CoChat
Sign Up
Cited by 103Functional Brain Connectivity StudiesStatistical Methods and Bayesian InferenceAdvanced MRI Techniques and Applications

The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.

Using SPM 12’s Second-Level Bayesian Inference Procedure for fMRI Analysis: Practical Guidelines for End Users

Open access
Han, Hyemin, Park, Joonsuk·2018·Frontiers in Neuroinformatics
DOI 10.3389/fninf.2018.00001PMC PMC5801291

Published version of a preprint

Open this paper in CoChat — free accountOpen PDF

Abstract

< 0.05, have spurred researchers to consider alternative ways to analyze fMRI data. A group of methodologists and statisticians have considered Bayesian inference as a candidate methodology. However, few previous studies have attempted to provide end users of fMRI analysis tools, such as SPM 12, with practical guidelines about how to conduct Bayesian inference. In the present study, we aim to demonstrate how to utilize Bayesian inference, Bayesian second-level inference in particular, implemented in SPM 12 by analyzing fMRI data available to public via NeuroVault. In addition, to help end users understand how Bayesian inference actually works in SPM 12, we examine outcomes from Bayesian second-level inference implemented in SPM 12 by comparing them with those from classical second-level inference. Finally, we provide practical guidelines about how to set the parameters for Bayesian inference and how to interpret the results, such as Bayes factors, from the inference. We also discuss the practical and philosophical benefits of Bayesian inference and directions for future research.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3389/fninf.2018.00001

Cites

13 of 13 references verified0 citation errors · 0 not checkable

Statistics

Figures

FIGURE 1 Batch editor settings for Bayesian second-level inference in SPM 12.
FIGURE 1 Batch editor settings for Bayesian second-level inference in SPM 12.CC BY · PubMed Central
FIGURE 2 Contrast manager settings for Bayesian second-level inference in SPM 12.
FIGURE 2 Contrast manager settings for Bayesian second-level inference in SPM 12.CC BY · PubMed Central
FIGURE 3 Illustrative example showing prior (dashed curve) and posterior distributions when 2logBF = 0 (left filled curve) and 2logBF = 10 (right filled curve), and ES threshold is Cohen’s d = 0.5. Figure originally created by JASP and visualized by Adobe Illustrator. H0: a difference in activity between conditions A and B IS NOT GREATER than d = 0.5. H1: a difference in activity between conditions A and B IS GREATER than d = 0.5.
FIGURE 3 Illustrative example showing prior (dashed curve) and posterior distributions when 2logBF = 0 (left filled curve) and 2logBF = 10 (right filled curve), and ES threshold is Cohen’s d = 0.5. Figure originally created by JASP and visualized by Adobe Illustrator. H0: a difference in activity between conditions A and B IS NOT GREATER than d = 0.5. H1: a difference in activity between conditions A and B IS GREATER than d = 0.5.CC BY · PubMed Central
FIGURE 4 Results from classical and Bayesian second-level inferences for the contrast of moral versus non-moral task conditions. Images created by XjView (Cui et al., 2015). Red: classical clusterwise inference. Yellow: classical voxelwise FWE inference. Green: Bayesian inference.
FIGURE 4 Results from classical and Bayesian second-level inferences for the contrast of moral versus non-moral task conditions. Images created by XjView (Cui et al., 2015). Red: classical clusterwise inference. Yellow: classical voxelwise FWE inference. Green: Bayesian inference.CC BY · PubMed Central
FIGURE 5 False alarm rate calculated from moral psychology fMRI data.
FIGURE 5 False alarm rate calculated from moral psychology fMRI data.CC BY · PubMed Central
FIGURE 6 Hit rate calculated from moral psychology fMRI data.
FIGURE 6 Hit rate calculated from moral psychology fMRI data.CC BY · PubMed Central
FIGURE 7 Changes in survived voxel numbers with different inference methods across different sample sizes.
FIGURE 7 Changes in survived voxel numbers with different inference methods across different sample sizes.CC BY · PubMed Central
FIGURE 8 Changes in FWE threshold value, and t- and logBF values in (–4, 48, 12) across different sample sizes.
FIGURE 8 Changes in FWE threshold value, and t- and logBF values in (–4, 48, 12) across different sample sizes.CC BY · PubMed Central
FIGURE 9 Results from classical and Bayesian second-level inferences with Kievit et al.’s (unpublished) data. Images created by XjView (Cui et al., 2015). Red: classical clusterwise inference. Yellow: classical voxelwise FWE inference. Green: Bayesian inference.
FIGURE 9 Results from classical and Bayesian second-level inferences with Kievit et al.’s (unpublished) data. Images created by XjView (Cui et al., 2015). Red: classical clusterwise inference. Yellow: classical voxelwise FWE inference. Green: Bayesian inference.CC BY · PubMed Central
FIGURE 10 Results from classical and Bayesian second-level inferences with Amalric and Dehaene’s (2016) data. Images created by XjView (Cui et al., 2015). Red: classical clusterwise inference. Yellow: classical voxelwise FWE inference. Green: Bayesian inference.
FIGURE 10 Results from classical and Bayesian second-level inferences with Amalric and Dehaene’s (2016) data. Images created by XjView (Cui et al., 2015). Red: classical clusterwise inference. Yellow: classical voxelwise FWE inference. Green: Bayesian inference.CC BY · PubMed Central
FIGURE 11 Results from classical and Bayesian second-level inferences with Henson et al.’s (2002) data. Images created by XjView (Cui et al., 2015). Red: classical clusterwise inference. Yellow: classical voxelwise FWE inference. Green: Bayesian inference.
FIGURE 11 Results from classical and Bayesian second-level inferences with Henson et al.’s (2002) data. Images created by XjView (Cui et al., 2015). Red: classical clusterwise inference. Yellow: classical voxelwise FWE inference. Green: Bayesian inference.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 52

  1. Amalric, 2016, Origins of the brain networks for advanced mathematics in expert mathematicians., Proc. Natl. Acad. Sci., 113, 4909, 10.1073/pnas.1603205113 · DOI · Paper page
  2. Ashburner, 2016, SPM 12 Manual.
  3. Benjamini, 1995, Controlling the false discovery rate: a practical and powerful approach to multiple testing., J. R. Stat. Soc. B, 57, 289, 10.2307/2346101 · DOI
  4. Chang, 2009, Relationship between respiration, end-tidal CO2, and BOLD signals in resting-state fMRI., Neuroimage, 47, 1381, 10.1016/j.neuroimage.2009.04.048 · DOI
  5. Cohen, 1992, A power primer., Psychol. Bull., 112, 155, 10.1037/0033-2909.112.1.155 · DOI · Paper page
  6. Cohen, 1994, The earth is round (p < 0.05): rejoinder., Am. Psychol., 50, 1103, 10.1037/0003-066X.50.12.1103 · DOI
  7. Cox, 2017, FMRI clustering in AFNI: false-Positive rates redux., Brain Connect., 7, 152, 10.1089/brain.2016.0475 · DOI
  8. Cui, 2015, Xjview.
  9. Eklund, 2016, Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates., Proc. Natl. Acad. Sci. U.S.A., 113, 7900, 10.1073/pnas.1602413113 · DOI · Paper page
  10. Ellison, 1996, An introduction to Bayesian inference for ecological research and environmental decision-making., Ecol. Appl., 6, 1036, 10.2307/2269588 · DOI · Paper page
  11. Eres, 2017, Common and distinct neural networks involved in fMRI studies investigating morality: an ALE meta-analysis., Soc. Neurosci., 1, 10.1080/17470919.2017.1357657 · DOI
  12. Flandin, 2017, Analysis of family-wise error rates in statistical parametric mapping using random field theory., Hum. Brain Mapp., 10.1002/hbm.23839 · DOI
  13. Francis, 2012, Too good to be true: publication bias in two prominent studies from experimental psychology., Psychon. Bull. Rev., 19, 151, 10.3758/s13423-012-0227-9 · DOI
  14. Friston, 2003, Dynamic causal modelling., Neuroimage, 19, 1273, 10.1016/S1053-8119(03)00202-7 · DOI · Paper page
  15. Friston, 2003, Posterior probability maps and SPMs., Neuroimage, 19, 1240, 10.1016/S1053-8119(03)00144-7 · DOI
  16. Gelman, 2012, Why we (Usually) don’t have to worry about multiple comparisons., J. Res. Educ. Effs., 5, 189, 10.1080/19345747.2011.618213 · DOI · Paper page
  17. Gigerenzer, 2004, Mindless statistics., J. Socio. Econ., 33, 587, 10.1016/j.socec.2004.09.033 · DOI · Paper page
  18. Glover, 2009, SPIRAL IN/OUT Postprocessing for FMRI.
  19. Glover, 2001, Spiral-in/out BOLD fMRI for increased SNR and reduced susceptibility artifacts., Magn. Reson. Med., 46, 515, 10.1002/Mrm.1222 · DOI
  20. Glover, 2000, Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR., Magn. Reson. Med., 44, 162, 10.1002/1522-2594(200007)44:1<162::AID-MRM23>3.0.CO;2-E · DOI
  21. Gordon, 2017, Precision functional mapping of individual human brains., Neuron, 95, 791.e7, 10.1016/j.neuron.2017.07.011 · DOI · Paper page
  22. Gorgolewski, 2015, NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain., Front. Neuroinform., 9, 10.3389/fninf.2015.00008 · DOI
  23. Greene, 2004, The neural bases of cognitive conflict and control in moral judgment., Neuron, 44, 389, 10.1016/j.neuron.2004.09.027 · DOI · Paper page
  24. Greene, 2001, An fMRI investigation of emotional engagement in moral judgment., Science, 293, 2105, 10.1126/science.1062872 · DOI · Paper page
  25. Han, 2017, Neural correlates of moral sensitivity and moral judgment associated with brain circuitries of selfhood: a meta-analysis., J. Moral Educ., 46, 97, 10.1080/03057240.2016.1262834 · DOI
  26. Han, 2016, Influence of the cortical midline structures on moral emotion and motivation in moral decision-making., Behav. Brain Res., 302, 237, 10.1016/j.bbr.2016.01.001 · DOI
  27. Han, 2017, Evaluating methods of correcting for multiple comparisons implemented in SPM12 in social neuroscience fMRI studies: an example from moral psychology., Soc. Neurosci., 1, 10.1080/17470919.2017.1324521 · DOI
  28. Henson, 2002, Detecting latency differences in event-related BOLD responses: application to words versus nonwords and initial versus repeated face presentations., Neuroimage, 15, 83, 10.1006/nimg.2001.0940 · DOI
  29. Ioannidis, 2005, Why most published research findings are false., PLOS Med., 2, 10.1371/journal.pmed.0020124 · DOI · Paper page
  30. Jeffreys, 1961, Theory of Probability.
  31. Kass, 1995, Bayes Factors., J. Am. Stat. Assoc., 90, 773, 10.2307/2291091 · DOI
  32. Lieberman, 2009, Type I and Type II error concerns in fMRI research: re-balancing the scale., Soc. Cogn. Affect. Neurosci., 4, 423, 10.1093/scan/nsp052 · DOI · Paper page
  33. Love, 2017, JASP (Version 0.8.2).
  34. Magerkurth, 2015, Objective Bayesian fMRI analysis-a pilot study in different clinical environments., Front. Neurosci., 9, 10.3389/fnins.2015.00168 · DOI
  35. Maxwell, 2004, The persistence of underpowered studies in psychological research: causes, consequences, and remedies., Psychol. Methods, 9, 147, 10.1037/1082-989X.9.2.147 · DOI
  36. Mueller, 2017, Commentary: cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates., Front. Hum. Neurosci., 11, 10.3389/fnhum.2017.00345 · DOI · Paper page
  37. Neumann, 2003, Bayesian second-level analysis of functional magnetic resonance images., Neuroimage, 20, 1346, 10.1016/S1053-8119(03)00443-9 · DOI
  38. Nichols, 2012, Multiple testing corrections, nonparametric methods, and random field theory., Neuroimage, 62, 811, 10.1016/j.neuroimage.2012.04.014 · DOI
  39. Nichols, 2003, Controlling the familywise error rate in functional neuroimaging: a comparative review., Stat. Methods Med. Res., 12, 419, 10.1191/0962280203sm341ra · DOI
  40. Peng, 2015, The reproducibility crisis in science: a statistical counterattack., Significance, 12, 30, 10.1111/j.1740-9713.2015.00827.x · DOI · Paper page
  41. Penny, 2005, Bayesian Analysis of Single-Subject fMRI Data: User Guide.
  42. Penny, 2005, “Bayesian analysis of fMRI data with spatial priors,” in, Proceedings of the Joint Statistical Meeting (JSM) American Statistical Association
  43. Penny, 2007, “Bayesian treatments of neuroimaging data,” in, Bayesian Brain: Probabilistic Approaches to Neural Coding, 91
  44. Poldrack, 2017, Scanning the horizon: towards transparent and reproducible neuroimaging research., Nat. Rev. Neurosci., 18, 115, 10.1038/nrn.2016.167 · DOI · Paper page
  45. Rouder, 2009, Bayesian t tests for accepting and rejecting the null hypothesis., Psychon. Bull. Rev., 16, 225, 10.3758/PBR.16.2.225 · DOI · Paper page
  46. Scott, 2010, Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem., Ann. Stat., 38, 2587, 10.1214/10-AOS792 · DOI
  47. Sidén, 2017, Fast Bayesian whole-brain fMRI analysis with spatial 3D priors., Neuroimage, 146, 211, 10.1016/j.neuroimage.2016.11.040 · DOI
  48. Stern, 2016, A test by any other name: P values, bayes factors, and statistical inference., Multivariate Behav. Res., 51, 23, 10.1080/00273171.2015.1099032 · DOI
  49. Trafimow, 2015, Editorial., Basic Appl. Soc. Psych., 37, 1, 10.1080/01973533.2015.1012991 · DOI
  50. Wagenmakers, 2017, Bayesian inference for psychology. Part I: theoretical advantages and practical ramifications., Psychon. Bull. Rev., 10.3758/s13423-017-1343-3 · DOI
  51. Webster, 2017, FEAT/UserGuide.
  52. Woolrich, 2012, Bayesian inference in FMRI., Neuroimage, 62, 801, 10.1016/j.neuroimage.2011.10.047 · DOI

Cited by

Sorted by citations

See all 103

Metadata

Authors
Han, H., & Park, J.
Affiliations
  • University of Alabama (US)
  • The Ohio State University (US)
Corresponding author
Han, Hyemin
ORCID
Han, Hyemin
Venue
Frontiers in Neuroinformatics
Publisher
Frontiers Media SA
Published
2018-02-02
Volume
12
Pages
1-1
ISSN
1662-5196 (ISSN-L)
DOI
10.3389/fninf.2018.00001
PMC
PMC5801291
OpenAlex
W2789413038
Cited by
103
References
13
Citations by year
2026: 2 · 2025: 9 · 2024: 3 · 2023: 7 · 2022: 4 · 2021: 39 · 2020: 18 · 2019: 18 · 2018: 3
FWCI
10.29
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Neuroinformatics (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • PsyArXiv (OSF Preprints) (submittedVersion)
  • OSF Preprints (OSF Preprints) (submittedVersion, cc-by)
  • OSF Preprints (OSF Preprints) (submittedVersion)
  • PsyArXiv (OSF Preprints) (submittedVersion, cc-by)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
Keywords
Bayesian inference, fMRI data analysis, Bayes factor

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review