Brain multiphysics最新文献

Sports concussion is a form of mild traumatic brain injury (mTBI) caused by an impulsive force transmitted to the head. While concussion is recognized as a complex pathophysiological process affecting the brain at multiple scales, the causal link between external load and cellular, molecular level damage in mTBI remains elusive. The present study proposes a multiscale framework to analyze concussion and demonstrates its applicability with a real-life concussion case. The multiscale analysis starts from inputting mouth guard-recorded head kinematic into a detailed finite element (FE) head model tailored to the subject's head and white matter (WM) tract morphology. The resulting WM tract-oriented strains are then extracted and input to histology-informed micromechanical models of corpus callosum subregions with axonal detail to obtain axolemma strains at a subcellular level. By comparing axolemma strains against mechanoporation thresholds obtained via molecular dynamics (MD) simulations, axonal damage is inferred corresponding to a likelihood of concussion, in line with clinical observation. This novel multiscale framework bridges the organ-to-molecule length scales and accounts both inter- and intra-subject regional variability, providing a new way of non-invasively predicting axonal damage and real-life concussion analysis. The framework may contribute to a better understanding of the mechanistic causes behind concussion.

Statement of Significance

This study reports a multiscale computational framework for concussion, for the first time revealing a picture of how a global impact to the head measured on the field transfers to the cellular level of axons and finally down to the molecular level. Demonstrated with a real-life concussion case using a detailed and subject-specific head model, the results show molecular level damage corresponds to a likelihood of concussion, in line with clinical observation. An insight into the multiscale mechanical consequences is critical for a better understanding of the complex pathophysiological process affecting the brain at impact, which today are still poorly understood. Analyzing the concussive injury mechanisms the whole way from brains to molecules may also have significant clinical relevance. We show that in a typical injury scenario, the axolemma sustains large enough strains to entail pore formation in the adjoining lipid bilayer. Proration is found to occur in bilayer regions lacking ganglioside lipids, which provides important implications for the treatment of brain injury and other related neurodegenerative diseases.

By just replacing the infinitesimal strains by logarithmic strains, the Hencky strain energy has proven to extend successfully the infinitesimal framework to moderately large strains, as those found in brain. However, as polymers and soft tissues, brain presents an important strain-stiffening towards locking. Based on both observations, in this paper we propose a simple two-parameter isotropic strain energy function for representing the inviscid (conservative) behavior of brain matter. The two parameters of the model are the Young modulus (or alternatively the shear modulus) and the locking stretch during a test. Through a comparison with experimental data, we show that with this simple model, employing the two material parameters directly measured from a tensile test, we capture the qualitative aspects and quantitative behavior of brain mater in tension, compression and simple shear tests with good accuracy.

Statement of Significance

This paper shows a simple mathematical model capable of reproducing qualitative aspects and quantitative behavior of brain matter in tension, compression and simple shear tests with good accuracy. The model is governed by only two parameters, namely Young's modulus (or alternatively the shear modulus) and the locking stretch.

In the context of Alzheimer’s disease (AD), in silico research aims at giving complementary and better insight into the complex mechanisms which determine the development of AD. One of its important aspects is the construction of macroscopic mathematical models which are the basis for numerical simulations. In this paper we discuss some of the general and fundamental difficulties of macroscopic modelling of AD. In addition we formulate a mathematical model in the case of a specific problem in an early stage of AD, namely the propagation of pathological $\tau$ protein from the entorhinal cortex to the hippocampal region. The main feature of this model consists in the representation of the brain through two superposed finite graphs, which have the same vertices (that, roughly speaking, can be thought as parcels of a brain atlas), but different edges. We call these graphs “proximity graph” and “connectivity graph”, respectively. The edges of the first graph take into account the distances of the vertices and the heterogeneity of the cerebral parenchyma, whereas the edges of the second graph represent the connections by white-matter fiber pathways between different structures. The diffusion of the proteins A$\beta$ and $\tau$ are described through the Laplace operators on the graphs, whereas the phenomenon of aggregation of the proteins leading ultimately to senile plaques and neuro-fibrillar tangles (as already observed by A. Alzheimer in 1907) is modelled by means of the classical Smoluchowski aggregation system.

Statement of significance

Alzheimer’s disease is a neurodegenerative disease leading to dementia with huge economic and social costs. Despite a fast growing amount of clinical data, there is no widely accepted medical treatment to stop or slow down AD. It is generally accepted that two proteins, beta amyloid and tau, play a key role in the progression of the disease, and the edge of the current biomedical research focuses on the interactions of the two proteins also in the perspective of the production of new effective drugs. In this context, flexible mathematical models may give better and deeper insight by testing different clinical hypotheses.

Successful detection and prevention of brain injuries relies on the quantitative identification of cellular injury thresholds associated with the underlying pathology. Here, by combining a recently developed inertial microcavitation rheology technique with a 3D in vitro neural tissue model, we quantify and resolve the structural pathology and critical injury strain thresholds of neural cells occurring at high loading rates such as encountered in blast, cavitation or directed energy exposures. We find that neuronal dendritic spines characterized by MAP2 displayed the lowest physical failure strain at 7.3%, whereas microtubules and filamentous actin were able to tolerate appreciably higher strains (14%) prior to injury. Interestingly, while these critical injury thresholds were similar to previous literature values reported for moderate and lower strain rates ($<100$ 1/s), the pathology of primary injury reported here was distinctly different by being purely physical in nature as compared to biochemical activation during apoptosis or necrosis.

Statement of Significance

Mitigation and prevention of cellular injury is challenging in part due to the lack of quantitative correlation between mechanical insult and cellular pathology, especially at high deformation rates (>104 s⁻¹) that occur in blast and directed energy related brain injury, or laser and sonic-based medical procedures. By utilizing a recently developed inertial microcavitation rheology technique for generating high-rate deformations in a 3D in vitro neural tissue model, we quantitatively correlate critical stretch, strain and stress-based injury criteria to observed cell pathology. These quantitative experimental measurements provide unprecedented new detail into the cellular pathology of neural tissues affected by high-rate injury including the first quantitative high-rate injury threshold metrics.

The estimation of functional connectivity (FC) from noninvasive electrophysiological data recorded from sensors outside the skull requires transforming these data into a source space. As the number of sensors is much lower than the number of electrophysiological sources, the brain activity is usually parcellated into anatomical regions, and the FC between each pair of regions is then estimated.

In this work, we generate a set of simulated scenarios with different configurations and coupling levels between synthetic time series. Then, this simulated brain activity is converted into simulated MEG sensor-space data and reconstructed back into the source space. Last, we estimated the FC between different regions using different approaches commonly used in the literature and compared them with a novel approach.

Our results show that this novel approach, based on using all the information in each region, clearly outperforms classical approaches based on a representative time series. The proposed approach is more sensitive to the level of coupling and the extent of the area synchronized, and the resulting estimate better reflects the underlying FC. Based on these results, we strongly discourage using a representative time series to summarize large brain areas' activity when calculating FC.

Statement of significance

While it is now well established that mechanical instabilities play an important role for cortical folding in the developing human brain, the mechanisms on the cellular scale leading to those macroscopic structural changes remain insufficiently understood. Here, we demonstrate that a two-field mechanical model coupling cell division and migration with volume growth is capable of capturing the spatial and temporal distribution of the cell density and the corresponding cortical folding pattern observed in the human fetal brain. The presented model provides a platform to obtain important insights into the cellular mechanisms underlying normal cortical folding and, even more importantly, malformations of cortical development.

The rapid deformation of brain tissue in response to head impact can lead to traumatic brain injury. In vivo measurements of brain deformation during non-injurious head impacts are necessary to understand the underlying mechanisms of traumatic brain injury and compare to computational models of brain biomechanics. Using tagged magnetic resonance imaging (MRI), we obtained measurements of three-dimensional strain tensors that resulted from a mild head impact after neck rotation or neck extension. Measurements of maximum principal strain (MPS) peaked shortly after impact, with maximal values of 0.019–0.053 that correlated strongly with peak angular velocity. Subject-specific patterns of MPS were spatially heterogeneous and consistent across subjects for the same motion, though regions of high deformation differed between motions. The largest MPS values were seen in the cortical gray matter and cerebral white matter for neck rotation and the brainstem and cerebellum for neck extension. Axonal fiber strain (Ef) was estimated by combining the strain tensor with diffusion tensor imaging data. As with MPS, patterns of Ef varied spatially within subjects, were similar across subjects within each motion, and showed group differences between motions. Values were highest and most strongly correlated with peak angular velocity in the corpus callosum for neck rotation and in the brainstem for neck extension. The different patterns of brain deformation between head motions highlight potential areas of greater risk of injury between motions at higher loading conditions. Additionally, these experimental measurements can be directly compared to predictions of generic or subject-specific computational models of traumatic brain injury.

Statement of Significance

Traumatic brain injury can result from the rapid acceleration of the skull, leading to deformation of brain tissue and elongation of axonal fibers. Because treatment options and prognostic models for patients are lacking, a better understanding of injury mechanisms is needed. Here, we use tagged magnetic resonance imaging to measure deformation throughout the live, human brain during non-injurious head accelerations. We present the first in vivo measurements of axonal stretch and compare MPS and axonal stretch experienced during neck rotation and neck extension. These results are important to elucidate brain regions at risk for injury. Additionally, they can be directly used to evaluate computational models of brain injury, which are used to predict risk of concussion during head impacts and design protective equipment.

Changes in axonal myelination are an important hallmark of aging and a number of neurological diseases. Demyelinated axons are impaired in their function and degenerate over time. Oligodendrocytes, the cells responsible for myelination of axons, are sensitive to mechanical properties of their environment. Growing evidence indicates that mechanical properties of demyelinating lesions are different from the healthy state and thus have the potential to affect myelinating potential of oligodendrocytes. We performed a high-resolution spatial mapping of the mechanical heterogeneity of demyelinating lesions using atomic force microscope-enabled indentation. Our results indicate that the stiffness of specific regions of mouse brain tissue is influenced by age and degree of myelination. Here we specifically demonstrate that acquired acute but not genetic demyelination leads to decreased tissue stiffness, which could influence the remyelination potential of oligodendrocytes. We also demonstrate that specific brain regions have unique ranges of stiffness in white and grey matter. Our ex vivo findings may help the design of future in vitro models to mimic the mechanical environment of the brain in healthy and diseased states. The mechanical properties of demyelinating lesions reported here may facilitate novel approaches in treating demyelinating diseases such as multiple sclerosis.

Statement of Significance

Mechanical characteristics of a cell's environment can have a profound influence on its biological properties. Neuronal and glial cells are sensitive to mechanical input during development, in disease and regeneration. Sustained tensile strain can promote differentiation of oligodendrocyte progenitor cells into mature oligodendrocytes, which are responsible for the myelination of axons. Changing myelination is an important hallmark in human aging and disease, such as multiple sclerosis. Our hypothesis is that these diseases might be characterized by altered tissue stiffness and that this has an influence on remyelination potential. Here we investigate tissue stiffness profiles of healthy, aged and disease model mice. Manipulating the tissue stiffness might be another promising approach for new treatments.

Considering that the propagation speed of action potentials in the brain connectome has a finite limit and that present is ill-defined in the brain we apply concepts borrowed from the theories of special and general relativity to introduce the view that time and space are tightly blended in the brain. It is shown that the brain functional and structural features can be unified through a combined brain “spacetime”. This 4-dimensional brain spacetime presents a functional curvature generated by brain activity, in a similar way gravitational masses give our 4-dimensional Universe spacetime its curvature. After laying its foundations and developing this framework using a relativistic pseudo-diffusion model of neural propagation, we explore how this whole-brain framework may shed light on brain functional features and dysfunction phenotypes (clinical expression of diseases) observed in some neuropsychiatric and consciousness disorders.

Statement of Significance

Because action potentials in the brain connectome propagate with a finite velocity limit, simultaneity across brain nodes is only relative. A new concept is emerging where time and space in the brain, as in the Universe, are tightly mingled through a combined “spacetime”. This 4-dimensional spacetime merging brain structure and function presents a curvature generated by brain activity, in a similar way gravitational masses give our Universe spacetime its curvature, driving activity flow within the brain.

The nervous system is a complex dynamical system that incorporates higher order biology (e.g., multicellular architecture) and lower-order biology (e.g., intra cellular pathway) that can be modeled via classical laws such as reaction-diffusion models. Simple reaction-diffusion models of neuronal tissue are shown to support bio-chemical wave effects that are analogous to quantum phenomena. These phenomena include quantum-like superpositions and classical entanglement which will not be affected by decoherence n the wet and warm brain environment. These classical phenomena could enable quantum-like complexity of brain functions. Conventional reaction-diffusion models of biological tissues challenge the current quantum brain hypothesis and suggest that the brain should perhaps be thought of as a classical quantum-like system.

Statement of Significance

This manuscript introduces the notion of nonseparability (classical entanglement) in the case of biochemical waves in arrays of coupled axons. We use a linear reaction-diffusion model with cross diffusion to address nonseparability between degrees of freedom (along and across the axon array). Perturbation theory applied to a nonlinear model with quadratic nonlinearity is used to illustrate nonseparability between modes along the axons. This paper suggests that the brain should perhaps be thought of as a classical quantum-like system.

The study of temperature profiles within the central nervous system (CNS) when exposed to an alternating magnetic field (AMF) as a plausible therapy for neuropsychiatric disorders is crucial. This new procedure can be a better alternative for conventional permanent implanted electrodes treatment for CNS diseases such as Parkinson's disease (PD). Hyperthermic treatments are highly dependent on biomaterial thermophysical properties, magnetic nanoparticle (MNP) solution and magnetic field characteristics. This manuscript aims to ascertain the optimum conditions for magnetothermal neuromodulation. Hence, we employ a comprehensive modeling and utilize finite element method (FEM) for simulations to obtain the temperature distribution across the exposed tissue by which the lesion size is evaluated. The results are compared against experimental data in the literature. Local temperature distribution demonstrates an elevated temperature of 57 °C particularly, at the center of the injected solution after exposure. It is shown that a high fraction of the tissue around the injected magnetic nanoparticle solution is damaged mainly due to crossing the safe temperature domain (43 °C < T_tissue < 50 °C). In this investigation, we advance an optimized approach to a theoretical model of neuromodulation, based on Pennes’ equation, that includes a novel stimulation constraint. We establish several new results with this technique; in particular, we demonstrate: the method can be utilized to compute optimized parameter values. Consequently, the minimum necessary activation temperature for magnetothermal stimulation is achieved. Meanwhile, the underlying biomaterial is maintained at low levels of thermal-induced damage.

Statement of Significance

The invasiveness of conventional therapy for neurodegenerative diseases has prompted neuroscientists to discover a new treatment with the least side effects. Magnetothermal stimulation as a great potential alternative, utilizes nano-transducers to convert magnetic field energy to heat and activate targeted neurons. This technique has exhibited promising test results that ameliorates the symptoms. This manuscript by employing an optimization method and damage analysis, establishes the methodology to diminish the adverse impacts of magnetothermal stimulation. The optimum stimulation was established which satisfies the neuron activation requirement while causing the least damage on the targeted brain tissue.