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He G, Fan L, Horstemeyer MF. Embedded finite element modeling of the mechanics of brain axonal fiber tracts under head impact conditions. Comput Biol Med 2024; 181:109063. [PMID: 39178807 DOI: 10.1016/j.compbiomed.2024.109063] [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: 04/22/2024] [Revised: 08/07/2024] [Accepted: 08/20/2024] [Indexed: 08/26/2024]
Abstract
Investigating and understanding the biomechanical kinematics and kinetics of human brain axonal fibers during head impact process is crucial to study the mechanisms of Traumatic Axonal Injury (TAI). Such a study may require the explicit incorporation of brain fiber tracts into the host brain in order to distinguish the mechanical states of axonal fibers and brain tissue. Herein we extend our previously developed human head model by using an embedded element method to include fiber tracts reconstructed from diffusion tensor images in a host brain with the purpose of numerically tracking the deformation state of axonal fiber tracts during a head impact simulation. The updated model is validated by comparing its prediction of intracranial pressures with experimental data, followed by a thorough study of the effects of element types used for fiber tracts and the stiffness ratios of fiber to host brain. The validated model is also used to predict and visualize the damaged region of fiber tracts during the head impact process based on different injury criteria. The model is promising in tracking the state of fiber tracts and can add more objective functions such as axonal fiber deformation if used in the future design optimization of head protective equipment such as a football helmet.
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Affiliation(s)
- Ge He
- Department of Biomedical Engineering, Lawrence Technological University, Southfield, MI, 48075, USA.
| | - Lei Fan
- The Joint Department of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - M F Horstemeyer
- School of Engineering, Liberty University, Lynchburg, VA, 24515, USA
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Yuan T, Zhan W, Terzano M, Holzapfel GA, Dini D. A comprehensive review on modeling aspects of infusion-based drug delivery in the brain. Acta Biomater 2024; 185:1-23. [PMID: 39032668 DOI: 10.1016/j.actbio.2024.07.015] [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: 03/21/2024] [Revised: 07/10/2024] [Accepted: 07/11/2024] [Indexed: 07/23/2024]
Abstract
Brain disorders represent an ever-increasing health challenge worldwide. While conventional drug therapies are less effective due to the presence of the blood-brain barrier, infusion-based methods of drug delivery to the brain represent a promising option. Since these methods are mechanically controlled and involve multiple physical phases ranging from the neural and molecular scales to the brain scale, highly efficient and precise delivery procedures can significantly benefit from a comprehensive understanding of drug-brain and device-brain interactions. Behind these interactions are principles of biophysics and biomechanics that can be described and captured using mathematical models. Although biomechanics and biophysics have received considerable attention, a comprehensive mechanistic model for modeling infusion-based drug delivery in the brain has yet to be developed. Therefore, this article reviews the state-of-the-art mechanistic studies that can support the development of next-generation models for infusion-based brain drug delivery from the perspective of fluid mechanics, solid mechanics, and mathematical modeling. The supporting techniques and database are also summarized to provide further insights. Finally, the challenges are highlighted and perspectives on future research directions are provided. STATEMENT OF SIGNIFICANCE: Despite the immense potential of infusion-based drug delivery methods for bypassing the blood-brain barrier and efficiently delivering drugs to the brain, achieving optimal drug distribution remains a significant challenge. This is primarily due to our limited understanding of the complex interactions between drugs and the brain that are governed by principles of biophysics and biomechanics, and can be described using mathematical models. This article provides a comprehensive review of state-of-the-art mechanistic studies that can help to unravel the mechanism of drug transport in the brain across the scales, which underpins the development of next-generation models for infusion-based brain drug delivery. More broadly, this review will serve as a starting point for developing more effective treatments for brain diseases and mechanistic models that can be used to study other soft tissue and biomaterials.
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Affiliation(s)
- Tian Yuan
- Department of Mechanical Engineering, Imperial College London, SW7 2AZ, UK.
| | - Wenbo Zhan
- School of Engineering, University of Aberdeen, Aberdeen, AB24 3UE, UK
| | - Michele Terzano
- Institute of Biomechanics, Graz University of Technology, Austria
| | - Gerhard A Holzapfel
- Institute of Biomechanics, Graz University of Technology, Austria; Department of Structural Engineering, NTNU, Trondheim, Norway
| | - Daniele Dini
- Department of Mechanical Engineering, Imperial College London, SW7 2AZ, UK.
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Zhan X, Liu Y, Cecchi NJ, Gevaert O, Zeineh MM, Grant GA, Camarillo DB. Brain Deformation Estimation With Transfer Learning for Head Impact Datasets Across Impact Types. IEEE Trans Biomed Eng 2024; 71:1853-1863. [PMID: 38224520 DOI: 10.1109/tbme.2024.3354192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2024]
Abstract
OBJECTIVE The machine-learning head model (MLHM) to accelerate the calculation of brain strain and strain rate, which are the predictors for traumatic brain injury (TBI), but the model accuracy was found to decrease sharply when the training/test datasets were from different head impacts types (i.e., car crash, college football), which limits the applicability of MLHMs to different types of head impacts and sports. Particularly, small sizes of target dataset for specific impact types with tens of impacts may not be enough to train an accurate impact-type-specific MLHM. METHODS To overcome this, we propose data fusion and transfer learning to develop a series of MLHMs to predict the maximum principal strain (MPS) and maximum principal strain rate (MPSR). RESULTS The strategies were tested on American football (338), mixed martial arts (457), reconstructed car crash (48) and reconstructed American football (36) and we found that the MLHMs developed with transfer learning are significantly more accurate in estimating MPS and MPSR than other models, with a mean absolute error (MAE) smaller than 0.03 in predicting MPS and smaller than [Formula: see text] in predicting MPSR on all target impact datasets. High performance in concussion detection was observed based on the MPS and MPSR estimated by the transfer-learning-based models. CONCLUSION The MLHMs can be applied to various head impact types for rapidly and accurately calculating brain strain and strain rate. SIGNIFICANCE This study enables developing MLHMs for the head impact type with limited availability of data, and will accelerate the applications of MLHMs.
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Bakhtiarydavijani A, Stone TW. Impact of prior axonal injury on subsequent injury during brain tissue stretching - A mesoscale computational approach. J Mech Behav Biomed Mater 2024; 153:106489. [PMID: 38428206 DOI: 10.1016/j.jmbbm.2024.106489] [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: 11/03/2023] [Revised: 02/24/2024] [Accepted: 02/26/2024] [Indexed: 03/03/2024]
Abstract
Epidemiology studies of traumatic brain injury (TBI) show individuals with a prior history of TBI experience an increased risk of future TBI with a significantly more detrimental outcome. But the mechanisms through which prior head injuries may affect risks of injury during future head insults have not been identified. In this work, we show that prior brain tissue injury in the form of mechanically induced axonal injury and glial scar formation can facilitate future mechanically induced tissue injury. To achieve this, we use finite element computational models of brain tissue and a history-dependent pathophysiology-based mechanically-induced axonal injury threshold to determine the evolution of axonal injury and scar tissue formation and their effects on future brain tissue stretching. We find that due to the reduced stiffness of injured tissue and glial scars, the existence of prior injury can increase the risk of future injury in the vicinity of prior injury during future brain tissue stretching. The softer brain scar tissue is shown to increase the strain and strain rate in its vicinity by as much as 40% in its vicinity during dynamic stretching that reduces the global strain required to induce injury by 20% when deformed at 15 s-1 strain rate. The results of this work highlight the need to account for patient history when determining the risk of brain injury.
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Affiliation(s)
| | - Tonya W Stone
- Center for Advanced Vehicular Systems, Mississippi State University, Starkville, MS, 39759, USA; Department of Mechanical Engineering, Mississippi State University, Mississippi State, MS, 39762, USA
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Singh A, Kumar D, Ganpule S. Biomechanical Response of Head Surrogate With and Without the Helmet. J Biomech Eng 2024; 146:031001. [PMID: 37470487 DOI: 10.1115/1.4062968] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 07/12/2023] [Indexed: 07/21/2023]
Abstract
Measurements of brain deformations under injurious loading scenarios are actively sought. In this work, we report experimentally measured head kinematics and corresponding dynamic, two-dimensional brain simulant deformations in head surrogates under a blunt impact, with and without a helmet. Head surrogates used in this work consisted of skin, skull, dura, falx, tentorium, and brain stimulants. The head surrogate geometry was based on the global human body models consortium's head model. A base head surrogate consisting of skin-skull-brain was considered. In addition, the response of two other head surrogates, skin-skull-dura-brain, and skin-skull-dura-brain-falx-tentorium, was investigated. Head surrogate response was studied for sagittal and coronal plane rotations for impactor velocities of 1 and 3 m/s. Response of head surrogates was compared against strain measurements in PMHS. The strain pattern in the brain simulant was heterogenous, and peak strains were established within ∼30 ms. The choice of head surrogate affect the spatiotemporal evolution of strain. For no helmet case, peak MPS of ∼50-60% and peak MSS of ∼35-50% were seen in brain simulant corresponding to peak rotational accelerations of ∼5000-7000 rad/s2. Peak head kinematics and peak MPS have been reduced by up to 75% and 45%, respectively, with the conventional helmet and by up to 90% and 85%, respectively, with the helmet with antirotational pads. Overall, these results provide important, new data on brain simulant strains under a variety of loading scenarios-with and without the helmets.
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Affiliation(s)
- Abhilash Singh
- Department of Mechanical and Industrial Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India
| | - Devendra Kumar
- Department of Mechanical and Industrial Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India
| | - Shailesh Ganpule
- Department of Mechanical and Industrial Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India; Department of Design, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India
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Delteil C, Manlius T, Marle O, Godio-Raboutet Y, Bailly N, Piercecchi-Marti MD, Tuchtan L, Thollon L. Head injury: Importance of the deep brain nuclei in force transmission to the brain. Forensic Sci Int 2024; 356:111952. [PMID: 38350415 DOI: 10.1016/j.forsciint.2024.111952] [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: 06/05/2023] [Revised: 10/20/2023] [Accepted: 01/26/2024] [Indexed: 02/15/2024]
Abstract
Finite element modeling provides a digital representation of the human body. It is currently the most pertinent method to study the mechanisms of head injury, and is becoming a scientific reference in forensic expert reports. Improved biofidelity is a recurrent aim of research studies in biomechanics in order to improve earlier models whose mechanical properties conformed to simplified elastic behavior and mechanic laws. We aimed to study force transmission to the brain following impacts to the head, using a finite element head model with increased biofidelity. To the model developed by the Laboratory of Applied Biomechanics of Marseille, we added new brain structures (thalamus, central gray nuclei and ventricular systems) as well as three tracts involved in the symptoms of head injury: the corpus callosum, uncinate tracts and corticospinal tracts. Three head impact scenarios were simulated: an uppercut with the prior model and an uppercut with the improved model in order to compare the two models, and a lateral impact with an impact velocity of 6.5 m/s in the improved model. In these conditions, in uppercuts the maximum stress values did not exceed the injury risk threshold. On the other hand, the deep gray matter (thalamus and central gray nuclei) was the region at highest risk of injury during lateral impacts. Even if injury to the deep gray matter is not immediately life-threatening, it could explain the chronic disabling symptoms of even low-intensity head injury.
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Affiliation(s)
- Clémence Delteil
- Forensic Department, Assistance Publique-Hôpitaux de Marseille, La Timone, 264 rue St Pierre, 13385 Marseille Cedex 05, France; Aix Marseille Univ, CNRS, EFS, ADES, Marseille, France.
| | - Thais Manlius
- Aix Marseille Univ, Univ Gustave Eiffel, LBA, Marseille, France
| | - Oceane Marle
- Aix Marseille Univ, Univ Gustave Eiffel, LBA, Marseille, France
| | | | - Nicolas Bailly
- Aix Marseille Univ, Univ Gustave Eiffel, LBA, Marseille, France
| | - Marie-Dominique Piercecchi-Marti
- Forensic Department, Assistance Publique-Hôpitaux de Marseille, La Timone, 264 rue St Pierre, 13385 Marseille Cedex 05, France; Aix Marseille Univ, CNRS, EFS, ADES, Marseille, France
| | - Lucile Tuchtan
- Forensic Department, Assistance Publique-Hôpitaux de Marseille, La Timone, 264 rue St Pierre, 13385 Marseille Cedex 05, France; Aix Marseille Univ, CNRS, EFS, ADES, Marseille, France
| | - Lionel Thollon
- Aix Marseille Univ, Univ Gustave Eiffel, LBA, Marseille, France
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Dietvorst S, Vervekken A, Depreitere B. Developing a porcine model of severe traumatic brain injury induced by high amplitude rotational acceleration. BRAIN & SPINE 2023; 4:102728. [PMID: 38510621 PMCID: PMC10951692 DOI: 10.1016/j.bas.2023.102728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 11/29/2023] [Accepted: 12/08/2023] [Indexed: 03/22/2024]
Abstract
Introduction It is unclear which pathophysiological processes initiate and drive dynamic cerebrovascular autoregulation (CA) impairment as seen in traumatic brain injury (TBI). This is not solely attributable to raised intracranial pressure (ICP), but also results from local tissue damage. Research question In order to investigate CA disturbing processes, a porcine model is needed that mimics severe TBI as seen in humans. This model requires high amplitude rotational acceleration. Material and methods A customized device was built to produce a rotational impulse with high amplitude and short pulse duration. Following preparatory tests on cadaver piglets, six piglets of six weeks old were sedated, ventilated and subjected to rotational impulses of different magnitudes. The impulse was immediately followed by installment of invasive monitoring of ICP, PbO₂, Laser Doppler Flowmetry and arterial blood pressure. TBI was further characterized by magnetic resonance brain imaging. Results The current setup enabled to reach sagittal head rotational maximal acceleration magnitudes up to 30 krad/s2. Half of the animals had an increase in ICP, measured shortly after the impulse. It has proved impossible so far to produce a sustained rise in ICP as seen in human severe TBI. MRI showed no anatomical abnormalities which would confirm severe TBI. Discussion and conclusion The challenge to build a porcine model in which severe TBI with ICP raise and MRI changes as seen in humans can be reliably reproduced is still ongoing. It might be that higher peak rotational accelerations are needed.
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Affiliation(s)
- Sofie Dietvorst
- Department of Neurosurgery, University Hospitals, Leuven, Belgium
- Research Group Experimental Neurosurgery and Neuroanatomy, KULeuven, Belgium
| | - Aline Vervekken
- Research Group Experimental Neurosurgery and Neuroanatomy, KULeuven, Belgium
| | - Bart Depreitere
- Department of Neurosurgery, University Hospitals, Leuven, Belgium
- Research Group Experimental Neurosurgery and Neuroanatomy, KULeuven, Belgium
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Zhan X, Li Y, Liu Y, Cecchi NJ, Raymond SJ, Zhou Z, Vahid Alizadeh H, Ruan J, Barbat S, Tiernan S, Gevaert O, Zeineh MM, Grant GA, Camarillo DB. Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematics. JOURNAL OF SPORT AND HEALTH SCIENCE 2023; 12:619-629. [PMID: 36921692 PMCID: PMC10466194 DOI: 10.1016/j.jshs.2023.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 12/06/2022] [Accepted: 02/16/2023] [Indexed: 05/26/2023]
Abstract
BACKGROUND Traumatic brain injury can be caused by head impacts, but many brain injury risk estimation models are not equally accurate across the variety of impacts that patients may undergo, and the characteristics of different types of impacts are not well studied. We investigated the spectral characteristics of different head impact types with kinematics classification. METHODS Data were analyzed from 3262 head impacts from lab reconstruction, American football, mixed martial arts, and publicly available car crash data. A random forest classifier with spectral densities of linear acceleration and angular velocity was built to classify head impact types (e.g., football, car crash, mixed martial arts). To test the classifier robustness, another 271 lab-reconstructed impacts were obtained from 5 other instrumented mouthguards. Finally, with the classifier, type-specific, nearest-neighbor regression models were built for brain strain. RESULTS The classifier reached a median accuracy of 96% over 1000 random partitions of training and test sets. The most important features in the classification included both low- and high-frequency features, both linear acceleration features and angular velocity features. Different head impact types had different distributions of spectral densities in low- and high-frequency ranges (e.g., the spectral densities of mixed martial arts impacts were higher in the high-frequency range than in the low-frequency range). The type-specific regression showed a generally higher R2 value than baseline models without classification. CONCLUSION The machine-learning-based classifier enables a better understanding of the impact kinematics spectral density in different sports, and it can be applied to evaluate the quality of impact-simulation systems and on-field data augmentation.
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Affiliation(s)
- Xianghao Zhan
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Yiheng Li
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Yuzhe Liu
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
| | - Nicholas J Cecchi
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Samuel J Raymond
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Zhou Zhou
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | | | - Jesse Ruan
- Ford Motor Company, 3001 Miller Rd, Dearborn, MI 48120, USA
| | - Saeed Barbat
- Ford Motor Company, 3001 Miller Rd, Dearborn, MI 48120, USA
| | | | - Olivier Gevaert
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Michael M Zeineh
- Department of Radiology, Stanford University, Stanford, CA 94305, USA
| | - Gerald A Grant
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA
| | - David B Camarillo
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
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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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He G, Fan L. A transversely isotropic viscohyperelastic-damage model for the brain tissue with strain rate sensitivity. J Biomech 2023; 151:111554. [PMID: 36958091 DOI: 10.1016/j.jbiomech.2023.111554] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 02/26/2023] [Accepted: 03/17/2023] [Indexed: 03/25/2023]
Abstract
Understanding the mechanical behaviors and properties of brain tissue are crucial to study the mechanisms of traumatic brain injury (TBI). Such injury may be associated with high rate loading conditions and the large deformation of brain tissue. Thus, constitutive models that consider the rate dependent large deformation of brain tissue and its possible damage initiation and evolution may help uncover the related mechanisms of TBI. Motivated from this, in this paper we present a fully three-dimensional large strain viscohyperelastic-damage model with the purpose of reproducing the experimentally observed rate sensitive elastic and damage-induced stress softening behaviors of brain tissue. The parameters of the proposed model can be identified using the experimental data from simple monotonic tests such as uniaxial tension, compression and simple shear. The proposed model is validated by comparing its prediction with experimental data. Good agreement between predictive results and experimental data is achieved indicating the potential of the proposed model in characterizing the mechanical behaviors of brain tissue considering rate dependence and damage effect.
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Affiliation(s)
- Ge He
- Shanghai Key Laboratory of Mechanics in Energy Engineering, Shanghai Institute of Applied Mathematics and Mechanics, School of Mechanics and Engineering Science Shanghai University, Shanghai 200444, China.
| | - Lei Fan
- Department of Mechanical Engineering, Michigan State University, East Lansing, MI 48824, USA
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Pupillary Light Response Deficits in 4-Week-Old Piglets and Adolescent Children after Low-Velocity Head Rotations and Sports-Related Concussions. Biomedicines 2023; 11:biomedicines11020587. [PMID: 36831121 PMCID: PMC9952885 DOI: 10.3390/biomedicines11020587] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 02/10/2023] [Accepted: 02/14/2023] [Indexed: 02/18/2023] Open
Abstract
Neurological disorders and traumatic brain injury (TBI) are among the leading causes of death and disability. The pupillary light reflex (PLR) is an emerging diagnostic tool for concussion in humans. We compared PLR obtained with a commercially available pupillometer in the 4 week old piglet model of the adolescent brain subject to rapid nonimpact head rotation (RNR), and in human adolescents with and without sports-related concussion (SRC). The 95% PLR reference ranges (RR, for maximum and minimum pupil diameter, latency, and average and peak constriction velocities) were established in healthy piglets (N = 13), and response reliability was validated in nine additional healthy piglets. PLR assessments were obtained in female piglets allocated to anesthetized sham (N = 10), single (sRNR, N = 13), and repeated (rRNR, N = 14) sagittal low-velocity RNR at pre-injury, as well as days 1, 4, and 7 post injury, and evaluated against RRs. In parallel, we established human PLR RRs in healthy adolescents (both sexes, N = 167) and compared healthy PLR to values obtained <28 days from a SRC (N = 177). In piglets, maximum and minimum diameter deficits were greater in rRNR than sRNR. Alterations peaked on day 1 post sRNR and rRNR, and remained altered at day 4 and 7. In SRC adolescents, the proportion of adolescents within the RR was significantly lower for maximum pupil diameter only (85.8%). We show that PLR deficits may persist in humans and piglets after low-velocity head rotations. Differences in timing of assessment after injury, developmental response to injury, and the number and magnitude of impacts may contribute to the differences observed between species. We conclude that PLR is a feasible, quantifiable involuntary physiological metric of neurological dysfunction in pigs, as well as humans. Healthy PLR porcine and human reference ranges established can be used for neurofunctional assessments after TBI or hypoxic exposures (e.g., stroke, apnea, or cardiac arrest).
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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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Translational models of mild traumatic brain injury tissue biomechanics. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2022. [DOI: 10.1016/j.cobme.2022.100422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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14
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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: 19] [Impact Index Per Article: 9.5] [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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15
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Zhan X, Liu Y, Cecchi NJ, Gevaert O, Zeineh MM, Grant GA, Camarillo DB. Finding the Spatial Co-Variation of Brain Deformation With Principal Component Analysis. IEEE Trans Biomed Eng 2022; 69:3205-3215. [PMID: 35349430 PMCID: PMC9580615 DOI: 10.1109/tbme.2022.3163230] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE Strain and strain rate are effective traumatic brain injury metrics. In finite element (FE) head model, thousands of elements were used to represent the spatial distribution of these metrics. Owing that these metrics are resulted from brain inertia, their spatial distribution can be represented in more concise pattern. Since head kinematic features and brain deformation vary largely across head impact types (Zhan et al., 2021), we applied principal component analysis (PCA) to find the spatial co-variation of injury metrics (maximum principal strain (MPS), MPS rate (MPSR) and MPS × MPSR) in four impact types: simulation, football, mixed martial arts and car crashes, and used the PCA to find patterns in these metrics and improve the machine learning head model (MLHM). METHODS We applied PCA to decompose the injury metrics for all impacts in each impact type, and investigate the spatial co-variation using the first principal component (PC1). Furthermore, we developed a MLHM to predict PC1 and then inverse-transform to predict for all brain elements. The accuracy, the model complexity and the size of training dataset of PCA-MLHM are compared with previous MLHM (Zhan et al., 2021). RESULTS PC1 explained variance on the datasets. Based on PC1 coefficients, the corpus callosum and midbrain exhibit high variance on all datasets. Finally, the PCA-MLHM reduced model parameters by 74% with a similar MPS estimation accuracy. CONCLUSION The brain injury metric in a dataset can be decomposed into mean components and PC1 with high explained variance. SIGNIFICANCE The spatial co-variation analysis enables better interpretation of the patterns in brain injury metrics. It also improves the efficiency of MLHM.
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16
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Rifkin JA, Wu T, Rayfield AC, Anderson ED, Panzer MB, Meaney DF. Brain architecture-based vulnerability to traumatic injury. Front Bioeng Biotechnol 2022; 10:936082. [PMID: 36091446 PMCID: PMC9448929 DOI: 10.3389/fbioe.2022.936082] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 08/01/2022] [Indexed: 02/03/2023] Open
Abstract
The white matter tracts forming the intricate wiring of the brain are subject-specific; this heterogeneity can complicate studies of brain function and disease. Here we collapse tractography data from the Human Connectome Project (HCP) into structural connectivity (SC) matrices and identify groups of similarly wired brains from both sexes. To characterize the significance of these architectural groupings, we examined how similarly wired brains led to distinct groupings of neural activity dynamics estimated with Kuramoto oscillator models (KMs). We then lesioned our networks to simulate traumatic brain injury (TBI) and finally we tested whether these distinct architecture groups’ dynamics exhibited differing responses to simulated TBI. At each of these levels we found that brain structure, simulated dynamics, and injury susceptibility were all related to brain grouping. We found four primary brain architecture groupings (two male and two female), with similar architectures appearing across both sexes. Among these groupings of brain structure, two architecture types were significantly more vulnerable than the remaining two architecture types to lesions. These groups suggest that mesoscale brain architecture types exist, and these architectural differences may contribute to differential risks to TBI and clinical outcomes across the population.
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Affiliation(s)
- Jared A. Rifkin
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
- Department of Mechanical and Aerospace Engineering, University of Virginia, Charlottesville, VA, United States
| | - Taotao Wu
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
| | - Adam C. Rayfield
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
| | - Erin D. Anderson
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
| | - Matthew B. Panzer
- Department of Mechanical and Aerospace Engineering, University of Virginia, Charlottesville, VA, United States
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, United States
| | - David F. Meaney
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, United States
- *Correspondence: David F. Meaney,
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17
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Fiber orientation downsampling compromises the computation of white matter tract-related deformation. J Mech Behav Biomed Mater 2022; 132:105294. [DOI: 10.1016/j.jmbbm.2022.105294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 04/13/2022] [Accepted: 05/21/2022] [Indexed: 11/18/2022]
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18
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Zhou Z, Li X, Domel AG, Dennis EL, Georgiadis M, Liu Y, Raymond SJ, Grant G, Kleiven S, Camarillo D, Zeineh M. The Presence of the Temporal Horn Exacerbates the Vulnerability of Hippocampus During Head Impacts. Front Bioeng Biotechnol 2022; 10:754344. [PMID: 35392406 PMCID: PMC8980591 DOI: 10.3389/fbioe.2022.754344] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 01/19/2022] [Indexed: 11/13/2022] Open
Abstract
Hippocampal injury is common in traumatic brain injury (TBI) patients, but the underlying pathogenesis remains elusive. In this study, we hypothesize that the presence of the adjacent fluid-containing temporal horn exacerbates the biomechanical vulnerability of the hippocampus. Two finite element models of the human head were used to investigate this hypothesis, one with and one without the temporal horn, and both including a detailed hippocampal subfield delineation. A fluid-structure interaction coupling approach was used to simulate the brain-ventricle interface, in which the intraventricular cerebrospinal fluid was represented by an arbitrary Lagrangian-Eulerian multi-material formation to account for its fluid behavior. By comparing the response of these two models under identical loadings, the model that included the temporal horn predicted increased magnitudes of strain and strain rate in the hippocampus with respect to its counterpart without the temporal horn. This specifically affected cornu ammonis (CA) 1 (CA1), CA2/3, hippocampal tail, subiculum, and the adjacent amygdala and ventral diencephalon. These computational results suggest that the presence of the temporal horn exacerbate the vulnerability of the hippocampus, highlighting the mechanobiological dependency of the hippocampus on the temporal horn.
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Affiliation(s)
- Zhou Zhou
- Department of Bioengineering, Stanford University, Stanford, CA, United States
- Neuronic Engineering, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Xiaogai Li
- Neuronic Engineering, KTH Royal Institute of Technology, Stockholm, Sweden
| | - August G. Domel
- Department of Bioengineering, Stanford University, Stanford, CA, United States
| | - Emily L. Dennis
- TBI and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT, United States
- Department of Radiology, Stanford University, Stanford, CA, United States
| | - Marios Georgiadis
- Department of Radiology, Stanford University, Stanford, CA, United States
| | - Yuzhe Liu
- Department of Bioengineering, Stanford University, Stanford, CA, United States
| | - Samuel J. Raymond
- Department of Bioengineering, Stanford University, Stanford, CA, United States
| | - Gerald Grant
- Department of Neurosurgery, Stanford University, Stanford, CA, United States
- Department of Neurology, Stanford University, Stanford, CA, United States
| | - Svein Kleiven
- Neuronic Engineering, KTH Royal Institute of Technology, Stockholm, Sweden
| | - David Camarillo
- Department of Bioengineering, Stanford University, Stanford, CA, United States
- Department of Neurosurgery, Stanford University, Stanford, CA, United States
- Department of Mechanical Engineering, Stanford University, Stanford, CA, United States
| | - Michael Zeineh
- Department of Radiology, Stanford University, Stanford, CA, United States
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19
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An interdisciplinary computational model for predicting traumatic brain injury: Linking biomechanics and functional neural networks. Neuroimage 2022; 251:119002. [PMID: 35176490 DOI: 10.1016/j.neuroimage.2022.119002] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 01/19/2022] [Accepted: 02/12/2022] [Indexed: 11/22/2022] Open
Abstract
The brain is a complex network consisting of neuron cell bodies in the gray matter and their axonal projections, forming the white matter tracts. These neurons are supported by an equally complex vascular network as well as glial cells. Traumatic brain injury (TBI) can lead to the disruption of the structural and functional brain networks due to disruption of both neuronal cell bodies in the gray matter as well as their projections and supporting cells. To explore how an impact can alter the function of brain networks, we integrated a finite element (FE) brain mechanics model with linked models of brain dynamics (Kuramoto oscillator) and vascular perfusion (Balloon-Windkessel) in this study. We used empirical resting-state functional magnetic resonance imaging (MRI) data to optimize the fit of our brain dynamics and perfusion models to clinical data. Results from the FE model were used to mimic injury in these optimized brain dynamics models: injury to the nodes (gray matter) led to a decrease in the nodal oscillation frequency, while damage to the edges (axonal connections/white matter) progressively decreased coupling among connected nodes. A total of 53 cases, including 33 non-injurious and 20 concussive head impacts experienced by professional American football players were simulated using this integrated model. We examined the correlation of injury outcomes with global measures of structural connectivity, neural dynamics, and functional connectivity of the brain networks when using different lesion methods. Results show that injurious head impacts cause significant alterations in global network topology regardless of lesion methods. Changes between the disrupted and healthy functional connectivity (measured by Pearson correlation) consistently correlated well with injury outcomes (AUC≥0.75), although the predictive performance is not significantly different (p>0.05) to that of traditional kinematic measures (angular acceleration). Intriguingly, our lesion model for gray matter damage predicted increases in global efficiency and clustering coefficient with increases in injury risk, while disrupting axonal connections led to lower network efficiency and clustering. When both injury mechanisms were combined into a single injury prediction model, the injury prediction performance depended on the thresholds used to determine neurodegeneration and mechanical tolerance for axonal injury. Together, these results point towards complex effects of mechanical trauma to the brain and provide a new framework for understanding brain injury at a causal mechanistic level and developing more effective diagnostic methods and therapeutic interventions.
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20
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Injury Metrics for Assessing the Risk of Acute Subdural Hematoma in Traumatic Events. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182413296. [PMID: 34948905 PMCID: PMC8702226 DOI: 10.3390/ijerph182413296] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 12/08/2021] [Accepted: 12/10/2021] [Indexed: 11/17/2022]
Abstract
Worldwide, the ocurrence of acute subdural hematomas (ASDHs) in road traffic crashes is a major public health problem. ASDHs are usually produced by loss of structural integrity of one of the cerebral bridging veins (CBVs) linking the parasagittal sinus to the brain. Therefore, to assess the risk of ASDH it is important to know the mechanical conditions to which the CBVs are subjected during a potentially traumatic event (such as a traffic accident or a fall from height). Recently, new studies on CBVs have been published allowing much more accurate prediction of the likelihood of mechanical failure of CBVs. These new data can be used to propose new damage metrics, which make more accurate predictions about the probability of occurrence of ASDH in road crashes. This would allow a better assessement of the effects of passive safety countermeasures and, consequently, to improve vehicle restraint systems. Currently, some widely used damage metrics are based on partially obsolete data and measurements of the mechanical behavior of CBVs that have not been confirmed by subsequent studies. This paper proposes a revision of some existing metrics and constructs a new metric based on more accurate recent data on the mechanical failure of human CBVs.
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21
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Wu T, Sato F, Antona-Makoshi J, Gabler L, Giudice JS, Alshareef A, Yaguchi M, Masuda M, Margulies S, Panzer MB. Integrating Human and Non-Human Primate Data to Estimate Human Tolerances for Traumatic Brain Injury. J Biomech Eng 2021; 144:1129238. [PMID: 34897386 DOI: 10.1115/1.4053209] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Indexed: 11/08/2022]
Abstract
Traumatic brain injury (TBI) contributes to a significant portion of the injuries resulting from motor vehicle crashes, falls, and sports collisions. The development of advanced countermeasures to mitigate these injuries requires a complete understanding of the tolerance of the human brain to injury. In this study, we developed a new method to establish human injury tolerance levels using an integrated database of reconstructed football impacts, sub-injurious human volunteer data, and non-human primate data. The human tolerance levels were analyzed using tissue-level metrics determined using harmonized species-specific finite element brain models. Kinematics-based metrics involving complete characterization of angular motion (e.g., DAMAGE) showed better power of predicting tissue-level deformation in a variety of impact conditions and were subsequently used to characterize injury tolerance. The proposed human brain tolerances for mild and severe TBI were estimated and presented in the form of injury risk curves based on selected tissue-level and kinematics-based injury metrics. The application of the estimated injury tolerances was finally demonstrated using real-world automotive crash data.
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Affiliation(s)
- Taotao Wu
- Center for Applied Biomechanics, University of Virginia, Charlottesville, VA, USA
| | - Fusako Sato
- Safety Research Division, Japan Automobile Research Institute, Tsukuba, Japan
| | | | - Lee Gabler
- Center for Applied Biomechanics, University of Virginia, Charlottesville, VA, USA
| | - J Sebastian Giudice
- Center for Applied Biomechanics, University of Virginia, Charlottesville, VA, USA
| | - Ahmed Alshareef
- Center for Applied Biomechanics, University of Virginia, Charlottesville, VA, USA
| | - Masayuki Yaguchi
- Safety Research Division, Japan Automobile Research Institute, Tsukuba, Japan
| | - Mitsutoshi Masuda
- Safety Subcommittee, Japan Automobile Manufacturers Association, Inc., Tokyo, Japan
| | - Susan Margulies
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Matthew B Panzer
- Center for Applied Biomechanics, University of Virginia, Charlottesville, VA, USA
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22
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Blinkouskaya Y, Caçoilo A, Gollamudi T, Jalalian S, Weickenmeier J. Brain aging mechanisms with mechanical manifestations. Mech Ageing Dev 2021; 200:111575. [PMID: 34600936 PMCID: PMC8627478 DOI: 10.1016/j.mad.2021.111575] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 09/09/2021] [Accepted: 09/22/2021] [Indexed: 12/14/2022]
Abstract
Brain aging is a complex process that affects everything from the subcellular to the organ level, begins early in life, and accelerates with age. Morphologically, brain aging is primarily characterized by brain volume loss, cortical thinning, white matter degradation, loss of gyrification, and ventricular enlargement. Pathophysiologically, brain aging is associated with neuron cell shrinking, dendritic degeneration, demyelination, small vessel disease, metabolic slowing, microglial activation, and the formation of white matter lesions. In recent years, the mechanics community has demonstrated increasing interest in modeling the brain's (bio)mechanical behavior and uses constitutive modeling to predict shape changes of anatomically accurate finite element brain models in health and disease. Here, we pursue two objectives. First, we review existing imaging-based data on white and gray matter atrophy rates and organ-level aging patterns. This data is required to calibrate and validate constitutive brain models. Second, we review the most critical cell- and tissue-level aging mechanisms that drive white and gray matter changes. We focuse on aging mechanisms that ultimately manifest as organ-level shape changes based on the idea that the integration of imaging and mechanical modeling may help identify the tipping point when normal aging ends and pathological neurodegeneration begins.
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Affiliation(s)
- Yana Blinkouskaya
- Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States
| | - Andreia Caçoilo
- Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States
| | - Trisha Gollamudi
- Department of Biomedical Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States
| | - Shima Jalalian
- 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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23
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Wofford KL, Grovola MR, Adewole DO, Browne KD, Putt ME, O’Donnell JC, Cullen DK. Relationships between injury kinematics, neurological recovery, and pathology following concussion. Brain Commun 2021; 3:fcab268. [PMID: 34934944 PMCID: PMC8684470 DOI: 10.1093/braincomms/fcab268] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 06/21/2021] [Accepted: 07/01/2021] [Indexed: 11/12/2022] Open
Abstract
Mild traumatic brain injury affects millions of individuals annually primarily through falls, traffic collisions, or blunt trauma and can generate symptoms that persist for years. Closed-head rotational loading is the most common cause of mild traumatic brain injury and is defined by a rapid rotational acceleration of brain tissue within an intact skull. Injury kinematics-the mechanical descriptors of injury-inducing motion-explain movement of the head, which govern energy transfer, and, therefore, determine injury severity. However, the relationship between closed-head rotational injury kinematics-such as angular velocity, angular acceleration, and injury duration-and outcome after mild traumatic brain injury is not completely understood. To address this gap in knowledge, we analysed archived surgical records of 24 swine experiencing a diffuse closed-head rotational acceleration mild traumatic brain injury against 12 sham animals. Kinematics were contrasted against acute recovery outcomes, specifically apnea time, extubation time, standing time, and recovery duration. Compared to controls, animals experiencing a mild traumatic brain injury were far more likely to have apnea (P < 0.001), shorter time to extubation (P = 0.023), and longer time from extubation to standing (P = 0.006). Using least absolute shrinkage and selection operator-based regressions, kinematic parameters, including maximum negative angular velocity and time from peak angular velocity to maximum angular deceleration, were selected to explain variation in apnea time, standing time, and recovery duration. Simplified linear models employing the least absolute shrinkage and selection operator-selected variables explained a modest degree of variation in apnea time (adjusted R 2 = 0.18), standing time (adjusted R 2 = 0.19), and recovery duration (adjusted R 2 = 0.27). Neuropathology was correlated with multiple injury kinematics, with maximum angular acceleration exhibiting the strongest correlation (R 2 = 0.66). Together, these data suggest the interplay between multiple injury kinematics, including maximum negative angular velocity (immediately preceding cessation of head motion) and time from peak angular velocity to maximum angular deceleration, best explain acute recovery metrics and neuropathology after mild traumatic brain injury in swine. Future experiments that independently manipulate individual kinematic parameters could be instrumental in developing translational diagnostics for clinical mild traumatic brain injury.
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Affiliation(s)
- Kathryn L Wofford
- Center for Brain Injury & Repair, Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neurotrauma, Neurodegeneration and Restoration, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA 19104, USA
| | - Michael R Grovola
- Center for Brain Injury & Repair, Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neurotrauma, Neurodegeneration and Restoration, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA 19104, USA
| | - Dayo O Adewole
- Center for Neurotrauma, Neurodegeneration and Restoration, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kevin D Browne
- Center for Brain Injury & Repair, Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neurotrauma, Neurodegeneration and Restoration, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA 19104, USA
| | - Mary E Putt
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - John C O’Donnell
- Center for Brain Injury & Repair, Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neurotrauma, Neurodegeneration and Restoration, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA 19104, USA
| | - D Kacy Cullen
- Center for Brain Injury & Repair, Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neurotrauma, Neurodegeneration and Restoration, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
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24
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Hoffe B, Mazurkiewicz A, Thomson H, Banton R, Piehler T, Petel OE, Holahan MR. Relating strain fields with microtubule changes in porcine cortical sulci following drop impact. J Biomech 2021; 128:110708. [PMID: 34492445 DOI: 10.1016/j.jbiomech.2021.110708] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 08/06/2021] [Accepted: 08/23/2021] [Indexed: 12/31/2022]
Abstract
The biomechanical response of brain tissue to strain and the immediate neural outcomes are of fundamental importance in understanding mild traumatic brain injury (mTBI). The sensitivity of neural tissue to dynamic strain events and the resulting strain-induced changes are considered to be a primary factor in injury. Rodent models have been used extensively to investigate impact-induced injury. However, the lissencephalic structure is inconsistent with the human brain, which is gyrencephalic (convoluted structure), and differs considerably in strain field localization effects. Porcine brains have a similar structure to the human brain, containing a similar ratio of white-grey matter and gyrification in the cortex. In this study, coronal brain slabs were extracted from female pig brains within 2hrs of sacrifice. Slabs were implanted with neutral density radiopaque markers, sealed inside an elastomeric encasement, and dropped from 0.9 m onto a steel anvil. Particle tracking revealed elevated tensile strains in the sulcus. One hour after impact, decreased microtubule associated protein 2 (MAP2) was found exclusively within the sulcus with no increase in cell death. These results suggest that elevated tensile strain in the sulcus may result in compromised cytoskeleton, possibly indicating a vulnerability to pathological outcomes under the right circumstances. The results demonstrated that the observed changes were unrelated to shear strain loading of the tissues but were more sensitive to tensile load.
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Affiliation(s)
- Brendan Hoffe
- Departement of Neuroscience, Carleton University, Ottawa Ontario K1S 5B6, Canada.
| | - Ashley Mazurkiewicz
- Department of Mechanical and Aerospace Engineering, Carleton University, Ottawa Ontario K1S 5B6, Canada
| | - Hannah Thomson
- Department of Mechanical and Aerospace Engineering, Carleton University, Ottawa Ontario K1S 5B6, Canada
| | - Rohan Banton
- U.S. Army Research Laboratory, Aberdeen Proving Ground, Maryland 21005-5066, United States
| | - Thuvan Piehler
- U.S. Army Research Laboratory, Aberdeen Proving Ground, Maryland 21005-5066, United States
| | - Oren E Petel
- Department of Mechanical and Aerospace Engineering, Carleton University, Ottawa Ontario K1S 5B6, Canada
| | - Matthew R Holahan
- Departement of Neuroscience, Carleton University, Ottawa Ontario K1S 5B6, Canada
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25
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Shoemaker AR, Jones IE, Jeffris KD, Gabrielli G, Togliatti AG, Pichika R, Martin E, Kiskinis E, Franz CK, Finan J. Biofidelic dynamic compression of human cortical spheroids reproduces neurotrauma phenotypes. Dis Model Mech 2021; 14:273823. [PMID: 34746950 PMCID: PMC8713991 DOI: 10.1242/dmm.048916] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 11/02/2021] [Indexed: 11/20/2022] Open
Abstract
Fundamental questions about patient heterogeneity and human-specific pathophysiology currently obstruct progress towards a therapy for traumatic brain injury (TBI). Human in vitro models have the potential to address these questions. 3D spheroidal cell culture protocols for human-origin neural cells have several important advantages over their 2D monolayer counterparts. Three dimensional spheroidal cultures may mature more quickly, develop more biofidelic electrophysiological activity and/or reproduce some aspects of brain architecture. Here, we present the first human in vitro model of non-penetrating TBI employing 3D spheroidal cultures. We used a custom-built device to traumatize these spheroids in a quantifiable, repeatable and biofidelic manner and correlated the heterogeneous, mechanical strain field with the injury phenotype. Trauma reduced cell viability, mitochondrial membrane potential and spontaneous, synchronous, electrophysiological activity in the spheroids. Electrophysiological deficits emerged at lower injury severities than changes in cell viability. Also, traumatized spheroids secreted lactate dehydrogenase, a marker of cell damage, and neurofilament light chain, a promising clinical biomarker of neurotrauma. These results demonstrate that 3D human in vitro models can reproduce important phenotypes of neurotrauma in vitro.
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Affiliation(s)
- Aaron R Shoemaker
- Department of Neurosurgery, NorthShore University Health System, Evanston, IL, USA
| | - Ian E Jones
- Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL, USA
| | - Kira D Jeffris
- Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL, USA
| | - Gina Gabrielli
- Department of Neurosurgery, NorthShore University Health System, Evanston, IL, USA
| | | | - Rajeswari Pichika
- Department of Physical Medicine and Rehabilitation, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Eric Martin
- Department of Physical Medicine and Rehabilitation, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Evangelos Kiskinis
- The Ken & Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Colin K Franz
- Shirley Ryan AbilityLab, Chicago, IL, USA.,Department of Physical Medicine and Rehabilitation, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.,The Ken & Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - John Finan
- Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL, USA
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26
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Menichetti A, Bartsoen L, Depreitere B, Vander Sloten J, Famaey N. A Machine Learning Approach to Investigate the Uncertainty of Tissue-Level Injury Metrics for Cerebral Contusion. Front Bioeng Biotechnol 2021; 9:714128. [PMID: 34692652 PMCID: PMC8531645 DOI: 10.3389/fbioe.2021.714128] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 08/10/2021] [Indexed: 11/13/2022] Open
Abstract
Controlled cortical impact (CCI) on porcine brain is often utilized to investigate the pathophysiology and functional outcome of focal traumatic brain injury (TBI), such as cerebral contusion (CC). Using a finite element (FE) model of the porcine brain, the localized brain strain and strain rate resulting from CCI can be computed and compared to the experimentally assessed cortical lesion. This way, tissue-level injury metrics and corresponding thresholds specific for CC can be established. However, the variability and uncertainty associated with the CCI experimental parameters contribute to the uncertainty of the provoked cortical lesion and, in turn, of the predicted injury metrics. Uncertainty quantification via probabilistic methods (Monte Carlo simulation, MCS) requires a large number of FE simulations, which results in a time-consuming process. Following the recent success of machine learning (ML) in TBI biomechanical modeling, we developed an artificial neural network as surrogate of the FE porcine brain model to predict the brain strain and the strain rate in a computationally efficient way. We assessed the effect of several experimental and modeling parameters on four FE-derived CC injury metrics (maximum principal strain, maximum principal strain rate, product of maximum principal strain and strain rate, and maximum shear strain). Next, we compared the in silico brain mechanical response with cortical damage data from in vivo CCI experiments on pig brains to evaluate the predictive performance of the CC injury metrics. Our ML surrogate was capable of rapidly predicting the outcome of the FE porcine brain undergoing CCI. The now computationally efficient MCS showed that depth and velocity of indentation were the most influential parameters for the strain and the strain rate-based injury metrics, respectively. The sensitivity analysis and comparison with the cortical damage experimental data indicate a better performance of maximum principal strain and maximum shear strain as tissue-level injury metrics for CC. These results provide guidelines to optimize the design of CCI tests and bring new insights to the understanding of the mechanical response of brain tissue to focal traumatic brain injury. Our findings also highlight the potential of using ML for computationally efficient TBI biomechanics investigations.
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Affiliation(s)
- Andrea Menichetti
- Biomechanics Section, Department of Mechanical Engineering, KU Leuven, Leuven, Belgium
| | - Laura Bartsoen
- Biomechanics Section, Department of Mechanical Engineering, KU Leuven, Leuven, Belgium
| | | | - Jos Vander Sloten
- Biomechanics Section, Department of Mechanical Engineering, KU Leuven, Leuven, Belgium
| | - Nele Famaey
- Biomechanics Section, Department of Mechanical Engineering, KU Leuven, Leuven, Belgium
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27
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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: 12] [Impact Index Per Article: 4.0] [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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28
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Singh A, Ganpule SG, Khan MK, Iqbal MA. Measurement of brain simulant strains in head surrogate under impact loading. Biomech Model Mechanobiol 2021; 20:2319-2334. [PMID: 34455505 DOI: 10.1007/s10237-021-01509-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Accepted: 08/13/2021] [Indexed: 10/20/2022]
Abstract
Impact-induced traumatic brain injury (TBI) is a major source of disability and mortality. Knowledge of brain strains during impact (accelerative) loading is critical for the overall management of TBI, including the development of injury thresholds, personal protective equipment, and validation of computational models. Despite these needs, the current understanding of brain strains in humans or humanlike surrogates is limited, especially for injury causing loading magnitudes. Toward this end, we measured full-field, in-plane (2D) strains in a brain simulant using the hemispherical head surrogate. The hemispherical head was mounted on the Hybrid-III neck and subjected to impact loading using a linear impactor system. The resulting head kinematics was measured using a triaxial accelerometer and angular rate sensors. Dynamic, 2D strains in a brain simulant were obtained using high-speed imaging and digital image correlation. Concurrent finite element (FE) simulations of the experiment were also performed to gain additional insights. The role of stiff membranes of the head was also studied using experiments. Our results suggest that rotational modes dominate the response of the brain simulant. The wave propagation in the brain simulant as a result of impact has a timescale of ~100 ms. We obtain peak strains of ~20%, ~40%, ~60% for peak rotational accelerations of ~838, ~5170, ~11,860 rad/s2, respectively. Further, peak strains in cortical regions are higher than subcortical regions by up to ~70%. The agreement between the experiments and FE simulations is reasonable in terms of spatiotemporal evolution of strain pattern and peak strain magnitudes. Experiments with the addition of falx and tentorium indicate significant strain concentration (up to 115%) in the brain simulant near the interface of falx or tentorium and brain simulant. Overall, this work provides important insights into the biomechanics of strain in the brain simulant during impact loading.
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Affiliation(s)
- A Singh
- Department of Mechanical and Industrial Engineering, Indian Institute of Technology Roorkee, Roorkee, 247667, India
| | - S G Ganpule
- Department of Mechanical and Industrial Engineering, Indian Institute of Technology Roorkee, Roorkee, 247667, India.
| | - M K Khan
- Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee, 247667, India
| | - M A Iqbal
- Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee, 247667, India
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29
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Braun NJ, Liao D, Alford PW. Orientation of neurites influences severity of mechanically induced tau pathology. Biophys J 2021; 120:3272-3282. [PMID: 34293301 PMCID: PMC8392125 DOI: 10.1016/j.bpj.2021.07.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 06/11/2021] [Accepted: 07/13/2021] [Indexed: 01/03/2023] Open
Abstract
Chronic traumatic encephalopathy is a neurodegenerative disease associated with repeated traumatic brain injury (TBI). Chronic traumatic encephalopathy is a tauopathy, in which cognitive decline is accompanied by the accumulation of neurofibrillary tangles of the protein tau in patients' brains. We recently found that mechanical force alone can induce tau mislocalization to dendritic spines and loss of synaptic function in in vitro neuronal cultures with random cell organization. However, in the brain, neurons are highly aligned, so here we aimed to determine how neuronal organization influences early-stage tauopathy caused by mechanical injury. Using microfabricated cell culture constructs to control the growth of neurites and an in vitro simulated TBI device to apply controlled mechanical deformation, we found that neuronal orientation with respect to the direction of a uniaxial high-strain-rate stretch injury influences the degree of tau pathology in injured neurons. We found that a mechanical stretch applied parallel to the neurite alignment induces greater mislocalization of tau proteins to dendritic spines than does a stretch with the same strain applied perpendicular to the neurites. Synaptic function, characterized by the amplitude of miniature excitatory postsynaptic currents, was similarly decreased in neurons with neurites aligned parallel to stretch, whereas in neurons aligned perpendicular to stretch, it had little to no functional loss. Experimental injury parameters (strain, strain rate, direction of stretch) were combined with a standard viscoelastic solid model to show that in our in vitro model, neurite work density during stretch correlates with tau mislocalization. These findings suggest that in a TBI, the magnitude of brain deformation is not wholly predictive of neurodegenerative consequences of TBI but that deformation relative to local neuronal architecture and the neurite mechanical energy during injury are better metrics for predicting trauma-induced tauopathy.
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Affiliation(s)
| | - Dezhi Liao
- Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota.
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30
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Zhan X, Li Y, Liu Y, Domel AG, Alizadeh HV, Raymond SJ, Ruan J, Barbat S, Tiernan S, Gevaert O, Zeineh MM, Grant GA, Camarillo DB. The relationship between brain injury criteria and brain strain across different types of head impacts can be different. J R Soc Interface 2021; 18:20210260. [PMID: 34062102 PMCID: PMC8169213 DOI: 10.1098/rsif.2021.0260] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 05/06/2021] [Indexed: 12/27/2022] Open
Abstract
Multiple brain injury criteria (BIC) are developed to quickly quantify brain injury risks after head impacts. These BIC originated from different head impact types (e.g. sports and car crashes) are widely used in risk evaluation. However, the accuracy of using the BIC on brain injury risk estimation across head impact types has not been evaluated. Physiologically, brain strain is often considered the key parameter of brain injury. To evaluate the BIC's risk estimation accuracy across five datasets comprising different head impact types, linear regression was used to model 95% maximum principal strain, 95% maximum principal strain at the corpus callosum and cumulative strain damage (15%) on 18 BIC. The results show significantly different relationships between BIC and brain strain across datasets, indicating the same BIC value may suggest different brain strain across head impact types. The accuracy of brain strain regression is generally decreasing if the BIC regression models are fitted on a dataset with a different type of head impact rather than on the dataset with the same type. Given this finding, this study raises concerns for applying BIC to estimate the brain injury risks for head impacts different from the head impacts on which the BIC was developed.
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Affiliation(s)
- Xianghao Zhan
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Yiheng Li
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Yuzhe Liu
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - August G. Domel
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | | | - Samuel J. Raymond
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Jesse Ruan
- Ford Motor Company, 3001 Miller Road, Dearborn, MI 48120, USA
| | - Saeed Barbat
- Ford Motor Company, 3001 Miller Road, Dearborn, MI 48120, USA
| | | | - Olivier Gevaert
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Michael M. Zeineh
- Department of Radiology, Stanford University, Stanford, CA 94305, USA
| | - Gerald A. Grant
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA
| | - David B. Camarillo
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
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31
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Mazurkiewicz A, Xu S, Frei H, Banton R, Piehler T, Petel OE. Impact-Induced Cortical Strain Concentrations at the Sulcal Base and Its Implications for Mild Traumatic Brain Injury. J Biomech Eng 2021; 143:061015. [PMID: 33625494 DOI: 10.1115/1.4050283] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Indexed: 11/08/2022]
Abstract
This study investigated impact-induced strain fields within brain tissue surrogates having different cortical gyrification. Two elastomeric surrogates, one representative of a lissencephalic brain and the other of a gyrencephalic brain, were drop impacted in unison at four different heights and in two different orientations. Each surrogate contained a radiopaque speckle pattern that was used to calculate strain fields. Two different approaches, digital image correlation (DIC) and a particle tracking method, enabled comparisons of full-field and localized strain responses. The DIC results demonstrated increased localized deviations from the mean strain field in the surrogate with a gyrified cortex. Particle tracking algorithms, defining four-node quadrilateral elements, were used to investigate the differences in the strain response of three regions: the base of a sulcus, the adjacent gyrus, and the internal capsule of the surrogates. The results demonstrated that the strains in the cortex were concentrated at the sulcal base. This mechanical mechanism of increased strain is consistent with neurodegenerative markers observed in postmortem analyses, suggesting a potential mechanism of local damage due to strain amplification at the sulcal bases in gyrencephalic brains. This strain amplification mechanism may be responsible for cumulative neurodegeneration from repeated subconcussive impacts. The observed results suggest that lissencephalic animal models, such as rodents, would not have the same modes of injury present in a gyrencephalic brain, such as that of a human. As such, a shift toward representative mild traumatic brain injury animal models having gyrencephalic cortical structures should be strongly considered.
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Affiliation(s)
- Ashley Mazurkiewicz
- Department of Mechanical and Aerospace Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada
| | - Sheng Xu
- Department of Mechanical and Aerospace Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada
| | - Hanspeter Frei
- Department of Mechanical and Aerospace Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada
| | - Rohan Banton
- U.S. Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD 21005-5066
| | - Thuvan Piehler
- U.S. Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD 21005-5066; U.S. Army Medical Research and Development Command, Fort Detrick, MD 21702
| | - Oren E Petel
- Department of Mechanical and Aerospace Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada
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32
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Wu T, Hajiaghamemar M, Giudice JS, Alshareef A, Margulies SS, Panzer MB. Evaluation of Tissue-Level Brain Injury Metrics Using Species-Specific Simulations. J Neurotrauma 2021; 38:1879-1888. [PMID: 33446011 PMCID: PMC8219195 DOI: 10.1089/neu.2020.7445] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Traumatic brain injury (TBI) is a significant public health burden, and the development of advanced countermeasures to mitigate and prevent these injuries during automotive, sports, and military impact events requires an understanding of the intracranial mechanisms related to TBI. In this study, the efficacy of tissue-level injury metrics for predicting TBI was evaluated using finite element reconstructions from a comprehensive, multi-species TBI database. The database consisted of human volunteer tests, laboratory-reconstructed head impacts from sports, in vivo non-human primate (NHP) tests, and in vivo pig tests. Eight tissue-level metrics related to brain tissue strain, axonal strain, and strain-rate were evaluated using survival analysis for predicting mild and severe TBI risk. The correlation between TBI risk and most of the assessed metrics were statistically significant, but when injury data was analyzed by species, the best metric was often inconclusive and limited by the small datasets. When the human and animal datasets were combined, the injury analysis was able to delineate maximum axonal strain as the best predictor of injury for all species and TBI severities, with maximum principal strain as a suitable alternative metric. The current study is the first to provide evidence to support the assumption that brain strain response between human, pig, and NHP result in similar injury outcomes through a multi-species analysis. This assumption is the biomechanical foundation for translating animal brain injury findings to humans. The findings in the study provide fundamental guidelines for developing injury criteria that would contribute towards the innovation of more effective safety countermeasures.
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Affiliation(s)
- Taotao Wu
- Center for Applied Biomechanics, University of Virginia, Charlottesville, Virginia, USA
| | - Marzieh Hajiaghamemar
- Department of Biomedical Engineering, University of Texas at San Antonio, San Antonio, Texas, USA
| | - J. Sebastian Giudice
- Center for Applied Biomechanics, University of Virginia, Charlottesville, Virginia, USA
| | - Ahmed Alshareef
- Center for Applied Biomechanics, University of Virginia, Charlottesville, Virginia, USA
| | - Susan S. Margulies
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA
| | - Matthew B. Panzer
- Center for Applied Biomechanics, University of Virginia, Charlottesville, Virginia, USA
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33
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Giudice JS, Alshareef A, Wu T, Knutsen AK, Hiscox LV, Johnson CL, Panzer MB. Calibration of a Heterogeneous Brain Model Using a Subject-Specific Inverse Finite Element Approach. Front Bioeng Biotechnol 2021; 9:664268. [PMID: 34017826 PMCID: PMC8129184 DOI: 10.3389/fbioe.2021.664268] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 04/12/2021] [Indexed: 12/02/2022] Open
Abstract
Central to the investigation of the biomechanics of traumatic brain injury (TBI) and the assessment of injury risk from head impact are finite element (FE) models of the human brain. However, many existing FE human brain models have been developed with simplified representations of the parenchyma, which may limit their applicability as an injury prediction tool. Recent advances in neuroimaging techniques and brain biomechanics provide new and necessary experimental data that can improve the biofidelity of FE brain models. In this study, the CAB-20MSym template model was developed, calibrated, and extensively verified. To implement material heterogeneity, a magnetic resonance elastography (MRE) template image was leveraged to define the relative stiffness gradient of the brain model. A multi-stage inverse FE (iFE) approach was used to calibrate the material parameters that defined the underlying non-linear deviatoric response by minimizing the error between model-predicted brain displacements and experimental displacement data. This process involved calibrating the infinitesimal shear modulus of the material using low-severity, low-deformation impact cases and the material non-linearity using high-severity, high-deformation cases from a dataset of in situ brain displacements obtained from cadaveric specimens. To minimize the geometric discrepancy between the FE models used in the iFE calibration and the cadaveric specimens from which the experimental data were obtained, subject-specific models of these cadaveric brain specimens were developed and used in the calibration process. Finally, the calibrated material parameters were extensively verified using independent brain displacement data from 33 rotational head impacts, spanning multiple loading directions (sagittal, coronal, axial), magnitudes (20–40 rad/s), durations (30–60 ms), and severity. Overall, the heterogeneous CAB-20MSym template model demonstrated good biofidelity with a mean overall CORA score of 0.63 ± 0.06 when compared to in situ brain displacement data. Strains predicted by the calibrated model under non-injurious rotational impacts in human volunteers (N = 6) also demonstrated similar biofidelity compared to in vivo measurements obtained from tagged magnetic resonance imaging studies. In addition to serving as an anatomically accurate model for further investigations of TBI biomechanics, the MRE-based framework for implementing material heterogeneity could serve as a foundation for incorporating subject-specific material properties in future models.
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Affiliation(s)
- J Sebastian Giudice
- Center for Applied Biomechanics, University of Virginia, Charlottesville, VA, United States
| | - Ahmed Alshareef
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Taotao Wu
- Center for Applied Biomechanics, University of Virginia, Charlottesville, VA, United States
| | - Andrew K Knutsen
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, United States
| | - Lucy V Hiscox
- Department of Biomedical Engineering, University of Delaware, Newark, DE, United States
| | - Curtis L Johnson
- Department of Biomedical Engineering, University of Delaware, Newark, DE, United States
| | - Matthew B Panzer
- Center for Applied Biomechanics, University of Virginia, Charlottesville, VA, United States
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34
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Zhou Z, Domel AG, Li X, Grant G, Kleiven S, Camarillo D, Zeineh M. White Matter Tract-Oriented Deformation Is Dependent on Real-Time Axonal Fiber Orientation. J Neurotrauma 2021; 38:1730-1745. [PMID: 33446060 DOI: 10.1089/neu.2020.7412] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Traumatic axonal injury (TAI) is a critical public health issue with its pathogenesis remaining largely elusive. Finite element (FE) head models are promising tools to bridge the gap between mechanical insult, localized brain response, and resultant injury. In particular, the FE-derived deformation along the direction of white matter (WM) tracts (i.e., tract-oriented strain) has been shown to be an appropriate predictor for TAI. The evolution of fiber orientation in time during the impact and its potential influence on the tract-oriented strain remains unknown, however. To address this question, the present study leveraged an embedded element approach to track real-time fiber orientation during impacts. A new scheme to calculate the tract-oriented strain was proposed by projecting the strain tensors from pre-computed simulations along the temporal fiber direction instead of its static counterpart directly obtained from diffuse tensor imaging. The results revealed that incorporating the real-time fiber orientation not only altered the direction but also amplified the magnitude of the tract-oriented strain, resulting in a generally more extended distribution and a larger volume ratio of WM exposed to high deformation along fiber tracts. These effects were exacerbated with the impact severities characterized by the acceleration magnitudes. Results of this study provide insights into how best to incorporate fiber orientation in head injury models and derive the WM tract-oriented deformation from computational simulations, which is important for furthering our understanding of the underlying mechanisms of TAI.
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Affiliation(s)
- Zhou Zhou
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - August G Domel
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Xiaogai Li
- Neuronic Engineering, KTH Royal Institute of Technology, Stockholm, Sweden
| | - 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 Neurosurgery, 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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35
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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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36
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37
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Wofford KL, Grovola MR, Adewole DO, Browne KD, Putt ME, O'Donnell JC, Cullen DK. Relationships between injury kinematics, neurological recovery, and pathology following concussion. Brain Commun 2021. [PMID: 34934944 DOI: 10.1093/braincomms/fcab268/6430108] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/11/2023] Open
Abstract
Mild traumatic brain injury affects millions of individuals annually primarily through falls, traffic collisions, or blunt trauma and can generate symptoms that persist for years. Closed-head rotational loading is the most common cause of mild traumatic brain injury and is defined by a rapid rotational acceleration of brain tissue within an intact skull. Injury kinematics-the mechanical descriptors of injury-inducing motion-explain movement of the head, which govern energy transfer, and, therefore, determine injury severity. However, the relationship between closed-head rotational injury kinematics-such as angular velocity, angular acceleration, and injury duration-and outcome after mild traumatic brain injury is not completely understood. To address this gap in knowledge, we analysed archived surgical records of 24 swine experiencing a diffuse closed-head rotational acceleration mild traumatic brain injury against 12 sham animals. Kinematics were contrasted against acute recovery outcomes, specifically apnea time, extubation time, standing time, and recovery duration. Compared to controls, animals experiencing a mild traumatic brain injury were far more likely to have apnea (P < 0.001), shorter time to extubation (P = 0.023), and longer time from extubation to standing (P = 0.006). Using least absolute shrinkage and selection operator-based regressions, kinematic parameters, including maximum negative angular velocity and time from peak angular velocity to maximum angular deceleration, were selected to explain variation in apnea time, standing time, and recovery duration. Simplified linear models employing the least absolute shrinkage and selection operator-selected variables explained a modest degree of variation in apnea time (adjusted R 2 = 0.18), standing time (adjusted R 2 = 0.19), and recovery duration (adjusted R 2 = 0.27). Neuropathology was correlated with multiple injury kinematics, with maximum angular acceleration exhibiting the strongest correlation (R 2 = 0.66). Together, these data suggest the interplay between multiple injury kinematics, including maximum negative angular velocity (immediately preceding cessation of head motion) and time from peak angular velocity to maximum angular deceleration, best explain acute recovery metrics and neuropathology after mild traumatic brain injury in swine. Future experiments that independently manipulate individual kinematic parameters could be instrumental in developing translational diagnostics for clinical mild traumatic brain injury.
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Affiliation(s)
- Kathryn L Wofford
- Center for Brain Injury & Repair, Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA 19104, USA.,Center for Neurotrauma, Neurodegeneration and Restoration, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA 19104, USA
| | - Michael R Grovola
- Center for Brain Injury & Repair, Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA 19104, USA.,Center for Neurotrauma, Neurodegeneration and Restoration, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA 19104, USA
| | - Dayo O Adewole
- Center for Neurotrauma, Neurodegeneration and Restoration, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA 19104, USA.,Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kevin D Browne
- Center for Brain Injury & Repair, Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA 19104, USA.,Center for Neurotrauma, Neurodegeneration and Restoration, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA 19104, USA
| | - Mary E Putt
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - John C O'Donnell
- Center for Brain Injury & Repair, Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA 19104, USA.,Center for Neurotrauma, Neurodegeneration and Restoration, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA 19104, USA
| | - D Kacy Cullen
- Center for Brain Injury & Repair, Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA 19104, USA.,Center for Neurotrauma, Neurodegeneration and Restoration, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA 19104, USA.,Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
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38
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Alshareef A, Knutsen AK, Johnson CL, Carass A, Upadhyay K, Bayly PV, Pham DL, Prince JL, Ramesh K. Integrating material properties from magnetic resonance elastography into subject-specific computational models for the human brain. BRAIN MULTIPHYSICS 2021; 2. [PMID: 37168236 PMCID: PMC10168673 DOI: 10.1016/j.brain.2021.100038] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Advances in brain imaging and computational methods have facilitated the creation of subject-specific computational brain models that aid researchers in investigating brain trauma using simulated impacts. The emergence of magnetic resonance elastography (MRE) as a non-invasive mechanical neuroimaging tool has enabled in vivo estimation of material properties at low-strain, harmonic loading. An open question in the field has been how this data can be integrated into computational models. The goals of this study were to use a novel MRI dataset acquired in human volunteers to generate models with subject-specific anatomy and material properties, and then to compare simulated brain deformations to subject-specific brain deformation data under non-injurious loading. Models of five subjects were simulated with linear viscoelastic (LVE) material properties estimated directly from MRE data. Model predictions were compared to experimental brain deformation acquired in the same subjects using tagged MRI. Outcomes from the models matched the spatial distribution and magnitude of the measured peak strain components as well as the 95th percentile in-plane peak strains within 0.005 mm/mm and maximum principal strain within 0.012 mm/mm. Sensitivity to material heterogeneity was also investigated. Simulated brain deformations from a model with homogenous brain properties and a model with brain properties discretized with up to ten regions were very similar (a mean absolute difference less than 0.0015 mm/mm in peak strains). Incorporating material properties directly from MRE into a biofidelic subject-specific model is an important step toward future investigations of higher-order model features and simulations under more severe loading conditions.
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39
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Mechanical threshold for concussion based on computation of axonal strain using a finite element rat brain model. BRAIN MULTIPHYSICS 2021. [DOI: 10.1016/j.brain.2021.100032] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
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Hajiaghamemar M, Margulies SS. Multi-Scale White Matter Tract Embedded Brain Finite Element Model Predicts the Location of Traumatic Diffuse Axonal Injury. J Neurotrauma 2020; 38:144-157. [PMID: 32772838 DOI: 10.1089/neu.2019.6791] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Abstract
Finite element models (FEMs) are used increasingly in the traumatic brain injury (TBI) field to provide an estimation of tissue responses and predict the probability of sustaining TBI after a biomechanical event. However, FEM studies have mainly focused on predicting the absence/presence of TBI rather than estimating the location of injury. In this study, we created a multi-scale FEM of the pig brain with embedded axonal tracts to estimate the sites of acute (≤6 h) traumatic axonal injury (TAI) after rapid head rotation. We examined three finite element (FE)-derived metrics related to the axonal bundle deformation and three FE-derived metrics based on brain tissue deformation for prediction of acute TAI location. Rapid head rotations were performed in pigs, and TAI neuropathological maps were generated and colocalized to the FEM. The distributions of the FEM-derived brain/axonal deformations spatially correlate with the locations of acute TAI. For each of the six metric candidates, we examined a matrix of different injury thresholds (thx) and distance to actual TAI sites (ds) to maximize the average of two optimization criteria. Three axonal deformation-related TAI candidates predicted the sites of acute TAI within 2.5 mm, but no brain tissue metric succeeded. The optimal range of thresholds for maximum axonal strain, maximum axonal strain rate, and maximum product of axonal strain and strain rate were 0.08-0.14, 40-90, and 2.0-7.5 s-1, respectively. The upper bounds of these thresholds resulted in higher true-positive prediction rate. In summary, this study confirmed the hypothesis that the large axonal-bundle deformations occur on/close to the areas that sustained TAI.
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Affiliation(s)
- Marzieh Hajiaghamemar
- Department of Biomedical Engineering, University of Texas at San Antonio, San Antonio, Texas, USA.,Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA
| | - Susan S Margulies
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA
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Knutsen AK, Gomez AD, Gangolli M, Wang WT, Chan D, Lu YC, Christoforou E, Prince JL, Bayly PV, Butman JA, Pham DL. In vivo estimates of axonal stretch and 3D brain deformation during mild head impact. BRAIN MULTIPHYSICS 2020; 1. [PMID: 33870238 DOI: 10.1016/j.brain.2020.100015] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
The rapid deformation of brain tissue in response to head impact can lead to traumatic brain injury. In vivo measurements of brain deformation during non-injurious head impacts are necessary to understand the underlying mechanisms of traumatic brain injury and compare to computational models of brain biomechanics. Using tagged magnetic resonance imaging (MRI), we obtained measurements of three-dimensional strain tensors that resulted from a mild head impact after neck rotation or neck extension. Measurements of maximum principal strain (MPS) peaked shortly after impact, with maximal values of 0.019-0.053 that correlated strongly with peak angular velocity. Subject-specific patterns of MPS were spatially heterogeneous and consistent across subjects for the same motion, though regions of high deformation differed between motions. The largest MPS values were seen in the cortical gray matter and cerebral white matter for neck rotation and the brainstem and cerebellum for neck extension. Axonal fiber strain (Ef) was estimated by combining the strain tensor with diffusion tensor imaging data. As with MPS, patterns of Ef varied spatially within subjects, were similar across subjects within each motion, and showed group differences between motions. Values were highest and most strongly correlated with peak angular velocity in the corpus callosum for neck rotation and in the brainstem for neck extension. The different patterns of brain deformation between head motions highlight potential areas of greater risk of injury between motions at higher loading conditions. Additionally, these experimental measurements can be directly compared to predictions of generic or subject-specific computational models of traumatic brain injury.
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Affiliation(s)
- Andrew K Knutsen
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, Maryland, USA
| | - Arnold D Gomez
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Mihika Gangolli
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, Maryland, USA
| | - Wen-Tung Wang
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, Maryland, USA
| | - Deva Chan
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, New York, USA
| | - Yuan-Chiao Lu
- Center for the Developing Brain, Children's National Hospital, Washington, D.C., USA
| | | | - Jerry L Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Philip V Bayly
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, Missouri, USA
| | - John A Butman
- Clinical Center, National Institutes of Health, Bethesda, Maryland, USA
| | - Dzung L Pham
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, Maryland, USA
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Pasquesi SA, Seidi M, Hajiaghamemar M, Margulies SS. Predictions of neonatal porcine bridging vein rupture and extra-axial hemorrhage during rapid head rotations. J Mech Behav Biomed Mater 2020; 106:103740. [PMID: 32250951 DOI: 10.1016/j.jmbbm.2020.103740] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Revised: 02/07/2020] [Accepted: 02/26/2020] [Indexed: 11/17/2022]
Abstract
When the head is rotated rapidly, the movement of the brain lags that of the skull. Intracranial contents between the brain and skull include meninges, cerebrospinal fluid (CSF), and cerebral vasculature. Among the cerebral vasculature in this space are the parasagittal bridging veins (BVs), which drain blood from the brain into the superior sagittal sinus (SSS), which is housed within the falx cerebri, adhered to the inner surface of the skull. Differential motion between the brain and skull that may occur during a traumatic event is thought to stretch BVs, causing damage and producing extra-axial hemorrhage (EAH). Finite element (FE) modeling is a technique often used to aid in the understanding and prediction of traumatic brain injury (TBI), and estimation of tissue deformation during traumatic events provides insight into kinematic injury thresholds. Using a FE model of the newborn porcine head with neonatal porcine brain and BV properties, single and cyclic rapid head rotations without impact were simulated. Measured BV failure properties were used to predict BV rupture. By comparing simulation outputs to observations of EAH in a development group of in vivo studies of rapid non-impact head rotations in the piglet, it was determined that failure of 16.7% of BV elements was associated with a 50% risk of EAH, and showed in a separate validation group that this threshold predicted the occurrence of EAH with 100% sensitivity and 100% specificity for single rapid non-impact rotations. This threshold for failed BV elements performed with 90% overall correct prediction in simulations of cyclic rotational head injuries. A 50% risk of EAH was associated with head angular velocities of 94.74 rad/s and angular accelerations of 29.60 krad/s2 in the newborn piglet. Future studies may build on these findings for BV failure in the piglet to develop predictive models for BV failure in human infants.
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Affiliation(s)
| | - Morteza Seidi
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, USA
| | - Marzieh Hajiaghamemar
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, USA
| | - Susan S Margulies
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, USA.
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Using Serum Amino Acids to Predict Traumatic Brain Injury: A Systematic Approach to Utilize Multiple Biomarkers. Int J Mol Sci 2020; 21:ijms21051786. [PMID: 32150890 PMCID: PMC7084695 DOI: 10.3390/ijms21051786] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Revised: 02/17/2020] [Accepted: 03/02/2020] [Indexed: 01/06/2023] Open
Abstract
Traumatic brain injury (TBI) can cause biochemical and metabolomic alterations in the brain tissue and serum. These alterations can be used for diagnosis and prognosis of TBI. Here, the serum concentrations of seventeen amino acids (AA) were studied for their potential utility as biomarkers of TBI. Twenty-five female, 4-week-old piglets received diffuse (n = 13) or focal (n = 12) TBI. Blood samples were obtained both pre-injury and at either 24-h or 4-days post-TBI. To find a robust panel of biomarkers, the results of focal and diffuse TBIs were combined and multivariate logistic regression analysis, coupled with the best subset selection technique and repeated k-fold cross-validation method, was used to perform a thorough search of all possible subsets of AAs. The combination of serum glycine, taurine, and ornithine was optimal for TBI diagnosis, with 80% sensitivity and 86% overall prediction rate, and showed excellent TBI diagnostic performance, with 100% sensitivity and 78% overall prediction rate, on a separate validation dataset including four uninjured and five injured animals. We found that combinations of biomarkers outperformed any single biomarker. We propose this 3-AA serum biomarker panel to diagnose mild-to-moderate focal/diffuse TBI. The systematic approaches implemented herein can be used for combining parameters from various TBI assessments to develop/evaluate optimal multi-factorial diagnostic/prognostic TBI metrics.
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