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How to avoid mismodelling in GLM-based fMRI data analysis: cross-validated Bayesian model selection.

Joram Soch1, John-Dylan Haynes2, Carsten Allefeld3

  • 1Bernstein Center for Computational Neuroscience, Berlin, Germany; Department of Psychology, Humboldt-Universität zu Berlin, Germany.

Neuroimage
|August 2, 2016
PubMed
Summary
This summary is machine-generated.

We introduce cross-validated Bayesian model selection (cvBMS) to select the best general linear model (GLM) for functional magnetic resonance imaging (fMRI) data, enhancing reproducibility.

Keywords:
Bayesian model selectioncross-validationfMRI-based neuroimagingmass-univariate GLMmodel misspecifcationunderfitting versus overfitting

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Statistical Modeling

Background:

  • General linear models (GLMs) are standard for fMRI analysis but offer many researcher choices, potentially impacting reproducibility.
  • Underfitting or overfitting GLMs can lead to missed effects or reduced statistical power.
  • Formal model comparison for GLMs is underutilized in fMRI research.

Purpose of the Study:

  • To present a systematic approach, cross-validated Bayesian model selection (cvBMS), for choosing the optimal GLM for fMRI data.
  • To enable non-nested model comparisons and spatially heterogeneous modeling.
  • To improve the reliability and reproducibility of fMRI GLM analyses.

Main Methods:

  • Developed a cross-validated Bayesian model selection (cvBMS) framework.
  • Implemented non-nested model comparison capabilities.
  • Allowed for spatially varying GLM specifications across the brain.

Main Results:

  • Validated the cvBMS method using simulated fMRI data.
  • Demonstrated the practical application of cvBMS on empirical fMRI datasets.
  • Showcased the ability to compare non-nested and spatially heterogeneous models.

Conclusions:

  • cvBMS provides a systematic method for selecting appropriate GLMs in fMRI.
  • This approach enhances the reliability of statistical findings in neuroimaging.
  • Wider adoption of model selection will bolster the reproducibility of fMRI studies.