Bulging brains

Modelling midline shift and ventricle collapse in cerebral oedema following acute ischaemic stroke

In ischaemic stroke, a large reduction in blood supply can lead to the breakdown of the blood-brain barrier and to cerebral oedema after reperfusion therapy. The resulting fluid accumulation in the brain may contribute to a significant rise in intracranial pressure (ICP) and tissue deformation. Changes in the level of ICP are essential for clinical decision-making and therapeutic strategies. However, the measurement of ICP is constrained by clinical techniques and obtaining the exact values of the ICP has proven challenging. In this study, we propose the first computational model for the simulation of cerebral oedema following acute ischaemic stroke for the investigation of ICP and midline shift (MLS) relationship. The model consists of three components for the simulation of healthy blood flow, occluded blood flow and oedema, respectively. The healthy and occluded blood flow components are utilized to obtain oedema core geometry and then imported into the oedema model for the simulation of oedema growth. The simulation results of the model are compared with clinical data from 97 traumatic brain injury patients for the validation of major model parameters. Midline shift has been widely used for the diagnosis, clinical decision-making, and prognosis of oedema patients. Therefore, we focus on quantifying the relationship between ICP and midline shift (MLS) and identify the factors that can affect the ICP-MLS relationship. Three major factors are investigated, including the brain geometry, blood-brain barrier damage severity and the types of oedema (including rare types of oedema). Meanwhile, the two major types (stress and tension/compression) of mechanical brain damage are also presented and the differences in the stress, tension, and compression between the intraparenchymal and periventricular regions are discussed. This work helps to predict ICP precisely and therefore provides improved clinical guidance for the treatment of brain oedema.

Prolonged course of brain edema and neurological recovery in a translational model of decompressive craniectomy after closed head injury in mice

Background

The use of decompressive craniectomy in traumatic brain injury (TBI) remains a matter of debate. According to the DECRA trial, craniectomy may have a negative impact on functional outcome, while the RescueICP trial revealed a positive effect of surgical decompression, which is evolving over time. This ambivalence of craniectomy has not been studied extensively in controlled laboratory experiments.

Objective

The goal of the current study was to investigate the prolonged effects of decompressive craniectomy (both positive and negative) in an animal model.

Methods

Male mice were assigned to the following groups: sham, decompressive craniectomy, TBI and TBI followed by craniectomy. The analysis of functional outcome was performed at time points 3d, 7d, 14d and 28d post trauma according to the Neurological Severity Score and Beam Balance Score. At the same time points, magnetic resonance imaging was performed, and brain edema was analyzed.

Results

Animals subjected to both trauma and craniectomy presented the exacerbation of the neurological impairment that was apparent mostly in the early course (up to 7d) after injury. Decompressive craniectomy also caused a significant increase in brain edema volume (initially cytotoxic with a secondary shift to vasogenic edema and gliosis). Notably, delayed edema plus gliosis appeared also after decompression even without preceding trauma.

Conclusion

In prolonged outcomes, craniectomy applied after closed head injury in mice aggravates posttraumatic brain edema, leading to additional functional impairment. This effect is, however, transient. Treatment options that reduce brain swelling after decompression may accelerate neurological recovery and should be explored in future experiments.

Subject-Specific Head Model Generation by Mesh Morphing: A Personalization Framework and Its Applications

Finite element (FE) head models have become powerful tools in many fields within neuroscience, especially for studying the biomechanics of traumatic brain injury (TBI). Subject-specific head models accounting for geometric variations among subjects are needed for more reliable predictions. However, the generation of such models suitable for studying TBIs remains a significant challenge and has been a bottleneck hindering personalized simulations. This study presents a personalization framework for generating subject-specific models across the lifespan and for pathological brains with significant anatomical changes by morphing a baseline model. The framework consists of hierarchical multiple feature and multimodality imaging registrations, mesh morphing, and mesh grouping, which is shown to be efficient with a heterogeneous dataset including a newborn, 1-year-old (1Y), 2Y, adult, 92Y, and a hydrocephalus brain. The generated models of the six subjects show competitive personalization accuracy, demonstrating the capacity of the framework for generating subject-specific models with significant anatomical differences. The family of the generated head models allows studying age-dependent and groupwise brain injury mechanisms. The framework for efficient generation of subject-specific FE head models helps to facilitate personalized simulations in many fields of neuroscience.

Decompressive hemicraniectomy in ischemic stroke

Malignant hemispheric stroke (MHS) is a life-threatening event, associated with high morbidity and mortality. Decompressive hemicraniectomy (DHS) is the treatment of choice to relieve the emerging space-occupying brain edema. This review details the pathophysiological and scientific background, considerations for clinical decision making, surgical treatment and impact on the patients' outcome. Although surgery reduces mortality significantly, the probability for unfavorable outcome is still high in selected cases. While former randomized controlled studies aimed for the prevention of the primary cause, the current research focuses on the treatment and prevention of secondary neurological injury.

Dynamic mechanical characterization and viscoelastic modeling of bovine brain tissue

Brain tissue is vulnerable and sensitive, predisposed to potential damage under various conditions of mechanical loading. Although its material properties have been investigated extensively, the frequency-dependent viscoelastic characterization is currently limited. Computational models can provide a non-invasive method by which to analyze brain injuries and predict the mechanical response of the tissue. The brain injuries are expected to be induced by dynamic loading, mostly in compression and measurement of dynamic viscoelastic properties are essential to improve the accuracy and variety of finite element simulations on brain tissue. Thus, the aim of this study was to investigate the compressive frequency-dependent properties of brain tissue and present a mathematical model in the frequency domain to capture the tissue behavior based on experimental results. Bovine brain specimens, obtained from four locations of corona radiata, corpus callosum, basal ganglia and cortex, were tested under compression using dynamic mechanical analysis over a range of frequencies between 0.5 and 35 Hz to characterize the regional and directional response of the tissue. The compressive dynamic properties of bovine brain tissue were heterogenous for regions but not sensitive to orientation showing frequency dependent statistical results, with viscoelastic properties increasing with frequency. The mean storage and loss modulus were found to be 12.41 kPa and 5.54 kPa, respectively. The material parameters were obtained using the linear viscoelastic model in the frequency domain and the numeric simulation can capture the compressive mechanical behavior of bovine brain tissue across a range of frequencies. The frequency-dependent viscoelastic characterization of brain tissue will improve the fidelity of the computational models of the head and provide essential information to the prediction and analysis of brain injuries in clinical treatments.

Decompressive craniectomy of post-traumatic brain injury: an in silico modelling approach for intracranial hypertension management

Traumatic brain injury (TBI) causes brain edema that induces increased intracranial pressure and decreased cerebral perfusion. Decompressive craniectomy has been recommended as a surgical procedure for the management of swollen brain and intracranial hypertension. Proper location and size of a decompressive craniectomy, however, remain controversial and no clinical guidelines are available. Mathematical and computational (in silico) models can predict the optimum geometric conditions and provide insights for the brain mechanical response following a decompressive craniectomy. In this work, we present a finite element model of post-traumatic brain injury and decompressive craniectomy that incorporates a biphasic, nonlinear biomechanical model of the brain. A homogenous pressure is applied in the brain to represent the intracranial pressure loading caused by the tissue swelling and the models calculate the deformations and stresses in the brain as well as the herniated volume of the brain tissue that exits the skull following craniectomy. Simulations for different craniectomy geometries (unilateral, bifrontal and bifrontal with midline bar) and sizes are employed to identify optimal clinical conditions of decompressive craniectomy. The reported results for the herniated volume of the brain tissue as a function of the intracranial pressure loading under a specific geometry and size of craniectomy are exceptionally relevant for decompressive craniectomy planning.

Mechanical Characterization and Modeling of the Porcine Cerebral Meninges

The cerebral meninges, made up of the dura, arachnoid, and pia mater, is a tri-layer membrane that surrounds the brain and the spinal cord and has an important function in protecting the brain from injury. Understanding its mechanical behavior is important to ensure the accuracy of finite element (FE) head model simulations which are commonly used in the study of traumatic brain injury (TBI). Mechanical characterization of freshly excised porcine dura-arachnoid mater (DAM) was achieved using uniaxial tensile testing and bulge inflation testing, highlighting the dependency of the identified parameters on the testing method. Experimental data was fit to the Ogden hyperelastic material model with best fit material parameters of μ = 450 ± 190 kPa and α = 16.55 ± 3.16 for uniaxial testing, and μ = 234 ± 193 kPa and α = 8.19 ± 3.29 for bulge inflation testing. The average ultimate tensile strength of the DAM was 6.91 ± 2.00 MPa (uniaxial), and the rupture stress at burst was 2.08 ± 0.41 MPa (inflation). A structural analysis using small angle light scattering (SALS) revealed that while local regions of highly aligned fibers exist, globally, there is no preferred orientation of fibers and the cerebral DAM can be considered to be structurally isotropic. This confirms the results of the uniaxial mechanical testing which found that there was no statistical difference between samples tested in the longitudinal and transversal direction (p = 0.13 for μ, p = 0.87 for α). A finite element simulation of a craniotomy procedure following brain swelling revealed that the mechanical properties of the meninges are important for predicting accurate stress and strain fields in the brain and meninges. Indeed, a simulation using a common linear elastic representation of the meninges was compared to the present material properties (Ogden model) and the intracranial pressure was found to differ by a factor of 3. The current study has provided researchers with primary experimental data on the mechanical behavior of the meninges which will further improve the accuracy of FE head models used in TBI.

Nonlinear elasticity of incompatible surface growth

Surface growth is a crucial component of many natural and artificial processes, from cell proliferation to additive manufacturing. In elastic systems surface growth is usually accompanied by the development of geometrical incompatibility, leading to residual stresses and triggering various instabilities. In a recent paper [G. Zurlo and L. Truskinovsky, Phys. Rev. Lett. 119, 048001 (2017)PRLTAO0031-900710.1103/PhysRevLett.119.048001] we presented a linearized elasticity theory of incompatible surface growth, which provides a quantitative link between deposition protocols and postgrowth states of stress. Here we extend this analysis to account for both physical and geometrical nonlinearities of an elastic solid. This development reveals the shortcomings of the linearized theory, in particular its inability to describe kinematically confined surface growth and to account for growth-induced elastic instabilities.

A computational model for microcirculation including Fahraeus-Lindqvist effect, plasma skimming and fluid exchange with the tissue interstitium

We present a two-phase model for microcirculation that describes the interaction of plasma with red blood cells. The model takes into account of typical effects characterizing the microcirculation, such as the Fahraeus-Lindqvist effect and plasma skimming. Besides these features, the model describes the interaction of capillaries with the surrounding tissue. More precisely, the model accounts for the interaction of capillary transmural flow with the surrounding interstitial pressure. Furthermore, the capillaries are represented as one-dimensional channels with arbitrary, possibly curved configuration. The latter two features rely on the unique ability of the model to account for variations of flow rate and pressure along the axis of the capillary, according to a local differential formulation of mass and momentum conservation. Indeed, the model stands on a solid mathematical foundation, which is also addressed in this work. In particular, we present the model derivation, the variational formulation, and its approximation using the finite element method. Finally, we conclude the work with a comparative computational study of the importance of the Fahraeus-Lindqvist, plasma skimming, and capillary leakage effects on the distribution of flow in a microvascular network.