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Using SPM 12’s Second-Level Bayesian Inference Procedure for fMRI Analysis: Practical Guidelines for End Users
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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.
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“To account for the liberal threshold of small volume corrections in the ROI analyses, the robustness of observed group differences in trust‐associated brain activity was probed by conducting Bayesian inference analyses as implemented in SPM.[ 55 ]”
From Loneliness and the Social Brain: How Perceived Social Isolation Impairs Human Interactions (2021) · cites this paper as [55]
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Cited by
Sorted by citations
Loneliness and the Social Brain: How Perceived Social Isolation Impairs Human Interactions
Lieberz, J., Shamay‐Tsoory, S. G., Saporta, N., Esser, T., Kuskova, E., Stoffel‐Wagner, B., Hurlemann, R., & Scheele, D. (2021) · Advanced Science · doi:10.1002/advs.202102076 · Cited by 116
Han, H., Glenn, A. L., & Dawson, K. J. (2019) · Brain Sciences · doi:10.3390/brainsci9080198 · Cited by 34
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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)
- 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
- gold
- doi.org
- 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
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