Dynamic Bayesian Network Modeling of Hippocampal Subfields Connectivity with 7T fMRI: A Case Study

Fernando P. Santos, Stephen F. Smagula, Helmet Karim, Tales S. Santini, Howard J. Aizenstein, Tamer S. Ibrahim, Carlos D. Maciel


The development of high resolution structural and functional magnetic resonance imaging, along with the new automatic segmentation procedures for identifying brain regions with high precision and level of detail, has made possible new studies on functional connectivity in the medial temporal lobe and hippocampal subfields, with important applications in the understanding of human memory and psychiatric disorders. Many previous analyses using high resolution data have focused on undirected measures between these subfields. Our work expands this by presenting Dynamic Bayesian Network (DBN) models as an useful tool for mapping directed functional connectivity in the hippocampal subfields. Besides revealing directional connections, DBNs use a model-free approach which also exclude indirect connections between nodes of a graph by means of conditional probability distribution. They also relax the constraint of acyclicity imposed by traditional Bayesian networks (BNs) by considering nodes at different time points through a Markovianity assumption. We apply the GlobalMIT DBN learning algorithm to one subject with fMRI time-series obtained from three regions: the cornu ammonis (CA), dentate gyrus (DG) and entorhinal cortex (ERC), and find an initial network structure, which can be further expanded with the inclusion of new regions and analyzed with a group analysis method.


  1. Bartsch, T. (2012). The clinical neurobiology of the hippocampus: An integrative view, volume 151. Oxford University Press.
  2. Bielza, C. and Larran˜aga, P. (2014). Bayesian networks in neuroscience: a survey. Frontiers in computational neuroscience, 8:131.
  3. Burge, J., Lane, T., Link, H., Qiu, S., and Clark, V. P. (2009). Discrete dynamic bayesian network analysis of fmri data. Human brain mapping, 30(1):122-137.
  4. Chen, R., Resnick, S. M., Davatzikos, C., and Herskovits, E. H. (2012). Dynamic bayesian network modeling for longitudinal brain morphometry. NeuroImage, 59(3):2330 - 2338.
  5. Das, S. R., Pluta, J., Mancuso, L., Kliot, D., Orozco, S., Dickerson, B. C., Yushkevich, P. A., and Wolk, D. A. (2013). Increased functional connectivity within medial temporal lobe in mild cognitive impairment. Hippocampus, 23(1):1-6.
  6. de Campos, L. M. (2006). A scoring function for learning bayesian networks based on mutual information and conditional independence tests. J. Mach. Learn. Res., 7:2149-2187.
  7. de Flores, R., Mutlu, J., Bejanin, A., Tomadesso, C., Landeau, B., Mézenge, F., de La Sayette, V., Eustache, F., and Chételat, G. (2015). Intrinsic connectivity of hippocampal subfields in normal elderly and its disturbance in mci patients. Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 11(7):P61.
  8. Duyn, J. H. (2012). The future of ultra-high field {MRI} and fmri for study of the human brain. NeuroImage, 62(2):1241 - 1248. 20 {YEARS} {OF} fMRI20 {YEARS} {OF} fMRI.
  9. Friedman, N., Murphy, K., and Russell, S. (1998). Learning the structure of dynamic probabilistic networks. In Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence , pages 139-147. Morgan Kaufmann Publishers Inc.
  10. Friston, K. J. (2011). Functional and effective connectivity: a review. Brain connectivity, 1(1):13-36.
  11. Friston, K. J., Harrison, L., and Penny, W. (2003). Dynamic causal modelling. Neuroimage, 19(4):1273-1302.
  12. Golyandina, N. and Zhigljavsky, A. (2013). Singular Spectrum Analysis for Time Series. SpringerBriefs in Statistics. Springer.
  13. Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: Journal of the Econometric Society, pages 424-438.
  14. Hassani, H. (2007). Singular spectrum analysis: Methodology and comparison. Journal of Data Science, 5(2):239-257.
  15. Ibrahim, T. S., Zhao, Y., Krishnamurthy, N., Raval, S. B., Zhao, T., Wood, S., and Kim, J. (2013). 20-to-8 channel tx array with 32-channel adjustable receive-only insert for 7t head imaging. In The 21th ISMRM Annual Meeting, page 4408.
  16. Ide, J. S., Zhang, S., and Chiang-shan, R. L. (2014). Bayesian network models in brain functional connectivity analysis. International Journal of Approximate Reasoning, 55(1):23-35.
  17. Iyer, S. P., Shafran, I., Grayson, D., Gates, K., Nigg, J. T., and Fair, D. A. (2013). Inferring functional connectivity in mri using bayesian network structure learning with a modified pc algorithm. Neuroimage, 75:165- 175.
  18. Jeneson, A. and Squire, L. R. (2012). Working memory, long-term memory, and medial temporal lobe function. Learning & Memory, 19(1):15-25.
  19. Koller, D. and Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques. MIT Press.
  20. Lacy, J. W. and Stark, C. E. (2012). Intrinsic functional connectivity of the human medial temporal lobe suggests a distinction between adjacent mtl cortices and hippocampus. Hippocampus, 22(12):2290-2302.
  21. Li, J., Wang, Z. J., and McKeown, M. J. (2007). A framework for group analysis of fmri data using dynamic bayesian networks. In 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages 5991-5994. IEEE.
  22. Li, J., Wang, Z. J., Palmer, S. J., and McKeown, M. J. (2008). Dynamic bayesian network modeling of fmri: a comparison of group-analysis methods. Neuroimage, 41(2):398-407.
  23. Li, R., Wu, X., Chen, K., Fleisher, A., Reiman, E., and Yao, L. (2013). Alterations of directional connectivity among resting-state networks in alzheimer disease. American Journal of Neuroradiology, 34(2):340-345.
  24. Libby, L. A., Ekstrom, A. D., Ragland, J. D., and Ranganath, C. (2012). Differential connectivity of perirhinal and parahippocampal cortices within human hippocampal subregions revealed by highresolution functional imaging. The Journal of Neuroscience, 32(19):6550-6560.
  25. Maruszak, A. and Thuret, S. (2015). Why looking at the whole hippocampus is not enougha critical role for anteroposterior axis, subfield and activation analyses to enhance predictive value of hippocampal changes for alzheimers disease diagnosis. 2015: Which new directions for Alzheimer's disease?, page 6.
  26. Mumford, J. A. and Ramsey, J. D. (2014). Bayesian networks for fmri: a primer. Neuroimage, 86:573-582.
  27. Neapolitan, R. E. et al. (2004). Learning bayesian networks.
  28. Patel, R. S., Bowman, F. D., and Rilling, J. K. (2006). A bayesian approach to determining connectivity of the human brain. Human brain mapping, 27(3):267-276.
  29. Penny, W. D., Friston, K. J., Ashburner, J. T., Kiebel, S. J., and Nichols, T. E. (2011). Statistical parametric mapping: the analysis of functional brain images. Academic press.
  30. Rajapakse, J. C. and Zhou, J. (2007). Learning effective brain connectivity with dynamic bayesian networks. Neuroimage, 37(3):749-760.
  31. Rykhlevskaia, E., Gratton, G., and Fabiani, M. (2008). Combining structural and functional neuroimaging data for studying brain connectivity: a review. Psychophysiology, 45(2):173-187.
  32. Santos, F. P. and Maciel, C. D. (2014). A pso approach for learning transition structures of higher-order dynamic bayesian networks. In Biosignals and Biorobotics Conference (2014): Biosignals and Robotics for Better and Safer Living (BRC), 5th ISSNIP-IEEE, pages 1-6.
  33. Schulz, M., Chau, W., Graham, S. J., McIntosh, A. R., Ross, B., Ishii, R., and Pantev, C. (2004). An integrative meg-fmri study of the primary somatosensory cortex using cross-modal correspondence analysis. NeuroImage, 22(1):120-133.
  34. Smith, S. M., Miller, K. L., Salimi-Khorshidi, G., Webster, M., Beckmann, C. F., Nichols, T. E., Ramsey, J. D., and Woolrich, M. W. (2011). Network modelling methods for fmri. Neuroimage, 54(2):875-891.
  35. Smith, V. A., Yu, J., Smulders, T. V., Hartemink, A. J., and Jarvis, E. D. (2006). Computational inference of neural information flow networks. PLoS computational biology, 2(11):e161.
  36. Valdes-Sosa, P. A., Roebroeck, A., Daunizeau, J., and Friston, K. (2011). Effective connectivity: influence, causality and biophysical modeling. Neuroimage, 58(2):339-361.
  37. Vinh, N. X., Chetty, M., Coppel, R., and Wangikar, P. P. (2011a). Globalmit: learning globally optimal dynamic bayesian network with the mutual information test criterion. Bioinformatics, 27(19):2765-2766.
  38. Vinh, N. X., Chetty, M., Coppel, R., and Wangikar, P. P. (2011b). Polynomial time algorithm for learning globally optimal dynamic bayesian network. In International Conference on Neural Information Processing, pages 719-729. Springer.
  39. Wang, C., Xu, J., Zhao, S., and Lou, W. (2015). Graph theoretical analysis of eeg effective connectivity in vascular dementia patients during a visual oddball task. Clinical Neurophysiology.
  40. Wisse, L., Kuijf, H., Honingh, A., Wang, H., Pluta, J., Das, S., Wolk, D., Zwanenburg, J., Yushkevich, P., and Geerlings, M. (2016). Automated hippocampal subfield segmentation at 7t mri. American Journal of Neuroradiology.
  41. Wisse, L. E., Biessels, G. J., Heringa, S. M., Kuijf, H. J., Luijten, P. R., Geerlings, M. I., Group, U. V. C. I. V. S., et al. (2014). Hippocampal subfield volumes at 7t in early alzheimer's disease and normal aging. Neurobiology of aging, 35(9):2039-2045.
  42. Wu, X., Wen, X., Li, J., and Yao, L. (2014). A new dynamic bayesian network approach for determining effective connectivity from fmri data. Neural Computing and Applications, 24(1):91-97.
  43. Xuan, N., Chetty, M., Coppel, R., and Wangikar, P. P. (2012). Gene regulatory network modeling via global optimization of high-order dynamic bayesian network. BMC bioinformatics, 13(1):131.
  44. Yushkevich, P. A., Pluta, J. B., Wang, H., Xie, L., Ding, S.- L., Gertje, E. C., Mancuso, L., Kliot, D., Das, S. R., and Wolk, D. A. (2015). Automated volumetry and regional thickness analysis of hippocampal subfields and medial temporal cortical structures in mild cognitive impairment. Human brain mapping, 36(1):258- 287.
  45. Zhang, L., Samaras, D., Alia-Klein, N., Volkow, N., and Goldstein, R. (2005). Modeling neuronal interactivity using dynamic bayesian networks. In Advances in neural information processing systems, pages 1593- 1600.
  46. Zhu, D., Zhang, T., Jiang, X., Hu, X., Chen, H., Yang, N., Lv, J., Han, J., Guo, L., and Liu, T. (2014). Fusing dti and fmri data: a survey of methods and applications. Neuroimage, 102:184-191.

Paper Citation

in Harvard Style

P. Santos F., F. Smagula S., Karim H., S. Santini T., J. Aizenstein H., S. Ibrahim T. and D. Maciel C. (2017). Dynamic Bayesian Network Modeling of Hippocampal Subfields Connectivity with 7T fMRI: A Case Study . In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2017) ISBN 978-989-758-212-7, pages 178-184. DOI: 10.5220/0006151601780184

in Bibtex Style

author={Fernando P. Santos and Stephen F. Smagula and Helmet Karim and Tales S. Santini and Howard J. Aizenstein and Tamer S. Ibrahim and Carlos D. Maciel},
title={Dynamic Bayesian Network Modeling of Hippocampal Subfields Connectivity with 7T fMRI: A Case Study},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2017)},

in EndNote Style

JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2017)
TI - Dynamic Bayesian Network Modeling of Hippocampal Subfields Connectivity with 7T fMRI: A Case Study
SN - 978-989-758-212-7
AU - P. Santos F.
AU - F. Smagula S.
AU - Karim H.
AU - S. Santini T.
AU - J. Aizenstein H.
AU - S. Ibrahim T.
AU - D. Maciel C.
PY - 2017
SP - 178
EP - 184
DO - 10.5220/0006151601780184