In Vitro Models for Biomechanical Studies of Neural Tissues

Thermosensitive chitosan-based hydrogels supporting motor neuron-like NSC-34 cell differentiation

Motor neuron diseases are neurodegenerative diseases that predominantly affect the neuromuscular system. To date, there are no valid therapeutic treatments for such diseases, and the classical experimental models fail in faithfully reproducing the pathological mechanisms behind them. In this regard, the use of three-dimensional (3D) culture systems, which more closely reproduce the native in vivo environment, can be a promising approach. Hydrogel-based systems are among the most used materials to reproduce the extracellular matrix, featuring an intrinsic similarity with its physiological characteristics. In this study, we developed a thermosensitive chitosan-based hydrogel combined with β-glycerophosphate (βGP) and sodium hydrogen carbonate (SHC), which give the system optimal mechanical properties and injectability, inducing the hydrogel sol-gel transition at 37 °C. An ad hoc protocol for the preparation of the hydrogel was established in order to obtain a highly homogeneous system, leading to reproducible physicochemical characteristics and easy cell encapsulation. All formulations supported the viability of a neuroblastoma/spinal cord hybrid cell line (NSC-34) beyond two weeks of culture and enabled cell differentiation towards a motor neuron-like morphology, characterized by the presence of extended neurites. Based on our results, these hydrogels represent excellent candidates for establishing 3D in vitro models of motor neuron diseases.

Emerging Brain-Pathophysiology-Mimetic Platforms for Studying Neurodegenerative Diseases: Brain Organoids and Brains-on-a-Chip

Neurodegenerative diseases are a group of disorders characterized by progressive degeneration of the structural and functional integrity of the central and peripheral nervous systems. Millions of people suffer from degenerative brain diseases worldwide, and the mortality continues to increase every year, causing a growing demand for knowledge of the underlying mechanisms and development of therapeutic targets. Conventional 2D-based cell culture platforms and animal models cannot fully recapitulate the pathophysiology, and this has limited the capability for estimating drug efficacy. Recently, engineered platforms, including brain organoids and brain-on-a-chip, have emerged. They mimic the physiology of brain tissue and reflect the fundamental pathophysiological signatures of neurodegenerative diseases, such as the accumulation of neurotoxic proteins, structural abnormalities, and functional loss. In this paper, recent advances in brain-mimetic platforms and their potential for modeling features of neurodegenerative diseases in vitro are reviewed. The development of a physiologically relevant model should help overcome unresolved neurodegenerative diseases.

Modelling human CNS injury with human neural stem cells in 2- and 3-Dimensional cultures

The adult human central nervous system (CNS) has very limited regenerative capability, and injury at the cellular and molecular level cannot be studied in vivo. Modelling neural damage in human systems is crucial to identifying species-specific responses to injury and potentially neurotoxic compounds leading to development of more effective neuroprotective agents. Hence we developed human neural stem cell (hNSC) 3-dimensional (3D) cultures and tested their potential for modelling neural insults, including hypoxic-ischaemic and Ca2+-dependent injury. Standard 3D conditions for rodent cells support neuroblastoma lines used as human CNS models, but not hNSCs, but in all cases changes in culture architecture alter gene expression. Importantly, response to damage differs in 2D and 3D cultures and this is not due to reduced drug accessibility. Together, this study highlights the impact of culture cytoarchitecture on hNSC phenotype and damage response, indicating that 3D models may be better predictors of in vivo response to damage and compound toxicity.

2D versus 3D human induced pluripotent stem cell-derived cultures for neurodegenerative disease modelling

Neurodegenerative diseases, such as Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD) and amyotrophic lateral sclerosis (ALS), affect millions of people every year and so far, there are no therapeutic cures available. Even though animal and histological models have been of great aid in understanding disease mechanisms and identifying possible therapeutic strategies, in order to find disease-modifying solutions there is still a critical need for systems that can provide more predictive and physiologically relevant results. One possible avenue is the development of patient-derived models, e.g. by reprogramming patient somatic cells into human induced pluripotent stem cells (hiPSCs), which can then be differentiated into any cell type for modelling. These systems contain key genetic information from the donors, and therefore have enormous potential as tools in the investigation of pathological mechanisms underlying disease phenotype, and progression, as well as in drug testing platforms. hiPSCs have been widely cultured in 2D systems, but in order to mimic human brain complexity, 3D models have been proposed as a more advanced alternative. This review will focus on the use of patient-derived hiPSCs to model AD, PD, HD and ALS. In brief, we will cover the available stem cells, types of 2D and 3D culture systems, existing models for neurodegenerative diseases, obstacles to model these diseases in vitro, and current perspectives in the field.

Do blast induced skull flexures result in axonal deformation?

Subject-specific computer models (male and female) of the human head were used to investigate the possible axonal deformation resulting from the primary phase blast-induced skull flexures. The corresponding axonal tractography was explicitly incorporated into these finite element models using a recently developed technique based on the embedded finite element method. These models were subjected to extensive verification against experimental studies which examined their pressure and displacement response under a wide range of loading conditions. Once verified, a parametric study was developed to investigate the axonal deformation for a wide range of loading overpressures and directions as well as varying cerebrospinal fluid (CSF) material models. This study focuses on early times during a blast event, just as the shock transverses the skull (< 5 milliseconds). Corresponding boundary conditions were applied to eliminate the rotation effects and the resulting axonal deformation. A total of 138 simulations were developed– 128 simulations for studying the different loading scenarios and 10 simulations for studying the effects of CSF material model variance–leading to a total of 10,702 simulation core hours. Extreme strains and strain rates along each of the fiber tracts in each of these scenarios were documented and presented here. The results suggest that the blast-induced skull flexures result in strain rates as high as 150–378 s^-1. These high-strain rates of the axonal fiber tracts, caused by flexural displacement of the skull, could lead to a rate dependent micro-structural axonal damage, as pointed by other researchers.

Multiscale modeling in the clinic: diseases of the brain and nervous system

Computational neuroscience is a field that traces its origins to the efforts of Hodgkin and Huxley, who pioneered quantitative analysis of electrical activity in the nervous system. While also continuing as an independent field, computational neuroscience has combined with computational systems biology, and neural multiscale modeling arose as one offshoot. This consolidation has added electrical, graphical, dynamical system, learning theory, artificial intelligence and neural network viewpoints with the microscale of cellular biology (neuronal and glial), mesoscales of vascular, immunological and neuronal networks, on up to macroscales of cognition and behavior. The complexity of linkages that produces pathophysiology in neurological, neurosurgical and psychiatric disease will require multiscale modeling to provide understanding that exceeds what is possible with statistical analysis or highly simplified models: how to bring together pharmacotherapeutics with neurostimulation, how to personalize therapies, how to combine novel therapies with neurorehabilitation, how to interlace periodic diagnostic updates with frequent reevaluation of therapy, how to understand a physical disease that manifests as a disease of the mind. Multiscale modeling will also help to extend the usefulness of animal models of human diseases in neuroscience, where the disconnects between clinical and animal phenomenology are particularly pronounced. Here we cover areas of particular interest for clinical application of these new modeling neurotechnologies, including epilepsy, traumatic brain injury, ischemic disease, neurorehabilitation, drug addiction, schizophrenia and neurostimulation.

Modeling the mechanics of axonal fiber tracts using the embedded finite element method

A subject-specific human head finite element model with embedded axonal fiber tractography obtained from diffusion tensor imaging was developed. The axonal fiber tractography finite element model was coupled with the volumetric elements in the head model using the embedded element method. This technique enables the calculation of axonal strains and real-time tracking of the mechanical response of the axonal fiber tracts. The coupled model was then verified using pressure and relative displacement-based (between skull and brain) experimental studies and was employed to analyze a head impact, demonstrating the applicability of this method in studying axonal injury. Following this, a comparison study of different injury criteria was performed. This model was used to determine the influence of impact direction on the extent of the axonal injury. The results suggested that the lateral impact loading is more dangerous compared to loading in the sagittal plane, a finding in agreement with previous studies. Through this analysis, we demonstrated the viability of the embedded element method as an alternative numerical approach for studying axonal injury in patient-specific human head models.

Toward modeling the human nervous system in a dish: recent progress and outstanding challenges

Studying the cellular and molecular bases governing development, and normal and abnormal functions of the human CNS is hampered by its complexity and the very limited possibility of experimentally manipulating it in vivo. Development of 3D, tissue-like culture systems offers much promise for boosting our understanding of human neural development, birth defects, neurodegenerative diseases and neural injury, and for providing platforms that will more accurately predict efficacy of putative therapeutic compounds and assess responses to potentially neurotoxic agents. Although novel technological developments and a more interdisciplinary approach to modeling the human CNS are accelerating the pace of discovery, increasing the complexity of in vitro systems increases the ordeals to be overcome to establish highly reproducible models amenable to quantitative analysis.

White Matter Injury Susceptibility via Fiber Strain Evaluation Using Whole-Brain Tractography

Microscale brain injury studies suggest axonal elongation as a potential mechanism for diffuse axonal injury (DAI). Recent studies have begun to incorporate white matter (WM) structural anisotropy in injury analysis, with initial evidence suggesting improved injury prediction performance. In this study, we further develop a tractography-based approach to analyze fiber strains along the entire lengths of fibers from voxel- or anatomically constrained whole-brain tractography. This technique potentially extends previous element- or voxel-based methods that instead utilize WM fiber orientations averaged from typically coarse elements or voxels. Perhaps more importantly, incorporating tractography-based axonal structural information enables assessment of the overall injury risks to functionally important neural pathways and the anatomical regions they connect, which is not possible with previous methods. A DAI susceptibility index was also established to quantify voxel-wise WM local structural integrity and tract-wise damage of individual neural pathways. This "graded" injury susceptibility potentially extends the commonly employed treatment of injury as a simple binary condition. As an illustration, we evaluate the DAI susceptibilities of WM voxels and transcallosal fiber tracts in three idealized head impacts. Findings suggest the potential importance of the tractography-based approach for injury prediction. These efforts may enable future studies to correlate WM mechanical responses with neuroimaging, cognitive alteration, and concussion, and to reveal the relative vulnerabilities of neural pathways and identify the most vulnerable ones in real-world head impacts.

Real-time, whole-brain, temporally resolved pressure responses in translational head impact

Theoretical debate still exists on the role of linear acceleration ( a lin) on the risk of brain injury. Recent injury metrics only consider head rotational acceleration ( a rot) but not a lin, despite that real-world on-field head impacts suggesting a lin significantly improves a concussion risk function. These controversial findings suggest a practical challenge in integrating theory and real-world experiment. Focusing on tissue-level mechanical responses estimated from finite-element (FE) models of the human head, rather than impact kinematics alone, may help address this debate. However, the substantial computational cost incurred (runtime and hardware) poses a significant barrier for their practical use. In this study, we established a real-time technique to estimate whole-brain a lin-induced pressures. Three hydrostatic atlas pressures corresponding to translational impacts (referred to as 'brain print') along the three major axes were pre-computed. For an arbitrary a lin profile at any instance in time, the atlas pressures were linearly scaled and then superimposed to estimate whole-brain responses. Using 12 publically available, independently measured or reconstructed real-world a lin profiles representative of a range of impact/injury scenarios, the technique was successfully validated (except for one case with an extremely short impulse of approx. 1 ms). The computational cost to estimate whole-brain pressure responses for an entire a lin profile was less than 0.1 s on a laptop versus typically hours on a high-end multicore computer. These findings suggest the potential of the simple, yet effective technique to enable future studies to focus on tissue-level brain responses, rather than solely relying on global head impact kinematics that have plagued early and contemporary brain injury research to date.

A Pre-computed Brain Response Atlas for Instantaneous Strain Estimation in Contact Sports

Finite element models of the human head play an important role in investigating the mechanisms of traumatic brain injury, including sports concussion. A critical limitation, however, is that they incur a substantial computational cost to simulate even a single impact. Therefore, current simulation schemes significantly hamper brain injury studies based on model-estimated tissue-level responses. In this study, we present a pre-computed brain response atlas (pcBRA) to substantially increase the simulation efficiency in estimating brain strains using isolated rotational acceleration impulses parameterized with four independent variables (peak magnitude and duration, and rotational axis azimuth and elevation angles) with values determined from on-field measurements. Using randomly generated testing datasets, the partially established pcBRA achieved a 100% success rate in interpolation based on element-wise differences in accumulated peak strain ([Formula: see text]) according to a "double-10%" criterion or average regional [Formula: see text] in generic regions and the corpus callosum. A similar performance was maintained in extrapolation. The pcBRA performance was further successfully validated against directly simulated responses from two independently measured typical real-world rotational profiles. The computational cost to estimate element-wise whole-brain or regional [Formula: see text] was 6 s and <0.01 s, respectively, vs. ~50 min directly simulating a 40 ms impulse. These findings suggest the pcBRA could substantially increase the throughput in impact simulation without significant loss of accuracy from the estimation itself and, thus, its potential to accelerate the exploration of the mechanisms of sports concussion in general. If successful, the pcBRA may also become a diagnostic adjunct in conjunction with sensors that measure head impact kinematics on the field to objectively monitor and identify tissue-level brain trauma in real-time for "return-to-play" decision-making on the sideline.

Head impact accelerations for brain strain-related responses in contact sports: a model-based investigation

Both linear [Formula: see text] and rotational [Formula: see text] accelerations contribute to head impacts on the field in contact sports; however, they are often isolated in injury studies. It is critical to evaluate the feasibility of estimating brain responses using isolated instead of full degrees-of-freedom (DOFs) accelerations. In this study, we investigated the sensitivities of regional brain strain-related responses to resultant [Formula: see text] and [Formula: see text] as well as the relative contributions of these acceleration components to the responses via random sampling and linear regression using parameterized, triangulated head impacts with kinematic variable values based on on-field measurements. Two independently established and validated finite element models of the human head were employed to evaluate model-consistency and dependency in results: the Dartmouth Head Injury Model and Simulated Injury Monitor. For the majority of the brain, volume-weighted regional peak strain, strain rate, and von Mises stress accumulated from the simulation significantly correlated with the product of the magnitude and duration of [Formula: see text], or effectively, the rotational velocity, but not to [Formula: see text]. Responses from [Formula: see text]-only were comparable to the full-DOF counterparts especially when normalized by injury-causing thresholds (e.g., volume fractions of large differences virtually diminished (i.e., [Formula: see text]1 %) at typical difference percentage levels of 1-4 % on average). These model-consistent results support the inclusion of both rotational acceleration magnitude and duration into kinematics-based injury metrics and demonstrate the feasibility of estimating strain-related responses from isolated [Formula: see text] for analyses of strain-induced injury relevant to contact sports without significant loss of accuracy, especially for the cerebrum.

Millimeter-sized neural building blocks for 3D heterogeneous neural network assembly

A millimeter-sized neural building block (NBB) shows high versatility to form a 3D heterogeneous neural component. A millimeter-sized 3D neural network between heterogeneous neural tissues is established, and an efficient technique is then developed to observe the spatiotemporal metrological changes of single neuron in the NBB. This technique allows the visualization of axonal extension, dendritic branching, and morphological changes of presynaptic components and synapses in real time.