through the ICT Centre of Excellence program. This work was also supported by the Human Frontier Science Program under Grant RGY 80/2008. Asterisk indicates corresponding author.