The DOI matches its Crossref record; 37 of the 41 references it cites were found in a registry or an index.
Bayesian networks for fMRI: A primer
Abstract
Bayesian network analysis is an attractive approach for studying the functional integration of brain networks, as it includes both the locations of connections between regions of the brain (functional connectivity) and more importantly the direction of the causal relationship between the regions (directed functional connectivity). Further, these approaches are more attractive than other functional connectivity analyses in that they can often operate on larger sets of nodes and run searches over a wide range of candidate networks. An important study by Smith et al. (2011) illustrated that many Bayesian network approaches did not perform well in identifying the directionality of connections in simulated single-subject data. Since then, new Bayesian network approaches have been developed that have overcome the failures in the Smith work. Additionally, an important discovery was made that shows a preprocessing step used in the Smith data puts some of the Bayesian network methods at a disadvantage. This work provides a review of Bayesian network analyses, focusing on the methods used in the Smith work as well as methods developed since 2011 that have improved estimation performance. Importantly, only approaches that have been specifically designed for fMRI data perform well, as they have been tailored to meet the challenges of fMRI data. Although this work does not suggest a single best model, it describes the class of models that perform best and highlights the features of these models that allow them to perform well on fMRI data. Specifically, methods that rely on non-Gaussianity to direct causal relationships in the network perform well.
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“As seen previously [10], GIMME reliably recovered the presence and true direction of connections at the group and individual levels and is one of the few approaches that can do so [46].”
From Organizing Heterogeneous Samples Using Community Detection of GIMME-Derived Resting State Functional Networks (2014) · cites this paper as [46]
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Metadata
- Authors
- Mumford, J. A., & Ramsey, J. D.
- Affiliations
- The University of Texas at Austin (US)
- Carnegie Mellon University (US)
- Corresponding author
- Mumford, Jeanette A.
- ORCID
- Mumford, Jeanette A.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2014-02
- Volume
- 86
- Pages
- 573-582
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2004026511
- Cited by
- 132
- References
- 41
- Citations by year
- 2026: 6 · 2025: 6 · 2024: 7 · 2023: 5 · 2022: 14 · 2021: 11 · 2020: 17 · 2019: 11 · 2018: 14 · 2017: 13 · 2016: 18 · 2015: 4 · 2014: 6
- FWCI
- 0.80
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- Bayesian networks, directed functional connectivity, functional MRI, causal inference, brain network integration, network structure estimation, non-Gaussianity
- MeSH terms
- Algorithms, Bayes Theorem, Brain, Computer Simulation, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Models, Neurological, Nerve Net, Pattern Recognition, Automated, Sensitivity and Specificity, Reproducibility of Results, Models, Statistical, Connectome
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