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Zhang C, Bartels L, Clansey A, Kloiber J, Bondi D, van Donkelaar P, Wu L, Rauscher A, Ji S. A computational pipeline towards large-scale and multiscale modeling of traumatic axonal injury. Comput Biol Med 2024; 171:108109. [PMID: 38364663 DOI: 10.1016/j.compbiomed.2024.108109] [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: 11/13/2023] [Revised: 01/26/2024] [Accepted: 02/04/2024] [Indexed: 02/18/2024]
Abstract
Contemporary biomechanical modeling of traumatic brain injury (TBI) focuses on either the global brain as an organ or a representative tiny section of a single axon. In addition, while it is common for a global brain model to employ real-world impacts as input, axonal injury models have largely been limited to inputs of either tension or compression with assumed peak strain and strain rate. These major gaps between global and microscale modeling preclude a systematic and mechanistic investigation of how tissue strain from impact leads to downstream axonal damage throughout the white matter. In this study, a unique subject-specific multimodality dataset from a male ice-hockey player sustaining a diagnosed concussion is used to establish an efficient and scalable computational pipeline. It is then employed to derive voxelized brain deformation, maximum principal strains and white matter fiber strains, and finally, to produce diverse fiber strain profiles of various shapes in temporal history necessary for the development and application of a deep learning axonal injury model in the future. The pipeline employs a structured, voxelized representation of brain deformation with adjustable spatial resolution independent of model mesh resolution. The method can be easily extended to other head impacts or individuals. The framework established in this work is critical for enabling large-scale (i.e., across the entire white matter region, head impacts, and individuals) and multiscale (i.e., from organ to cell length scales) modeling for the investigation of traumatic axonal injury (TAI) triggering mechanisms. Ultimately, these efforts could enhance the assessment of concussion risks and design of protective headgear. Therefore, this work contributes to improved strategies for concussion detection, mitigation, and prevention.
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Affiliation(s)
- Chaokai Zhang
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Lara Bartels
- Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada
| | - Adam Clansey
- Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Julian Kloiber
- Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada
| | - Daniel Bondi
- Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Paul van Donkelaar
- School of Health and Exercise Sciences, University of British Columbia, Kelowna, BC, Canada
| | - Lyndia Wu
- Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Alexander Rauscher
- Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada
| | - Songbai Ji
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, USA; Department of Mechanical Engineering, Worcester Polytechnic Institute, Worcester, MA, USA.
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2
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Atashgar F, Shafieian M, Abolfathi N. The effect of the properties of cell nucleus and underlying substrate on the response of finite element models of astrocytes undergoing mechanical stimulations. Comput Methods Biomech Biomed Engin 2023; 26:1572-1581. [PMID: 36324266 DOI: 10.1080/10255842.2022.2128673] [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: 05/23/2022] [Revised: 08/23/2022] [Accepted: 09/21/2022] [Indexed: 11/06/2022]
Abstract
Astrocyte cells play a critical role in the mechanical behaviour of the brain tissue; hence understanding the properties of Astrocytes is a big step toward understanding brain diseases and abnormalities. Conventionally, atomic force microscopy (AFM) has been used as one of the most powerful tools to characterize the mechanical properties of cells. However, due to the complexities of experimental work and the complex behaviour of living cells, the finite element method (FEM) is commonly used to estimate the cells' response to mechanical stimulations. In this study, we developed a finite element model of the Astrocyte cells to investigate the effect of two key parameters that could affect the response of the cell to mechanical loading; the properties of the underlying substrate and the nucleus. In this regard, the cells were placed on two different substrates in terms of thickness and stiffness (gel and glass) with varying properties of the nucleus. The main achievement of this study was to develop an insight to investigate the response of the Astrocytes to mechanical loading for future studies, both experimentally and computationally.
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Affiliation(s)
- Fatemeh Atashgar
- Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran
| | - Mehdi Shafieian
- Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran
| | - Nabiollah Abolfathi
- Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran
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3
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Lin N, Wu S, Wu Z, Ji S. Efficient Generation of Pretraining Samples for Developing a Deep Learning Brain Injury Model via Transfer Learning. Ann Biomed Eng 2023:10.1007/s10439-023-03354-3. [PMID: 37642795 DOI: 10.1007/s10439-023-03354-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 08/18/2023] [Indexed: 08/31/2023]
Abstract
The large amount of training samples required to develop a deep learning brain injury model demands enormous computational resources. Here, we study how a transformer neural network (TNN) of high accuracy can be used to efficiently generate pretraining samples for a convolutional neural network (CNN) brain injury model to reduce computational cost. The samples use synthetic impacts emulating real-world events or augmented impacts generated from limited measured impacts. First, we verify that the TNN remains highly accurate for the two impact types (N = 100 each; [Formula: see text] of 0.948-0.967 with root mean squared error, RMSE, ~ 0.01, for voxelized peak strains). The TNN-estimated samples (1000-5000 for each data type) are then used to pretrain a CNN, which is further finetuned using directly simulated training samples (250-5000). An independent measured impact dataset considered of complete capture of impact event is used to assess estimation accuracy (N = 191). We find that pretraining can significantly improve CNN accuracy via transfer learning compared to a baseline CNN without pretraining. It is most effective when the finetuning dataset is relatively small (e.g., 2000-4000 pretraining synthetic or augmented samples improves success rate from 0.72 to 0.81 with 500 finetuning samples). When finetuning samples reach 3000 or more, no obvious improvement occurs from pretraining. These results support using the TNN to rapidly generate pretraining samples to facilitate a more efficient training strategy for future deep learning brain models, by limiting the number of costly direct simulations from an alternative baseline model. This study could contribute to a wider adoption of deep learning brain injury models for large-scale predictive modeling and ultimately, enhancing safety protocols and protective equipment.
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Affiliation(s)
- Nan Lin
- Department of Biomedical Engineering, Worcester Polytechnic Institute, 60 Prescott Street, Worcester, MA, 01605, USA
| | - Shaoju Wu
- Department of Biomedical Engineering, Worcester Polytechnic Institute, 60 Prescott Street, Worcester, MA, 01605, USA
| | - Zheyang Wu
- Mathematical Sciences, Worcester Polytechnic Institute, Worcester, MA, 01609, USA
| | - Songbai Ji
- Department of Biomedical Engineering, Worcester Polytechnic Institute, 60 Prescott Street, Worcester, MA, 01605, USA.
- Department of Mechanical Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA, 01609, USA.
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4
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Wang P, Du Z, Shi H, Liu J, Liu Z, Zhuang Z. Origins of brain tissue elasticity under multiple loading modes by analyzing the microstructure-based models. Biomech Model Mechanobiol 2023; 22:1239-1252. [PMID: 37184689 DOI: 10.1007/s10237-023-01714-5] [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: 11/04/2022] [Accepted: 03/15/2023] [Indexed: 05/16/2023]
Abstract
Constitutive behaviors and material properties of brain tissue play an essential role in accurately modeling its mechanical responses. However, the measured mechanical behaviors of brain tissue exhibit a large variability, and the reported elastic modulus can differ by orders of magnitude. Here we develop the micromechanical models based on the actual microstructure of the longitudinally anisotropic plane of brain tissue to investigate the microstructural origins of the large variability. Specifically, axonal fiber bundles with the specified configurations are distributed in an equivalent matrix. All micromechanical models are subjected to multiple loading modes, such as tensile, compressive, and shear loading, under periodic boundary conditions. The predicted results agree well with the experimental results. Furthermore, we investigate how brain tissue elasticity varies with its microstructural features. It is revealed that the large variability in brain tissue elasticity stems from the volume fraction of axonal fiber, the aspect ratio of axonal fiber, and the distribution of axonal fiber orientation. The volume fraction has the greatest impact on the mechanical behaviors of brain tissue, followed by the distribution of axonal fiber orientation, then the aspect ratio. This study provides critical insights for understanding the microstructural origins of the large variability in brain tissue elasticity.
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Affiliation(s)
- Peng Wang
- School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai, 200092, China
- 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
| | - Huibin Shi
- Applied Mechanics Laboratory, School of Aerospace Engineering, Tsinghua University, Beijing, 100084, China
| | - Junjie Liu
- Applied Mechanics and Structure Safety Key Laboratory of Sichuan Province, School of Mechanics and Aerospace Engineering, Southwest Jiaotong University, Chengdu, 610031, China
| | - Zhanli Liu
- Applied Mechanics Laboratory, School of Aerospace Engineering, Tsinghua University, Beijing, 100084, China.
| | - Zhuo Zhuang
- Applied Mechanics Laboratory, School of Aerospace Engineering, Tsinghua University, Beijing, 100084, China
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5
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Wang X, Wang Y, He L. An intelligent data analysis-based medical management method for lower limb health of football athletes. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:14005-14022. [PMID: 37679121 DOI: 10.3934/mbe.2023624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
With increasingly mature commercial operations, football has become the most popular sport in the world. As the main body of football, athletes are prone to injury due to an increasing degree of competition intensity. Their health determines the length of these athletes careers, especially regarding the lower limbs that are mainly used. Therefore, the smart visualization approaches that can realize such function are in urgent demand in the area of sports healthcare. Benefitted by the strong ability of perception and analysis, a convolutional neural network (CNN) is utilized to construct an intelligent data analysis-based medical management method for the lower limb health of football athletes. First, the CNN is formulated as the main backbone, and its parameters are optimized for multiple rounds during the training stage. Then, a statistical analysis software named SPSS is introduced to assess the effect mechanism of different postures on lower limbs. Some experiments are carried out on simulative data to evaluate the proposed method, and results show a good performance of the proposed method.
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Affiliation(s)
- Xiang Wang
- Department of Physical Education, Gansu University of Political Science and Law, Lanzhou 730030, China
| | - Yongcheng Wang
- Volleyball Teaching and Research Department, Xi'an Physical Education University, Xi'an 710000, China
| | - Limin He
- Department of Physical Education, Lanzhou University, Lanzhou 730000, China
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6
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Reiter N, Paulsen F, Budday S. Mechanisms of mechanical load transfer through brain tissue. Sci Rep 2023; 13:8703. [PMID: 37248296 DOI: 10.1038/s41598-023-35768-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 05/23/2023] [Indexed: 05/31/2023] Open
Abstract
Brain injuries are often characterized by diffusely distributed axonal and vascular damage invisible to medical imaging techniques. The spatial distribution of mechanical stresses and strains plays an important role, but is not sufficient to explain the diffuse distribution of brain lesions. It remains unclear how forces are transferred from the organ to the cell scale and why some cells are damaged while neighboring cells remain unaffected. To address this knowledge gap, we subjected histologically stained fresh human and porcine brain tissue specimens to compressive loading and simultaneously tracked cell and blood vessel displacements. Our experiments reveal different mechanisms of load transfer from the organ or tissue scale to single cells, axons, and blood vessels. Our results show that cell displacement fields are inhomogeneous at the interface between gray and white matter and in the vicinity of blood vessels-locally inducing significant deformations of individual cells. These insights have important implications to better understand injury mechanisms and highlight the importance of blood vessels for the local deformation of the brain's cellular structure during loading.
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Affiliation(s)
- Nina Reiter
- Institute of Continuum Mechanics and Biomechanics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Egerlandstr. 5, 91058, Erlangen, Germany
| | - Friedrich Paulsen
- Institute for Functional and Clinical Anatomy, Friedrich-Alexander-Universität Erlangen Nürnberg, Universitätsstr. 19, 91054, Erlangen, Germany
| | - Silvia Budday
- Institute of Continuum Mechanics and Biomechanics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Egerlandstr. 5, 91058, Erlangen, Germany.
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7
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Menghani RR, Das A, Kraft RH. A sensor-enabled cloud-based computing platform for computational brain biomechanics. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 233:107470. [PMID: 36958108 DOI: 10.1016/j.cmpb.2023.107470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 02/24/2023] [Accepted: 03/06/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVES Driven by the risk of repetitive head trauma, sensors have been integrated into mouthguards to measure head impacts in contact sports and military activities. These wearable devices, referred to as "instrumented" or "smart" mouthguards are being actively developed by various research groups and organizations. These instrumented mouthguards provide an opportunity to further study and understand the brain biomechanics due to impact. In this study, we present a brain modeling service that can use information from these sensors to predict brain injury metrics in an automated fashion. METHODS We have built a brain modeling platform using several of Amazon's Web Services (AWS) to enable cloud computing and scalability. We use a custom-built cloud-based finite element modeling code to compute the physics-based nonlinear response of the intracranial brain tissue and provide a frontend web application and an application programming interface for groups working on head impact sensor technology to include simulated injury predictions into their research pipeline. RESULTS The platform results have been validated against experimental data available in literature for brain-skull relative displacements, brain strains and intracranial pressure. The parallel processing capability of the platform has also been tested and verified. We also studied the accuracy of the custom head surfaces generated by Avatar 3D. CONCLUSION We present a validated cloud-based computational brain modeling platform that uses sensor data as input for numerical brain models and outputs a quantitative description of brain tissue strains and injury metrics. The platform is expected to generate transparent, reproducible, and traceable brain computing results.
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Affiliation(s)
- Ritika R Menghani
- Department of Mechanical Engineering, The Pennsylvania State University, University Park, 16802, USA
| | - Anil Das
- Department of Mechanical Engineering, The Pennsylvania State University, University Park, 16802, USA
| | - Reuben H Kraft
- Department of Mechanical Engineering, The Pennsylvania State University, University Park, 16802, USA; Department of Biomedical Engineering, The Pennsylvania State University, University Park, 16802, USA; Institute for Computational and Data Sciences, The Pennsylvania State University, University Park, 16802, USA.
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8
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Griffiths E, Budday S. Finite element modeling of traumatic brain injury: Areas of future interest. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2022. [DOI: 10.1016/j.cobme.2022.100421] [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]
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9
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Ji S, Ghajari M, Mao H, Kraft RH, Hajiaghamemar M, Panzer MB, Willinger R, Gilchrist MD, Kleiven S, Stitzel JD. Use of Brain Biomechanical Models for Monitoring Impact Exposure in Contact Sports. Ann Biomed Eng 2022; 50:1389-1408. [PMID: 35867314 PMCID: PMC9652195 DOI: 10.1007/s10439-022-02999-w] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 06/22/2022] [Indexed: 02/03/2023]
Abstract
Head acceleration measurement sensors are now widely deployed in the field to monitor head kinematic exposure in contact sports. The wealth of impact kinematics data provides valuable, yet challenging, opportunities to study the biomechanical basis of mild traumatic brain injury (mTBI) and subconcussive kinematic exposure. Head impact kinematics are translated into brain mechanical responses through physics-based computational simulations using validated brain models to study the mechanisms of injury. First, this article reviews representative legacy and contemporary brain biomechanical models primarily used for blunt impact simulation. Then, it summarizes perspectives regarding the development and validation of these models, and discusses how simulation results can be interpreted to facilitate injury risk assessment and head acceleration exposure monitoring in the context of contact sports. Recommendations and consensus statements are presented on the use of validated brain models in conjunction with kinematic sensor data to understand the biomechanics of mTBI and subconcussion. Mainly, there is general consensus that validated brain models have strong potential to improve injury prediction and interpretation of subconcussive kinematic exposure over global head kinematics alone. Nevertheless, a major roadblock to this capability is the lack of sufficient data encompassing different sports, sex, age and other factors. The authors recommend further integration of sensor data and simulations with modern data science techniques to generate large datasets of exposures and predicted brain responses along with associated clinical findings. These efforts are anticipated to help better understand the biomechanical basis of mTBI and improve the effectiveness in monitoring kinematic exposure in contact sports for risk and injury mitigation purposes.
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Affiliation(s)
- Songbai Ji
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, USA.
| | - Mazdak Ghajari
- Dyson School of Design Engineering, Imperial College London, London, UK
| | - Haojie Mao
- Department of Mechanical and Materials Engineering, Faculty of Engineering, Western University, London, ON, N6A 5B9, Canada
| | - Reuben H Kraft
- Department of Mechanical and Nuclear Engineering, Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Marzieh Hajiaghamemar
- Department of Biomedical Engineering, The University of Texas at San Antonio, San Antonio, TX, USA
| | - Matthew B Panzer
- Department of Mechanical and Aerospace Engineering, University of Virginia, Charlottesville, VA, USA
| | - Remy Willinger
- University of Strasbourg, IMFS-CNRS, 2 rue Boussingault, 67000, Strasbourg, France
| | - Michael D Gilchrist
- School of Mechanical & Materials Engineering, University College Dublin, Belfield, Dublin 4, Ireland
| | - Svein Kleiven
- Division of Neuronic Engineering, KTH Royal Institute of Technology, Hälsovägen 11C, 141 57, Huddinge, Sweden
| | - Joel D Stitzel
- Department of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, NC, USA.
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10
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Hasan F, Mahmud KAHA, Khan MI, Adnan A. Viscoelastic damage evaluation of the axon. Front Bioeng Biotechnol 2022; 10:904818. [PMID: 36277388 PMCID: PMC9583024 DOI: 10.3389/fbioe.2022.904818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 08/22/2022] [Indexed: 11/13/2022] Open
Abstract
In this manuscript, we have studied the microstructure of the axonal cytoskeleton and adopted a bottom-up approach to evaluate the mechanical responses of axons. The cytoskeleton of the axon includes the microtubules (MT), Tau proteins (Tau), neurofilaments (NF), and microfilaments (MF). Although most of the rigidity of the axons is due to the MT, the viscoelastic response of axons comes from the Tau. Early studies have shown that NF and MF do not provide significant elasticity to the overall response of axons. Therefore, the most critical aspect of the mechanical response of axons is the microstructural topology of how MT and Tau are connected and construct the cross-linked network. Using a scanning electron microscope (SEM), the cross-sectional view of the axons revealed that the MTs are organized in a hexagonal array and cross-linked by Tau. Therefore, we have developed a hexagonal Representative Volume Element (RVE) of the axonal microstructure with MT and Tau as fibers. The matrix of the RVE is modeled by considering a combined effect of NF and MF. A parametric study is done by varying fiber geometric and mechanical properties. The Young’s modulus and spacing of MT are varied between 1.5 and 1.9 GPa and 20–38 nm, respectively. Tau is modeled as a 3-parameter General Maxwell viscoelastic material. The failure strains for MT and Tau are taken to be 50 and 40%, respectively. A total of 4 RVEs are prepared for finite element analysis, and six loading cases are inspected to quantify the three-dimensional (3D) viscoelastic relaxation response. The volume-averaged stress and strain are then used to fit the relaxation Prony series. Next, we imposed varying strain rates (between 10/sec to 50/sec) on the RVE and analyzed the axonal failure process. We have observed that the 40% failure strain of Tau is achieved in all strain rates before the MT reaches its failure strain of 50%. The corresponding axonal failure strain and stress vary between 6 and 11% and 5–19.8 MPa, respectively. This study can be used to model macroscale axonal aggregate typical of the white matter region of the brain tissue.
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Affiliation(s)
- Fuad Hasan
- Department of Mechanical and Aerospace Engineering, The University of Texas at Arlington, Arlington, TX, United States
| | - KAH Al Mahmud
- Department of Mechanical and Aerospace Engineering, The University of Texas at Arlington, Arlington, TX, United States
| | - Md. Ishak Khan
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Ashfaq Adnan
- Department of Mechanical and Aerospace Engineering, The University of Texas at Arlington, Arlington, TX, United States
- *Correspondence: Ashfaq Adnan,
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11
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Agarwal M, Pasupathy P, Pelegri AA. Oligodendrocyte tethering effect on hyperelastic 3D response of axons in white matter. J Mech Behav Biomed Mater 2022; 134:105394. [DOI: 10.1016/j.jmbbm.2022.105394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 05/06/2022] [Accepted: 07/19/2022] [Indexed: 10/16/2022]
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12
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Chen H, Felix C, Folloni D, Verhagen L, Sallet J, Jerusalem A. Modelling transcranial ultrasound neuromodulation: an energy-based multiscale framework. Acta Biomater 2022; 151:317-332. [PMID: 35902037 DOI: 10.1016/j.actbio.2022.07.034] [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: 02/08/2022] [Revised: 07/18/2022] [Accepted: 07/19/2022] [Indexed: 11/26/2022]
Abstract
Several animal and human studies have now established the potential of low intensity, low frequency transcranial ultrasound (TUS) for non-invasive neuromodulation. Paradoxically, the underlying mechanisms through which TUS neuromodulation operates are still unclear, and a consensus on the identification of optimal sonication parameters still remains elusive. One emerging hypothesis based on thermodynamical considerations attributes the acoustic-induced nerve activity alterations to the mechanical energy and/or entropy conversions occurring during TUS action. Here, we propose a multiscale modelling framework to examine the energy states of neuromodulation under TUS. First, macroscopic tissue-level acoustic simulations of the sonication of a whole monkey brain are conducted under different sonication protocols. For each one of them, mechanical loading conditions of the received waves in the anterior cingulate cortex region are recorded and exported into a microscopic cell-level 3D viscoelastic finite element model of neuronal axon embedded extracellular medium. Pulse-averaged elastically stored and viscously dissipated energy rate densities during axon deformation are finally computed under different sonication incident angles and are mapped against distinct combinations of sonication parameters of the TUS. The proposed multiscale framework allows for the analysis of vibrational patterns of the axons and its comparison against the spectrograms of stimulating ultrasound. The results are in agreement with literature data on neuromodulation, demonstrating the potential of this framework to identify optimised acoustic parameters in TUS neuromodulation. The proposed approach is finally discussed in the context of multiphysics energetic considerations, argued here to be a promising avenue towards a scalable framework for TUS in silico predictions. STATEMENT OF SIGNIFICANCE: Low-intensity transcranial ultrasound (TUS) is poised to become a leading neuromodulation technique for the treatment of neurological disorders. Paradoxically, how it operates at the cellular scale remains unknown, hampering progress in personalised treatment. To this end, models of the multiphysics of neurons able to upscale results to the organ scale are required. We propose here to achieve this by considering an axon submitted to an ultrasound wave extracted from a simulation at the organ scale. Doing so, information pertaining to both stored and dissipated axonal energies can be extracted for a given head/brain morphology. This two-scale multiphysics energetic approach is a promising scalable framework for in silico predictions in the context of personalised TUS treatment.
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Affiliation(s)
- Haoyu Chen
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Ciara Felix
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Davide Folloni
- Wellcome Centre for Integrative Neuroimaging (WIN), Department of Experimental Psychology, University of Oxford, Oxford, UK; Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lennart Verhagen
- Wellcome Centre for Integrative Neuroimaging (WIN), Department of Experimental Psychology, University of Oxford, Oxford, UK; Donders Institute, Radboud University, Nijmegen, Netherlands
| | - Jérôme Sallet
- Wellcome Centre for Integrative Neuroimaging (WIN), Department of Experimental Psychology, University of Oxford, Oxford, UK; Inserm, Stem Cell and Brain Research Institute, Université Lyon 1, Bron, France
| | - Antoine Jerusalem
- Department of Engineering Science, University of Oxford, Oxford, UK.
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13
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Wu S, Zhao W, Ji S. Real-time dynamic simulation for highly accurate spatiotemporal brain deformation from impact. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2022; 394:114913. [PMID: 35572209 PMCID: PMC9097909 DOI: 10.1016/j.cma.2022.114913] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Real-time dynamic simulation remains a significant challenge for spatiotemporal data of high dimension and resolution. In this study, we establish a transformer neural network (TNN) originally developed for natural language processing and a separate convolutional neural network (CNN) to estimate five-dimensional (5D) spatiotemporal brain-skull relative displacement resulting from impact (isotropic spatial resolution of 4 mm with temporal resolution of 1 ms). Sequential training is applied to train (N = 5184 samples) the two neural networks for estimating the complete 5D displacement across a temporal duration of 60 ms. We find that TNN slightly but consistently outperforms CNN in accuracy for both displacement and the resulting voxel-wise four-dimensional (4D) maximum principal strain (e.g., root mean squared error (RMSE) of ~1.0% vs. ~1.6%, with coefficient of determination, R 2 >0.99 vs. >0.98, respectively, and normalized RMSE (NRMSE) at peak displacement of 2%-3%, based on an independent testing dataset; N = 314). Their accuracies are similar for a range of real-world impacts drawn from various published sources (dummy, helmet, football, soccer, and car crash; average RMSE/NRMSE of ~0.3 mm/~4%-5% and average R 2 of ~0.98 at peak displacement). Sequential training is effective for allowing instantaneous estimation of 5D displacement with high accuracy, although TNN poses a heavier computational burden in training. This work enables efficient characterization of the intrinsically dynamic brain strain in impact critical for downstream multiscale axonal injury model simulation. This is also the first application of TNN in biomechanics, which offers important insight into how real-time dynamic simulations can be achieved across diverse engineering fields.
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Affiliation(s)
- Shaoju Wu
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, United States of America
| | - Wei Zhao
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, United States of America
| | - Songbai Ji
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, United States of America
- Department of Mechanical Engineering, Worcester Polytechnic Institute, Worcester, MA, United States of America
- Correspondence to: Department of Biomedical Engineering, Worcester Polytechnic Institute, 60 Prescott Street, Worcester, MA 01506, USA., (S. Ji)
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14
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15
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Li X. Subject-Specific Head Model Generation by Mesh Morphing: A Personalization Framework and Its Applications. Front Bioeng Biotechnol 2021; 9:706566. [PMID: 34733827 PMCID: PMC8558307 DOI: 10.3389/fbioe.2021.706566] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 09/03/2021] [Indexed: 11/30/2022] Open
Abstract
Finite element (FE) head models have become powerful tools in many fields within neuroscience, especially for studying the biomechanics of traumatic brain injury (TBI). Subject-specific head models accounting for geometric variations among subjects are needed for more reliable predictions. However, the generation of such models suitable for studying TBIs remains a significant challenge and has been a bottleneck hindering personalized simulations. This study presents a personalization framework for generating subject-specific models across the lifespan and for pathological brains with significant anatomical changes by morphing a baseline model. The framework consists of hierarchical multiple feature and multimodality imaging registrations, mesh morphing, and mesh grouping, which is shown to be efficient with a heterogeneous dataset including a newborn, 1-year-old (1Y), 2Y, adult, 92Y, and a hydrocephalus brain. The generated models of the six subjects show competitive personalization accuracy, demonstrating the capacity of the framework for generating subject-specific models with significant anatomical differences. The family of the generated head models allows studying age-dependent and groupwise brain injury mechanisms. The framework for efficient generation of subject-specific FE head models helps to facilitate personalized simulations in many fields of neuroscience.
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Affiliation(s)
- Xiaogai Li
- Division of Neuronic Engineering, Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Stockholm, Sweden
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16
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Zhou Z, Li X, Liu Y, Fahlstedt M, Georgiadis M, Zhan X, Raymond SJ, Grant G, Kleiven S, Camarillo D, Zeineh M. Toward a Comprehensive Delineation of White Matter Tract-Related Deformation. J Neurotrauma 2021; 38:3260-3278. [PMID: 34617451 DOI: 10.1089/neu.2021.0195] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Finite element (FE) models of the human head are valuable instruments to explore the mechanobiological pathway from external loading, localized brain response, and resultant injury risks. The injury predictability of these models depends on the use of effective criteria as injury predictors. The FE-derived normal deformation along white matter (WM) fiber tracts (i.e., tract-oriented strain) recently has been suggested as an appropriate predictor for axonal injury. However, the tract-oriented strain only represents a partial depiction of the WM fiber tract deformation. A comprehensive delineation of tract-related deformation may improve the injury predictability of the FE head model by delivering new tract-related criteria as injury predictors. Thus, the present study performed a theoretical strain analysis to comprehensively characterize the WM fiber tract deformation by relating the strain tensor of the WM element to its embedded fiber tract. Three new tract-related strains with exact analytical solutions were proposed, measuring the normal deformation perpendicular to the fiber tracts (i.e., tract-perpendicular strain), and shear deformation along and perpendicular to the fiber tracts (i.e., axial-shear strain and lateral-shear strain, respectively). The injury predictability of these three newly proposed strain peaks along with the previously used tract-oriented strain peak and maximum principal strain (MPS) were evaluated by simulating 151 impacts with known outcome (concussion or non-concussion). The results preliminarily showed that four tract-related strain peaks exhibited superior performance than MPS in discriminating concussion and non-concussion cases. This study presents a comprehensive quantification of WM tract-related deformation and advocates the use of orientation-dependent strains as criteria for injury prediction, which may ultimately contribute to an advanced mechanobiological understanding and enhanced computational predictability of brain injury.
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Affiliation(s)
- Zhou Zhou
- Department of Bioengineering, Stanford University, Stanford, California, USA.,Neuronic Engineering, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Xiaogai Li
- Neuronic Engineering, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Yuzhe Liu
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Madelen Fahlstedt
- Neuronic Engineering, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Marios Georgiadis
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Xianghao Zhan
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Samuel J Raymond
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Gerald Grant
- Department of Neurosurgery, Stanford University, Stanford, California, USA.,Department of Neurology, Stanford University, Stanford, California, USA
| | - Svein Kleiven
- Neuronic Engineering, KTH Royal Institute of Technology, Stockholm, Sweden
| | - David Camarillo
- Department of Bioengineering, Stanford University, Stanford, California, USA.,Department of Neurology, Stanford University, Stanford, California, USA.,Department of Mechanical Engineering, Stanford University, Stanford, California, USA
| | - Michael Zeineh
- Department of Radiology, Stanford University, Stanford, California, USA
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17
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Human brain FE modeling including incompressible fluid dynamics of intraventricular cerebrospinal fluid. BRAIN MULTIPHYSICS 2021. [DOI: 10.1016/j.brain.2021.100037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
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