Craig S. Henriquez

Image of 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.

Appointments and Affiliations

  • Professor of Biomedical Engineering
  • Professor in the Department of Mechanical Engineering and Materials Science
  • Faculty Network Member of the Duke Institute for Brain Sciences
  • Bass Fellow

Contact Information:

  • Office Location: 274 Hudson Hall Annex, Durham, NC 27708
  • Office Phone: (919) 660-5168
  • Email Address:


  • Ph.D. Duke University, 1988
  • B.S. Duke University, 1981

Awards, Honors, and Distinctions:

  • Fellows. American Institute for Medical and Biological Engineering. 2008

Courses Taught:

  • BME 244L9: Quantitative Physiology with Biostatistical Applications
  • BME 244L: Quantitative Physiology with Biostatistical Applications
  • BME 394: Projects in Biomedical Engineering (GE)
  • BME 493: Projects in Biomedical Engineering (GE)
  • BME 494: Projects in Biomedical Engineering (GE)
  • BME 503: Computational Neuroengineering (GE, EL)
  • BME 790: Advanced Topics for Graduate Students in Biomedical Engineering
  • BME 791: Graduate Independent Study
  • BME 792: Graduate Independent Study
  • ECE 493: Projects in Electrical and Computer Engineering
  • NEUROSCI 503: Computational Neuroengineering (GE, EL)

Representative Publications:

    • Gokhale, TA; Medvescek, E; Henriquez, CS, Modeling dynamics in diseased cardiac tissue: Impact of model choice., Chaos, vol 27 no. 9 (2017) [10.1063/1.4999605] [abs].
    • Gokhale, TA; Kim, JM; Kirkton, RD; Bursac, N; Henriquez, CS, Modeling an Excitable Biosynthetic Tissue with Inherent Variability for Paired Computational-Experimental Studies., PLoS computational biology, vol 13 no. 1 (2017) [10.1371/journal.pcbi.1005342] [abs].
    • Zhang, X; Foderaro, G; Henriquez, C; Ferrari, S, A Scalable Weight-Free Learning Algorithm for Regulatory Control of Cell Activity in Spiking Neuronal Networks. (2016) [10.1142/s0129065717500150] [abs].
    • Gokhale, TA; Medvescek, E; Henriquez, CS, Continuous models fail to capture details of reentry in fibrotic myocardium, Computing in cardiology, vol 43 (2016), pp. 1-4 [abs].
    • Ying, W; Henriquez, CS, Adaptive Mesh Refinement and Adaptive Time Integration for Electrical Wave Propagation on the Purkinje System., BioMed Research International, vol 2015 (2015) [10.1155/2015/137482] [abs].