ArticlePloS one2013
Efficient posterior probability mapping using Savage-Dickey ratios.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evidence for non-selective response inhibition in uncertain contexts revealed by combined meta-analysis and Bayesian analysis of fMRI data.Scientific reports · 2022Pooled it
- Suppression of non-selected solutions as a possible brain mechanism for ambiguity resolution in the word fragment task completion task.Scientific reports · 2022Article
- Providing Evidence for the Null Hypothesis in Functional Magnetic Resonance Imaging Using Group-Level Bayesian Inference.Frontiers in neuroinformatics · 2021Article
- Bayesian Model Selection Maps for Group Studies Using M/EEG Data.Frontiers in neuroscience · 2018Article
- Improved estimates for the role of grey matter volume and GABA in bistable perception.Cortex; a journal devoted to the study of the nervous system and behavior · 2016Article
- Article
- Objective Bayesian fMRI analysis-a pilot study in different clinical environments.Frontiers in neuroscience · 2015Article
- Relative valuation of pain in human orbitofrontal cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2014Article
- Estimating neural response functions from fMRI.Frontiers in neuroinformatics · 2014Article
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Authors and funding
2 authors.
Funding
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.
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