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Bayesian model selection for group studies - revisited.

L Rigoux1, K E Stephan, K J Friston

  • 1Brain and Spine Institute, Paris, France.

Neuroimage
|September 11, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces Bayesian Omnibus Risk (BOR) for group-level Bayesian model selection (BMS). It refines methods for comparing models across groups and clarifies when to use random effects BMS versus classical analyses.

Keywords:
Between-condition comparisonBetween-group comparisonDCMExceedance probabilityMixed effectsRandom effectsStatistical risk

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Area of Science:

  • Neuroimaging
  • Cognitive Neuroscience
  • Statistical Modeling

Background:

  • Bayesian model selection (BMS) at the group level is crucial for analyzing neuroimaging and behavioral data.
  • Previous work (Stephan et al., 2009) treated models as random effects with unknown population distributions.
  • Extending group-level BMS requires addressing statistical risk and model comparison across groups.

Purpose of the Study:

  • Introduce Bayesian Omnibus Risk (BOR) to quantify statistical risk in group BMS.
  • Differentiate group-level random effects BMS from classical random effects analyses.
  • Provide methods for comparing models between different groups or conditions.

Main Methods:

  • Quantify the probability of chance differences in model frequencies, leading to protected exceedance probabilities.
  • Develop guidance for choosing between classical second-level analyses and random effects BMS for group-level parameter inference.
  • Establish methods for assessing evidence of differing model frequencies across groups or conditions.

Main Results:

  • The Bayesian Omnibus Risk (BOR) provides a principled measure of statistical risk in group BMS.
  • Protected exceedance probabilities offer a way to control for chance findings in model comparisons.
  • Clear distinctions are made between random effects BMS and classical analyses for group-level questions.

Conclusions:

  • This work enhances group-level Bayesian model selection for neuroimaging and behavioral data analysis.
  • The introduced methods, including BOR and protected exceedance probabilities, refine statistical inference.
  • The paper offers practical guidance for researchers conducting group-level model comparisons.