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Authors: Fernando P. Santos 1 ; Stephen F. Smagula 2 ; Helmet Karim 3 ; Tales S. Santini 3 ; Howard J. Aizenstein 2 ; Tamer S. Ibrahim 3 and Carlos D. Maciel 1

Affiliations: 1 University of São Paulo, Brazil ; 2 University of Pittsburgh School of Medicine, United States ; 3 University of Pittsburgh, United States

ISBN: 978-989-758-212-7

Keyword(s): Dynamic Bayesian Networks, fMRI, Network Modelling, Hippocampus.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Biomedical Engineering ; Biomedical Signal Processing ; Computer Vision, Visualization and Computer Graphics ; Medical Image Detection, Acquisition, Analysis and Processing ; Physiological Processes and Bio-Signal Modeling, Non-Linear Dynamics

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. (More)

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Paper citation in several formats:
P. Santos, F.; P. Santos, F.; F. Smagula, S.; Karim, H.; S. Santini, T.; J. Aizenstein, H.; S. Ibrahim, . 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

@conference{biosignals17,
author={Fernando P. Santos. and 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},
}

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 - P. Santos, F.
AU - F. Smagula, S.
AU - Karim, H.
AU - S. Santini, T.
AU - J. Aizenstein, H.
AU - S. Ibrahim, .
AU - D. Maciel, C.
PY - 2017
SP - 178
EP - 184
DO - 10.5220/0006151601780184

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