Penny, WD;
Ridgway, GR;
(2013)
Efficient posterior probability mapping using savage-dickey ratios.
PLoS One
, 8
(3)
, Article e59655. 10.1371/journal.pone.0059655.
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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.
| Type: | Article |
|---|---|
| Title: | Efficient posterior probability mapping using savage-dickey ratios. |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1371/journal.pone.0059655 |
| Publisher version: | http://dx.doi.org/10.1371/journal.pone.0059655 |
| Language: | English |
| Additional information: | © 2013 Penny, Ridgway. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was supported by the Wellcome Trust [grant number 091593/Z/10/Z]; and the Medical Research Council [grant number MR/J014257/1]. The Wellcome Trust Centre for Neuroimaging is supported by core funding from the Wellcome Trust [grant number 091593/Z/10/Z]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1389768 |
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