title: A hierarchical model for simultaneous detection and estimation in multi-subject fMRI studies.
abstract: In this paper we introduce a new hierarchical model for the simultaneous detection of brain activation and estimation of the shape of the hemodynamic response in multi-subject fMRI studies. The proposed approach circumvents a major stumbling block in standard multi-subject fMRI data analysis, in that it both allows the shape of the hemodynamic response function to vary across region and subjects, while still providing a straightforward way to estimate population-level activation. An efficient estimation algorithm is presented, as is an inferential framework that allows for not only tests of activation, but also tests for deviations from some canonical shape. The model is validated through simulations and application to a multi-subject fMRI study of thermal pain.
authors.affiliation: Department of Mathematical Sciences, DePaul University, USA.
authors.forename: David
authors.initials: D
authors.lastname: Degras
authors.affiliation: Department of Biostatistics, Johns Hopkins University, USA. Electronic address: mlindqui@jhsph.edu.
authors.forename: Martin A
authors.initials: MA
authors.lastname: Lindquist
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping; D003198:Computer Simulation; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008959:Models, Neurological; D015233:Models, Statistical