Craig S. Henriquez
Professor of Biomedical Engineering
Dr. Henriquez is also a Professor of Computer Science and Co-Director of the Center for Neuroengineering. Henriquez's research interests include large scale computing, heart modeling, and brain modeling.
A breakdown of the normal electrical activation sequence of the heart can sometimes lead to a state of ventricular fibrillation in which the heart ceases to function as an effective pump. Abnormal rhythms or arrhythmias often result after an episode of ischemia (a localized reduction of blood flow to the heart itself) which affects both the ionic processes necessary to elicit an impulse and the passive electrical properties of the tissue. Identifying the complex mechanisms of arrhythmogenesis will require experimentation as well as mathematical and computer models.
Current projects include the application of the bidomain model to diseased tissue to investigate how changes in tissue structure (both natural and diseased induced) and changes in ionic current flow influences the nature of conduction and the onset of arrhythmia.
Dr. Henriquez's group is also interested in developing realistic models that will enable investigators to better interpret electrophysiological measurements made in the clinic. For example, activation maps at the surface of the heart are typically constructed based on the detection of specific features of the surface extracellular recordings. However, for complex activation, such as might arise during an arrhythmia, the features are difficult to distinguish.
The use of models that simulate both activation and the resulting extracellular potential and the application of signal processing techniques can lead to a tool for constructing more meaningful maps from experimental recordings during abnormal conduction. This works involves direct interaction with experimental research performed in the Experimental Electrophysiology Laboratory under the direction of Dr. Patrick Wolf and the Cardiac Electrophysiology & Tissue Engineering lab under the direction of Dr. Nenad Bursac.
Medical Scientist Training Program awarded by National Institutes of Health (Mentor). 1997 to 2022
Modeling Activation and Block of Autonomic Nerves for Analysis and Design awarded by National Institutes of Health (Co Investigator). 2017 to 2021
Multiscale Modeling of Clotting Risk in Atrial Fibrillation awarded by University of North Carolina - Chapel Hill (Principal Investigator). 2018 to 2020
Engineering of Human Excitable Tissues from Unexcitable Cells awarded by National Institutes of Health (Co Investigator). 2016 to 2020
In Vitro and In Situ Engineering of Fibroblasts for Cardiac Repair awarded by National Institutes of Health (Co Investigator). 2016 to 2020
Multiscale Modeling of Clotting Risk in Atrial Fibrillation awarded by University of North Carolina - Chapel Hill (Principal Investigator). 2018 to 2019
Heart Risk Model awarded by (Co Investigator). 2013 to 2016
Modeling Cardiac Impulse Propagation at the Microscale awarded by National Institutes of Health (Principal Investigator). 2009 to 2015
Analysis and Design of Cultured Neuronal Networks for Adaptive and Reconfigurable Control awarded by National Science Foundation (Investigator). 2009 to 2014
Duke Coulter Translational Partnership - Yr 2 post-endowment awarded by (Principal Investigator). 2013 to 2014
Li, Guoshi, et al. “Rhythmic modulation of thalamic oscillations depends on intrinsic cellular dynamics..” Journal of Neural Engineering, vol. 16, no. 1, Feb. 2019. Epmc, doi:10.1088/1741-2552/aaeb03. Full Text
Gokhale, Tanmay A., et al. “Microheterogeneity-induced conduction slowing and wavefront collisions govern macroscopic conduction behavior: A computational and experimental study..” Plos Computational Biology, vol. 14, no. 7, July 2018. Epmc, doi:10.1371/journal.pcbi.1006276. Full Text
Zhang, Xu, et al. “A Scalable Weight-Free Learning Algorithm for Regulatory Control of Cell Activity in Spiking Neuronal Networks..” International Journal of Neural Systems, vol. 28, no. 2, Mar. 2018. Epmc, doi:10.1142/s0129065717500150. Full Text
Rossi, Simone, et al. “Muscle Thickness and Curvature Influence Atrial Conduction Velocities..” Front Physiol, vol. 9, 2018. Pubmed, doi:10.3389/fphys.2018.01344. Full Text
Barth, Bradley B., et al. “Electrical stimulation of gut motility guided by an in silico model..” Journal of Neural Engineering, vol. 14, no. 6, Dec. 2017. Epmc, doi:10.1088/1741-2552/aa86c8. Full Text
Li, Guoshi, et al. “Unified thalamic model generates multiple distinct oscillations with state-dependent entrainment by stimulation..” Plos Computational Biology, vol. 13, no. 10, Oct. 2017. Epmc, doi:10.1371/journal.pcbi.1005797. Full Text
Gokhale, Tanmay A., et al. “Modeling dynamics in diseased cardiac tissue: Impact of model choice..” Chaos (Woodbury, N.Y.), vol. 27, no. 9, Sept. 2017. Epmc, doi:10.1063/1.4999605. Full Text
Gokhale, Tanmay A., et al. “Modeling an Excitable Biosynthetic Tissue with Inherent Variability for Paired Computational-Experimental Studies..” Plos Computational Biology, vol. 13, no. 1, Jan. 2017. Epmc, doi:10.1371/journal.pcbi.1005342. Full Text Open Access Copy
Ying, Wenjun, and Craig S. Henriquez. “Adaptive Mesh Refinement and Adaptive Time Integration for Electrical Wave Propagation on the Purkinje System..” Biomed Research International, vol. 2015, Jan. 2015. Epmc, doi:10.1155/2015/137482. Full Text
Hubbard, Marjorie Letitia, and Craig S. Henriquez. “A microstructural model of reentry arising from focal breakthrough at sites of source-load mismatch in a central region of slow conduction..” American Journal of Physiology. Heart and Circulatory Physiology, vol. 306, no. 9, May 2014, pp. H1341–52. Epmc, doi:10.1152/ajpheart.00385.2013. Full Text
Gokhale, T. A., et al. “Continuous models fail to capture details of reentry in fibrotic myocardium.” Computing in Cardiology, vol. 43, 2016, pp. 1–4.
Hubbard, M. L., et al. “The effect of random cell decoupling on electrogram morphology near the percolation threshold in microstructural models of cardiac tissue.” Computing in Cardiology, vol. 41, no. January, 2014, pp. 65–68.
Hugh, G. S., and C. S. Henriquez. “Application of local learning and biological activation functions to networks of neurons for motor control.” International Ieee/Embs Conference on Neural Engineering, Ner, vol. 2003-January, 2003, pp. 233–36. Scopus, doi:10.1109/CNE.2003.1196801. Full Text
Jacquemet, V., et al. “Simulated atrial fibrillation in a computer model of human atria.” International Conference on Digital Signal Processing, Dsp, vol. 1, 2002, pp. 393–98. Scopus, doi:10.1109/ICDSP.2002.1189668. Full Text
Pollard, A. E., et al. “A comparison of iterative methods for the determination of the interstitial potential distribution with the bidomain model.” Proceedings of the Annual International Conference of the Ieee Engineering in Medicine and Biology Society, Embs, vol. 2, 1992, pp. 602–03. Scopus, doi:10.1109/IEMBS.1992.5761131. Full Text
Henriquez, C. S., and N. F. Hooke. “Effect of interstitial anisotropy and the extracellular volume conductor on action potential morphology in a thin layer of cardiac tissue.” Proceedings of the Annual International Conference of the Ieee Engineering in Medicine and Biology Society, Embs, vol. 2, 1992, pp. 600–01. Scopus, doi:10.1109/IEMBS.1992.5761130. Full Text