title: A Bayesian mixture approach to modeling spatial activation patterns in multisite fMRI data.
authors.affiliation: Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA. sssykim@cs.cmu.edu
authors.forename: Seyoung
authors.initials: S
authors.lastname: Kim
authors.forename: Padhraic
authors.initials: P
authors.lastname: Smyth
authors.forename: Hal
authors.initials: H
authors.lastname: Stern
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity