Spatially Adaptive Mixture Modeling for Analysis of fMRI Time Series

Within-subject analysis in fMRI essentially addresses two problems, the detection of brain regions eliciting evoked activity and the estimation of the underlying dynamics. In [1, 2], a detection-estimation framework has been proposed to tackle these problems jointly, since they are connected to one another. In the Bayesian formalism, detection is achieved by modeling activating and non-activating voxels through independent mixture models (IMM) within each region while hemodynamic response estimation is performed at a regional scale in a nonparametric way. Instead of IMMs, in this paper we take advantage of spatial mixture models (SMM) for their non-linear spatial regularizing properties. The proposed method is unsupervised and spatially adaptive in the sense that the amount of spatial correlation is automatically tuned from the data and this setting automatically varies across brain regions. In addition, the level of regularization is specific to each experimental condition since both ...

• Type: Journal
• Year: 2010
• Where: TMI
• Authors: Thomas Vincent, Laurent Risser, Philippe Ciuciu
• Keywords: Brain Region, Management, Mixture Models, Spatial Mixture Models, TMI 2010