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   Brain Biomechanics  


Awards: Thrasher Research Foundation Early Career Award, Lucile Packard Children's Hospital Research Grant

In moderate to severe TBI, Diffuse Axonal Injury (DAI) can be observed through imaging where axons are sheared and ruptured. However, mild TBI (mTBI or concussion) is clinically defined as the absence of structural injury evidence in imaging. It is more likely, however, that our tools are simply insufficient to observe the microstructural injury that occurs in the white matter at the axon level. Even athletes without diagnosed concussions show pathological brain changes in fMRI and DTI studies. More recently, MRI tagging has been used to measure relative brain-skull motion in live humans. To study mTBI injury mechanisms, we need to gain a better understanding of the governing dynamics of skull-brain interactions both in micro and macro scales.











At KurtLab, in collaboration with Athletics Department at Stevens, we use machine learning methods to investigate the mechanics of traumatic brain injury in boxing, soccer and other contact sports (Figure 1A) We aim to investigate the biomechanics of TBI in injured populations in the following stages : (1) field studies to obtain more mTBI injury data in collaboration with the Athletics department (2) conducting brain imaging experiments to gather in vivo human brain dynamics data where we apply methods from structural dynamics to investigate various mechanical loads on the human brain (Figure 1C) (3) Combining the data obtained in (1) and (2) to create accurate brain fi nite element models and determine mTBI thresholds in humans (Figure 1B). Our lab's ultimate dream is to be able to predict axonal strains and damage as a function of the mechanical loads on the head.

We are especially interested in the spatiotemporal dynamics of the human brain in response to the impact. Our goal is to tie the extreme complexity in the tissue-level response (as seen in the displacement fields above) with the underlying cellular damage mechanisms.  

Neuromechanics Imaging


Awards: Lucile Packard Children's Hospital Research Grant

1) Magnetic Resonance Elastography (MRE):

Magnetic resonance elastography (MRE) has the promise of mapping the viscoelastic properties of the human brain. Viscoelasticity is a measure of the microstructural composition of the soft biological tissue and is increasingly used as a diagnostic marker. It is quantified with two parameters: storage modulus, which describes the elastic behavior of the tissue and the loss modulus, which describes the viscous behavior. MRE has been gaining attention as a non-invasive means to measure the viscoelasticity of tissues in vivo, most prominently brain tissue. MRE obtains information about the stiffness of the tissue by assessing the propagation of mechanical waves through the tissue with a magnetic resonance imaging (MRI) technique. The brain is usually excited through an actuator that is placed under the head of the subject inside the MRI coil . Several studies have shown that it can potentially be used as a diagnostic tool for detecting the onset of neurological diseases including Alzheimer’s and traumatic brain injury,

At KurtLab, we aim to understand the unique mechanical properties of the developing pediatric brain and aging adult brain through MRE, which will enable us to predict the functional and structural tolerance of the human brain to traumatic brain injury, and develop protective equipment for the pediatric population. Specifically, if the susceptible regions of the pediatric brain to trauma over different ages can be determined, this would have a major interventional effect in the form of novel diagnostic/therapeutic/preventive tools.


The characterization of brain tissue mechanical properties is also of crucial importance in the development of realistic numerical models of the human head. However, properties used in finite element human brain models normally vary in a large range in published literature due to the lack of in vivo data. Our unprecedented research aims to incorporate in vivo  human brain measurements into brain finite element models.

2) Amplified MRI: Through a collaboration with Stanford (Dr. Samantha Holdsworth), we utilize amplified MRI (aMRI) method which takes retrospective cardiac-gated cine MRI data as input, applies a spatial decomposition, followed by temporal filtering and frequency-selective amplification of the MRI cardiac-gated frames before synthesizing a motion-amplified cine data set. This sequence has been developed by Dr. Holdsworth at Stanford. This approach reveals deformations of the brain parenchyma and displacements of arteries due to cardiac pulsatility, especially in the brainstem, cerebellum, and spinal cord. We aim to use this methodology to characterize often barely perceptible motion and identify possible abnormalities in the brain, such as Chiari malformation in children.

Nonlinear System Identification in Mechanical and Biological Systems


Awards: Thomas Bernard Hall Best Paper Award (2012)

Practical engineering applications necessitate system identi cation techniques for analyses, design and maintenance. System identi cation techniques existing in the literature work quite well for linear dynamical systems, e.g. linear modal analyses. However, these techniques fail to produce meaningful results when structures with nonlinear couplings are analyzed. The same holds for biological systems as well: As biological systems become more complex and assumptions of linearity no longer hold, a need for more advanced system identi cation and reduced order modeling techniques arises.

Our system identification methodology investigates the dynamics in local and global scales by using empirical mode decomposition (EMD) and frequency-energy dependence of the system. The proposed methodology holds promise of broad applicability,  is general and (at least in principle) can be applied to general classes of dynamical systems, even to systems with non-smooth non-linearities. We  are currently working to develop system identification methodologies , with the goals of constructing data-driven ROMs in biological and multi-physics problems, of identifying and modeling non-smooth structural dynamics including the effects of clearance and friction, and to extend it to wave-related applications. Basic open questions include the construction of local models for non-linear dynamic transitions with closely spaced fast frequencies, or with slowly varying fast frequencies; improving and providing a theoretical basis for separating smooth and nonsmooth effects in dynamics of systems with nonsmooth non-linearities; and extending local model constructions to distributed parameter systems (i.e. adding a spatial dimension to the analysis).

Figure Credits: Hovding, Sweden

Smart Biomedical Devices


Awards: Thrasher Research Foundation Early Career Award

A current particular focus at KurtLab is smart preventive equipment design in sports.

The classical approach in designing a helmet has not been completely e ffective. Helmets have proven eff ective in mitigating moderate to severe head injuries by attenuating translational head accelerations. However, reduced acceleration levels do not seem to prevent mild traumatic brain injury (mTBI). Despite the ubiquity of helmets in contact sports, mTBI is highly prevalent.

With the availability of high-rate MEMS sensors and high performance batteries, a new class of helmets, i.e. smart, expandable helmets, can be designed that can sense an impending collision and expand to protect the brain in an optimized manner. We currently work on developing smart and expandable helmets and neck protection systems for contact sports. In previous work, (Kurt et al, 2016, ABME) we have shown that such helmets can reduce the risk of head injuries by 8-fold compared to conventional sports helmets in football and bicycling.