Paper Info
Title
HRF Estimation in fMRI Data With an Unknown Drift Matrix by Iterative Minimization of the Kullback–Leibler Divergence
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 considering an unknown and therefore flexible drift matrix. This allows the effective representation of a broader class of drift signals and therefore the reduction of the error in approximating the drift component. Estimates of the HRF and the hyperparameters are derived by iterative minimization of the Kullback-Leibler divergence between a model family of probability distributions defined using the mixed-effects model and a desired family of probability distributions constrained to be concentrated on the observed data. The performance of proposed method is demonstrated on simulated and real fMRI data, the latter originating from both event-related and block design fMRI experiments.
Year
DOI
Venue
2012
10.1109/TMI.2011.2167238
Medical Imaging, IEEE Transactions
Keywords
Field
DocType
biomedical MRI,haemodynamics,iterative methods,maximum likelihood estimation,minimisation,probability,Kullback-Leibler divergence,brain region response,drift signals,fMRI data,hemodynamic response function,iterative minimization,noisy functional magnetic resonance imaging,nonparametric maximum likelihood HRF estimation,nonparametric method,probability distribution,random effect,Functional magnetic resonance imaging (fMRI),Kullback–Leibler divergence,hemodynamic response function,maximum likelihood (ML) estimation
Random effects model,Data modeling,Pattern recognition,Hyperparameter,Iterative method,Block design,Nonparametric statistics,Probability distribution,Artificial intelligence,Mathematics,Kullback–Leibler divergence
Journal
Volume
Issue
ISSN
31
2
0278-0062
Citations 
PageRank 
References 
21
1.30
6
Authors
2
Name
Order
Citations
PageRank
Abd-Krim Seghouane119324.99
Adnan Shah2476.08