A Bayesian Spatial Mixture Model for FMRI Analysis

Geliazkova, Maya. "A Bayesian Spatial Mixture Model for FMRI Analysis." Serdica Journal of Computing 4.4 (2010): 417-434. <http://eudml.org/doc/11397>.

@article{Geliazkova2010,
abstract = {We develop, implement and study a new Bayesian spatial mixture model (BSMM). The proposed BSMM allows for spatial structure in the binary activation indicators through a latent thresholded Gaussian Markov random field. We develop a Gibbs (MCMC) sampler to perform posterior inference on the model parameters, which then allows us to assess the posterior probabilities of activation for each voxel. One purpose of this article is to compare the HJ model and the BSMM in terms of receiver operating characteristics (ROC) curves. Also we consider the accuracy of the spatial mixture model and the BSMM for estimation of the size of the activation region in terms of bias, variance and mean squared error. We perform a simulation study to examine the aforementioned characteristics under a variety of configurations of spatial mixture model and BSMM both as the size of the region changes and as the magnitude of activation changes.},
author = {Geliazkova, Maya},
journal = {Serdica Journal of Computing},
keywords = {Spatial Mixture Models; CAR Model; ROC Analysis; Procedure; Bias; Variance; Mean Squared Error},
language = {eng},
number = {4},
pages = {417-434},
publisher = {Institute of Mathematics and Informatics Bulgarian Academy of Sciences},
title = {A Bayesian Spatial Mixture Model for FMRI Analysis},
url = {http://eudml.org/doc/11397},
volume = {4},
year = {2010},
}

TY - JOUR
AU - Geliazkova, Maya
TI - A Bayesian Spatial Mixture Model for FMRI Analysis
JO - Serdica Journal of Computing
PY - 2010
PB - Institute of Mathematics and Informatics Bulgarian Academy of Sciences
VL - 4
IS - 4
SP - 417
EP - 434
AB - We develop, implement and study a new Bayesian spatial mixture model (BSMM). The proposed BSMM allows for spatial structure in the binary activation indicators through a latent thresholded Gaussian Markov random field. We develop a Gibbs (MCMC) sampler to perform posterior inference on the model parameters, which then allows us to assess the posterior probabilities of activation for each voxel. One purpose of this article is to compare the HJ model and the BSMM in terms of receiver operating characteristics (ROC) curves. Also we consider the accuracy of the spatial mixture model and the BSMM for estimation of the size of the activation region in terms of bias, variance and mean squared error. We perform a simulation study to examine the aforementioned characteristics under a variety of configurations of spatial mixture model and BSMM both as the size of the region changes and as the magnitude of activation changes.
LA - eng
KW - Spatial Mixture Models; CAR Model; ROC Analysis; Procedure; Bias; Variance; Mean Squared Error
UR - http://eudml.org/doc/11397
ER -