title: A general statistical analysis for fMRI data.
abstract: We propose a method for the statistical analysis of fMRI data that seeks a compromise between efficiency, generality, validity, simplicity, and execution speed. The main differences between this analysis and previous ones are: a simple bias reduction and regularization for voxel-wise autoregressive model parameters; the combination of effects and their estimated standard deviations across different runs/sessions/subjects via a hierarchical random effects analysis using the EM algorithm; overcoming the problem of a small number of runs/session/subjects using a regularized variance ratio to increase the degrees of freedom.
authors.affiliation: Department of Mathematics and Statistics, McGill University, Montreal, Canada.
authors.forename: K J
authors.initials: KJ
authors.lastname: Worsley
authors.forename: C H
authors.initials: CH
authors.lastname: Liao
authors.forename: J
authors.initials: J
authors.lastname: Aston
authors.forename: V
authors.initials: V
authors.lastname: Petre
authors.forename: G H
authors.initials: GH
authors.lastname: Duncan
authors.forename: F
authors.initials: F
authors.lastname: Morales
authors.forename: A C
authors.initials: AC
authors.lastname: Evans
mesh_terms: D000465:Algorithms; D016477:Artifacts; D015982:Bias; D001921:Brain / Q000098:blood supply*; D006439:Hemodynamics / Q000502:physiology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data*; D021621:Imaging, Three-Dimensional / Q000706:statistics & numerical data*; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D008432:Mathematical Computing; D012044:Regression Analysis; D015203:Reproducibility of Results