Combining Images Across Multiple Subjects A Study of Direct Cortical Electrical Interference

• Published in: Journal of the American Statistical Association Vol. 97; no. 457; pp. 125 - 135
• Main Authors: Miglioretti, Diana L, McCulloch, Colin, Zeger, Scott L
• Format: Journal Article
• Language: English
• Published: Alexandria, VA Taylor & Francis 01.03.2002; American Statistical Association; Taylor & Francis Ltd
• Subjects: Applications; Applications and Case Studies; Bayesian analysis; Binary image variation; Binary system; Biology, psychology, social sciences; Brain; Brain mapping; Electrodes; Exact sciences and technology; Functional brain mapping; Hierarchical model; Linear inference, regression; Markov analysis; Markov chains; Markovian processes; Mathematics; Monte Carlo simulation; Parametric inference; Parametric models; Perceptual localization; Population distributions; Population estimates; Population mean; Population parameters; Probability and statistics; Reversible jump Markov chain Monte Carlo; Sciences and techniques of general use; Statistical methods; Statistics; Template mixture model; Bayes estimation; Statistical analysis; Cortical reaction; Algorithmics; Biophysics; Logistic model; Electrical model; Template specificity; Parametric method; Logistic regression; MCMC algorithm; Reversible jump; Mixed model; Binary image; Observation data; Random effect; Functional brain mapping; Hierarchical model; Activation analysis

This article introduces a Bayesian hierarchical model for combining information across multiple images. Our work was motivated by an invasive functional brain mapping technique called direct cortical electrical interference that gives a sparse set of binary observations of an underlying "true&q...