HRF Estimation in fMRI Data with an Unknown Drift Matrix by Iterative Minimization of the Kullback-Leibler Divergence
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...[Show more]
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|Source:||IEEE Transactions on Medical Imaging|
|01_Seghouane_HRF_Estimation_in_fMRI_Data_2012.pdf||1.9 MB||Adobe PDF||Request a copy|
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