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P. A. Valdes-Sosa, “Spatio-Temporal Autoregressive Models Defined over Brain Manifolds,” Neuroinformatics, Vol. 2, No. 2, 2004, pp. 239-250. doi:10.1385/NI:2:2:239

has been cited by the following article:

• TITLE: A Neighborhood Method for Statistical Analysis of fMRI Data

  AUTHORS: Fayyaz Ahmad, Ghanim Ullah, Sung-Ho Kim

  KEYWORDS: Statistical Parametric Mapping; Autoregressive Model; Initial Values; ROC Curve; GLM; Regions of Interest

  JOURNAL NAME: Open Journal of Biophysics, Vol.2 No.1, January 19, 2012

  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.