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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“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]
Citations
Cites
Estimating the Dimension of a Model
Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033
(1988) · Cited by 16,582
Causality : Models , Reasoning , and Inference
Bentler, P., Heslenfeld, D., Molenaar, P., & Mcdonald, R. (2002) · Choice Reviews Online · doi:10.5860/choice.47-3771 · Cited by 13,039
Causality: Models, Reasoning, and Inference
Pearl, J. (2009) · doi:10.1017/cbo9780511803161 · Cited by 11,653
Independent component analysis, A new concept?
Comon, P. (1994) · Signal Processing · doi:10.1016/0165-1684(94)90029-9 · Cited by 8,324
Asymptotic Theory of Certain "Goodness of Fit" Criteria Based on Stochastic Processes
Anderson, T. W., & Darling, D. A. (1952) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729437 · Cited by 3,542
The rician distribution of noisy mri data
Guðbjartsson, H., & Patz, S. (1995) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910340618 · Cited by 2,498
The interpretation of interaction in contingency tables
Simpson, E. H. (1951) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1951.tb00088.x · Cited by 2,301 · cited 2×
“…in Ramsey et al. (2010), if data are simply concatenated over subjects and analyzed, network characteristics may arise in the estimated group network that are not present in any of the single subject datasets, a phenomenon commonly known as the Yule–Simpson paradox (Simpson, 1951; Yule, 1903).”
Network modelling methods for FMRI
Smith, S. M., Miller, K. L., Salimi-Khorshidi, G., Webster, M., Beckmann, C. F., Nichols, T. E., Ramsey, J. D., & Woolrich, M. W. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.08.063 · Cited by 1,878
Self-citationA Linear Non-Gaussian Acyclic Model for Causal Discovery
Shimizu, S., Hoyer, P. O., Hyvärinen, A., & Kerminen, A. (2006) · Cited by 1,152
Shipley, B. (2000) · Cited by 1,085
Bronstein, L. R., Kovacs, P. J., & Vega, A. (2007) · Social Work in Health Care · doi:10.1300/j010v45n02_04 · Cited by 787
大西, 仁. (1994) · Medical Entomology and Zoology · doi:10.11225/jcss.1.2_100 · Cited by 552
Richardson, T., & Spirtes, P. (2002) · The Annals of Statistics · doi:10.1214/aos/1031689015 · Cited by 545
Gates, K. M., & Molenaar, P. C. M. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.06.026 · Cited by 478
NOTES ON THE THEORY OF ASSOCIATION OF ATTRIBUTES IN STATISTICS
Yule, G. U. (1903) · Biometrika · doi:10.1093/biomet/2.2.121 · Cited by 466
Uncertainty in Artificial Intelligence
(1990) · Machine intelligence and pattern recognition · doi:10.1016/c2009-0-14501-4 · Cited by 430
Sörbom, D. (1989) · Psychometrika · doi:10.1007/bf02294623 · Cited by 424
Causal Inference and Causal Explanation with Background KnowledgePreprint
Meek, C. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1302.4972 · Cited by 378
Zhang, J. (2008) · Artificial Intelligence · doi:10.1016/j.artint.2008.08.001 · Cited by 367
Six problems for causal inference from fMRI
Ramsey, J. D., Hanson, S. J., Hanson, C., Halchenko, Y. O., Poldrack, R. A., & Glymour, C. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.08.065 · Cited by 337
Self-citationPairwise Likelihood Ratios for Estimation of Non-Gaussian Structural Equation Models.
Hyvärinen, A., & Smith, S. M. (2013) · PubMed · doi:10.5555/2567709.2502585 · Cited by 185
Adjacency-Faithfulness and Conservative Causal InferencePreprint
Ramsey, J., Zhang, J., & Spirtes, P. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1206.6843 · Cited by 171
Self-citationGraphical Models: Selecting causal and statistical models
Meek, C. (2023) · Figshare · doi:10.1184/r1/22696393 · Cited by 133
Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence (2002)Preprint
Darwiche, A. Y., & Friedman, N. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.4608 · Cited by 117
Finding Optimal Bayesian NetworksPreprint
Chickering, D. M., & Meek, C. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.0561 · Cited by 109
Computation, Causation and Discovery
MTW, Glymour, C., & Copper, G. F. (2000) · Journal of the American Statistical Association · doi:10.2307/2669507 · Cited by 109
Ramsey, J., Hanson, S. J., & Glymour, C. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.06.068 · Cited by 102
Self-citationHow to Test Normality and Other Distributional Assumptions
Coleman, D. R., & Shapiro, S. S. (1992) · Technometrics · doi:10.2307/1270050 · Cited by 96
On Asymmetric Properties of the Correlation Coeffcient in the Regression Setting
Dodge, Y., & Rousson, V. (2001) · The American Statistician · doi:10.1198/000313001300339932 · Cited by 83
Automated Discovery of Linear Feedback Models
Richardson, T. S., & Spirtes, P. (1999) · AAAI Press eBooks · doi:10.7551/mitpress/2006.003.0010 · Cited by 67
Non-Gaussian methods and high-pass filters in the estimation of effective connections
Ramsey, J., Sánchez-Romero, R., & Glymour, C. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.09.062 · Cited by 48
Self-citationNotes on the Theory of Association of Attributes in Statistics
Yule, G. U. (1903) · Biometrika · doi:10.2307/2331677 · Cited by 42
Distinguishing causes from effects using nonlinear acyclic causal models
Zhang, K., & Hyvärinen, A. (2010) · neural information processing systems · Cited by 36
Effective connectivity of fMRI data using ancestral graph theory: Dealing with missing regions
Waldorp, L., Christoffels, I. K., & van de Ven, V. G. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.10.054 · Cited by 33
Pairwise Measures of Causal Direction in Linear Non-Gaussian Acyclic Models
Hyvärinen, A. (2010) · Cited by 18
A Fast Algorithm for Discovering Sparse Causal Graphs
Spirtes, P., & Glymour, C. (2018) · KiltHub Repository · doi:10.1184/r1/6490700 · Cited by 9
Research works printed but not linked: 1
- Chickering, 2002, Optimal structure identification with greedy search, J. Mach. Learn. Res., 3, 507
Cited by
Sorted by citations
Advancing functional connectivity research from association to causation
Reid, A., Headley, D. B., Mill, R. D., Sánchez-Romero, R., Uddin, L. Q., Marinazzo, D., Lurie, D. J., Valdes-Sosa, P. A., Hanson, S. J., Biswal, B. B., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0510-4 · Cited by 303
Challenges and future directions for representations of functional brain organization
Bijsterbosch, J. D., Harrison, S. J., Jbabdi, S., Woolrich, M. W., Beckmann, C. F., Smith, S. M., & Duff, E. P. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-00726-z · Cited by 167
Bayesian networks in neuroscience: a survey
Bielza, C., & Larrañaga, P. (2014) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2014.00131 · Cited by 152
Uncovering general, shared, and unique temporal patterns in ambulatory assessment data.
Lane, S., Gates, K. M., Pike, H., Beltz, A. M., & Wright, A. G. C. (2018) · Psychological Methods · doi:10.1037/met0000192 · Cited by 136
Unsupervised Classification During Time-Series Model Building
Gates, K. M., Lane, S., Varangis, E. M. L., Giovanello, K. S., & Guiskewicz, K. (2016) · Multivariate Behavioral Research · doi:10.1080/00273171.2016.1256187 · Cited by 145
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
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
