Title of article :
Unsupervised robust nonparametric estimation of the hemodynamic response function for any fMRI experiment
Author/Authors :
J.-B.، Poline, نويسنده , , P.، Ciuciu, نويسنده , , G.، Marrelec, نويسنده , , J.، Idier, نويسنده , , C.، Pallier, نويسنده , , H.، Benali, نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2003
Pages :
-1234
From page :
1235
To page :
0
Abstract :
This paper deals with the estimation of the blood oxygen level-dependent response to a stimulus, as measured in functional magnetic resonance imaging (fMRI) data. A precise estimation is essential for a better understanding of cerebral activations. The most recent works have used a nonparametric framework for this estimation, considering each brain region as a system characterized by its impulse response, the so-called hemodynamic response function (HRF). However, the use of these techniques has remained limited since they are not well-adapted to real fMRI data. Here, we develop a threefold extension to previous works. We consider asynchronous event-related paradigms account for different trial types and integrate several fMRI sessions into the estimation. These generalizations are simultaneously addressed through a badly conditioned observation model. Bayesian formalism is used to model temporal prior information of the underlying physiological process of the brain hemodynamic response. By this way, the HRF estimate results from a tradeoff between information brought by the data and by our prior knowledge. This tradeoff is modeled with hyperparameters that are set to the maximum-likelihood estimate using an expectation conditional maximization algorithm. The proposed unsupervised approach is validated on both synthetic and real fMRI data, the latter originating from a speech perception experiment.
Keywords :
Power-aware
Journal title :
IEEE Transactions on Medical Imaging
Serial Year :
2003
Journal title :
IEEE Transactions on Medical Imaging
Record number :
100719
Link To Document :
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