What is he best known for?
The fields of study he is best known for:
- Internal medicine
- Geometry
- Artificial intelligence
Craig S. Henriquez spends much of his time researching Magnetic resonance imaging, Neuroscience, Internal medicine, Anisotropy and Electrocardiography.
His studies deal with areas such as Orientation, Fiber orientation and Biomedical engineering as well as Magnetic resonance imaging.
His research in Motor cortex and Brain–computer interface are components of Neuroscience.
His research integrates issues of Impulse, Endocrinology, Coupling and Cardiology in his study of Internal medicine.
His Anisotropy study incorporates themes from Isotropy, Mechanics, Conductor and Bidomain model.
As part of the same scientific family, Craig S. Henriquez usually focuses on Electrocardiography, concentrating on Transient and intersecting with Electrophysiology.
His most cited work include:
- Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates (1444 citations)
- Simulating the electrical behavior of cardiac tissue using the bidomain model. (514 citations)
- Magnetic resonance myocardial fiber-orientation mapping with direct histological correlation (327 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Biomedical engineering, Anisotropy, Mechanics, Bidomain model and Wavefront.
His studies examine the connections between Biomedical engineering and genetics, as well as such issues in Membrane potential, with regards to Anatomy and Extracellular.
His Anatomy research includes themes of Myocyte and Electrophysiology.
His Anisotropy research incorporates elements of Isotropy, Geometry and Condensed matter physics.
His work in Mechanics covers topics such as Nerve conduction velocity which are related to areas like Action potential duration.
The various areas that Craig S. Henriquez examines in his Wavefront study include Transverse plane, Curvature and Nanotechnology.
He most often published in these fields:
- Biomedical engineering (20.67%)
- Anisotropy (16.00%)
- Mechanics (15.33%)
What were the highlights of his more recent work (between 2013-2021)?
- Neuroscience (9.33%)
- Stimulation (4.00%)
- Amplitude (6.00%)
In recent papers he was focusing on the following fields of study:
Craig S. Henriquez focuses on Neuroscience, Stimulation, Amplitude, Wavefront and Entrainment.
His Neuroscience research incorporates themes from Movement and Motility.
His Amplitude research is multidisciplinary, relying on both Substrate mapping, Physical medicine and rehabilitation, Rhythm, Neural activity and Transcranial alternating current stimulation.
The concepts of his Wavefront study are interwoven with issues in Transverse plane, Curvature, Condensed matter physics, Electrical resistivity and conductivity and Reentry.
His Curvature study combines topics in areas such as Nerve conduction velocity, Boundary value problem, Bidomain model, Mechanics and Anisotropy.
His studies in Reentry integrate themes in fields like Conductance, Biophysics, Coupling and Ablation.
Between 2013 and 2021, his most popular works were:
- A Brief History of Tissue Models For Cardiac Electrophysiology (26 citations)
- A Scalable Weight-Free Learning Algorithm for Regulatory Control of Cell Activity in Spiking Neuronal Networks (23 citations)
- Unified thalamic model generates multiple distinct oscillations with state-dependent entrainment by stimulation. (22 citations)
In his most recent research, the most cited papers focused on:
- Geometry
- Internal medicine
- Artificial intelligence
His primary scientific interests are in Gap junction, Biological system, Cardiology, Internal medicine and Medical physics.
Craig S. Henriquez has researched Gap junction in several fields, including Wavefront, Biophysics, Ablation, Conductance and Coupling.
As part of his studies on Biological system, Craig S. Henriquez often connects relevant subjects like Tissue conductivity.
His Electrocardiography study in the realm of Cardiology interacts with subjects such as Patient treatment.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
