Posterior probability maps and SPMs.

Full Abstract

This technical note describes the construction of posterior probability maps that enable conditional or Bayesian inferences about regionally specific effects in neuroimaging. Posterior probability maps are images of the probability or confidence that an activation exceeds some specified threshold, given the data. Posterior probability maps (PPMs) represent a complementary alternative to statistical parametric maps (SPMs) that are used to make classical inferences. However, a key problem in Bayesian inference is the specification of appropriate priors. This problem can be finessed using empirical Bayes in which prior variances are estimated from the data, under some simple assumptions about their form. Empirical Bayes requires a hierarchical observation model, in which higher levels can be regarded as providing prior constraints on lower levels. In neuroimaging, observations of the same effect over voxels provide a natural, two-level hierarchy that enables an empirical Bayesian approach. In this note we present a brief motivation and the operational details of a simple empirical Bayesian method for computing posterior probability maps. We then compare Bayesian and classical inference through the equivalent PPMs and SPMs testing for the same effect in the same data.

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Author information

Author/s: Friston, K J (KJ); Penny, W (W);

Affiliation: The Wellcome Department of Imaging Neuroscience, London, Queen Square, London WC1N 3BG, UK. k.friston(-atsign-)fil.ion.ucl.ac.uk

Journal and publication information

Publication Type: Journal Article; Research Support, Non-U.S. Gov't

Journal: NeuroImage (Neuroimage), published in United States. (Language: eng)

Reference: 2003-Jul; vol 19 (issue 3) : pp 1240-9

Dates: Created 2003/07/25; Completed 2003/09/09; Revised 2006/11/15;

PMID: 12880849, status: MEDLINE (last retrieval date: 12/26/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Algorithms
Artifacts
Attention - physiology
Bayes Theorem
Brain Mapping - methods
Humans
Image Processing, Computer-Assisted - statistics & numerical data

Linear Models
Magnetic Resonance Imaging - statistics & numerical data
Models, Neurological
Models, Statistical
Probability Theory
Tomography, Emission-Computed - statistics & numerical data
Visual Cortex - physiology

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