Author(s): Sung-Ho Kim | Ghanim Ullah | Fayyaz Ahmad
Journal: Advances in Molecular Imaging
ISSN 2161-6728
Volume: 02;
Issue: 01;
Start page: 15;
Date: 2012;
Original page
Keywords: Statistical Parametric Mapping | Autoregressive Model | Initial Values | ROC Curve | GLM | Regions of Interest
ABSTRACT
In an effort to cope with the fact that functional magnetic resonance imaging (fMRI) data are spatiotemporally correlated, we propose a novel statistical method with a view to improve the detection of brain regions with increased neu-ronal activity in fMRI. In this method, we make use of information from neighboring voxels of a voxel, for estimation at the voxel. We examined performance of the method against the statistical parametric mapping (SPM) method using both simulated and real data. The proposed method is shown to be considerably better than the SPM in the context of receiver operating characteristics (ROC) curves.
Journal: Advances in Molecular Imaging
ISSN 2161-6728
Volume: 02;
Issue: 01;
Start page: 15;
Date: 2012;
Original page
Keywords: Statistical Parametric Mapping | Autoregressive Model | Initial Values | ROC Curve | GLM | Regions of Interest
ABSTRACT
In an effort to cope with the fact that functional magnetic resonance imaging (fMRI) data are spatiotemporally correlated, we propose a novel statistical method with a view to improve the detection of brain regions with increased neu-ronal activity in fMRI. In this method, we make use of information from neighboring voxels of a voxel, for estimation at the voxel. We examined performance of the method against the statistical parametric mapping (SPM) method using both simulated and real data. The proposed method is shown to be considerably better than the SPM in the context of receiver operating characteristics (ROC) curves.
