1
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Bradfield C, Voo L, Bhaduri A, Ramesh KT. Validation of a computational biomechanical mouse brain model for rotational head acceleration. Biomech Model Mechanobiol 2024; 23:1347-1367. [PMID: 38662175 DOI: 10.1007/s10237-024-01843-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 03/17/2024] [Indexed: 04/26/2024]
Abstract
Recent mouse brain injury experiments examine diffuse axonal injury resulting from accelerative head rotations. Evaluating brain deformation during these events would provide valuable information on tissue level thresholds for brain injury, but there are many challenges to imaging the brain's mechanical response during dynamic loading events, such as a blunt head impact. To address this shortcoming, we present an experimentally validated computational biomechanics model of the mouse brain that predicts tissue deformation, given the motion of the mouse head during laboratory experiments. First, we developed a finite element model of the mouse brain that computes tissue strains, given the same head rotations as previously conducted in situ hemicephalic mouse brain experiments. Second, we calibrated the model using a single brain segment, and then validated the model based on the spatial and temporal strain responses of other regions. The result is a computational tool that will provide researchers with the ability to predict brain tissue strains that occur during mouse laboratory experiments, and to link the experiments to the resulting neuropathology, such as diffuse axonal injury.
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Affiliation(s)
- Connor Bradfield
- Applied Physics Laboratory, Johns Hopkins University, Laurel, MD, 20723, USA, 11100 Johns Hopkins Road.
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA, 3400 North Charles Street.
| | - Liming Voo
- Applied Physics Laboratory, Johns Hopkins University, Laurel, MD, 20723, USA, 11100 Johns Hopkins Road
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA, 3400 North Charles Street
| | - Anindya Bhaduri
- Department of Civil Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA, 3400 North Charles Street
| | - K T Ramesh
- Applied Physics Laboratory, Johns Hopkins University, Laurel, MD, 20723, USA, 11100 Johns Hopkins Road
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA, 3400 North Charles Street
- Hopkins Extreme Materials Institute, Johns Hopkins University, Baltimore, MD, 21218, USA, 3400 North Charles Street
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2
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Li W, Shepherd DET, Espino DM. Frequency and time dependent viscoelastic characterization of pediatric porcine brain tissue in compression. Biomech Model Mechanobiol 2024; 23:1197-1207. [PMID: 38483696 DOI: 10.1007/s10237-024-01833-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 02/20/2024] [Indexed: 08/24/2024]
Abstract
Understanding the viscoelastic behavior of pediatric brain tissue is critical to interpret how external mechanical forces affect head injury in children. However, knowledge of the viscoelastic properties of pediatric brain tissue is limited, and this reduces the biofidelity of developed numeric simulations of the pediatric head in analysis of brain injury. Thus, it is essential to characterize the viscoelastic behavior of pediatric brain tissue in various loading conditions and to identify constitutive models. In this study, the pediatric porcine brain tissue was investigated in compression with determine the viscoelasticity under small and large strain, respectively. A range of frequencies between 0.1 and 40 Hz was applied to determine frequency-dependent viscoelastic behavior via dynamic mechanical analysis, while brain samples were divided into three strain rate groups of 0.01/s, 1/s and 10/s for compression up to 0.3 strain level and stress relaxation to obtain time-dependent viscoelastic properties. At frequencies above 20 Hz, the storage modulus did not increase, while the loss modulus increased continuously. With strain rate increasing from 0.01/s to 10/s, the mean stress at 0.1, 0.2 and 0.3 strain increased to approximate 6.8, 5.6 and 4.4 times, respectively. The brain compressive response was sensitive to strain rate and frequency. The characterization of brain tissue will be valuable for development of head protection systems and prediction of brain injury.
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Affiliation(s)
- Weiqi Li
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.
| | - Duncan E T Shepherd
- Department of Mechanical Engineering, University of Birmingham, Birmingham, B15 2TT, UK
| | - Daniel M Espino
- Department of Mechanical Engineering, University of Birmingham, Birmingham, B15 2TT, UK
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3
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Bradfield C, Voo L, Drewry D, Koliatsos V, Ramesh KT. Dynamic strain fields of the mouse brain during rotation. Biomech Model Mechanobiol 2024; 23:397-412. [PMID: 37891395 DOI: 10.1007/s10237-023-01781-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 10/06/2023] [Indexed: 10/29/2023]
Abstract
Mouse models are used to better understand brain injury mechanisms in humans, yet there is a limited understanding of biomechanical relevance, beginning with how the murine brain deforms when the head undergoes rapid rotation from blunt impact. This problem makes it difficult to translate some aspects of diffuse axonal injury from mouse to human. To address this gap, we present the two-dimensional strain field of the mouse brain undergoing dynamic rotation in the sagittal plane. Using a high-speed camera with digital image correlation measurements of the exposed mid-sagittal brain surface, we found that pure rotations (no direct impact to the skull) of 100-200 rad/s are capable of producing complex strain fields that evolve over time with respect to rotational acceleration and deceleration. At the highest rotational velocity tested, the largest tensile strains (≥ 21% elongation) in selected regions of the mouse brain approach strain thresholds previously associated with axonal injury in prior work. These findings provide a benchmark to validate the mechanical response in biomechanical computational models predicting diffuse axonal injury, but much work remains in correlating tissue deformation patterns from computational models with underlying neuropathology.
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Affiliation(s)
- Connor Bradfield
- Applied Physics Laboratory, Johns Hopkins University, 11100 Johns Hopkins Road, Laurel, MD, 20723, USA.
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA.
| | - Liming Voo
- Applied Physics Laboratory, Johns Hopkins University, 11100 Johns Hopkins Road, Laurel, MD, 20723, USA
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - David Drewry
- Applied Physics Laboratory, Johns Hopkins University, 11100 Johns Hopkins Road, Laurel, MD, 20723, USA
| | - Vassilis Koliatsos
- Division of Neuropathology, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - K T Ramesh
- Applied Physics Laboratory, Johns Hopkins University, 11100 Johns Hopkins Road, Laurel, MD, 20723, USA
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
- Hopkins Extreme Materials Institute, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA
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4
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Li Y, Zhang Q, Zhao J, Wang Z, Zong X, Yang L, Zhang C, Zhao H. Mechanical behavior and microstructure of porcine brain tissues under pulsed electric fields. Biomech Model Mechanobiol 2024; 23:241-254. [PMID: 37861916 DOI: 10.1007/s10237-023-01771-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 08/29/2023] [Indexed: 10/21/2023]
Abstract
Pulsed electric fields are extensively utilized in clinical treatments, such as subthalamic deep brain stimulation, where electric field loading is in direct contact with brain tissue. However, the alterations in brain tissue's mechanical properties and microstructure due to changes in electric field parameters have not received adequate attention. In this study, the mechanical properties and microstructure of the brain tissue under pulsed electric fields were focused on. Herein, a custom indentation device was equipped with a module for electric field loading. Parameters such as pulse amplitude and frequency were adjusted. The results demonstrated that following an indentation process lasting 5 s and reaching a depth of 1000 μm, and a relaxation process of 175 s, the average shear modulus of brain tissue was reduced, and viscosity decreased. At the same amplitude, high-frequency pulsed electric fields had a smaller effect on brain tissue than low-frequency ones. Furthermore, pulsed electric fields induced cell polarization and reduced the proteoglycan concentration in brain tissue. As pulse frequency increased, cell polarization diminished, and proteoglycan concentration decreased significantly. High-frequency pulsed electric fields applied to brain tissue were found to reduce impedance fluctuation amplitude. This study revealed the effect of pulsed electric fields on the mechanical properties and microstructure of ex vivo brain tissue, providing essential information to promote the advancement of brain tissue electrotherapy in clinical settings.
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Affiliation(s)
- Yiqiang Li
- School of Mechanical & Aerospace Engineering, Jilin University, 5988 Renmin Street, Changchun, 130025, People's Republic of China
- Key Laboratory of CNC Equipment Reliability, Ministry of Education, Jilin University, 5988 Renmin Street, Changchun, 130025, People's Republic of China
| | - Qixun Zhang
- School of Mechanical & Aerospace Engineering, Jilin University, 5988 Renmin Street, Changchun, 130025, People's Republic of China
- Key Laboratory of CNC Equipment Reliability, Ministry of Education, Jilin University, 5988 Renmin Street, Changchun, 130025, People's Republic of China
- Chongqing Research Institute, Jilin University, Chongqing, 401100, People's Republic of China
| | - Jiucheng Zhao
- School of Mechanical & Aerospace Engineering, Jilin University, 5988 Renmin Street, Changchun, 130025, People's Republic of China
- Key Laboratory of CNC Equipment Reliability, Ministry of Education, Jilin University, 5988 Renmin Street, Changchun, 130025, People's Republic of China
| | - Zhaoxin Wang
- School of Mechanical & Aerospace Engineering, Jilin University, 5988 Renmin Street, Changchun, 130025, People's Republic of China
- Key Laboratory of CNC Equipment Reliability, Ministry of Education, Jilin University, 5988 Renmin Street, Changchun, 130025, People's Republic of China
| | - Xiangyu Zong
- School of Mechanical & Aerospace Engineering, Jilin University, 5988 Renmin Street, Changchun, 130025, People's Republic of China
- Key Laboratory of CNC Equipment Reliability, Ministry of Education, Jilin University, 5988 Renmin Street, Changchun, 130025, People's Republic of China
| | - Li Yang
- School of Mechanical & Aerospace Engineering, Jilin University, 5988 Renmin Street, Changchun, 130025, People's Republic of China
- Key Laboratory of Zoonosis Research, Ministry of Education, Institute of Zoonosis, College of Veterinary Medicine, Jilin University, Changchun, 130062, People's Republic of China
| | - Chi Zhang
- School of Mechanical & Aerospace Engineering, Jilin University, 5988 Renmin Street, Changchun, 130025, People's Republic of China.
- Key Laboratory of CNC Equipment Reliability, Ministry of Education, Jilin University, 5988 Renmin Street, Changchun, 130025, People's Republic of China.
| | - Hongwei Zhao
- School of Mechanical & Aerospace Engineering, Jilin University, 5988 Renmin Street, Changchun, 130025, People's Republic of China.
- Key Laboratory of CNC Equipment Reliability, Ministry of Education, Jilin University, 5988 Renmin Street, Changchun, 130025, People's Republic of China.
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Zhang X, Weickenmeier J. Brain Stiffness Follows Cuprizone-Induced Variations in Local Myelin Content. Acta Biomater 2023; 170:507-518. [PMID: 37660962 DOI: 10.1016/j.actbio.2023.08.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 08/08/2023] [Accepted: 08/17/2023] [Indexed: 09/05/2023]
Abstract
Brain maturation and neurological diseases are intricately linked to microstructural changes that inherently affect the brain's mechanical behavior. Animal models are frequently used to explore relative brain stiffness changes as a function of underlying microstructure. Here, we are using the cuprizone mouse model to study indentation-derived stiffness changes resulting from acute and chronic demyelination during a 15-week observation period. We focus on the corpus callosum, cingulum, and cortex which undergo different degrees of de- and remyelination and, therefore, result in region-specific stiffness changes. Mean stiffness of the corpus callosum starts at 1.1 ± 0.3 kPa in untreated mice, then cuprizone treatment causes stiffness to drop to 0.6 ± 0.1 kPa by week 3, temporarily increase to 0.9 ± 0.3 kPa by week 6, and ultimately stabilize around 0.7 ± 0.1 kPa by week 9 for the rest of the observation period. The cingulum starts at 3.2 ± 0.9 kPa, then drops to 1.6 ± 0.4 kPa by week 3, and then gradually stabilizes around 1.4 ± 0.3 kPa by week 9. Cortical stiffness exhibits less stiffness variations overall; it starts at 4.2 ± 1.3 kPa, drops to 2.4 ± 0.6 kPa by week 3, and stabilizes around 2.7 ± 0.9 kPa by week 6. We also assess the impact of tissue fixation on indentation-based mechanical tissue characterization. On the one hand, fixation drastically increases untreated mean tissue stiffness by a factor of 3.3 for the corpus callosum, 2.9 for the cingulum, and 3.6 for the cortex; on the other hand, fixation influences interregional stiffness ratios during demyelination, thus suggesting that fixation affects individual brain tissues differently. Lastly, we determine the spatial correlation between stiffness measurements and myelin density and observe a region-specific proportionality between myelin content and tissue stiffness. STATEMENT OF SIGNIFICANCE: Despite extensive work, the relationship between microstructure and mechanical behavior in the brain remains mostly unknown. Additionally, the existing variation of measurement results reported in literature requires in depth investigation of the impact of individual cell and protein populations on tissue stiffness and interregional stiffness ratios. Here, we used microindentation measurements to show that brain stiffness changes with myelin density in the cuprizone-based demyelination mouse model. Moreover, we explored the impact of tissue fixation prior to mechanical characterization because of conflicting results reported in literature. We observe that fixation has a distinctly different impact on our three regions of interest, thus causing region-specific tissue stiffening and, more importantly, changing interregional stiffness ratios.
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Affiliation(s)
- Xuesong Zhang
- Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ 07030 United States
| | - Johannes Weickenmeier
- Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ 07030 United States.
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Morrison O, Destrade M, Tripathi BB. An atlas of the heterogeneous viscoelastic brain with local power-law attenuation synthesised using Prony-series. Acta Biomater 2023; 169:66-87. [PMID: 37507033 DOI: 10.1016/j.actbio.2023.07.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 07/16/2023] [Accepted: 07/24/2023] [Indexed: 07/30/2023]
Abstract
This review addresses the acute need to acknowledge the mechanical heterogeneity of brain matter and to accurately calibrate its local viscoelastic material properties accordingly. Specifically, it is important to compile the existing and disparate literature on attenuation power-laws and dispersion to make progress in wave physics of brain matter, a field of research that has the potential to explain the mechanisms at play in diffuse axonal injury and mild traumatic brain injury in general. Currently, viscous effects in the brain are modelled using Prony-series, i.e., a sum of decaying exponentials at different relaxation times. Here we collect and synthesise the Prony-series coefficients appearing in the literature for twelve regions: brainstem, basal ganglia, cerebellum, corona radiata, corpus callosum, cortex, dentate gyrus, hippocampus, thalamus, grey matter, white matter, homogeneous brain, and for eight different mammals: pig, rat, human, mouse, cow, sheep, monkey and dog. Using this data, we compute the fractional-exponent attenuation power-laws for different tissues of the brain, the corresponding dispersion laws resulting from causality, and the averaged Prony-series coefficients. STATEMENT OF SIGNIFICANCE: Traumatic brain injuries are considered a silent epidemic and finite element methods (FEMs) are used in modelling brain deformation, requiring access to viscoelastic properties of brain. To the best of our knowledge, this work presents 1) the first multi-frequency viscoelastic atlas of the heterogeneous brain, 2) the first review focusing on viscoelastic modelling in both FEMs and experimental works, 3) the first attempt to conglomerate the disparate existing literature on the viscoelastic modelling of the brain and 4) the largest collection of viscoelastic parameters for the brain (212 different Prony-series spanning 12 different tissues and 8 different animal surrogates). Furthermore, this work presents the first brain atlas of attenuation power-laws essential for modelling shear waves in brain.
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Affiliation(s)
- Oisín Morrison
- School of Mathematical and Statistical Sciences, University of Galway, University Road, Galway, Ireland
| | - Michel Destrade
- School of Mathematical and Statistical Sciences, University of Galway, University Road, Galway, Ireland
| | - Bharat B Tripathi
- School of Mathematical and Statistical Sciences, University of Galway, University Road, Galway, Ireland.
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7
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Wang P, Yan Z, Du Z, Fu Y, Liu Z, Qu S, Zhuang Z. A Bayesian method with nonlinear noise model to calibrate constitutive parameters of soft tissue. J Mech Behav Biomed Mater 2023; 146:106070. [PMID: 37567066 DOI: 10.1016/j.jmbbm.2023.106070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 07/23/2023] [Accepted: 08/06/2023] [Indexed: 08/13/2023]
Abstract
The measured mechanical responses of soft tissue exhibit large variability and errors, especially for the softest brain tissue, while calibrating its constitutive parameters in a deterministic way remains a common practice. Here we implement a Bayesian method considering the nonlinear noise model to calibrate constitutive parameters of brain tissue. A probability model is first developed based on the measured experimental data, likelihood function, and prior function, from which the posterior distributions of model parameters are formulated. The likelihood function considers the nonlinear behaviors of the constitutive response and noise distribution of the experimentally measured data. Meanwhile, the prior predictive distribution is computed to check the probability model preliminarily. Secondly, the Markov Chain Monte Carlo (MCMC) method is used to compute the posterior distributions of model parameters, enabling assessment of parameter uncertainty, correlation, and model calibration error. Finally, the posterior predictive distributions of the overall response, constitutive response, and noise response are computed to validate the probabilistic model, all of which are consistent with the corresponding data. Furthermore, the effect of the prior distribution, experimental data, and noise model on posterior distribution is studied. Our study provides a general approach to calibrating constitutive parameters of soft tissue despite errors and large variability in experimental data.
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Affiliation(s)
- Peng Wang
- School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai, 200092, China
| | - Ziming Yan
- Applied Mechanics Laboratory, School of Aerospace Engineering, Tsinghua University, Beijing, 100084, China
| | - Zhibo Du
- Applied Mechanics Laboratory, School of Aerospace Engineering, Tsinghua University, Beijing, 100084, China
| | - Yimou Fu
- State Key Laboratory of Fluid Power & Mechatronic System, Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province, Center for X-Mechanics, Eye Center of the Second Affiliated Hospital, and Department of Engineering Mechanics, Zhejiang University, Hangzhou, 310027, China
| | - Zhanli Liu
- Applied Mechanics Laboratory, School of Aerospace Engineering, Tsinghua University, Beijing, 100084, China.
| | - Shaoxing Qu
- State Key Laboratory of Fluid Power & Mechatronic System, Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province, Center for X-Mechanics, Eye Center of the Second Affiliated Hospital, and Department of Engineering Mechanics, Zhejiang University, Hangzhou, 310027, China.
| | - Zhuo Zhuang
- Applied Mechanics Laboratory, School of Aerospace Engineering, Tsinghua University, Beijing, 100084, China
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8
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Exton J, Higgins JMG, Chen J. Acute brain slice elastic modulus decreases over time. Sci Rep 2023; 13:12826. [PMID: 37550376 PMCID: PMC10406937 DOI: 10.1038/s41598-023-40074-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 08/04/2023] [Indexed: 08/09/2023] Open
Abstract
A common benchmark in the brain tissue mechanics literature is that the properties of acute brain slices should be measured within 8 h of the experimental animal being sacrificed. The core assumption is that-since there is no substantial protein degradation during this time-there will be no change to elastic modulus. This assumption overlooks the possibility of other effects (such as osmotic swelling) that may influence the mechanical properties of the tissue. To achieve consistent and accurate analysis of brain mechanics, it is important to account for or mitigate these effects. Using atomic force microscopy (AFM), tissue hydration and volume measurements, we find that acute brain slices in oxygenated artificial cerebrospinal fluid (aCSF) with a standard osmolarity of 300 mOsm/l experience rapid swelling, softening, and increases in hydration within the first 2 hours after slicing. Reductions in elastic modulus can be partly mitigated by addition of chondroitinase ABC enzyme (CHABC). Increasing aCSF osmolarity to 400 mOsm/l does not prevent softening but may hasten equilibration of samples to a point where measurements of relative elastic modulus are consistent across experiments.
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Affiliation(s)
- John Exton
- School of Engineering, Newcastle University, Newcastle Upon Tyne, NE1 7RU, UK
| | - Jonathan M G Higgins
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Framlington Place, Newcastle Upon Tyne, NE2 4HH, UK
| | - Jinju Chen
- School of Engineering, Newcastle University, Newcastle Upon Tyne, NE1 7RU, UK.
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Carmo GP, Dymek M, Ptak M, Alves-de-Sousa RJ, Fernandes FAO. Development, validation and a case study: The female finite element head model (FeFEHM). COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 231:107430. [PMID: 36827824 DOI: 10.1016/j.cmpb.2023.107430] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 01/18/2023] [Accepted: 02/16/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Traumatic brain injuries are one of the leading causes of death and disability in the world. To better understand the interactions and forces applied in different constituents of the human head, several finite element head models have been developed throughout the years, for offering a good cost-effective and ethical approach compared to experimental tests. Once validated, the female finite element head model (FeFEHM) will allow a better understanding of injury mechanisms resulting in neuronal damage, which can later evolve into neurodegenerative diseases. METHODS This work encompasses the approached methodology starting from medical images and finite element modelling until the validation process using novel experimental data of brain displacements conducted on human cadavers. The material modelling of the brain is performed using an age-specific characterization of the brain using microindentation at dynamic rates and under large deformation, with a similar age to the patient used to model the FeFEHM. RESULTS The numerical displacement curves are in good accordance with the experimental data, displaying similar peak times and values, in all three anatomical planes. The case study result shows a similarity between the pressure fields of the FeFEHM compared to another model, highlighting the future potential of the model. CONCLUSIONS The initial objective was met, and a new female finite element head model has been developed with biofidelic brain motion. This model will be used for the assessment of repetitive impact scenarios and its repercussions on the female brain.
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Affiliation(s)
- Gustavo P Carmo
- Centre for Mechanical Technology and Automation (TEMA), Department of Mechanical Engineering, Campus Universitário de Santiago, University of Aveiro, Aveiro 3810-193, Portugal; LASI-Intelligent Systems Associate Laboratory, Portugal.
| | - Mateusz Dymek
- Faculty of Mechanical Engineering, Wroclaw University of Science and Technology, Łukasiewicza 5/7, Wrocław 50-370, Poland
| | - Mariusz Ptak
- Faculty of Mechanical Engineering, Wroclaw University of Science and Technology, Łukasiewicza 5/7, Wrocław 50-370, Poland
| | - Ricardo J Alves-de-Sousa
- Centre for Mechanical Technology and Automation (TEMA), Department of Mechanical Engineering, Campus Universitário de Santiago, University of Aveiro, Aveiro 3810-193, Portugal; LASI-Intelligent Systems Associate Laboratory, Portugal
| | - Fábio A O Fernandes
- Centre for Mechanical Technology and Automation (TEMA), Department of Mechanical Engineering, Campus Universitário de Santiago, University of Aveiro, Aveiro 3810-193, Portugal; LASI-Intelligent Systems Associate Laboratory, Portugal
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10
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Material properties of human brain tissue suitable for modelling traumatic brain injury. BRAIN MULTIPHYSICS 2022. [DOI: 10.1016/j.brain.2022.100059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
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11
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Faber J, Hinrichsen J, Greiner A, Reiter N, Budday S. Tissue-Scale Biomechanical Testing of Brain Tissue for the Calibration of Nonlinear Material Models. Curr Protoc 2022; 2:e381. [PMID: 35384412 DOI: 10.1002/cpz1.381] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 01/14/2022] [Indexed: 06/14/2023]
Abstract
Brain tissue is one of the most complex and softest tissues in the human body. Due to its ultrasoft and biphasic nature, it is difficult to control the deformation state during biomechanical testing and to quantify the highly nonlinear, time-dependent tissue response. In numerous experimental studies that have investigated the mechanical properties of brain tissue over the last decades, stiffness values have varied significantly. One reason for the observed discrepancies is the lack of standardized testing protocols and corresponding data analyses. The tissue properties have been tested on different length and time scales depending on the testing technique, and the corresponding data have been analyzed based on simplifying assumptions. In this review, we highlight the advantage of using nonlinear continuum mechanics based modeling and finite element simulations to carefully design experimental setups and protocols as well as to comprehensively analyze the corresponding experimental data. We review testing techniques and protocols that have been used to calibrate material model parameters and discuss artifacts that might falsify the measured properties. The aim of this work is to provide standardized procedures to reliably quantify the mechanical properties of brain tissue and to more accurately calibrate appropriate constitutive models for computational simulations of brain development, injury and disease. Computational models can not only be used to predictively understand brain tissue behavior, but can also serve as valuable tools to assist diagnosis and treatment of diseases or to plan neurosurgical procedures. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC.
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Affiliation(s)
- Jessica Faber
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute of Applied Mechanics, Egerlandstraße 5, 91058 Erlangen, Germany
| | - Jan Hinrichsen
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute of Applied Mechanics, Egerlandstraße 5, 91058 Erlangen, Germany
| | - Alexander Greiner
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute of Applied Mechanics, Egerlandstraße 5, 91058 Erlangen, Germany
| | - Nina Reiter
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute of Applied Mechanics, Egerlandstraße 5, 91058 Erlangen, Germany
| | - Silvia Budday
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute of Applied Mechanics, Egerlandstraße 5, 91058 Erlangen, Germany
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12
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Sundaresh SN, Finan JD, Elkin BS, Basilio AV, McKhann GM, Morrison B. Region-Dependent Viscoelastic Properties of Human Brain Tissue Under Large Deformations. Ann Biomed Eng 2022; 50:1452-1460. [PMID: 35034227 DOI: 10.1007/s10439-022-02910-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 01/01/2022] [Indexed: 11/01/2022]
Abstract
This study characterizes the mechanical properties of human brain tissue resected during the course of surgery under multistep indentation loading up to 30% strain. The experimental characterization using fresh, post-operative, human brain tissue is highly advantageous since postmortem times can affect its biomechanical behavior. Although the quasilinear theory of viscoelasticity (QLV) approach has been widely used to model brain tissue mechanical properties, our analysis concluded that the linear viscoelastic approach provided a better fit to the experimental data overall. The only statistically significant regional difference in observed stiffness was between the cortex gray and dentate gyrus. There were no statistically significant age or sex dependent differences, although the data suggested that the cortex white matter in males was stiffer than that in females. Our results can help improve the accuracy of finite element models of brain tissue deformation to predict its response to traumatic brain injury.
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Affiliation(s)
- Sowmya N Sundaresh
- Department of Biomedical Engineering, Columbia University, 351 Engineering Terrace MC 8904, 1210 Amsterdam Avenue, New York, NY, 10027, United States
| | - John D Finan
- Department of Biomedical Engineering, Columbia University, 351 Engineering Terrace MC 8904, 1210 Amsterdam Avenue, New York, NY, 10027, United States
| | - Benjamin S Elkin
- Department of Biomedical Engineering, Columbia University, 351 Engineering Terrace MC 8904, 1210 Amsterdam Avenue, New York, NY, 10027, United States
| | - Andrew V Basilio
- Department of Biomedical Engineering, Columbia University, 351 Engineering Terrace MC 8904, 1210 Amsterdam Avenue, New York, NY, 10027, United States
| | - Guy M McKhann
- Department of Neurological Surgery, Columbia University Medical Center, New York Presbyterian Hospital, 710 West 168th St, New York, NY, 10032, United States
| | - Barclay Morrison
- Department of Biomedical Engineering, Columbia University, 351 Engineering Terrace MC 8904, 1210 Amsterdam Avenue, New York, NY, 10027, United States.
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13
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Urcun S, Rohan PY, Sciumè G, Bordas SPA. Cortex tissue relaxation and slow to medium load rates dependency can be captured by a two-phase flow poroelastic model. J Mech Behav Biomed Mater 2021; 126:104952. [PMID: 34906865 DOI: 10.1016/j.jmbbm.2021.104952] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Revised: 10/16/2021] [Accepted: 10/27/2021] [Indexed: 11/28/2022]
Abstract
This paper investigates the complex time-dependent behavior of cortex tissue, under adiabatic condition, using a two-phase flow poroelastic model. Motivated by experiments and Biot's consolidation theory, we tackle time-dependent uniaxial loading, confined and unconfined, with various geometries and loading rates from 1μm/s to 100μm/s. The cortex tissue is modeled as the porous solid saturated by two immiscible fluids, with dynamic viscosities separated by four orders, resulting in two different characteristic times. These are respectively associated to interstitial fluid and glial cells. The partial differential equations system is discretized in space by the finite element method and in time by Euler-implicit scheme. The solution is computed using a monolithic scheme within the open-source computational framework FEniCS. The parameters calibration is based on Sobol sensitivity analysis, which divides them into two groups: the tissue specific group, whose parameters represent general properties, and sample specific group, whose parameters have greater variations. Our results show that the experimental curves can be reproduced without the need to resort to viscous solid effects, by adding an additional fluid phase. Through this process, we aim to present multiphase poromechanics as a promising way to a unified brain tissue modeling framework in a variety of settings.
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Affiliation(s)
- Stéphane Urcun
- Institute for Computational Engineering Sciences, Department of Engineering Sciences, Faculté des Sciences, de la Technologie et de Médecine, Université du Luxembourg, Campus Kirchberg, Luxembourg; Institut de Biomécanique Humaine Georges Charpak, Arts et Métiers ParisTech, Paris, France; Institut de Mécanique et d'Ingénierie (I2M), Univ. Bordeaux, CNRS, ENSAM, Bordeaux INP, Talence, France
| | - Pierre-Yves Rohan
- Institut de Biomécanique Humaine Georges Charpak, Arts et Métiers ParisTech, Paris, France
| | - Giuseppe Sciumè
- Institut de Mécanique et d'Ingénierie (I2M), Univ. Bordeaux, CNRS, ENSAM, Bordeaux INP, Talence, France
| | - Stéphane P A Bordas
- Institute for Computational Engineering Sciences, Department of Engineering Sciences, Faculté des Sciences, de la Technologie et de Médecine, Université du Luxembourg, Campus Kirchberg, Luxembourg.
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14
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Cabeza-Gil I, Calvo B, Rico A, Reinhards-Hervás C, Rodríguez J. Mechanical characterisation of hydrophobic and hydrophilic acrylates used in intraocular lenses through depth sensing indentation. J Mech Behav Biomed Mater 2021; 126:104997. [PMID: 34848137 DOI: 10.1016/j.jmbbm.2021.104997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 11/08/2021] [Accepted: 11/23/2021] [Indexed: 10/19/2022]
Abstract
In this work, the mechanical behaviour of hydrophilic and hydrophobic acrylates has been characterised by depth sensing indentation. Time-dependent behaviour has been studied using load-relaxation tests. Experiments have been simulated with a finite element software using a visco-hyperelastic material model. The parameters of this model have been determined using deep learning techniques. The developed material models have been used to mechanically simulate a standard compression test of a prototype intraocular lens.
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Affiliation(s)
- I Cabeza-Gil
- Aragon Institute of Engineering Research (i3A), University of Zaragoza, Spain
| | - B Calvo
- Aragon Institute of Engineering Research (i3A), University of Zaragoza, Spain; Centro de Investigacion Biomedica en Red en Bioingenieria, Biomateriales y Nanomedicina (CIBER-BBN), Spain
| | - A Rico
- Durability and Mechanical Integrity of Structural Materials, Rey Juan Carlos University, Spain
| | - C Reinhards-Hervás
- Durability and Mechanical Integrity of Structural Materials, Rey Juan Carlos University, Spain
| | - J Rodríguez
- Durability and Mechanical Integrity of Structural Materials, Rey Juan Carlos University, Spain.
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15
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Solano Fonseca R, Metang P, Egge N, Liu Y, Zuurbier KR, Sivaprakasam K, Shirazi S, Chuah A, Arneaud SL, Konopka G, Qian D, Douglas PM. Glycolytic preconditioning in astrocytes mitigates trauma-induced neurodegeneration. eLife 2021; 10:69438. [PMID: 34473622 PMCID: PMC8448530 DOI: 10.7554/elife.69438] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 08/24/2021] [Indexed: 01/02/2023] Open
Abstract
Concussion is associated with a myriad of deleterious immediate and long-term consequences. Yet the molecular mechanisms and genetic targets promoting the selective vulnerability of different neural subtypes to dysfunction and degeneration remain unclear. Translating experimental models of blunt force trauma in C. elegans to concussion in mice, we identify a conserved neuroprotective mechanism in which reduction of mitochondrial electron flux through complex IV suppresses trauma-induced degeneration of the highly vulnerable dopaminergic neurons. Reducing cytochrome C oxidase function elevates mitochondrial-derived reactive oxygen species, which signal through the cytosolic hypoxia inducing transcription factor, Hif1a, to promote hyperphosphorylation and inactivation of the pyruvate dehydrogenase, PDHE1α. This critical enzyme initiates the Warburg shunt, which drives energetic reallocation from mitochondrial respiration to astrocyte-mediated glycolysis in a neuroprotective manner. These studies demonstrate a conserved process in which glycolytic preconditioning suppresses Parkinson-like hypersensitivity of dopaminergic neurons to trauma-induced degeneration via redox signaling and the Warburg effect. Concussion is a type of traumatic brain injury that results from a sudden blow or jolt to the head. Symptoms can include a passing headache, dizziness, confusion or sensitivity to light, but experiencing multiple concussions can have drastic repercussions in later life. Studies of professional athletes have shown that those who experience one or more concussions are prone to developing Alzheimer’s and Parkinson’s disease, two well-known neurodegenerative diseases. Both conditions involve the progressive loss or breakdown of nerve cells, called neurons. But exactly how this so-called neurodegeneration of brain cells stems from the original, physical injury remains unclear. Head trauma may cause damage to the structural support of a cell or disrupt the flow of electrical impulses through neurons. Energy use and production in damaged cells could shift into overdrive to repair the damage. The chemical properties of different types of brain cells could also make some more vulnerable to trauma than others. Besides neurons, star-shaped support cells in the brain called astrocytes, which may have some protective ability, could also be affected. To investigate which cells may be more susceptible to traumatic injuries, Solano Fonseca et al. modelled the impacts of concussion-like head trauma in roundworms (C. elegans) and mice. In both animals, one type of neuron was extremely vulnerable to cell death after trauma. Neurons that release dopamine, a chemical involved in cell-to-cell communication and the brain’s reward system, showed signs of cell damage and deteriorated after injury. Dopaminergic cells, as these cells are called, are involved in motor coordination, and the loss of dopaminergic cells has been linked to both Alzheimer’s and Parkinson’s disease. Astrocytes, however, had a role in reducing the death of dopaminergic neurons after trauma. In experiments, astrocytes appeared to restore the balance of energy production to meet the increased energy demands of impacted neurons. Single-cell analyses showed that genes involved in metabolism were switched on in astrocytes to produce energy via an alternative pathway. This energetic shift facilitated via astrocytes may help mitigate against some damage to dopamine-producing neurons after trauma, reducing cell death. This work furthers our understanding of cellular changes in the concussed brain. More research will be required to better characterise how this immediate trauma to cells, and the subsequent loss of dopaminergic neurons, impacts brain health long-term. Efforts to design effective therapies to slow or reverse these changes could then follow.
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Affiliation(s)
- Rene Solano Fonseca
- Department of Molecular Biology, University of Texas Southwestern Medical Center, Dallas, United States
| | - Patrick Metang
- Department of Molecular Biology, University of Texas Southwestern Medical Center, Dallas, United States
| | - Nathan Egge
- Department of Molecular Biology, University of Texas Southwestern Medical Center, Dallas, United States
| | - Yingjian Liu
- Department of Mechanical Engineering, University of Texas at Dallas, Dallas, United States
| | - Kielen R Zuurbier
- Department of Molecular Biology, University of Texas Southwestern Medical Center, Dallas, United States.,O'Donnell Brain Institute, University of Texas Southwestern Medical Center, Dallas, United States
| | - Karthigayini Sivaprakasam
- O'Donnell Brain Institute, University of Texas Southwestern Medical Center, Dallas, United States.,Department of Neuroscience, University of Texas Southwestern Medical Center, Dallas, United States
| | - Shawn Shirazi
- Department of Integrative Biology, University of California, Berkeley, Berkeley, United States
| | - Ashleigh Chuah
- Department of Molecular Biology, University of Texas Southwestern Medical Center, Dallas, United States
| | - Sonja Lb Arneaud
- Department of Molecular Biology, University of Texas Southwestern Medical Center, Dallas, United States
| | - Genevieve Konopka
- O'Donnell Brain Institute, University of Texas Southwestern Medical Center, Dallas, United States.,Department of Neuroscience, University of Texas Southwestern Medical Center, Dallas, United States
| | - Dong Qian
- Department of Mechanical Engineering, University of Texas at Dallas, Dallas, United States
| | - Peter M Douglas
- Department of Molecular Biology, University of Texas Southwestern Medical Center, Dallas, United States.,Hamon Center for Regenerative Science and Medicine, University of Texas Southwestern Medical Center, Dallas, United States
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16
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Keating CE, Cullen DK. Mechanosensation in traumatic brain injury. Neurobiol Dis 2020; 148:105210. [PMID: 33259894 DOI: 10.1016/j.nbd.2020.105210] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 11/10/2020] [Accepted: 11/24/2020] [Indexed: 12/14/2022] Open
Abstract
Traumatic brain injury (TBI) is distinct from other neurological disorders because it is induced by a discrete event that applies extreme mechanical forces to the brain. This review describes how the brain senses, integrates, and responds to forces under both normal conditions and during injury. The response to forces is influenced by the unique mechanical properties of brain tissue, which differ by region, cell type, and sub-cellular structure. Elements such as the extracellular matrix, plasma membrane, transmembrane receptors, and cytoskeleton influence its properties. These same components also act as force-sensors, allowing neurons and glia to respond to their physical environment and maintain homeostasis. However, when applied forces become too large, as in TBI, these components may respond in an aberrant manner or structurally fail, resulting in unique pathological sequelae. This so-called "pathological mechanosensation" represents a spectrum of cellular responses, which vary depending on the overall biomechanical parameters of the injury and may be compounded by repetitive injuries. Such aberrant physical responses and/or damage to cells along with the resulting secondary injury cascades can ultimately lead to long-term cellular dysfunction and degeneration, often resulting in persistent deficits. Indeed, pathological mechanosensation not only directly initiates secondary injury cascades, but this post-physical damage environment provides the context in which these cascades unfold. Collectively, these points underscore the need to use experimental models that accurately replicate the biomechanics of TBI in humans. Understanding cellular responses in context with injury biomechanics may uncover therapeutic targets addressing various facets of trauma-specific sequelae.
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Affiliation(s)
- Carolyn E Keating
- Department of Neurosurgery, Center for Brain Injury and Repair, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Center for Neurotrauma, Neurodegeneration, and Restoration, Corporal Michael J. Crescenz VA Medical Center, USA
| | - D Kacy Cullen
- Department of Neurosurgery, Center for Brain Injury and Repair, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA; Center for Neurotrauma, Neurodegeneration, and Restoration, Corporal Michael J. Crescenz VA Medical Center, USA.
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17
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Lai C, Chen Y, Wang T, Liu J, Wang Q, Du Y, Feng Y. A machine learning approach for magnetic resonance image-based mouse brain modeling and fast computation in controlled cortical impact. Med Biol Eng Comput 2020; 58:2835-2844. [PMID: 32954460 DOI: 10.1007/s11517-020-02262-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2020] [Accepted: 08/29/2020] [Indexed: 10/23/2022]
Abstract
Computational modeling of the brain is crucial for the study of traumatic brain injury. An anatomically accurate model with refined details could provide the most accurate computational results. However, computational models with fine mesh details could take prolonged computation time that impedes the clinical translation of the models. Therefore, a way to construct a model with low computational cost while maintaining a computational accuracy comparable with that of the high-fidelity model is desired. In this study, we constructed magnetic resonance (MR) image-based finite element (FE) models of a mouse brain for simulations of controlled cortical impact. The anatomical details were kept by mapping each image voxel to a corresponding FE mesh element. We constructed a super-resolution neural network that could produce computational results of a refined FE model with a mesh size of 70 μm from a coarse FE model with a mesh size of 280 μm. The peak signal-to-noise ratio of the reconstructed results was 33.26 dB, while the computational speed was increased by 50-fold. This proof-of-concept study showed that using machine learning techniques, MR image-based computational modeling could be applied and evaluated in a timely fashion. This paved ways for fast FE modeling and computation based on MR images. Results also support the potential clinical applications of MR image-based computational modeling of the human brain in a variety of scenarios such as brain impact and intervention.Graphical abstract MR image-based FE models with different mesh sizes were generated for CCI. The training and testing data sets were computed with 5 different impact locations and 3 different impact velocities. High-resolution strain maps were estimated using a SR neural network with greatly reduced computational cost.
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Affiliation(s)
- Changxin Lai
- Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Yu Chen
- Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Tianyao Wang
- Department of Radiology, The Fifth People's Hospital of Shanghai, Fudan University, 801 Heqing Road, Shanghai, 200240, China
| | - Jun Liu
- Department of Radiology, The Fifth People's Hospital of Shanghai, Fudan University, 801 Heqing Road, Shanghai, 200240, China
| | - Qian Wang
- Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Yiping Du
- Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Yuan Feng
- Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China.
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18
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Chen Y, Qiu S, Wang C, Li X, Tang Y, Feng Y. Measurement of viscoelastic properties of injured mouse brain after controlled cortical impact. BIOPHYSICS REPORTS 2020. [DOI: 10.1007/s41048-020-00110-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
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19
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Eskandari F, Shafieian M, Aghdam MM, Laksari K. A knowledge map analysis of brain biomechanics: Current evidence and future directions. Clin Biomech (Bristol, Avon) 2020; 75:105000. [PMID: 32361083 DOI: 10.1016/j.clinbiomech.2020.105000] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Revised: 01/27/2020] [Accepted: 03/18/2020] [Indexed: 02/07/2023]
Abstract
Although brain, one of the most complex organs in the mammalian body, has been subjected to many studies from physiological and pathological points of view, there remain significant gaps in the available knowledge regarding its biomechanics. This article reviews the research trends in brain biomechanics with a focus on injury. We used published scientific articles indexed by Web of Science database over the past 40 years and tried to address the gaps that still exist in this field. We analyzed the data using VOSviewer, which is a software tool designed for scientometric studies. The results of this study showed that the response of brain tissue to external forces has been one of the significant research topics among biomechanicians. These studies have addressed the effects of mechanical forces on the brain and mechanisms of traumatic brain injury, as well as characterized changes in tissue behavior under trauma and other neurological diseases to provide new diagnostic and monitoring methods. In this study, some challenges in the field of brain injury biomechanics have been identified and new directions toward understanding the gaps in this field are suggested.
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Affiliation(s)
- Faezeh Eskandari
- Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
| | - Mehdi Shafieian
- Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.
| | - Mohammad M Aghdam
- Department of Mechanical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
| | - Kaveh Laksari
- Department of Biomedical Engineering, University of Arizona, Tucson, AZ, USA
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20
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Frequency dependent viscoelastic properties of porcine brain tissue. J Mech Behav Biomed Mater 2020; 102:103460. [DOI: 10.1016/j.jmbbm.2019.103460] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 09/24/2019] [Accepted: 09/28/2019] [Indexed: 02/06/2023]
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21
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Viscoelastic characterization of injured brain tissue after controlled cortical impact (CCI) using a mouse model. J Neurosci Methods 2020; 330:108463. [DOI: 10.1016/j.jneumeth.2019.108463] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 10/08/2019] [Accepted: 10/09/2019] [Indexed: 01/01/2023]
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22
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Ayad NME, Kaushik S, Weaver VM. Tissue mechanics, an important regulator of development and disease. Philos Trans R Soc Lond B Biol Sci 2019; 374:20180215. [PMID: 31431174 DOI: 10.1098/rstb.2018.0215] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
A growing body of work describes how physical forces in and around cells affect their growth, proliferation, migration, function and differentiation into specialized types. How cells receive and respond biochemically to mechanical signals is a process termed mechanotransduction. Disease may arise if a disruption occurs within this mechanism of sensing and interpreting mechanics. Cancer, cardiovascular diseases and developmental defects, such as during the process of neural tube formation, are linked to changes in cell and tissue mechanics. A breakdown in normal tissue and cellular forces activates mechanosignalling pathways that affect their function and can promote disease progression. The recent advent of high-resolution techniques enables quantitative measurements of mechanical properties of the cell and its extracellular matrix, providing insight into how mechanotransduction is regulated. In this review, we will address the standard methods and new technologies available to properly measure mechanical properties, highlighting the challenges and limitations of probing different length-scales. We will focus on the unique environment present throughout the development and maintenance of the central nervous system and discuss cases where disease, such as brain cancer, arises in response to changes in the mechanical properties of the microenvironment that disrupt homeostasis. This article is part of a discussion meeting issue 'Forces in cancer: interdisciplinary approaches in tumour mechanobiology'.
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Affiliation(s)
- Nadia M E Ayad
- Center for Bioengineering and Tissue Regeneration, Department of Surgery, University of California San Francisco, San Francisco, CA, USA.,UC Berkeley-UCSF Graduate Program in Bioengineering, San Francisco, CA, USA
| | - Shelly Kaushik
- Center for Bioengineering and Tissue Regeneration, Department of Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Valerie M Weaver
- Center for Bioengineering and Tissue Regeneration, Department of Surgery, University of California San Francisco, San Francisco, CA, USA.,Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco, San Francisco, CA, USA.,UCSF Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA.,Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA.,Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA
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23
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Finan JD. Biomechanical simulation of traumatic brain injury in the rat. Clin Biomech (Bristol, Avon) 2019; 64:114-121. [PMID: 29449041 PMCID: PMC6068009 DOI: 10.1016/j.clinbiomech.2018.01.015] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Revised: 12/08/2017] [Accepted: 01/18/2018] [Indexed: 02/07/2023]
Abstract
BACKGROUND Traumatic brain injury poses an enormous clinical challenge. Rats are the animals most widely used in pre-clinical experiments. Biomechanical simulations of these experiments predict the distribution of mechanical stress and strain across key tissues. It is in theory possible to dramatically increase our understanding of traumatic brain injury pathophysiology by correlating stress and strain with histological and functional injury outcomes. This review summarizes the state of the art in biomechanical simulation of traumatic brain injury in the rat. It also places this body of knowledge in the context of the wider effort to understand traumatic brain injury in rats and in humans. METHODS Peer-reviewed research articles on biomechanical simulation of traumatic brain injury in the rat were reviewed and summarized. FINDINGS When mathematical models of traumatic brain injury in the rat first emerged, they relied on scant data regarding biomechanical properties. The data on relevant biomechanical properties has increased recently. However, experimental models of traumatic brain injury in the rat have also become less homogeneous. New and modified models have emerged that are biomechanically distinct from traditional models. INTERPRETATION Important progress in mathematical modeling and measurement of biomechanical properties has led to credible, predictive simulations of traditional, experimental models of traumatic brain injury in the rat, such as controlled cortical impact. However, recent trends such as the increasing popularity of closed head models and blast models create new biomechanical challenges. Investigators studying rat brain biomechanics must continue to innovate to keep pace with these developments.
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24
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MacManus DB, Murphy JG, Gilchrist MD. Mechanical characterisation of brain tissue up to 35% strain at 1, 10, and 100/s using a custom-built micro-indentation apparatus. J Mech Behav Biomed Mater 2018; 87:256-266. [DOI: 10.1016/j.jmbbm.2018.07.025] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2018] [Revised: 07/10/2018] [Accepted: 07/17/2018] [Indexed: 10/28/2022]
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25
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Influence of strain rate on indentation response of porcine brain. J Mech Behav Biomed Mater 2018; 82:210-217. [DOI: 10.1016/j.jmbbm.2018.03.031] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2017] [Revised: 03/19/2018] [Accepted: 03/26/2018] [Indexed: 11/20/2022]
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26
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MacManus DB, Pierrat B, Murphy JG, Gilchrist MD. Region and species dependent mechanical properties of adolescent and young adult brain tissue. Sci Rep 2017; 7:13729. [PMID: 29061984 PMCID: PMC5653834 DOI: 10.1038/s41598-017-13727-z] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Accepted: 09/27/2017] [Indexed: 11/19/2022] Open
Abstract
Traumatic brain injuries, the leading cause of death and disability in children and young adults, are the result of a rapid acceleration or impact of the head. In recent years, a global effort to better understand the biomechanics of TBI has been undertaken, with many laboratories creating detailed computational models of the head and brain. For these models to produce realistic results they require accurate regional constitutive data for brain tissue. However, there are large differences in the mechanical properties reported in the literature. These differences are likely due to experimental parameters such as specimen age, brain region, species, test protocols, and fiber direction which are often not reported. Furthermore, there is a dearth of reported viscoelastic properties for brain tissue at large-strain and high rates. Mouse, rat, and pig brains are impacted at 10/s to a strain of ~36% using a custom-built micro-indenter with a 125 μm radius. It is shown that the resultant mechanical properties are dependent on specimen-age, species, and region, under identical experimental parameters.
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Affiliation(s)
- David B MacManus
- School of Mechanical & Materials Engineering, University College Dublin, Dublin, Ireland
| | - Baptiste Pierrat
- School of Mechanical & Materials Engineering, University College Dublin, Dublin, Ireland.,Ecole Nationale Supérieure des Mines de Saint-Etienne, CIS-EMSE, SAINBIOSE, F-42023, Saint Etienne, France.,INSERM, U1059, F-42000, Saint Etienne, France
| | - Jeremiah G Murphy
- School of Mechanical & Manufacturing Engineering, Dublin City University, Dublin, Ireland
| | - Michael D Gilchrist
- School of Mechanical & Materials Engineering, University College Dublin, Dublin, Ireland.
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27
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MacManus DB, Pierrat B, Murphy JG, Gilchrist MD. Protection of cortex by overlying meninges tissue during dynamic indentation of the adolescent brain. Acta Biomater 2017; 57:384-394. [PMID: 28501711 DOI: 10.1016/j.actbio.2017.05.022] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2017] [Revised: 05/04/2017] [Accepted: 05/09/2017] [Indexed: 10/19/2022]
Abstract
Traumatic brain injury (TBI) has become a recent focus of biomedical research with a growing international effort targeting material characterization of brain tissue and simulations of trauma using computer models of the head and brain to try to elucidate the mechanisms and pathogenesis of TBI. The meninges, a collagenous protective tri-layer, which encloses the entire brain and spinal cord has been largely overlooked in these material characterization studies. This has resulted in a lack of accurate constitutive data for the cranial meninges, particularly under dynamic conditions such as those experienced during head impacts. The work presented here addresses this lack of data by providing for the first time, in situ large deformation material properties of the porcine dura-arachnoid mater composite under dynamic indentation. It is demonstrated that this tissue is substantially stiffer (shear modulus, μ=19.10±8.55kPa) and relaxes at a slower rate (τ1=0.034±0.008s, τ2=0.336±0.077s) than the underlying brain tissue (μ=6.97±2.26kPa, τ1=0.021±0.007s, τ2=0.199±0.036s), reducing the magnitudes of stress by 250% and 65% for strains that arise during indentation-type deformations in adolescent brains. STATEMENT OF SIGNIFICANCE We present the first mechanical analysis of the protective capacity of the cranial meninges using in situ micro-indentation techniques. Force-relaxation tests are performed on in situ meninges and cortex tissue, under large strain dynamic micro-indentation. A quasi-linear viscoelastic model is used subsequently, providing time-dependent mechanical properties of these neural tissues under loading conditions comparable to what is experienced in TBI. The reported data highlights the large differences in mechanical properties between these two tissues. Finite element simulations of the indentation experiments are also performed to investigate the protective capacity of the meninges. These simulations show that the meninges protect the underlying brain tissue by reducing the overall magnitude of stress by 250% and up to 65% for strains.
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28
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Barnes JM, Przybyla L, Weaver VM. Tissue mechanics regulate brain development, homeostasis and disease. J Cell Sci 2017; 130:71-82. [PMID: 28043968 PMCID: PMC5394781 DOI: 10.1242/jcs.191742] [Citation(s) in RCA: 191] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
All cells sense and integrate mechanical and biochemical cues from their environment to orchestrate organismal development and maintain tissue homeostasis. Mechanotransduction is the evolutionarily conserved process whereby mechanical force is translated into biochemical signals that can influence cell differentiation, survival, proliferation and migration to change tissue behavior. Not surprisingly, disease develops if these mechanical cues are abnormal or are misinterpreted by the cells - for example, when interstitial pressure or compression force aberrantly increases, or the extracellular matrix (ECM) abnormally stiffens. Disease might also develop if the ability of cells to regulate their contractility becomes corrupted. Consistently, disease states, such as cardiovascular disease, fibrosis and cancer, are characterized by dramatic changes in cell and tissue mechanics, and dysregulation of forces at the cell and tissue level can activate mechanosignaling to compromise tissue integrity and function, and promote disease progression. In this Commentary, we discuss the impact of cell and tissue mechanics on tissue homeostasis and disease, focusing on their role in brain development, homeostasis and neural degeneration, as well as in brain cancer.
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Affiliation(s)
- J Matthew Barnes
- Center for Bioengineering and Tissue Regeneration, Department of Surgery, University of California San Francisco (UCSF), San Francisco, CA 94143, USA
| | - Laralynne Przybyla
- Center for Bioengineering and Tissue Regeneration, Department of Surgery, University of California San Francisco (UCSF), San Francisco, CA 94143, USA
| | - Valerie M Weaver
- Center for Bioengineering and Tissue Regeneration, Department of Surgery, University of California San Francisco (UCSF), San Francisco, CA 94143, USA
- Departments of Anatomy, Bioengineering and Therapeutic Sciences, Radiation Oncology, and the Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research and The Helen Diller Family Comprehensive Cancer Center, UCSF, San Francisco, CA 94143, USA
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