Mehmet Kurt

Magnetic resonance elastography of the brain: A comparison between pigs and humans

Magnetic resonance elastography holds promise as a non-invasive, easy-to-use, in vivo biomarker for neurodegenerative diseases. Throughout the past decade, pigs have gained increased popularity as large animal models for human neurodegeneration. However, the volume of a pig brain is an order of magnitude smaller than the human brain, its skull is 40% thicker, and its head is about twice as big. This raises the question to which extent established vibration devices, actuation frequencies, and analysis tools for humans translate to large animal studies in pigs. Here we explored the feasibility of using human brain magnetic resonance elastography to characterize the dynamic properties of the porcine brain. In contrast to humans, where vibration devices induce an anterior-posterior displacement recorded in transverse sections, the porcine anatomy requires a dorsal-ventral displacement recorded in coronal sections. Within these settings, we applied a wide range of actuation frequencies, from 40Hz to 90Hz, and recorded the storage and loss moduli for human and porcine brains. Strikingly, we found that optimal actuation frequencies for humans translate one-to-one to pigs and reliably generate shear waves for elastographic post-processing. In a direct comparison, human and porcine storage and loss moduli followed similar trends and increased with increasing frequency. When translating these frequency-dependent storage and loss moduli into the frequency-independent stiffnesses and viscosities of a standard linear solid model, we found human values of μ1=1.3kPa, μ2=2.1kPa, and η=0.025kPas and porcine values of μ1=2.0kPa, μ2=4.9kPa, and η=0.046kPas. These results suggest that living human brain is softer and less viscous than dead porcine brain. Our study compares, for the first time, magnetic resonance elastography in human and porcine brains, and paves the way towards systematic interspecies comparison studies and ex vivo validation of magnetic resonance elastography as a whole.

Brain stiffens post mortem

Alterations in brain rheology are increasingly recognized as a diagnostic marker for various neurological conditions. Magnetic resonance elastography now allows us to assess brain rheology repeatably, reproducibly, and non-invasively in vivo. Recent elastography studies suggest that brain stiffness decreases one percent per year during normal aging, and is significantly reduced in Alzheimer’s disease and multiple sclerosis. While existing studies successfully compare brain stiffnesses across different populations, they fail to provide insight into changes within the same brain. Here we characterize rheological alterations in one and the same brain under extreme metabolic changes: alive and dead. Strikingly, the storage and loss moduli of the cerebrum increased by 26% and 60% within only three minutes post mortem and continued to increase by 40% and 103% within 45 minutes. Immediate post mortem stiffening displayed pronounced regional variations; it was largest in the corpus callosum and smallest in the brainstem. We postulate that post mortem stiffening is a manifestation of alterations in polarization, oxidation, perfusion, and metabolism immediately after death. Our results suggest that the stiffness of our brain–unlike any other organ–is a dynamic property that is highly sensitive to the metabolic environment Our findings emphasize the importance of characterizing brain tissue in vivo and question the relevance of ex vivo brain tissue testing as a whole. Knowing the true stiffness of the living brain has important consequences in diagnosing neurological conditions, planning neurosurgical procedures, and modeling the brain’s response to high impact loading.

Detection of American Football Head Impacts Using Biomechanical Features and Support Vector Machine Classification

Accumulation of head impacts may contribute to acute and long-term brain trauma. Wearable sensors can measure impact exposure, yet current sensors do not have validated impact detection methods for accurate exposure monitoring. Here we demonstrate a head impact detection method that can be implemented on a wearable sensor for detecting field football head impacts. Our method incorporates a support vector machine classifier that uses biomechanical features from the time domain and frequency domain, as well as model predictions of head-neck motions. The classifier was trained and validated using instrumented mouthguard data from collegiate football games and practices, with ground truth data labels established from video review. We found that low frequency power spectral density and wavelet transform features (10~30u2009Hz) were the best performing features. From forward feature selection, fewer than ten features optimized classifier performance, achieving 87.2% sensitivity and 93.2% precision in cross-validation on the collegiate dataset (nu2009=u2009387), and over 90% sensitivity and precision on an independent youth dataset (nu2009=u200932). Accurate head impact detection is essential for studying and monitoring head impact exposure on the field, and the approach in the current paper may help to improve impact detection performance on wearable sensors.

Utility of preoperative meningioma consistency measurement with magnetic resonance elastography (MRE): a review

Meningioma consistency is a critical factor that influences preoperative planning for surgical resection. Recent studies have investigated the utility of preoperative magnetic resonance elastography (MRE) in predicting meningioma consistency. However, it is unclear whether existing methods are optimal for application to clinical practice. The results and conclusions of these studies are limited by their imaging acquisition methods, such as the use of a single MRE frequency and the use of shear modulus as the final measurement variable, rather than its storage and loss modulus components. In addition, existing studies do not account for the effects of cranial anatomy, which have been shown to significantly distort the MRE signal. Given the interaction of meningiomas with these anatomic structures and the lack of supporting evidence with more accurate imaging parameters, MRE may not yet be reliable for use in clinical practice.

Direct Detection of Nonlinear Modal Interactions and Model Updating Using Measured Time Series

We describe a new method for identifying mechanical systems with strongly nonlinear attachments using measured transient response data. The procedure is motivated by the desire to quantify the degree of nonlinearity of a system, with the ultimate goal of updating a finite-element or other mathematical model to capture the nonlinear effects accurately. Our method relies on the proper orthogonal decomposition to extract proper orthogonal mode shapes (POMs), which are inherently energy dependent, directly from the measured transient response. Using known linear properties, the system’s frequencies are estimated using the Rayleigh quotient and an estimated frequency-energy plot (FEP) is created by them as functions of the system’s mechanical energy. The estimated FEP reveals distinct linear and nonlinear regimes which h are characterized by constant frequency (horizontal lines) and large frequency changes, respectively. The nonlinear regimes also contain spikes that connect different modes and indicate strongly nonlinear modal interactions. The nonlinearity is identified by plotting the estimated frequencies as functions of characteristic displacement and fitting a frequency equation based on the model of the nonlinearity. We demonstrate the method on the response of a cantilevered, model airplane wing with a nonlinear energy sink attached at its free end.

Advanced Nonlinear System Identification for Modal Interactions in Nonlinear Structures: A Review

In this work, we review a recently developed method for the characterization and identification of strongly nonlinear dynamical systems, including the detection of strongly nonlinear modal interactions, directly from transient response data. The method synergistically combines the proper orthogonal decomposition and the Rayleigh quotient to create estimated frequency-energy plots (FEPs) that capture the rich and interesting nonlinear dynamical interactions. The method is first applied to the experimentally measured response of a cantilever beam with a local, smooth nonlinearity. In this application, the estimated FEP reveals the presence of nonsmooth perturbations that connect different nonlinear normal modes (NNMs) of the system. The wavelet-bounded empirical mode decomposition and slow-flow analysis are used to demonstrate that the nonsmooth perturbations correspond to strongly nonlinear internal resonances between two NNMs. In the second example, the method is applied to the experimentally measured response of a cantilever beam with a local, nonlinear attachment in the form of a nonlinear energy sink (NES). An estimated frequency-displacement plot for the NES is created, and an optimization routine is then used to identify the unknown parameters for a given model of the nonlinearity. Ultimately, the method is conceptually and computationally simple compared to traditional methods while providing significant insight into the nonlinear physics governing dynamical systems with strong, local nonlinearity directly from measured time series data.

Elements of a Nonlinear System Identification Methodology of Broad Applicability with Application to Bolted Joints

This chapter presents a recently proposed nonlinear system identification methodology that has the promise of broad applicability based on a global–local approach. In the proposed nonlinear system identification methodology, measured time series are decomposed in terms of approximately monochromatic dominant intrinsic mode functions, which are either depicted in frequency-energy plots (the global aspect of nonlinear system identification), or are used to construct local models in terms of sets of intrinsic modal oscillators (the local aspect of nonlinear system identification). The proposed nonlinear system identification methodology is applied to the analysis and modeling of the nonlinear damping effects induced by a frictional interface on the dynamics of a beam with a bolted joint connection. In particular, we show that by studying the temporal decays of the logarithms of the moduli of the complex amplitudes of the forcing functions of the intrinsic modal oscillators, we can deduce the nonlinear damping effects in the dynamics. The nonlinear system identification methodology can be employed to study nonlinear damping effects in structural assemblies with more complex mechanical joints, and nonlinear stiffness effects in structural components with local or distributed nonlinearities of a different source. Moreover, it is possible to study the effects of nonproportional (linear or nonlinear) damping distribution on the modal responses, and conceive methods for modeling such effects and for examining how these effects perturb the result of classical modal analysis.

Revealing sub-voxel motions of brain tissue using phase-based amplified MRI (aMRI)

Amplified magnetic resonance imaging (aMRI) was recently introduced as a new brain motion detection and visualization method. The original aMRI approach used a video‐processing algorithm, Eulerian video magnification (EVM), to amplify cardio‐ballistic motion in retrospectively cardiac‐gated MRI data. Here, we strive to improve aMRI by incorporating a phase‐based motion amplification algorithm.