title: Graphical-model-based multivariate analysis of functional magnetic-resonance data.
abstract: We propose a method for the analysis of functional magnetic-resonance (fMR) data, based on a Bayesian-network representation. Our method identifies multivariate linear/nonlinear voxel-activation pattern differences across groups, which may provide information complementary to that resulting from a general linear model (GLM)-based analysis. In addition, we describe a model-stabilization method based on data resampling, which may be helpful in the presence of small numbers of subjects, or when data are noisy.
authors.affiliation: Department of Radiology, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, USA. rong.chen@uphs.edu
authors.forename: Rong
authors.initials: R
authors.lastname: Chen
authors.forename: Edward H
authors.initials: EH
authors.lastname: Herskovits
mesh_terms: D000328:Adult; D000368:Aged; D001921:Brain / Q000502:physiology* / Q000503:physiopathology; D003704:Dementia / Q000503:physiopathology*; D005260:Female; D006801:Humans; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D008297:Male; D015999:Multivariate Analysis