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

Abstract

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.

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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

@conference{biosignals17,
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)},
year={2017},
pages={178-184},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006151601780184},
isbn={978-989-758-212-7},
}


in EndNote Style

TY - CONF
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