Efficient posterior probability mapping using Savage-Dickey ratios

Penny, William D and Ridgway, Gerard R (2013) Efficient posterior probability mapping using Savage-Dickey ratios. PLoS One, 8 (3). ISSN 1932-6203

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Abstract

Statistical Parametric Mapping (SPM) is the dominant paradigm for mass-univariate analysis of neuroimaging data. More recently, a bayesian approach termed Posterior Probability Mapping (PPM) has been proposed as an alternative. PPM offers two advantages: (i) inferences can be made about effect size thus lending a precise physiological meaning to activated regions, (ii) regions can be declared inactive. This latter facility is most parsimoniously provided by PPMs based on bayesian model comparisons. To date these comparisons have been implemented by an Independent Model Optimization (IMO) procedure which separately fits null and alternative models. This paper proposes a more computationally efficient procedure based on Savage-Dickey approximations to the Bayes factor, and Taylor-series approximations to the voxel-wise posterior covariance matrices. Simulations show the accuracy of this Savage-Dickey-Taylor (SDT) method to be comparable to that of IMO. Results on fMRI data show excellent agreement between SDT and IMO for second-level models, and reasonable agreement for first-level models. This Savage-Dickey test is a bayesian analogue of the classical SPM-F and allows users to implement model comparison in a truly interactive manner.

Item Type: Article
Uncontrolled Keywords: bayes theorem,humans,magnetic resonance imaging,neurological models,statistical models,probability
Faculty \ School: Faculty of Social Sciences > School of Psychology
Depositing User: Pure Connector
Date Deposited: 19 Aug 2017 05:06
Last Modified: 22 Apr 2020 05:22
URI: https://ueaeprints.uea.ac.uk/id/eprint/64585
DOI: 10.1371/journal.pone.0059655

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