Bayesian spatiotemporal model of fMRI data using transfer functions
Citation:
Alicia Quiros Carretero, Raquel Montes Diez and Simon P. Wilson, Bayesian spatiotemporal model of fMRI data using transfer functions, Neuroimage, 52, 3, 2010, 995-1004Download Item:
Abstract:
This research describes a new Bayesian spatiotemporal model to analyse
BOLD fMRI studies. In the temporal dimension, we describe the shape of
the hemodynamic response function (HRF) with a transfer function model.
In the spatial dimension, we use a Gaussian Markov random field prior on
the parameter indicating activations that embody our prior knowledge that
evoked responses are spatially contiguous. The proposal constitutes an extension
of the spatiotemporal model presented in a previous approach [Quir'os,
A., Montes Diez, R. and Gamerman, D. (2010). Bayesian spatiotemporal
model of fMRI data, Neuroimage, 49: 442-456.], o?ering more flexibility in
the estimation of the HRF and computational advantages in the resulting
MCMC algorithm. Simulations from the model are performed in order to
ascertain the performance of the sampling scheme and the ability of the posterior
to estimate model parameters, as well as to check the model sensitivity
to signal to noise ratio. Results are shown on synthetic data and on a real
data set from a block-design fMRI experiment, showing good performance
in the detection of activity and significant flexibility in the estimation of the
Author's Homepage:
http://people.tcd.ie/swilsonDescription:
PUBLISHED
Author: WILSON, SIMON PAUL
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Neuroimage;52;
3;
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