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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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.
“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]
Cites
Estimating the Dimension of a Model
Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033 · see citing works
(1988) · Cited by 16,582 · see citing works
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 · see citing works
Causality: Models, Reasoning, and Inference
Pearl, J. (2009) · doi:10.1017/cbo9780511803161 · Cited by 11,653 · see citing works
Independent component analysis, A new concept?
Comon, P. (1994) · Signal Processing · doi:10.1016/0165-1684(94)90029-9 · Cited by 8,150 · see citing works
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 · see citing works
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 · see citing works
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× · see citing works
“…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 · see citing works
Self-citationA Linear Non-Gaussian Acyclic Model for Causal Discovery
Shimizu, S., Hoyer, P. O., Hyvärinen, A., & Kerminen, A. (2006) · Cited by 1,152 · see citing works
Shipley, B. (2000) · Cited by 1,085 · see citing works
Bronstein, L. R., Kovacs, P. J., & Vega, A. (2007) · Social Work in Health Care · doi:10.1300/j010v45n02_04 · Cited by 787 · see citing works
大西, 仁. (1994) · Medical Entomology and Zoology · doi:10.11225/jcss.1.2_100 · Cited by 552 · see citing works
Richardson, T., & Spirtes, P. (2002) · The Annals of Statistics · doi:10.1214/aos/1031689015 · Cited by 545 · see citing works
Gates, K. M., & Molenaar, P. C. M. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.06.026 · Cited by 478 · see citing works
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 · see citing works
Uncertainty in Artificial Intelligence
(1990) · Machine intelligence and pattern recognition · doi:10.1016/c2009-0-14501-4 · Cited by 430 · see citing works
Sörbom, D. (1989) · Psychometrika · doi:10.1007/bf02294623 · Cited by 424 · see citing works
Causal Inference and Causal Explanation with Background KnowledgePreprint
Meek, C. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1302.4972 · Cited by 378 · see citing works
Zhang, J. (2008) · Artificial Intelligence · doi:10.1016/j.artint.2008.08.001 · Cited by 367 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Self-citationGraphical Models: Selecting causal and statistical models
Meek, C. (2023) · Figshare · doi:10.1184/r1/22696393 · Cited by 133 · see citing works
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 · see citing works
Finding Optimal Bayesian NetworksPreprint
Chickering, D. M., & Meek, C. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.0561 · Cited by 109 · see citing works
Computation, Causation and Discovery
MTW, Glymour, C., & Copper, G. F. (2000) · Journal of the American Statistical Association · doi:10.2307/2669507 · Cited by 105 · see citing works
Ramsey, J., Hanson, S. J., & Glymour, C. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.06.068 · Cited by 102 · see citing works
Self-citationHow to Test Normality and Other Distributional Assumptions
Coleman, D. R., & Shapiro, S. S. (1992) · Technometrics · doi:10.2307/1270050 · Cited by 96 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Self-citationNotes on the Theory of Association of Attributes in Statistics
Yule, G. U. (1903) · Biometrika · doi:10.2307/2331677 · Cited by 42 · see citing works
Distinguishing causes from effects using nonlinear acyclic causal models
Zhang, K., & Hyvärinen, A. (2010) · neural information processing systems · Cited by 36 · see citing works
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 35 · see citing works
Pairwise Measures of Causal Direction in Linear Non-Gaussian Acyclic Models
Hyvärinen, A. (2010) · Cited by 18 · see citing works
A Fast Algorithm for Discovering Sparse Causal Graphs
Spirtes, P., & Glymour, C. (2018) · KiltHub Repository · doi:10.1184/r1/6490700 · Cited by 9 · see citing works
Flagged first, then by citations
4 of the 41 references couldn’t be matched to a known paper.
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 · see citing works
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 165 · see citing works
Bayesian networks in neuroscience: a survey
Bielza, C., & Larrañaga, P. (2014) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2014.00131 · Cited by 152 · see citing works
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 · see citing works
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 136 · see citing works
From connectome to cognition: The search for mechanism in human functional brain networks
Mill, R. D., Ito, T., & Cole, M. W. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.01.060 · Cited by 118 · see citing works
Anticipation-related brain connectivity in bipolar and unipolar depression: a graph theory approach
Manelis, A., Almeida, J., Stiffler, R. S., Lockovich, J., Aslam, H. A., & Phillips, M. L. (2016) · Brain · doi:10.1093/brain/aww157 · Cited by 110 · see citing works
Wright, A. G. C., Gates, K. M., Arizmendi, C., Lane, S., Woods, W. C., & Edershile, E. A. (2019) · Psychological Assessment · doi:10.1037/pas0000617 · Cited by 107 · see citing works
Gates, K. M., Molenaar, P. C. M., Iyer, S. P., Nigg, J. T., & Fair, D. A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0091322 · Cited by 107 · see citing works
Parsing Heterogeneity in the Brain Connectivity of Depressed and Healthy Adults During Positive Mood
Price, R. B., Lane, S., Gates, K., Kraynak, T. E., Horner, M. S., Thase, M. E., & Siegle, G. J. (2017) · Biological Psychiatry · doi:10.1016/j.biopsych.2016.06.023 · Cited by 106 · see citing works
Data-Driven Subgroups in Depression Derived from Directed Functional Connectivity Paths at Rest
Price, R. B., Gates, K., Kraynak, T. E., Thase, M. E., & Siegle, G. J. (2017) · Neuropsychopharmacology · doi:10.1038/npp.2017.97 · Cited by 98 · see citing works
Causal interpretation rules for encoding and decoding models in neuroimaging
Weichwald, S., Meyer, T., Özdenizci, O., Schölkopf, B., Tonio Ball, A., & Grosse‐Wentrup, M. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.01.036 · Cited by 95 · see citing works
Automated Selection of Robust Individual-Level Structural Equation Models for Time Series Data
Lane, S., & Gates, K. M. (2017) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705511.2017.1309978 · Cited by 66 · see citing works
Bielczyk, N., Uithol, S., van Mourik, T., Anderson, P., Glennon, J. C., & Buitelaar, J. K. (2018) · Network Neuroscience · doi:10.1162/netn_a_00062 · Cited by 70 · see citing works
Spontaneous Neural Dynamics and Multi-scale Network Organization
Foster, B. L., He, B. J., Honey, C. J., Jerbi, K., Maier, A., & Saalmann, Y. B. (2016) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2016.00007 · Cited by 62 · see citing works
Hick–Hyman Law is Mediated by the Cognitive Control Network in the Brain
Wu, T., Dufford, A. J., Egan, L. J., Mackie, M., Chen, C., Yuan, C., Chen, C., Li, X., Liu, X., Hof, P. R., et al. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx127 · Cited by 59 · see citing works
Combining Multiple Functional Connectivity Methods to Improve Causal Inferences
Sánchez-Romero, R., & Cole, M. W. (2020) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01580 · Cited by 58 · see citing works
Comparing directed functional connectivity between groups with confirmatory subgrouping GIMME
Henry, T. R., Feczko, E., Cordova, M., Earl, E., Williams, S., Nigg, J. T., Fair, D. A., & Gates, K. M. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.12.040 · Cited by 45 · see citing works
Latent variable GIMME using model implied instrumental variables (MIIVs).
Gates, K. M., Fisher, Z. F., & Bollen, K. A. (2019) · Psychological Methods · doi:10.1037/met0000229 · Cited by 46 · see citing works
Causality in Cognitive Neuroscience: Concepts, Challenges, and Distributional Robustness
Weichwald, S., & Peters, J. (2020) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01623 · Cited by 47 · see citing works
Mutual connectivity analysis of resting-state functional MRI data with local models
DSouza, A. M., Abidin, A. Z., Chockanathan, U., Schifitto, G., & Wismüller, A. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.05.038 · Cited by 47 · see citing works
Wiedermann, W., & Li, X. (2018) · Behavior Research Methods · doi:10.3758/s13428-018-1031-x · Cited by 46 · see citing works
Grosse‐Wentrup, M., Janzing, D., Siegel, M., & Schölkopf, B. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.10.062 · Cited by 41 · see citing works
Intrinsic, dynamic and effective connectivity among large-scale brain networks modulated by oxytocin
Jiang, X., Ma, X., Geng, Y., Zhao, Z., Zhou, F., Zhao, W., Yao, S., Yang, S., Zhao, Z., Becker, B., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117668 · Cited by 39 · see citing works
Zelle, S. L., Gates, K. M., Fiez, J. A., Sayette, M. A., & Wilson, S. J. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.03.015 · Cited by 40 · see citing works
Emotion reactivity-related brain network analysis in generalized anxiety disorder: a task fMRI study
Li, J., Zhong, Y., Ma, Z., Wu, Y., Pang, M., Wang, C., Liu, N., Wang, C., & Zhang, N. (2020) · BMC Psychiatry · doi:10.1186/s12888-020-02831-6 · Cited by 40 · see citing works
Empirical validation of directed functional connectivity
Mill, R. D., Bagic, A. I., Bostan, A. C., Schneider, W., & Cole, M. W. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.11.037 · Cited by 37 · see citing works
Whole-brain estimates of directed connectivity for human connectomics
Frässle, S., Manjaly, Z. M., Tri, C., Kasper, L., Pruessmann, K. P., & Stephan, K. E. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117491 · Cited by 37 · see citing works
Lerner, I., Lupkin, S. M., Sinha, N., Tsai, A., & Gluck, M. A. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.0578-17.2017 · Cited by 36 · see citing works
The center for causal discovery of biomedical knowledge from big data
Cooper, G. F., Bahar, I., Becich, M. J., Benos, P. V., Berg, J., Espino, J., Glymour, C., Jacobson, R. C., Kienholz, M., Lee, A. V., et al. (2015) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv059 · Cited by 36 · see citing works
Recent developments in representations of the connectome
Bijsterbosch, J. D., Valk, S. L., Wang, D., & Glasser, M. F. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118533 · Cited by 33 · see citing works
Inferring Effective Connectivity Networks From fMRI Time Series With a Temporal Entropy-Score
Liu, J., Ji, J., Xun, G., & Zhang, A. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3072149 · Cited by 32 · see citing works
Integrative Bayesian Analysis of Brain Functional Networks Incorporating Anatomical Knowledge
Higgins, I., Kundu, S., & Guo, Y. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.07.015 · Cited by 32 · see citing works
Analysing brain networks in population neuroscience: a case for the Bayesian philosophy
Bzdok, D., Floris, D. L., & Marquand, A. F. (2020) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2019.0661 · Cited by 28 · see citing works
Estimating Effective Connectivity by Recurrent Generative Adversarial Networks
Ji, J., Liu, J., Han, L., & Wang, F. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3083984 · Cited by 28 · see citing works
Causal search procedures for fMRI: review and suggestions
Henry, T. R., & Gates, K. M. (2016) · Behaviormetrika · doi:10.1007/s41237-016-0010-8 · Cited by 28 · see citing works
Unsupervised classification reveals consistency and degeneracy in neural network patterns of emotion
Doyle, C. M., Lane, S., Brooks, J. A., Wilkins, R. W., Gates, K. M., & Lindquist, K. A. (2022) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsac028 · Cited by 25 · see citing works
GLP-1-Based Strategies: A Physiological Analysis of Differential Mode of Action
Burcelin, R., Gourdy, P., & Dalle, S. (2014) · Physiology · doi:10.1152/physiol.00009.2013 · Cited by 26 · see citing works
Multi-source brain computing with systematic fusion for smart health
Kuai, H., Zhong, N., Chen, J., Yang, Y., Zhang, X., Liang, P., Imamura, K., Ma, L., & Wang, H. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.03.009 · Cited by 26 · see citing works
Manelis, A., & Reder, L. M. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00593 · Cited by 25 · see citing works
Chen, Y., Rosen, B. Q., & B, T. J. S. A. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2117234119 · Cited by 24 · see citing works
Bayesian Community Detection in the Space of Group-Level Functional Differences
Venkataraman, A., Yang, D. Y. ‐., Pelphrey, K. A., & Duncan, J. S. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2536559 · Cited by 24 · see citing works
Dobryakova, E., Boukrina, O. V., & Wylie, G. R. (2014) · Brain Connectivity · doi:10.1089/brain.2014.0283 · Cited by 22 · see citing works
Ombao, H., & Pinto-Orellana, M. A. (2022) · Econometrics and Statistics · doi:10.1016/j.ecosta.2022.10.005 · Cited by 24 · see citing works
Aung, T., Tenney, J. R., & Bagic, A. I. (2022) · Frontiers in Neurology · doi:10.3389/fneur.2022.831546 · Cited by 23 · see citing works
Mill, R. D., Hamilton, J. L., Winfield, E. C., Lalta, N., Chen, R. H., & Cole, M. W. (2022) · PLoS Biology · doi:10.1371/journal.pbio.3001686 · Cited by 22 · see citing works
Bernaola, N., Michiels, M., Larrañaga, P., & Bielza, C. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011443 · Cited by 20 · see citing works
Weigard, A. S., Beltz, A. M., Reddy, S. N., & Wilson, S. J. (2018) · Human Brain Mapping · doi:10.1002/hbm.24493 · Cited by 19 · see citing works
Bayesian Joint Modeling of Multiple Brain Functional Networks
Lukemire, J., Kundu, S., Pagnoni, G., & Guo, Y. (2020) · Journal of the American Statistical Association · doi:10.1080/01621459.2020.1796357 · Cited by 20 · see citing works
Rawls, E., Kummerfeld, E., Mueller, B. A., Ma, S., & Zilverstand, A. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119211 · Cited by 24 · see citing works
A comparative study on swarm intelligence for structure learning of Bayesian networks
Ji, J., Yang, C., Liu, J., Liu, J., & Yin, B. (2016) · Soft Computing · doi:10.1007/s00500-016-2223-x · Cited by 18 · see citing works
Duffy, K. A., Fisher, Z. F., Arizmendi, C., Molenaar, P. C. M., Hopfinger, J. B., Cohen, J. R., Beltz, A. M., Lindquist, M. A., Hallquist, M. N., & Gates, K. M. (2021) · Brain Connectivity · doi:10.1089/brain.2020.0864 · Cited by 16 · see citing works
Price, R. B., Beltz, A. M., Woody, M. L., Cummings, L. R., Gilchrist, D., & Siegle, G. J. (2020) · Clinical Psychological Science · doi:10.1177/2167702620906149 · Cited by 17 · see citing works
Weigard, A. S., Lane, S., Gates, K. M., & Beltz, A. M. (2021) · Psychological Methods · doi:10.1037/met0000460 · Cited by 20 · see citing works
Rare Disease Discovery: An Optimized Disease Ranking System
Piñol, M., Alves, R., Teixidó, I., Mateo, J., Solsona, F., & Vilaprinyó, E. (2017) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2686380 · Cited by 16 · see citing works
Specifying exogeneity and bilinear effects in data-driven model searches
Arizmendi, C., Gates, K. M., Fredrickson, B. L., & Wright, A. G. C. (2020) · Behavior Research Methods · doi:10.3758/s13428-020-01469-2 · Cited by 18 · see citing works
Ji, J., Liu, J., Liang, P., & Zhang, A. (2016) · PLoS ONE · doi:10.1371/journal.pone.0152600 · Cited by 14 · see citing works
Xu, N., Spreng, R. N., & Doerschuk, P. C. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00271 · Cited by 14 · see citing works
Altered Effective Connectivity during a Processing Speed Task in Individuals with Multiple Sclerosis
Dobryakova, E., Costa, S. L., Wylie, G. R., DeLuca, J., & Genova, H. M. (2016) · Journal of the International Neuropsychological Society · doi:10.1017/s1355617715001034 · Cited by 14 · see citing works
Marchitelli, R., Martinot, M. P., Trouvé, A., Banaschewski, T., Bokde, A. L. W., Desrivières, S., Flor, H., Garavan, H., Gowland, P. A., Heinz, A., et al. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02610-9 · Cited by 14 · see citing works
Draining the pond and catching the fish: Uncovering the ecosystem of auditory verbal hallucinations
Looijestijn, J., Blom, J. D., Hoek, H. W., Renken, R. J., Liemburg, E. J., Sommer, I. E., Aleman, A., & Goekoop, R. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.09.016 · Cited by 13 · see citing works
Time‐varying dynamic Bayesian network learning for an fMRI study of emotion processing
Sun, L., Zhang, A., & Liang, F. (2024) · Statistics in Medicine · doi:10.1002/sim.10096 · Cited by 12 · see citing works
Inferring brain causal and temporal-lag networks for recognizing abnormal patterns of dementia
Xia, Z., Zhou, T., Mamoon, S., & Lu, J. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103133 · Cited by 11 · see citing works
Statistical Perspective on Functional and Causal Neural Connectomics: A Comparative Study
Biswas, R., & Shlizerman, E. (2022) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2022.817962 · Cited by 11 · see citing works
Learning Continuous Time Bayesian Networks in Non-stationary Domains
Villa, S., & Stella, F. A. (2016) · Journal of Artificial Intelligence Research · doi:10.1613/jair.5126 · Cited by 11 · see citing works
Woody, M. L., Panny, B., Degutis, M., Griffo, A., & Price, R. B. (2021) · Behaviour Research and Therapy · doi:10.1016/j.brat.2021.103960 · Cited by 10 · see citing works
Demidenko, M. I., Huntley, E. D., Weigard, A. S., Keating, D. P., & Beltz, A. M. (2022) · Journal of Neuroscience Research · doi:10.1002/jnr.25005 · Cited by 9 · see citing works
Ji, J., Liu, J., Zou, A., & Zhang, A. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.01290 · Cited by 10 · see citing works
Estimating Brain Effective Connectivity in fMRI data by Non-stationary Dynamic Bayesian Networks
Liu, J., Ji, J., Yao, L., & Zhang, A. (2019) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm47256.2019.8982982 · Cited by 10 · see citing works
A review of Gaussian Markov models for conditional independence
Córdoba, I., Bielza, C., & Larrañaga, P. (2019) · Journal of Statistical Planning and Inference · doi:10.1016/j.jspi.2019.09.008 · Cited by 9 · see citing works
Interictal epileptiform discharges changed epilepsy-related brain network architecture in BECTS
Dai, X., Yang, Y., & Wang, Y. (2021) · Brain Imaging and Behavior · doi:10.1007/s11682-021-00566-w · Cited by 9 · see citing works
Neto, S. R. M., Castiglione, R. C., Pontes, A., de Oliveira, D. F., Ferraz, E. B., Nascimento, J. H. M., & Bouskela, E. (2016) · PLoS ONE · doi:10.1371/journal.pone.0148402 · Cited by 9 · see citing works
Uncovering general, shared, and unique temporal patterns in ambulatory assessment dataPreprint
Lane, S., Gates, K. M., Pike, H., Beltz, A. M., & Wright, A. G. C. (2018) · doi:10.31234/osf.io/d3nk9 · Cited by 14 · see citing works
Wright, A. G. C., Gates, K. M., Arizmendi, C., Lane, S., Woods, W., & Edershile, E. A. (2017) · doi:10.31219/osf.io/nf5me · Cited by 12 · see citing works
Causality-Based Feature Fusion for Brain Neuro-Developmental Analysis
Kassani, P. H., Xiao, L., Zhang, G., Stephen, J. M., Wilson, T. W., Calhoun, V. D., & Wang, Y. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2990371 · Cited by 8 · see citing works
Liu, J., Ji, J., Zhang, A., & Liang, P. (2016) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2016.7822546 · Cited by 8 · see citing works
Inferring functional connectivity in fMRI using minimum partial correlation
Nie, L., Yang, X., Matthews, P. M., Xu, Z., & Guo, Y. (2017) · Machine Intelligence Research · doi:10.1007/s11633-017-1084-9 · Cited by 8 · see citing works
Sinha, N., & Glass, A. L. (2017) · The Journal of General Psychology · doi:10.1080/00221309.2016.1276044 · Cited by 8 · see citing works
Ahmed, I., Reeves, W. D., LaBalle, M. H., Taber, M. F., Sneed, S. E., Kaiser, E. E., West, F. D., & Zhao, Q. C. (2025) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2025.110459 · Cited by 7 · see citing works
Strigo, I. A., Spadoni, A. D., & Simmons, A. N. (2022) · Frontiers in Pain Research · doi:10.3389/fpain.2022.871961 · Cited by 7 · see citing works
Han, X., Cramer, S. R., & Zhang, N. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac9d66 · Cited by 7 · see citing works
The maternal-fetal neurodevelopmental groundings of preterm birth risk
Miglioli, C., Canini, M., Vignotto, E., Pecco, N., Pozzoni, M., Victoria‐Feser, M., Guerrier, S., Candiani, M., Falini, A., Baldoli, C., et al. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e28825 · Cited by 7 · see citing works
Bayesian Network Classification: Application to Epilepsy Type Prediction Using PET Scan Data
Jebreen, K., & Ghattas, B. (2016) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2016.0174 · Cited by 6 · see citing works
Category representations in the brain are both discretely localized and widely distributed
Shehzad, Z. E., & McCarthy, G. (2018) · Journal of Neurophysiology · doi:10.1152/jn.00912.2017 · Cited by 6 · see citing works
Hays, C. C., Simmons, A. N., Strigo, I. A., Timtim, S., Ellis, R. J., & Keltner, J. R. (2022) · Frontiers in Pain Research · doi:10.3389/fpain.2022.1004060 · Cited by 5 · see citing works
Irzan, H., Hütel, M., O’Reilly, H., Sebastien Ourselin, A., Marlow, N., & Melbourne, A. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.103037 · Cited by 5 · see citing works
Analysis of Symptoms Wise Disease Inference System Using Data Mining Technique
Burange, T. P., & Chatur, P. N. (2018) · International Conference on Intelligent Computing and Control Systems (ICICCS) · doi:10.1109/iccons.2018.8663062 · Cited by 5 · see citing works
Guo, X., Wang, Y., Chen, K., Wu, X., Zhang, J., Li, K., Jin, Z., & Li, Y. (2014) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2014.00122 · Cited by 4 · see citing works
Characterizing Network Search Algorithms Developed for Dynamic Causal Modeling
Aranyi, C., Nagy, M., Opposits, G., Berényi, E. L., & Emri, M. (2021) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2021.656486 · Cited by 4 · see citing works
Favela, L. H., & Amon, M. J. (2023) · Dynamics · doi:10.3390/dynamics3010008 · Cited by 4 · see citing works
Wang, X., Wong, W.-W., Fang, Y., Chu, C. W., Wong, K. S., & Tong, R. K. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8512430 · Cited by 4 · see citing works
Scalable Bayesian matrix normal graphical models for brain functional networks
Kundu, S., & Risk, B. B. (2020) · Biometrics · doi:10.1111/biom.13319 · Cited by 4 · see citing works
Differential Covariance: A New Method to Estimate Functional Connectivity in fMRI
Lin, T. W., Chen, Y., Bukhari, Q., Krishnan, G. P., Bazhenov, M., & B, T. J. S. A. (2020) · Neural Computation · doi:10.1162/neco_a_01323 · Cited by 4 · see citing works
Bayesian Joint Modeling of Multiple Brain Functional NetworksPreprint
Lukemire, J., Kundu, S., Pagnoni, G., & Guo, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.02123 · Cited by 4 · see citing works
Learning Continuous Time Bayesian Networks in Non-stationary Domains
Villa, S., & Stella, F. A. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/804 · Cited by 3 · see citing works
MERLiN: Mixture Effect Recovery in Linear Networks
Weichwald, S., Grosse‐Wentrup, M., & Gretton, A. (2016) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2016.2601144 · Cited by 3 · see citing works
Measuring Asymmetric Interactions in Resting State Brain Networks
Joshi, A. A., Salloum, R., Bhushan, C., & Leahy, R. M. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19992-4_31 · Cited by 3 · see citing works
Regularized Structural Equation Modeling for Individual-Level Directed Functional Connectivity
Lane, S. (2019) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/0prc-qf02 · Cited by 3 · see citing works
Weigard, A. S., Lane, S., Gates, K. M., & Beltz, A. M. (2020) · doi:10.31234/osf.io/frpvm · Cited by 3 · see citing works
Latent Variable GIMME Using Model Implied Instrumental Variables (MIIVs)Preprint
Gates, K. M., Bollen, K. A., & Fisher, Z. F. (2019) · doi:10.31234/osf.io/kuedz · Cited by 3 · see citing works
- 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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