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Bradfield C, Voo L, Bhaduri A, Ramesh KT. Validation of a computational biomechanical mouse brain model for rotational head acceleration. Biomech Model Mechanobiol 2024:10.1007/s10237-024-01843-5. [PMID: 38662175 DOI: 10.1007/s10237-024-01843-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 03/17/2024] [Indexed: 04/26/2024]
Abstract
Recent mouse brain injury experiments examine diffuse axonal injury resulting from accelerative head rotations. Evaluating brain deformation during these events would provide valuable information on tissue level thresholds for brain injury, but there are many challenges to imaging the brain's mechanical response during dynamic loading events, such as a blunt head impact. To address this shortcoming, we present an experimentally validated computational biomechanics model of the mouse brain that predicts tissue deformation, given the motion of the mouse head during laboratory experiments. First, we developed a finite element model of the mouse brain that computes tissue strains, given the same head rotations as previously conducted in situ hemicephalic mouse brain experiments. Second, we calibrated the model using a single brain segment, and then validated the model based on the spatial and temporal strain responses of other regions. The result is a computational tool that will provide researchers with the ability to predict brain tissue strains that occur during mouse laboratory experiments, and to link the experiments to the resulting neuropathology, such as diffuse axonal injury.
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Affiliation(s)
- Connor Bradfield
- Applied Physics Laboratory, Johns Hopkins University, Laurel, MD, 20723, USA, 11100 Johns Hopkins Road.
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA, 3400 North Charles Street.
| | - Liming Voo
- Applied Physics Laboratory, Johns Hopkins University, Laurel, MD, 20723, USA, 11100 Johns Hopkins Road
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA, 3400 North Charles Street
| | - Anindya Bhaduri
- Department of Civil Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA, 3400 North Charles Street
| | - K T Ramesh
- Applied Physics Laboratory, Johns Hopkins University, Laurel, MD, 20723, USA, 11100 Johns Hopkins Road
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA, 3400 North Charles Street
- Hopkins Extreme Materials Institute, Johns Hopkins University, Baltimore, MD, 21218, USA, 3400 North Charles Street
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2
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Keresztes L, Szögi E, Varga B, Grolmusz V. Discovering sex and age implicator edges in the human connectome. Neurosci Lett 2022; 791:136913. [DOI: 10.1016/j.neulet.2022.136913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 09/07/2022] [Accepted: 10/10/2022] [Indexed: 11/16/2022]
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3
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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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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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5
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Yang S, Tang J, Nie B, Zhou Q. Assessment of brain injury characterization and influence of modeling approaches. Sci Rep 2022; 12:13597. [PMID: 35948588 PMCID: PMC9365784 DOI: 10.1038/s41598-022-16713-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 07/14/2022] [Indexed: 11/09/2022] Open
Abstract
In this study, using computational biomechanics models, we investigated influence of the skull-brain interface modeling approach and the material property of cerebrum on the kinetic, kinematic and injury outputs. Live animal head impact tests of different severities were reconstructed in finite element simulations and DAI and ASDH injury results were compared. We used the head/brain models of Total HUman Model for Safety (THUMS) and Global Human Body Models Consortium (GHBMC), which had been validated under several loading conditions. Four modeling approaches of the skull-brain interface in the head/brain models were evaluated. They were the original models from THUMS and GHBMC, the THUMS model with skull-brain interface changed to sliding contact, and the THUMS model with increased shear modulus of cerebrum, respectively. The results have shown that the definition of skull-brain interface would significantly influence the magnitude and distribution of the load transmitted to the brain. With sliding brain-skull interface, the brain had lower maximum principal stress compared to that with strong connected interface, while the maximum principal strain slightly increased. In addition, greater shear modulus resulted in slightly higher the maximum principal stress and significantly lower the maximum principal strain. This study has revealed that using models with different modeling approaches, the same value of injury metric may correspond to different injury severity.
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Affiliation(s)
- Saichao Yang
- State Key Lab of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing, 100084, China
| | - Jisi Tang
- State Key Lab of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing, 100084, China
| | - Bingbing Nie
- State Key Lab of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing, 100084, China
| | - Qing Zhou
- State Key Lab of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing, 100084, China.
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Kim C, Choi WJ, Kang W. Cavitation nucleation and its ductile-to-brittle shape transition in soft gels under translational mechanical impact. Acta Biomater 2022; 142:160-173. [PMID: 35189381 DOI: 10.1016/j.actbio.2022.02.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 02/08/2022] [Accepted: 02/14/2022] [Indexed: 02/05/2023]
Abstract
Cavitation bubbles in the human body, when subjected to impact, are being increasingly considered as a possible brain injury mechanism. However, the onset of cavitation and its complex dynamics in biological materials remain unclear. Our experimental results using soft gels as a tissue simulant show that the critical acceleration (acr) at cavitation nucleation monotonically increases with increasing stiffness of gelatin A/B, while acr for agarose and agar initially increases but is followed by a plateau or even decrease after stiffness reach to ∼100 kPa. Our image analyses of cavitation bubbles and theoretical work reveal that the observed trends in acr are directly linked to how bubbles grow in each gel. Gelatin A/B, regardless of their stiffness, form a localized damaged zone (tens of nanometers) at the gel-bubble interface during bubble growth. In contrary, the damaged zone in agar/agarose becomes significantly larger (> 100 times) with increasing shear modulus, which triggers the transition from formation of a small, damaged zone to activation of crack propagation. STATEMENT OF SIGNIFICANCE: We have studied cavitation nucleation and bubble growth in four different types of soft gels (i.e., tissue simulants) under translational impact. The critical linear acceleration for cavitation nucleation has been measured in the simulants by utilizing a recently developed method that mimics acceleration profiles of typical head blunt events. Each gel type exhibits significantly different trends in the critical acceleration and bubble shape (e.g., A gel-specific sphere-to-saucer transition) with increasing gel stiffness. Our theoretical framework, based on the concepts of a damaged zone and crack propagation in each gel, explains underlying mechanisms of the experimental observations. Our in-depth studies shed light on potential links between traumatic brain injuries and cavitation bubbles induced by translational acceleration, the overlooked mechanism in the literature.
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Affiliation(s)
- Chunghwan Kim
- Mechanical Engineering, School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ 85281, United States
| | - Won June Choi
- Mechanical Engineering, School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ 85281, United States
| | - Wonmo Kang
- Mechanical Engineering, School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ 85281, United States.
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7
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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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8
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Dagro AM, Wilkerson JW, Thomas TP, Kalinosky BT, Payne JA. Computational modeling investigation of pulsed high peak power microwaves and the potential for traumatic brain injury. SCIENCE ADVANCES 2021; 7:eabd8405. [PMID: 34714682 PMCID: PMC8555891 DOI: 10.1126/sciadv.abd8405] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Accepted: 09/09/2021] [Indexed: 06/13/2023]
Abstract
When considering safety standards for human exposure to radiofrequency (RF) and microwave energy, the dominant concerns pertain to a thermal effect. However, in the case of high-power pulsed RF/microwave energy, a rapid thermal expansion can lead to stress waves within the body. In this study, a computational model is used to estimate the temperature profile in the human brain resulting from exposure to various RF/microwave incident field parameters. The temperatures are subsequently used to simulate the resulting mechanical response of the brain. Our simulations show that, for certain extremely high-power microwave exposures (permissible by current safety standards), very high stresses may occur within the brain that may have implications for neuropathological effects. Although the required power densities are orders of magnitude larger than most real-world exposure conditions, they can be achieved with devices meant to emit high-power electromagnetic pulses in military and research applications.
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Affiliation(s)
- Amy M. Dagro
- U.S. Army Research Laboratory, Aberdeen Proving Ground, MD 21005, USA
| | - Justin W. Wilkerson
- J. Mike ‘66 Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843, USA
| | | | - Benjamin T. Kalinosky
- General Dynamics Information Technology, JBSA Fort Sam Houston, San Antonio, TX 78234, USA
| | - Jason A. Payne
- Air Force Research Laboratory, 711th Human Performance Wing, Airman Systems Directorate, Bioeffects Division, Radio Frequency Bioeffects Branch, JBSA Fort Sam Houston, San Antonio, TX 78234, USA
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9
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Miller LE, Urban JE, Davenport EM, Powers AK, Whitlow CT, Maldjian JA, Stitzel JD. Brain Strain: Computational Model-Based Metrics for Head Impact Exposure and Injury Correlation. Ann Biomed Eng 2021; 49:1083-1096. [PMID: 33258089 PMCID: PMC10032321 DOI: 10.1007/s10439-020-02685-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Accepted: 10/20/2020] [Indexed: 12/20/2022]
Abstract
Athletes participating in contact sports are exposed to repetitive subconcussive head impacts that may have long-term neurological consequences. To better understand these impacts and their effects, head impacts are often measured during football to characterize head impact exposure and estimate injury risk. Despite widespread use of kinematic-based metrics, it remains unclear whether any single metric derived from head kinematics is well-correlated with measurable changes in the brain. This shortcoming has motivated the increasing use of finite element (FE)-based metrics, which quantify local brain deformations. Additionally, quantifying cumulative exposure is of increased interest to examine the relationship to brain changes over time. The current study uses the atlas-based brain model (ABM) to predict the strain response to impacts sustained by 116 youth football athletes and proposes 36 new, or derivative, cumulative strain-based metrics that quantify the combined burden of head impacts over the course of a season. The strain-based metrics developed and evaluated for FE modeling and presented in the current study present potential for improved analytics over existing kinematically-based and cumulative metrics. Additionally, the findings highlight the importance of accounting for directional dependence and expand the techniques to explore spatial distribution of the strain response throughout the brain.
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Affiliation(s)
- Logan E Miller
- Department of Biomedical Engineering, Wake Forest School of Medicine, 575 N. Patterson Avenue, Suite 530, Winston-Salem, NC, 27101, USA.
- School of Biomedical Engineering and Sciences, Virginia Tech - Wake Forest University, 575 N. Patterson Avenue, Suite 530, Winston-Salem, NC, 27101, USA.
| | - Jillian E Urban
- Department of Biomedical Engineering, Wake Forest School of Medicine, 575 N. Patterson Avenue, Suite 530, Winston-Salem, NC, 27101, USA
- School of Biomedical Engineering and Sciences, Virginia Tech - Wake Forest University, 575 N. Patterson Avenue, Suite 530, Winston-Salem, NC, 27101, USA
| | - Elizabeth M Davenport
- Department of Radiology, Southwestern Medical School, University of Texas, 5323 Harry Hines Boulevard, Dallas, TX, 75390, USA
| | - Alexander K Powers
- Department of Biomedical Engineering, Wake Forest School of Medicine, 575 N. Patterson Avenue, Suite 530, Winston-Salem, NC, 27101, USA
- Department of Neurosurgery, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC, 27157, USA
| | - Christopher T Whitlow
- Department of Biomedical Engineering, Wake Forest School of Medicine, 575 N. Patterson Avenue, Suite 530, Winston-Salem, NC, 27101, USA
- Department of Radiology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC, 27157, USA
| | - Joseph A Maldjian
- Department of Radiology, Southwestern Medical School, University of Texas, 5323 Harry Hines Boulevard, Dallas, TX, 75390, USA
| | - Joel D Stitzel
- Department of Biomedical Engineering, Wake Forest School of Medicine, 575 N. Patterson Avenue, Suite 530, Winston-Salem, NC, 27101, USA
- School of Biomedical Engineering and Sciences, Virginia Tech - Wake Forest University, 575 N. Patterson Avenue, Suite 530, Winston-Salem, NC, 27101, USA
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Dymek M, Ptak M, Ratajczak M, Fernandes FAO, Kwiatkowski A, Wilhelm J. Analysis of HIC and Hydrostatic Pressure in the Human Head during NOCSAE Tests of American Football Helmets. Brain Sci 2021; 11:287. [PMID: 33669105 PMCID: PMC7996556 DOI: 10.3390/brainsci11030287] [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: 01/25/2021] [Revised: 02/10/2021] [Accepted: 02/22/2021] [Indexed: 11/21/2022] Open
Abstract
Brain damage is a serious economic and social burden. Contact sports such as American football, are one of the most common sources of concussions. The biomechanical response of the head-helmet system caused by dynamic loading plays a major role. The literature has focused on measuring the resultant kinematics that act on the head and helmet during tackles. However, few studies have focused on helmet validation tests, supported by recent findings and emerging numerical approaches. The future of helmet standards could benefit from insights at the level of injury mechanisms, using numerical tools to assess the helmets. Therefore, in this work, a numerical approach is employed to investigate the influence of intracranial pressure (ICP) on brain pathophysiology during and after helmeted impacts, which are common in American football. The helmeted impacts were performed at several impact locations according to the NOCSAE standard (configurations A, AP, B, C, D, F, R, UT). In order to evaluate the ICP levels, the αHEAD finite element head and brain model was combined with a Hybrid III-neck structure and then coupled with an American football helmet to simulate the NOCSAE impacts. In addition, the ICP level was analyzed together with the resulting HIC value, since the latter is commonly used, in this application and others, as the injury criterion. The obtained results indicate that ICP values exceed the common threshold of head injury criteria and do not correlate with HIC values. Thus, this work raises concern about applying the HIC to predict brain injury in American football direct head impacts, since it does not correlate with ICP predicted with the FE head model.
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Affiliation(s)
- Mateusz Dymek
- Faculty of Mechanical Engineering, Wroclaw University of Science and Technology, Lukasiewicza 7/9, 50-371 Wroclaw, Poland
| | - Mariusz Ptak
- Faculty of Mechanical Engineering, Wroclaw University of Science and Technology, Lukasiewicza 7/9, 50-371 Wroclaw, Poland
| | - Monika Ratajczak
- Faculty of Mechanical Engineering, University of Zielona Gora, ul. Szafrana 4, 65-516 Zielona Gora, Poland;
| | - Fábio A. O. Fernandes
- TEMA—Centre for Mechanical Technology and Automation, Department of Mechanical Engineering, Campus de Santiago, University of Aveiro, 3810-193 Aveiro, Portugal;
| | - Artur Kwiatkowski
- Department of Neurosurgery, Provincial Specialist Hospital in Legnica, ul. Iwaszkiewicza 5, 59-220 Legnica, Poland;
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Subramaniam DR, Unnikrishnan G, Sundaramurthy A, Rubio JE, Kote VB, Reifman J. The importance of modeling the human cerebral vasculature in blunt trauma. Biomed Eng Online 2021; 20:11. [PMID: 33446217 PMCID: PMC7809851 DOI: 10.1186/s12938-021-00847-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 01/04/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Multiple studies describing human head finite element (FE) models have established the importance of including the major cerebral vasculature to improve the accuracy of the model predictions. However, a more detailed network of cerebral vasculature, including the major veins and arteries as well as their branch vessels, can further enhance the model-predicted biomechanical responses and help identify correlates to observed blunt-induced brain injury. METHODS We used an anatomically accurate three-dimensional geometry of a 50th percentile U.S. male head that included the skin, eyes, sinuses, spine, skull, brain, meninges, and a detailed network of cerebral vasculature to develop a high-fidelity model. We performed blunt trauma simulations and determined the intracranial pressure (ICP), the relative displacement (RD), the von Mises stress, and the maximum principal strain. We validated our detailed-vasculature model by comparing the model-predicted ICP and RD values with experimental measurements. To quantify the influence of including a more comprehensive network of brain vessels, we compared the biomechanical responses of our detailed-vasculature model with those of a reduced-vasculature model and a no-vasculature model. RESULTS For an inclined frontal impact, the predicted ICP matched well with the experimental results in the fossa, frontal, parietal, and occipital lobes, with peak-pressure differences ranging from 2.4% to 9.4%. For a normal frontal impact, the predicted ICP matched the experimental results in the frontal lobe and lateral ventricle, with peak-pressure discrepancies equivalent to 1.9% and 22.3%, respectively. For an offset parietal impact, the model-predicted RD matched well with the experimental measurements, with peak RD differences of 27% and 24% in the right and left cerebral hemispheres, respectively. Incorporating the detailed cerebral vasculature did not influence the ICP but redistributed the brain-tissue stresses and strains by as much as 30%. In addition, our detailed-vasculature model predicted strain reductions by as much as 28% when compared to current reduced-vasculature FE models that only include the major cerebral vessels. CONCLUSIONS Our study highlights the importance of including a detailed representation of the cerebral vasculature in FE models to more accurately estimate the biomechanical responses of the human brain to blunt impact.
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Affiliation(s)
- Dhananjay Radhakrishnan Subramaniam
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, United States Army Medical Research and Development Command, FCMR-TT, 504 Scott Street, Fort Detrick, MD 21702-5012 USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc, 6720A Rockledge Drive, Bethesda, MD 20817 USA
| | - Ginu Unnikrishnan
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, United States Army Medical Research and Development Command, FCMR-TT, 504 Scott Street, Fort Detrick, MD 21702-5012 USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc, 6720A Rockledge Drive, Bethesda, MD 20817 USA
| | - Aravind Sundaramurthy
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, United States Army Medical Research and Development Command, FCMR-TT, 504 Scott Street, Fort Detrick, MD 21702-5012 USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc, 6720A Rockledge Drive, Bethesda, MD 20817 USA
| | - Jose E. Rubio
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, United States Army Medical Research and Development Command, FCMR-TT, 504 Scott Street, Fort Detrick, MD 21702-5012 USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc, 6720A Rockledge Drive, Bethesda, MD 20817 USA
| | - Vivek Bhaskar Kote
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, United States Army Medical Research and Development Command, FCMR-TT, 504 Scott Street, Fort Detrick, MD 21702-5012 USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc, 6720A Rockledge Drive, Bethesda, MD 20817 USA
| | - Jaques Reifman
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, United States Army Medical Research and Development Command, FCMR-TT, 504 Scott Street, Fort Detrick, MD 21702-5012 USA
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12
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Carlsen RW, Fawzi AL, Wan Y, Kesari H, Franck C. A quantitative relationship between rotational head kinematics and brain tissue strain from a 2-D parametric finite element analysis. BRAIN MULTIPHYSICS 2021. [DOI: 10.1016/j.brain.2021.100024] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
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13
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Dagro A, Wilkerson J. A computational investigation of strain concentration in the brain in response to a rapid temperature rise. J Mech Behav Biomed Mater 2020; 115:104228. [PMID: 33316549 DOI: 10.1016/j.jmbbm.2020.104228] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Revised: 10/20/2020] [Accepted: 11/21/2020] [Indexed: 01/18/2023]
Abstract
Following the mysterious health attacks on U.S. diplomats in Cuba in 2016, the cause of concussion-like symptoms concurrent with strange noises heard by the diplomats remains undetermined. A wide range of possible causes of the sensations have been proposed: pulsed microwave exposure, infrasound acoustic devices, pesticides/neurotoxins, and even mass hysteria (psychogenic illness). Here, we numerically examine the pulsed microwave exposure hypothesis and the simulated mechanical response of brain tissue. A computational model is used to examine the influence of various spatially varying temperature gradients and pulse durations on the mechanical response of brain tissue. We show that a stress-focusing effect due to a rapid temperature increase may result in brain tissue strains larger than the initially applied thermal strains.
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Affiliation(s)
- Amy Dagro
- CCDC U.S. Army Research Laboratory, Aberdeen Proving Ground, MD, 21005, USA.
| | - Justin Wilkerson
- Texas A&M University, J. Mike Walker '66 Dept. of Mechanical Engineering, College Station, TX, USA.
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14
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Kara E, Rahman A, Aulisa E, Ghosh S. Tumor ablation due to inhomogeneous anisotropic diffusion in generic three-dimensional topologies. Phys Rev E 2020; 102:062425. [PMID: 33466110 DOI: 10.1103/physreve.102.062425] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 11/23/2020] [Indexed: 11/07/2022]
Abstract
In recent decades computer-aided technologies have become prevalent in medicine, however, cancer drugs are often only tested on in vitro cell lines from biopsies. We derive a full three-dimensional model of inhomogeneous -anisotropic diffusion in a tumor region coupled to a binary population model, which simulates in vivo scenarios faster than traditional cell-line tests. The diffusion tensors are acquired using diffusion tensor magnetic resonance imaging from a patient diagnosed with glioblastoma multiform. Then we numerically simulate the full model with finite element methods and produce drug concentration heat maps, apoptosis hotspots, and dose-response curves. Finally, predictions are made about optimal injection locations and volumes, which are presented in a form that can be employed by doctors and oncologists.
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Affiliation(s)
- Erdi Kara
- Department of Mathematics and Statistics, Texas Tech University, Lubbock TX
| | - Aminur Rahman
- Department of Applied Mathematics, University of Washington, Seattle WA
| | - Eugenio Aulisa
- Department of Mathematics and Statistics, Texas Tech University, Lubbock TX
| | - Souparno Ghosh
- Department of Statistics, University of Nebraska - Lincoln, Lincoln NB
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15
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Fagan BT, Satapathy SS, Rutledge JN, Kornguth SE. Simulation of the Strain Amplification in Sulci Due to Blunt Impact to the Head. Front Neurol 2020; 11:998. [PMID: 33013659 PMCID: PMC7506117 DOI: 10.3389/fneur.2020.00998] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 07/29/2020] [Indexed: 11/13/2022] Open
Abstract
Traumatic brain injury (TBI) has become a concern in sports, automobile accidents and combat operations. A better understanding of the mechanics leading to a TBI is required to cope with both the short-term life-threatening effects and long-term effects of TBIs, such as the development chronic traumatic encephalopathy (CTE). Kornguth et al. (1) proposed that an inflammatory and autoimmune process initiated by a water hammer effect at the bases of the sulci of the brain is a mechanism of TBI leading to CTE. A major objective of this study is to investigate whether the water hammer effect is present due to blunt impacts through the use of computational models. Frontal blunt impacts were simulated with 2D finite element models developed to capture the biofidelic geometry of a human head. The models utilized the Arbitrary Lagrangian Eulerian (ALE) method to model a layer of cerebrospinal fluid (CSF) as a deforming fluid allowing for CSF to move in and out of sulci. During the simulated impacts, CSF was not observed to be driven into the sulci during the transient response. However, elevated shear strain levels near the base of the sulci were exhibited. Further, increased shear strain was present when differentiation between white and gray matter was taken into account. Both of the results support clinical observations of (1).
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Affiliation(s)
- Brian T Fagan
- U.S. Army Combat Capabilities Development Command - Army Research Laboratory, Aberdeen Proving Ground, MD, United States
| | - Sikhanda S Satapathy
- U.S. Army Combat Capabilities Development Command - Army Research Laboratory, Aberdeen Proving Ground, MD, United States
| | | | - Steven E Kornguth
- Dell Medical School, University of Texas at Austin, Austin, TX, United States.,Department of Kinesiology and Health Education, University of Texas at Austin, Austin, TX, United States
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16
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Bobo J, Garg A, Venkatraman P, Puthenveedu M, LeDuc PR. 3D In Vitro Neuron on a Chip for Probing Calcium Mechanostimulation. ACTA ACUST UNITED AC 2020; 4:e2000080. [PMID: 32875741 DOI: 10.1002/adbi.202000080] [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: 03/20/2020] [Revised: 08/05/2020] [Indexed: 12/13/2022]
Abstract
The evolution of tissue on a chip systems holds promise for mimicking the response of biological functionality of physiological systems. One important direction for tissue on a chip approaches are neuron-based systems that could mimic neurological responses and lessen the need for in vivo experimentation. For neural research, more attention has been devoted recently to understanding mechanics due to issues in areas such as traumatic brain injury (TBI) and pain, among others. To begin to address these areas, a 3D Nerve Integrated Tissue on a Chip (NITC) approach combined with a Mechanical Excitation Testbed (MET) System is developed to impose external mechanical stimulation toward more realistic physiological environments. PC12 cells differentiated with nerve growth factor, which were cultured in a controlled 3D scaffolds, are used. The cells are labeled in a 3D NITC system with Fluo-4-AM to examine their calcium response under mechanical stimulation synchronized with image capture. Understanding the neural responses to mechanical stimulation beyond 2D systems is very important for neurological studies and future personalized strategies. This work will have implications in a diversity of areas including tissue-on-a-chip systems, biomaterials, and neuromechanics.
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Affiliation(s)
- Justin Bobo
- Department of Mechanical Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, 15213, USA
| | - Akash Garg
- Department of Mechanical Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, 15213, USA
| | - Prahatha Venkatraman
- Department of Pharmacology, University of Michigan, 1150 W Medical Center Dr, Ann Arbor, MI, 48109, USA
| | - Manoj Puthenveedu
- Department of Pharmacology, University of Michigan, 1150 W Medical Center Dr, Ann Arbor, MI, 48109, USA
| | - Philip R LeDuc
- Department of Mechanical Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, 15213, USA.,Department of Biological Sciences, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, 15213, USA
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17
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Gabrieli D, Vigilante NF, Scheinfeld R, Rifkin JA, Schumm SN, Wu T, Gabler LF, Panzer MB, Meaney DF. A Multibody Model for Predicting Spatial Distribution of Human Brain Deformation Following Impact Loading. J Biomech Eng 2020; 142:091015. [PMID: 32266930 PMCID: PMC7247535 DOI: 10.1115/1.4046866] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Revised: 04/06/2020] [Indexed: 11/08/2022]
Abstract
With an increasing focus on long-term consequences of concussive brain injuries, there is a new emphasis on developing tools that can accurately predict the mechanical response of the brain to impact loading. Although finite element models (FEM) estimate the brain response under dynamic loading, these models are not capable of delivering rapid (∼seconds) estimates of the brain's mechanical response. In this study, we develop a multibody spring-mass-damper model that estimates the regional motion of the brain to rotational accelerations delivered either about one anatomic axis or across three orthogonal axes simultaneously. In total, we estimated the deformation across 120 locations within a 50th percentile human brain. We found the multibody model (MBM) correlated, but did not precisely predict, the computed finite element response (average relative error: 18.4 ± 13.1%). We used machine learning (ML) to combine the prediction from the MBM and the loading kinematics (peak rotational acceleration, peak rotational velocity) and significantly reduced the discrepancy between the MBM and FEM (average relative error: 9.8 ± 7.7%). Using an independent sports injury testing set, we found the hybrid ML model also correlated well with predictions from a FEM (average relative error: 16.4 ± 10.2%). Finally, we used this hybrid MBM-ML approach to predict strains appearing in different locations throughout the brain, with average relative error estimates ranging from 8.6% to 25.2% for complex, multi-axial acceleration loading. Together, these results show a rapid and reasonably accurate method for predicting the mechanical response of the brain for single and multiplanar inputs, and provide a new tool for quickly assessing the consequences of impact loading throughout the brain.
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Affiliation(s)
- David Gabrieli
- Department of Bioengineering, University of Pennsylvania, 240 Skirkanich Hall, 210. S. 33rd Street, Philadelphia, PA 19104
| | - Nicholas F. Vigilante
- Department of Bioengineering, University of Pennsylvania, 240 Skirkanich Hall, 210. S. 33rd Street, Philadelphia, PA 19104
| | - Rich Scheinfeld
- Department of Bioengineering, University of Pennsylvania, 240 Skirkanich Hall, 210. S. 33rd Street, Philadelphia, PA 19104
| | - Jared A. Rifkin
- Department of Bioengineering, University of Pennsylvania, 240 Skirkanich Hall, 210. S. 33rd Street, Philadelphia, PA 19104
| | - Samantha N. Schumm
- Department of Bioengineering, University of Pennsylvania, 240 Skirkanich Hall, 210. S. 33rd Street, Philadelphia, PA 19104
| | - Taotao Wu
- Department of Mechanical and Aerospace Engineering, Center for Applied Biomechanics, University of Virginia, P.O. Box 400237, Charlottesville, VA 22904
| | - Lee F. Gabler
- Department of Mechanical and Aerospace Engineering, Center for Applied Biomechanics, University of Virginia, P.O. Box 400237, Charlottesville, VA 22904
| | - Matthew B. Panzer
- Departments of Mechanical and Aerospace Engineering and Biomedical Engineering, Center for Applied Biomechanics, University of Virginia, P.O. Box 400237, Charlottesville, VA 22904
| | - David F. Meaney
- Departments of Bioengineering and Neurosurgery, University of Pennsylvania, 240 Skirkanich Hall, 210 S. 33rd Street, Philadelphia, PA 19104
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18
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Anderson ED, Giudice JS, Wu T, Panzer MB, Meaney DF. Predicting Concussion Outcome by Integrating Finite Element Modeling and Network Analysis. Front Bioeng Biotechnol 2020; 8:309. [PMID: 32351948 PMCID: PMC7174699 DOI: 10.3389/fbioe.2020.00309] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2019] [Accepted: 03/23/2020] [Indexed: 12/11/2022] Open
Abstract
Concussion is a significant public health problem affecting 1.6-2.4 million Americans annually. An alternative to reducing the burden of concussion is to reduce its incidence with improved protective equipment and injury mitigation systems. Finite element (FE) models of the brain response to blunt trauma are often used to estimate injury potential and can lead to improved helmet designs. However, these models have yet to incorporate how the patterns of brain connectivity disruption after impact affects the relay of information in the injured brain. Furthermore, FE brain models typically do not consider the differences in individual brain structural connectivities and their purported role in concussion risk. Here, we use graph theory techniques to integrate brain deformations predicted from FE modeling with measurements of network efficiency to identify brain regions whose connectivity characteristics may influence concussion risk. We computed maximum principal strain in 129 brain regions using head kinematics measured from 53 professional football impact reconstructions that included concussive and non-concussive cases. In parallel, using diffusion spectrum imaging data from 30 healthy subjects, we simulated structural lesioning of each of the same 129 brain regions. We simulated lesioning by removing each region one at a time along with all its connections. In turn, we computed the resultant change in global efficiency to identify regions important for network communication. We found that brain regions that deformed the most during an impact did not overlap with regions most important for network communication (Pearson's correlation, ρ = 0.07; p = 0.45). Despite this dissimilarity, we found that predicting concussion incidence was equally accurate when considering either areas of high strain or of high importance to global efficiency. Interestingly, accuracy for concussion prediction varied considerably across the 30 healthy connectomes. These results suggest that individual network structure is an important confounding variable in concussion prediction and that further investigation of its role may improve concussion prediction and lead to the development of more effective protective equipment.
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Affiliation(s)
- Erin D. Anderson
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
| | - J. Sebastian Giudice
- Department of Mechanical and Aerospace Engineering, University of Virginia, Charlottesville, VA, United States
| | - Taotao Wu
- Department of Mechanical and Aerospace Engineering, University of Virginia, Charlottesville, VA, 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
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19
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Montanino A, Saeedimasine M, Villa A, Kleiven S. Localized Axolemma Deformations Suggest Mechanoporation as Axonal Injury Trigger. Front Neurol 2020; 11:25. [PMID: 32082244 PMCID: PMC7005088 DOI: 10.3389/fneur.2020.00025] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Accepted: 01/09/2020] [Indexed: 12/19/2022] Open
Abstract
Traumatic brain injuries are a leading cause of morbidity and mortality worldwide. With almost 50% of traumatic brain injuries being related to axonal damage, understanding the nature of cellular level impairment is crucial. Experimental observations have so far led to the formulation of conflicting theories regarding the cellular primary injury mechanism. Disruption of the axolemma, or alternatively cytoskeletal damage has been suggested mainly as injury trigger. However, mechanoporation thresholds of generic membranes seem not to overlap with the axonal injury deformation range and microtubules appear too stiff and too weakly connected to undergo mechanical breaking. Here, we aim to shed a light on the mechanism of primary axonal injury, bridging finite element and molecular dynamics simulations. Despite the necessary level of approximation, our models can accurately describe the mechanical behavior of the unmyelinated axon and its membrane. More importantly, they give access to quantities that would be inaccessible with an experimental approach. We show that in a typical injury scenario, the axonal cortex sustains deformations large enough to entail pore formation in the adjoining lipid bilayer. The observed axonal deformation of 10–12% agree well with the thresholds proposed in the literature for axonal injury and, above all, allow us to provide quantitative evidences that do not exclude pore formation in the membrane as a result of trauma. Our findings bring to an increased knowledge of axonal injury mechanism that will have positive implications for the prevention and treatment of brain injuries.
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Affiliation(s)
- Annaclaudia Montanino
- Division of Neuronic Engineering, Royal Institute of Technology (KTH), Stockholm, Sweden
| | - Marzieh Saeedimasine
- Department of Biosciences and Nutrition, Karolinska Institutet (KI), Stockholm, Sweden
| | - Alessandra Villa
- Department of Biosciences and Nutrition, Karolinska Institutet (KI), Stockholm, Sweden
| | - Svein Kleiven
- Division of Neuronic Engineering, Royal Institute of Technology (KTH), Stockholm, Sweden
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20
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Wu S, Zhao W, Rowson B, Rowson S, Ji S. A network-based response feature matrix as a brain injury metric. Biomech Model Mechanobiol 2019; 19:927-942. [PMID: 31760600 DOI: 10.1007/s10237-019-01261-y] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Accepted: 11/11/2019] [Indexed: 01/06/2023]
Abstract
Conventional brain injury metrics are scalars that treat the whole head/brain as a single unit but do not characterize the distribution of brain responses. Here, we establish a network-based "response feature matrix" to characterize the magnitude and distribution of impact-induced brain strains. The network nodes and edges encode injury risks to the gray matter regions and their white matter interconnections, respectively. The utility of the metric is illustrated in injury prediction using three independent, real-world datasets: two reconstructed impact datasets from the National Football League (NFL) and Virginia Tech, respectively, and measured concussive and non-injury impacts from Stanford University. Injury predictions with leave-one-out cross-validation are conducted using the two reconstructed datasets separately, and then by combining all datasets into one. Using support vector machine, the network-based injury predictor consistently outperforms four baseline scalar metrics including peak maximum principal strain of the whole brain (MPS), peak linear/rotational acceleration, and peak rotational velocity across all five selected performance measures (e.g., maximized accuracy of 0.887 vs. 0.774 and 0.849 for MPS and rotational acceleration with corresponding positive predictive values of 0.938, 0.772, and 0.800, respectively, using the reconstructed NFL dataset). With sufficient training data, real-world injury prediction is similar to leave-one-out in-sample evaluation, suggesting the potential advantage of the network-based injury metric over conventional scalar metrics. The network-based response feature matrix significantly extends scalar metrics by sampling the brain strains more completely, which may serve as a useful framework potentially allowing for other applications such as characterizing injury patterns or facilitating targeted multi-scale modeling in the future.
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Affiliation(s)
- Shaoju Wu
- Department of Biomedical Engineering, Worcester Polytechnic Institute, 60 Prescott Street, Worcester, MA, 01605, USA
| | - Wei Zhao
- Department of Biomedical Engineering, Worcester Polytechnic Institute, 60 Prescott Street, Worcester, MA, 01605, USA
| | - Bethany Rowson
- Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA, USA
| | - Steven Rowson
- Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA, USA
| | - Songbai Ji
- Department of Biomedical Engineering, Worcester Polytechnic Institute, 60 Prescott Street, Worcester, MA, 01605, USA.
- Department of Mechanical Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA, 01609, USA.
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21
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van Montfort SJT, van Dellen E, Stam CJ, Ahmad AH, Mentink LJ, Kraan CW, Zalesky A, Slooter AJC. Brain network disintegration as a final common pathway for delirium: a systematic review and qualitative meta-analysis. NEUROIMAGE-CLINICAL 2019; 23:101809. [PMID: 30981940 PMCID: PMC6461601 DOI: 10.1016/j.nicl.2019.101809] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 03/25/2019] [Accepted: 03/31/2019] [Indexed: 01/05/2023]
Abstract
Delirium is an acute neuropsychiatric syndrome characterized by altered levels of attention and awareness with cognitive deficits. It is most prevalent in elderly hospitalized patients and related to poor outcomes. Predisposing risk factors, such as older age, determine the baseline vulnerability for delirium, while precipitating factors, such as use of sedatives, trigger the syndrome. Risk factors are heterogeneous and the underlying biological mechanisms leading to vulnerability for delirium are poorly understood. We tested the hypothesis that delirium and its risk factors are associated with consistent brain network changes. We performed a systematic review and qualitative meta-analysis and included 126 brain network publications on delirium and its risk factors. Findings were evaluated after an assessment of methodological quality, providing N=99 studies of good or excellent quality on predisposing risk factors, N=10 on precipitation risk factors and N=7 on delirium. Delirium was consistently associated with functional network disruptions, including lower EEG connectivity strength and decreased fMRI network integration. Risk factors for delirium were associated with lower structural connectivity strength and less efficient structural network organization. Decreased connectivity strength and efficiency appear to characterize structural brain networks of patients at risk for delirium, possibly impairing the functional network, while functional network disintegration seems to be a final common pathway for the syndrome. Delirium is consistently associated with functional network impairments. Risk factors are associated with lower structural connectivity strength. Risk factors are associated with a less efficient structural network organization. Structural impairments make the functional network more vulnerable to deterioration. Functional network disintegration seems to be a final common pathway for delirium.
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Affiliation(s)
- S J T van Montfort
- Department of Intensive Care Medicine and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands.
| | - E van Dellen
- Department of Psychiatry and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands; Melbourne Neuropsychiatry Center, Department of Psychiatry, Level 3, Alan Gilbert Building, 161 Barry Street, Carlton South, 3053 Victoria, University of Melbourne and Melbourne Health, Australia
| | - C J Stam
- Department of Clinical Neurophysiology and MEG Center, Neuroscience Campus Amsterdam, VU University Medical Center, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - A H Ahmad
- Department of Intensive Care Medicine and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands; Faculty of Psychology, Utrecht University, Heidelberglaan 1, 3584 CS Utrecht, The Netherlands
| | - L J Mentink
- Department of Intensive Care Medicine and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands; Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
| | - C W Kraan
- Department of Intensive Care Medicine and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands; Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
| | - A Zalesky
- Melbourne Neuropsychiatry Center, Department of Psychiatry, Level 3, Alan Gilbert Building, 161 Barry Street, Carlton South, 3053 Victoria, University of Melbourne and Melbourne Health, Australia
| | - A J C Slooter
- Department of Intensive Care Medicine and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
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22
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Madhukar A, Ostoja-Starzewski M. Finite Element Methods in Human Head Impact Simulations: A Review. Ann Biomed Eng 2019; 47:1832-1854. [DOI: 10.1007/s10439-019-02205-4] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Accepted: 01/12/2019] [Indexed: 12/01/2022]
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23
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A mechanism for injury through cerebral arteriole inflation. Biomech Model Mechanobiol 2019; 18:651-663. [PMID: 30604301 DOI: 10.1007/s10237-018-01107-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2018] [Accepted: 12/12/2018] [Indexed: 10/27/2022]
Abstract
An increase in arterial pressure within the cerebral vasculature appears to coincide with ischemia and dysfunction of the neurovascular unit in some cases of traumatic brain injury. In this study, we examine a new mechanism of brain tissue damage that results from excessive cerebral arteriole pressurization. We begin by considering the morphological and material properties of normotensive and hypertensive arterioles and present a computational model that captures the interaction of neighboring pressurized arterioles and the surrounding brain tissue. Assuming an axonal strain-induced injury criterion, we find that the injury depends on vessel spacing, proximity to an unconfined free surface, and the relative difference in stiffness between the arterioles and the surrounding tissue. We find that a steeper heterogeneity (stiffer vessels surrounded by softer brain tissue) causes larger axial strains to develop at some distance from the arteriole wall, within the brain parenchyma. For a more gradual heterogeneity (softer vessels), we observe more larger strain fields close to the arteriole walls. Both deformation patterns are comparable to damage seen in previous pathology studies on postmortem TBI patients. Finally, we use an analytical model to approximate the interplay between internal pressure, arteriole thickness, and the variation in mechanical properties of arterioles.
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24
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A three-dimensional micromechanical model of brain white matter with histology-informed probabilistic distribution of axonal fibers. J Mech Behav Biomed Mater 2018; 88:288-295. [PMID: 30196184 DOI: 10.1016/j.jmbbm.2018.08.042] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Revised: 08/17/2018] [Accepted: 08/28/2018] [Indexed: 11/20/2022]
Abstract
This paper presents a three-dimensional micromechanical model of brain white matter tissue as a transversely isotropic soft composite described by the generalized Ogden hyperelastic model. The embedded element technique, with corrected stiffness redundancy in large deformations, was used for the embedment of a histology-informed probabilistic distribution of the axonal fibers in the extracellular matrix. The model was linked to a multi-objective, multi-parametric optimization algorithm, using the response surface methodology, for characterization of material properties of the axonal fibers and extracellular matrix in an inverse finite element analysis. The optimum hyperelastic characteristics of the tissue constituents, obtained based on the axonal and transverse direction test results of the corona radiata tissue samples, indicated that the axonal fibers were almost thirteen times stiffer than the extracellular matrix under large deformations. Simulation of the same tissue under a different loading condition, as well as that of another white matter tissue, i.e., the corpus callosum, in the axonal and transverse directions, using the optimized hyperelastic characteristics revealed tissue responses very close to those of the experiments. The results of the model at the sub-tissue level indicated that the stress concentrations were considerably large around the small axons, which might contribute into the brain injury.
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25
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Johnson VE, Weber MT, Xiao R, Cullen DK, Meaney DF, Stewart W, Smith DH. Mechanical disruption of the blood-brain barrier following experimental concussion. Acta Neuropathol 2018; 135:711-726. [PMID: 29460006 DOI: 10.1007/s00401-018-1824-0] [Citation(s) in RCA: 95] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Revised: 02/09/2018] [Accepted: 02/10/2018] [Indexed: 12/14/2022]
Abstract
Although concussion is now recognized as a major health issue, its non-lethal nature has limited characterization of the underlying pathophysiology. In particular, potential neuropathological changes have typically been inferred from non-invasive techniques or post-mortem examinations of severe traumatic brain injury (TBI). Here, we used a swine model of head rotational acceleration based on human concussion to examine blood-brain barrier (BBB) integrity after injury in association with diffuse axonal injury and glial responses. We then determined the potential clinical relevance of the swine concussion findings through comparisons with pathological changes in human severe TBI, where post-mortem examinations are possible. At 6-72 h post-injury in swine, we observed multifocal disruption of the BBB, demonstrated by extravasation of serum proteins, fibrinogen and immunoglobulin-G, in the absence of hemorrhage or other focal pathology. BBB disruption was observed in a stereotyped distribution consistent with biomechanical insult. Specifically, extravasated serum proteins were frequently observed at interfaces between regions of tissue with differing material properties, including the gray-white boundary, periventricular and subpial regions. In addition, there was substantial overlap of BBB disruption with regions of axonal pathology in the white matter. Acute perivascular cellular uptake of blood-borne proteins was observed to be prominent in astrocytes (GFAP-positive) and neurons (MAP-2-positive), but not microglia (IBA1-positive). Parallel examination of human severe TBI revealed similar patterns of serum extravasation and glial uptake of serum proteins, but to a much greater extent than in the swine model, attributed to the higher injury severity. These data suggest that BBB disruption represents a new and important pathological feature of concussion.
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26
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Do blast induced skull flexures result in axonal deformation? PLoS One 2018; 13:e0190881. [PMID: 29547663 PMCID: PMC5856259 DOI: 10.1371/journal.pone.0190881] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2017] [Accepted: 12/21/2017] [Indexed: 12/28/2022] Open
Abstract
Subject-specific computer models (male and female) of the human head were used to investigate the possible axonal deformation resulting from the primary phase blast-induced skull flexures. The corresponding axonal tractography was explicitly incorporated into these finite element models using a recently developed technique based on the embedded finite element method. These models were subjected to extensive verification against experimental studies which examined their pressure and displacement response under a wide range of loading conditions. Once verified, a parametric study was developed to investigate the axonal deformation for a wide range of loading overpressures and directions as well as varying cerebrospinal fluid (CSF) material models. This study focuses on early times during a blast event, just as the shock transverses the skull (< 5 milliseconds). Corresponding boundary conditions were applied to eliminate the rotation effects and the resulting axonal deformation. A total of 138 simulations were developed– 128 simulations for studying the different loading scenarios and 10 simulations for studying the effects of CSF material model variance–leading to a total of 10,702 simulation core hours. Extreme strains and strain rates along each of the fiber tracts in each of these scenarios were documented and presented here. The results suggest that the blast-induced skull flexures result in strain rates as high as 150–378 s-1. These high-strain rates of the axonal fiber tracts, caused by flexural displacement of the skull, could lead to a rate dependent micro-structural axonal damage, as pointed by other researchers.
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Bradfield C, Vavalle N, DeVincentis B, Wong E, Luong Q, Voo L, Carneal C. Combat Helmet Suspension System Stiffness Influences Linear Head Acceleration and White Matter Tissue Strains: Implications for Future Helmet Design. Mil Med 2018; 183:276-286. [PMID: 29635587 DOI: 10.1093/milmed/usx181] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Indexed: 11/13/2022] Open
Abstract
Combat helmets are expected to protect the warfighter from a variety of blunt, blast, and ballistic threats. Their blunt impact performance is evaluated by measuring linear headform acceleration in drop tower tests, which may be indicative of skull fracture, but not necessarily brain injury. The current study leverages a blunt impact biomechanics model consisting of a head, neck, and helmet with a suspension system to predict how pad stiffness affects both (1) linear acceleration alone and (2) brain tissue response induced by both linear and rotational acceleration. The approach leverages diffusion tensor imaging information to estimate how pad stiffness influences white matter tissue strains, which may be representative of diffuse axonal injury. Simulation results demonstrate that a softer pad material reduces linear head accelerations for mild and moderate impact velocities, but a stiffer pad design minimizes linear head accelerations at high velocities. Conversely, white matter tract-oriented strains were found to be smallest with the softer pads at the severe impact velocity. The results demonstrate that the current helmet blunt impact testing standards' standalone measurement of linear acceleration does not always convey how the brain tissue responds to changes in helmet design. Consequently, future helmet testing should consider the brain's mechanical response when evaluating new designs.
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Affiliation(s)
- Connor Bradfield
- Applied Physics Laboratory, The Johns Hopkins University, 11100 Johns Hopkins Rd, Laurel, MD 20723
| | - Nicholas Vavalle
- Applied Physics Laboratory, The Johns Hopkins University, 11100 Johns Hopkins Rd, Laurel, MD 20723
| | - Brian DeVincentis
- Applied Physics Laboratory, The Johns Hopkins University, 11100 Johns Hopkins Rd, Laurel, MD 20723
| | - Edna Wong
- Applied Physics Laboratory, The Johns Hopkins University, 11100 Johns Hopkins Rd, Laurel, MD 20723
| | - Quang Luong
- Applied Physics Laboratory, The Johns Hopkins University, 11100 Johns Hopkins Rd, Laurel, MD 20723
| | - Liming Voo
- Applied Physics Laboratory, The Johns Hopkins University, 11100 Johns Hopkins Rd, Laurel, MD 20723
| | - Catherine Carneal
- Applied Physics Laboratory, The Johns Hopkins University, 11100 Johns Hopkins Rd, Laurel, MD 20723
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Ganpule S, Daphalapurkar NP, Cetingul MP, Ramesh K. Effect of bulk modulus on deformation of the brain under rotational accelerations. SHOCK WAVES 2018; 28:127-139. [PMID: 29662272 PMCID: PMC5898454 DOI: 10.1007/s00193-017-0791-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2016] [Revised: 11/09/2017] [Accepted: 11/17/2017] [Indexed: 06/08/2023]
Abstract
Traumatic brain injury such as that developed as a consequence of blast is a complex injury with a broad range of symptoms and disabilities. Computational models of brain biomechanics hold promise for illuminating the mechanics of traumatic brain injury (TBI) and for developing preventive devices. However, reliable material parameters are needed for models to be predictive. Unfortunately, the properties of human brain tissue are difficult to measure, and the bulk modulus of brain tissue in particular is not well-characterized. Thus, a wide range of bulk modulus values are used in computational models of brain biomechanics, spanning up to three orders of magnitude in the differences between values. However, the sensitivity of these variations on computational predictions is not known. In this work, we study the sensitivity of a 3D computational human head model to various bulk modulus values. A subject-specific human head model was constructed from T1-weighted MRI images at 2 mm3 voxel resolution. Diffusion tensor imaging provided data on spatial distribution and orientation of axonal fiber-bundles for modeling white-matter anisotropy. Non-injurious, full-field brain deformations in a human volunteer were used to assess the simulated predictions. The comparison suggests that a bulk modulus value on the order of GPa gives the best agreement with experimentally measured in vivo deformation in the human brain. Further, simulations of injurious loading suggest that bulk modulus values on the order of GPa provide the closest match with the clinical findings in terms of predicated injured regions and extent of injury.
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Affiliation(s)
- S. Ganpule
- Indian Institute of Technology Roorkee, Roorkee, India, 247667
- Hopkins Extreme Materials Institute, Johns Hopkins University, Baltimore, MD, 21218
| | - N. P. Daphalapurkar
- Hopkins Extreme Materials Institute, Johns Hopkins University, Baltimore, MD, 21218
| | | | - K.T. Ramesh
- Hopkins Extreme Materials Institute, Johns Hopkins University, Baltimore, MD, 21218
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Somayaji MR, Przekwas AJ, Gupta RK. Combination Therapy for Multi-Target Manipulation of Secondary Brain Injury Mechanisms. Curr Neuropharmacol 2018; 16:484-504. [PMID: 28847295 PMCID: PMC6018188 DOI: 10.2174/1570159x15666170828165711] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2016] [Revised: 02/10/2017] [Accepted: 03/28/2017] [Indexed: 12/14/2022] Open
Abstract
Traumatic brain injury (TBI) is a major healthcare problem that affects millions of people worldwide. Despite advances in understanding and developing preventative and treatment strategies using preclinical animal models, clinical trials to date have failed, and a 'magic bullet' for effectively treating TBI-induced damage does not exist. Thus, novel pharmacological strategies to effectively manipulate the complex and heterogeneous pathophysiology of secondary injury mechanisms are needed. Given that goal, this paper discusses the relevance and advantages of combination therapies (COMTs) for 'multi-target manipulation' of the secondary injury cascade by administering multiple drugs to achieve an optimal therapeutic window of opportunity (e.g., temporally broad window) and compares these regimens to monotherapies that manipulate a single target with a single drug at a given time. Furthermore, we posit that integrated mechanistic multiscale models that combine primary injury biomechanics, secondary injury mechanobiology/neurobiology, physiology, pharmacology and mathematical programming techniques could account for vast differences in the biological space and time scales and help to accelerate drug development, to optimize pharmacological COMT protocols and to improve treatment outcomes.
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Affiliation(s)
| | | | - Raj K. Gupta
- Department of Defense Blast Injury Research Program Coordinating Office, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD, USA
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30
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Lytton WW, Arle J, Bobashev G, Ji S, Klassen TL, Marmarelis VZ, Schwaber J, Sherif MA, Sanger TD. Multiscale modeling in the clinic: diseases of the brain and nervous system. Brain Inform 2017; 4:219-230. [PMID: 28488252 PMCID: PMC5709279 DOI: 10.1007/s40708-017-0067-5] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Accepted: 04/27/2017] [Indexed: 12/26/2022] Open
Abstract
Computational neuroscience is a field that traces its origins to the efforts of Hodgkin and Huxley, who pioneered quantitative analysis of electrical activity in the nervous system. While also continuing as an independent field, computational neuroscience has combined with computational systems biology, and neural multiscale modeling arose as one offshoot. This consolidation has added electrical, graphical, dynamical system, learning theory, artificial intelligence and neural network viewpoints with the microscale of cellular biology (neuronal and glial), mesoscales of vascular, immunological and neuronal networks, on up to macroscales of cognition and behavior. The complexity of linkages that produces pathophysiology in neurological, neurosurgical and psychiatric disease will require multiscale modeling to provide understanding that exceeds what is possible with statistical analysis or highly simplified models: how to bring together pharmacotherapeutics with neurostimulation, how to personalize therapies, how to combine novel therapies with neurorehabilitation, how to interlace periodic diagnostic updates with frequent reevaluation of therapy, how to understand a physical disease that manifests as a disease of the mind. Multiscale modeling will also help to extend the usefulness of animal models of human diseases in neuroscience, where the disconnects between clinical and animal phenomenology are particularly pronounced. Here we cover areas of particular interest for clinical application of these new modeling neurotechnologies, including epilepsy, traumatic brain injury, ischemic disease, neurorehabilitation, drug addiction, schizophrenia and neurostimulation.
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Affiliation(s)
- William W. Lytton
- Department of Physiology and Pharmacology and Neurology, SUNY Downstate, Kings County Hospital, Brooklyn, NY 11203 USA
| | | | | | - Songbai Ji
- Thayer School of Engineering, Department of Surgery and of Orthopaedic Surgery, Geisel School of Medicine, Dartmouth College, Hanover, NH 3755 USA
| | | | | | | | - Mohamed A. Sherif
- Yale U, New Haven, CT USA
- VA Connecticut Healthcare System, West Haven, CT USA
- Ain Shams U Institute of Psychiatry, Cairo, Egypt
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Zhao W, Cai Y, Li Z, Ji S. Injury prediction and vulnerability assessment using strain and susceptibility measures of the deep white matter. Biomech Model Mechanobiol 2017; 16:1709-1727. [PMID: 28500358 PMCID: PMC5682246 DOI: 10.1007/s10237-017-0915-5] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2017] [Accepted: 04/29/2017] [Indexed: 10/19/2022]
Abstract
Reliable prediction and diagnosis of concussion is important for its effective clinical management. Previous model-based studies largely employ peak responses from a single element in a pre-selected anatomical region of interest (ROI) and utilize a single training dataset for injury prediction. A more systematic and rigorous approach is necessary to scrutinize the entire white matter (WM) ROIs as well as ROI-constrained neural tracts. To this end, we evaluated injury prediction performances of the 50 deep WM regions using predictor variables based on strains obtained from simulating the 58 reconstructed American National Football League head impacts. To objectively evaluate performance, repeated random subsampling was employed to split the impacts into independent training and testing datasets (39 and 19 cases, respectively, with 100 trials). Univariate logistic regressions were conducted based on training datasets to compute the area under the receiver operating characteristic curve (AUC), while accuracy, sensitivity, and specificity were reported based on testing datasets. Two tract-wise injury susceptibilities were identified as the best overall via pair-wise permutation test. They had comparable AUC, accuracy, and sensitivity, with the highest values occurring in superior longitudinal fasciculus (SLF; 0.867-0.879, 84.4-85.2, and 84.1-84.6%, respectively). Using metrics based on WM fiber strain, the most vulnerable ROIs included genu of corpus callosum, cerebral peduncle, and uncinate fasciculus, while genu and main body of corpus callosum, and SLF were among the most vulnerable tracts. Even for one un-concussed athlete, injury susceptibility of the cingulum (hippocampus) right was elevated. These findings highlight the unique injury discriminatory potentials of computational models and may provide important insight into how best to incorporate WM structural anisotropy for investigation of brain injury.
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Affiliation(s)
- Wei Zhao
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, 01609, USA
| | - Yunliang Cai
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, 01609, USA
| | - Zhigang Li
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Lebanon, NH, 03766, USA
| | - Songbai Ji
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, 01609, USA.
- Department of Mechanical Engineering, Worcester Polytechnic Institute, Worcester, MA, 01609, USA.
- Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, USA.
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32
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Haimovici A, Balenzuela P, Tagliazucchi E. Dynamical Signatures of Structural Connectivity Damage to a Model of the Brain Posed at Criticality. Brain Connect 2017; 6:759-771. [PMID: 27758115 DOI: 10.1089/brain.2016.0455] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Synchronization of brain activity fluctuations is believed to represent communication between spatially distant neural processes. These interareal functional interactions develop in the background of a complex network of axonal connections linking cortical and subcortical neurons, termed the human "structural connectome." Theoretical considerations and experimental evidence support the view that the human brain can be modeled as a system operating at a critical point between ordered (subcritical) and disordered (supercritical) phases. Here, we explore the hypothesis that pathologies resulting from brain injury of different etiologies are related to this model of a critical brain. For this purpose, we investigate how damage to the integrity of the structural connectome impacts on the signatures of critical dynamics. Adopting a hybrid modeling approach combining an empirical weighted network of human structural connections with a conceptual model of critical dynamics, we show that lesions located at highly transited connections progressively displace the model toward the subcritical regime. The topological properties of the nodes and links are of less importance when considered independently of their weight in the network. We observe that damage to midline hubs such as the middle and posterior cingulate cortex is most crucial for the disruption of criticality in the model. However, a similar effect can be achieved by targeting less transited nodes and links whose connection weights add up to an equivalent amount. This implies that brain pathology does not necessarily arise due to insult targeted at well-connected areas and that intersubject variability could obscure lesions located at nonhub regions. Finally, we discuss the predictions of our model in the context of clinical studies of traumatic brain injury and neurodegenerative disorders.
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Affiliation(s)
- Ariel Haimovici
- 1 Departamento de Física, Facultad de Cs. Exactas y Naturales, Universidad de Buenos Aires , Buenos Aires, Argentina .,2 Instituto de Física de Buenos Aires (IFIBA) , CONICET, Buenos Aires, Argentina
| | - Pablo Balenzuela
- 1 Departamento de Física, Facultad de Cs. Exactas y Naturales, Universidad de Buenos Aires , Buenos Aires, Argentina .,2 Instituto de Física de Buenos Aires (IFIBA) , CONICET, Buenos Aires, Argentina
| | - Enzo Tagliazucchi
- 3 Netherlands Institute for Neuroscience , Amsterdam-Zuidoost, The Netherlands
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33
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Franck C. Microcavitation: the key to modeling blast traumatic brain injury? Concussion 2017; 2:CNC47. [PMID: 30202586 PMCID: PMC6122696 DOI: 10.2217/cnc-2017-0011] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2017] [Accepted: 05/17/2017] [Indexed: 12/02/2022] Open
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34
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Agoston DV, Langford D. Big Data in traumatic brain injury; promise and challenges. Concussion 2017; 2:CNC45. [PMID: 30202589 PMCID: PMC6122694 DOI: 10.2217/cnc-2016-0013] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2016] [Accepted: 05/25/2017] [Indexed: 01/14/2023] Open
Abstract
Traumatic brain injury (TBI) is a spectrum disease of overwhelming complexity, the research of which generates enormous amounts of structured, semi-structured and unstructured data. This resulting big data has tremendous potential to be mined for valuable information regarding the "most complex disease of the most complex organ". Big data analyses require specialized big data analytics applications, machine learning and artificial intelligence platforms to reveal associations, trends, correlations and patterns not otherwise realized by current analytical approaches. The intersection of potential data sources between experimental TBI and clinical TBI research presents inherent challenges for setting parameters for the generation of common data elements and to mine existing legacy data that would allow highly translatable big data analyses. In order to successfully utilize big data analyses in TBI, we must be willing to accept the messiness of data, collect and store all data and give up causation for correlation. In this context, coupling the big data approach to established clinical and pre-clinical data sources will transform current practices for triage, diagnosis, treatment and prognosis into highly integrated evidence-based patient care.
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Affiliation(s)
- Denes V Agoston
- Department of Anatomy, Physiology & Genetics, Uniformed Services University, Bethesda, MD 20814, USA.,Department of Neuroscience, Karolinska Institute, Stockholm, Sweden.,Department of Anatomy, Physiology & Genetics, Uniformed Services University, Bethesda, MD 20814, USA.,Department of Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Dianne Langford
- Department of Neuroscience, Lewis Katz School of Medicine, Temple University, Philadelphia, PA 19140, USA.,Department of Neuroscience, Lewis Katz School of Medicine, Temple University, Philadelphia, PA 19140, USA
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Ng LJ, Volman V, Gibbons MM, Phohomsiri P, Cui J, Swenson DJ, Stuhmiller JH. A Mechanistic End-to-End Concussion Model That Translates Head Kinematics to Neurologic Injury. Front Neurol 2017; 8:269. [PMID: 28663736 PMCID: PMC5471336 DOI: 10.3389/fneur.2017.00269] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2016] [Accepted: 05/26/2017] [Indexed: 11/13/2022] Open
Abstract
Past concussion studies have focused on understanding the injury processes occurring on discrete length scales (e.g., tissue-level stresses and strains, cell-level stresses and strains, or injury-induced cellular pathology). A comprehensive approach that connects all length scales and relates measurable macroscopic parameters to neurological outcomes is the first step toward rationally unraveling the complexity of this multi-scale system, for better guidance of future research. This paper describes the development of the first quantitative end-to-end (E2E) multi-scale model that links gross head motion to neurological injury by integrating fundamental elements of tissue and cellular mechanical response with axonal dysfunction. The model quantifies axonal stretch (i.e., tension) injury in the corpus callosum, with axonal functionality parameterized in terms of axonal signaling. An internal injury correlate is obtained by calculating a neurological injury measure (the average reduction in the axonal signal amplitude) over the corpus callosum. By using a neurologically based quantity rather than externally measured head kinematics, the E2E model is able to unify concussion data across a range of exposure conditions and species with greater sensitivity and specificity than correlates based on external measures. In addition, this model quantitatively links injury of the corpus callosum to observed specific neurobehavioral outcomes that reflect clinical measures of mild traumatic brain injury. This comprehensive modeling framework provides a basis for the systematic improvement and expansion of this mechanistic-based understanding, including widening the range of neurological injury estimation, improving concussion risk correlates, guiding the design of protective equipment, and setting safety standards.
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Affiliation(s)
- Laurel J Ng
- Simulation Engineering and Testing, L-3 Applied Technologies, Inc., San Diego, CA, United States
| | - Vladislav Volman
- Simulation Engineering and Testing, L-3 Applied Technologies, Inc., San Diego, CA, United States
| | - Melissa M Gibbons
- Simulation Engineering and Testing, L-3 Applied Technologies, Inc., San Diego, CA, United States
| | - Pi Phohomsiri
- Simulation Engineering and Testing, L-3 Applied Technologies, Inc., San Diego, CA, United States
| | - Jianxia Cui
- Simulation Engineering and Testing, L-3 Applied Technologies, Inc., San Diego, CA, United States
| | - Darrell J Swenson
- Cardiac Rhythm and Heart Failure Numerical Modeling, Medtronic, Mounds View, MN, United States
| | - James H Stuhmiller
- Simulation Engineering and Testing, L-3 Applied Technologies, Inc., San Diego, CA, United States
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Garimella HT, Kraft RH. Modeling the mechanics of axonal fiber tracts using the embedded finite element method. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2017; 33. [PMID: 27502006 DOI: 10.1002/cnm.2823] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2015] [Revised: 07/11/2016] [Accepted: 07/16/2016] [Indexed: 05/10/2023]
Abstract
A subject-specific human head finite element model with embedded axonal fiber tractography obtained from diffusion tensor imaging was developed. The axonal fiber tractography finite element model was coupled with the volumetric elements in the head model using the embedded element method. This technique enables the calculation of axonal strains and real-time tracking of the mechanical response of the axonal fiber tracts. The coupled model was then verified using pressure and relative displacement-based (between skull and brain) experimental studies and was employed to analyze a head impact, demonstrating the applicability of this method in studying axonal injury. Following this, a comparison study of different injury criteria was performed. This model was used to determine the influence of impact direction on the extent of the axonal injury. The results suggested that the lateral impact loading is more dangerous compared to loading in the sagittal plane, a finding in agreement with previous studies. Through this analysis, we demonstrated the viability of the embedded element method as an alternative numerical approach for studying axonal injury in patient-specific human head models.
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Affiliation(s)
- Harsha T Garimella
- Department of Mechanical and Nuclear Engineering, The Pennsylvania State University, University Park, PA 16802, USA
| | - Reuben H Kraft
- Department of Mechanical and Nuclear Engineering, The Pennsylvania State University, University Park, PA 16802, USA
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
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37
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Giordano C, Zappalà S, Kleiven S. Anisotropic finite element models for brain injury prediction: the sensitivity of axonal strain to white matter tract inter-subject variability. Biomech Model Mechanobiol 2017; 16:1269-1293. [PMID: 28233136 PMCID: PMC5511602 DOI: 10.1007/s10237-017-0887-5] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2016] [Accepted: 02/08/2017] [Indexed: 01/17/2023]
Abstract
Computational models incorporating anisotropic features of brain tissue have become a valuable tool for studying the occurrence of traumatic brain injury. The tissue deformation in the direction of white matter tracts (axonal strain) was repeatedly shown to be an appropriate mechanical parameter to predict injury. However, when assessing the reliability of axonal strain to predict injury in a population, it is important to consider the predictor sensitivity to the biological inter-subject variability of the human brain. The present study investigated the axonal strain response of 485 white matter subject-specific anisotropic finite element models of the head subjected to the same loading conditions. It was observed that the biological variability affected the orientation of the preferential directions (coefficient of variation of 39.41% for the elevation angle—coefficient of variation of 29.31% for the azimuth angle) and the determination of the mechanical fiber alignment parameter in the model (gray matter volume 55.55–70.75%). The magnitude of the maximum axonal strain showed coefficients of variation of 11.91%. On the contrary, the localization of the maximum axonal strain was consistent: the peak of strain was typically located in a 2 cm3 volume of the brain. For a sport concussive event, the predictor was capable of discerning between non-injurious and concussed populations in several areas of the brain. It was concluded that, despite its sensitivity to biological variability, axonal strain is an appropriate mechanical parameter to predict traumatic brain injury.
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Affiliation(s)
- Chiara Giordano
- Royal Institute of Technology KTH, School of Technology and Health, Hälsovägen 11C, 141 57, Huddinge, Sweden.
| | - Stefano Zappalà
- Royal Institute of Technology KTH, School of Technology and Health, Hälsovägen 11C, 141 57, Huddinge, Sweden
| | - Svein Kleiven
- Royal Institute of Technology KTH, School of Technology and Health, Hälsovägen 11C, 141 57, Huddinge, Sweden
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38
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Garimella HT, Kraft RH. A new computational approach for modeling diffusion tractography in the brain. Neural Regen Res 2017; 12:23-26. [PMID: 28250733 PMCID: PMC5319226 DOI: 10.4103/1673-5374.198967] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
Computational models provide additional tools for studying the brain, however, many techniques are currently disconnected from each other. There is a need for new computational approaches that span the range of physics operating in the brain. In this review paper, we offer some new perspectives on how the embedded element method can fill this gap and has the potential to connect a myriad of modeling genre. The embedded element method is a mesh superposition technique used within finite element analysis. This method allows for the incorporation of axonal fiber tracts to be explicitly represented. Here, we explore the use of the approach beyond its original goal of predicting axonal strain in brain injury. We explore the potential application of the embedded element method in areas of electrophysiology, neurodegeneration, neuropharmacology and mechanobiology. We conclude that this method has the potential to provide us with an integrated computational framework that can assist in developing improved diagnostic tools and regeneration technologies.
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Affiliation(s)
- Harsha T Garimella
- Department of Mechanical and Nuclear Engineering, Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Reuben H Kraft
- Department of Mechanical and Nuclear Engineering, Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA, USA
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39
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Bigler ED, Zielinski BA, Goodrich-Hunsaker N, Black GM, Huff BST, Christiansen Z, Wood DM, Abildskov TJ, Dennis M, Taylor HG, Rubin K, Vannatta K, Gerhardt CA, Stancin T, Yeates KO. The Relation of Focal Lesions to Cortical Thickness in Pediatric Traumatic Brain Injury. J Child Neurol 2016; 31:1302-11. [PMID: 27342577 PMCID: PMC5525324 DOI: 10.1177/0883073816654143] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2016] [Accepted: 05/09/2016] [Indexed: 12/22/2022]
Abstract
In a sample of children with traumatic brain injury, this magnetic resonance imaging (MRI)-based investigation examined whether presence of a focal lesion uniquely influenced cortical thickness in any brain region. Specifically, the study explored the relation of cortical thickness to injury severity as measured by Glasgow Coma Scale score and length of stay, along with presence of encephalomalacia, focal white matter lesions or presence of hemosiderin deposition as a marker of shear injury. For comparison, a group of children without head injury but with orthopedic injury of similar age and sex were also examined. Both traumatic brain injury and orthopedic injury children had normally reduced cortical thickness with age, assumed to reflect neuronal pruning. However, the reductions observed within the traumatic brain injury sample were similar to those in the orthopedic injury group, suggesting that in this sample traumatic brain injury, per se, did not uniquely alter cortical thickness in any brain region at the group level. Injury severity in terms of Glasgow Coma Scale or longer length of stay was associated with greater reductions in frontal and occipitoparietal cortical thickness. However, presence of focal lesions were not related to unique changes in cortical thickness despite having a prominent distribution of lesions within frontotemporal regions among children with traumatic brain injury. Because focal lesions were highly heterogeneous, their association with cortical thickness and development appeared to be idiosyncratic, and not associated with group level effects.
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Affiliation(s)
- Erin D Bigler
- Department of Psychology and the Neuroscience Center, Brigham Young University, Provo, UT, USA Department of Psychiatry, University of Utah, Salt Lake City, UT, USA
| | - Brandon A Zielinski
- Departments of Pediatrics and Neurology, University of Utah, Salt Lake City, UT, USA
| | | | - Garrett M Black
- Department of Psychology, Brigham Young University, Provo, UT, USA
| | - B S Trevor Huff
- Department of Psychology, Brigham Young University, Provo, UT, USA
| | | | - Dawn-Marie Wood
- Department of Psychology, Brigham Young University, Provo, UT, USA
| | | | - Maureen Dennis
- Program in Neuroscience & Mental Health, The Hospital for Sick Children, Toronto, Canada Department of Surgery and Department of Psychology, University of Toronto, Toronto, Canada
| | - H Gerry Taylor
- Department of Pediatrics, Case Western Reserve University and Rainbow Babies & Children's Hospital, University Hospitals Case Medical Center, Cleveland, OH, USA
| | - Kenneth Rubin
- Department of Psychology, University of Maryland, College Park, MD, USA
| | - Kathryn Vannatta
- Department of Pediatrics, The Ohio State University, Columbus, OH, USA Center for Behavioral Health, Columbus Children's Research Institute, Columbus, OH, USA
| | - Cynthia A Gerhardt
- Department of Pediatrics, The Ohio State University, Columbus, OH, USA Center for Behavioral Health, Columbus Children's Research Institute, Columbus, OH, USA
| | - Terry Stancin
- Department of Pediatrics, Case Western Reserve University and Rainbow Babies & Children's Hospital, University Hospitals Case Medical Center, Cleveland, OH, USA Department of Psychiatry, MetroHealth Medical Center, Cleveland, OH, USA
| | - Keith Owen Yeates
- Department of Pediatrics, The Ohio State University, Columbus, OH, USA Center for Biobehavioral Health, The Research Institute at Nationwide Children's Hospital, Columbus, OH, USA
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40
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Affiliation(s)
- Graham Martin
- Accident Compensation Corporation of New Zealand, Wellington, New Zealand
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41
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Bigler ED. Systems Biology, Neuroimaging, Neuropsychology, Neuroconnectivity and Traumatic Brain Injury. Front Syst Neurosci 2016; 10:55. [PMID: 27555810 PMCID: PMC4977319 DOI: 10.3389/fnsys.2016.00055] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2016] [Accepted: 06/08/2016] [Indexed: 01/03/2023] Open
Abstract
The patient who sustains a traumatic brain injury (TBI) typically undergoes neuroimaging studies, usually in the form of computed tomography (CT) and magnetic resonance imaging (MRI). In most cases the neuroimaging findings are clinically assessed with descriptive statements that provide qualitative information about the presence/absence of visually identifiable abnormalities; though little if any of the potential information in a scan is analyzed in any quantitative manner, except in research settings. Fortunately, major advances have been made, especially during the last decade, in regards to image quantification techniques, especially those that involve automated image analysis methods. This review argues that a systems biology approach to understanding quantitative neuroimaging findings in TBI provides an appropriate framework for better utilizing the information derived from quantitative neuroimaging and its relation with neuropsychological outcome. Different image analysis methods are reviewed in an attempt to integrate quantitative neuroimaging methods with neuropsychological outcome measures and to illustrate how different neuroimaging techniques tap different aspects of TBI-related neuropathology. Likewise, how different neuropathologies may relate to neuropsychological outcome is explored by examining how damage influences brain connectivity and neural networks. Emphasis is placed on the dynamic changes that occur following TBI and how best to capture those pathologies via different neuroimaging methods. However, traditional clinical neuropsychological techniques are not well suited for interpretation based on contemporary and advanced neuroimaging methods and network analyses. Significant improvements need to be made in the cognitive and behavioral assessment of the brain injured individual to better interface with advances in neuroimaging-based network analyses. By viewing both neuroimaging and neuropsychological processes within a systems biology perspective could represent a significant advancement for the field.
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Affiliation(s)
- Erin D. Bigler
- Department of Psychology, Neuroscience Center, Brigham Young UniversityProvo, UT, USA
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42
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Strain and rate-dependent neuronal injury in a 3D in vitro compression model of traumatic brain injury. Sci Rep 2016; 6:30550. [PMID: 27480807 PMCID: PMC4969749 DOI: 10.1038/srep30550] [Citation(s) in RCA: 105] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2016] [Accepted: 07/06/2016] [Indexed: 12/11/2022] Open
Abstract
In the United States over 1.7 million cases of traumatic brain injury are reported yearly, but predictive correlation of cellular injury to impact tissue strain is still lacking, particularly for neuronal injury resulting from compression. Given the prevalence of compressive deformations in most blunt head trauma, this information is critically important for the development of future mitigation and diagnosis strategies. Using a 3D in vitro neuronal compression model, we investigated the role of impact strain and strain rate on neuronal lifetime, viability, and pathomorphology. We find that strain magnitude and rate have profound, yet distinctively different effects on the injury pathology. While strain magnitude affects the time of neuronal death, strain rate influences the pathomorphology and extent of population injury. Cellular injury is not initiated through localized deformation of the cytoskeleton but rather driven by excess strain on the entire cell. Furthermore we find that, mechanoporation, one of the key pathological trigger mechanisms in stretch and shear neuronal injuries, was not observed under compression.
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43
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Zhao W, Ford JC, Flashman LA, McAllister TW, Ji S. White Matter Injury Susceptibility via Fiber Strain Evaluation Using Whole-Brain Tractography. J Neurotrauma 2016; 33:1834-1847. [PMID: 26782139 DOI: 10.1089/neu.2015.4239] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Microscale brain injury studies suggest axonal elongation as a potential mechanism for diffuse axonal injury (DAI). Recent studies have begun to incorporate white matter (WM) structural anisotropy in injury analysis, with initial evidence suggesting improved injury prediction performance. In this study, we further develop a tractography-based approach to analyze fiber strains along the entire lengths of fibers from voxel- or anatomically constrained whole-brain tractography. This technique potentially extends previous element- or voxel-based methods that instead utilize WM fiber orientations averaged from typically coarse elements or voxels. Perhaps more importantly, incorporating tractography-based axonal structural information enables assessment of the overall injury risks to functionally important neural pathways and the anatomical regions they connect, which is not possible with previous methods. A DAI susceptibility index was also established to quantify voxel-wise WM local structural integrity and tract-wise damage of individual neural pathways. This "graded" injury susceptibility potentially extends the commonly employed treatment of injury as a simple binary condition. As an illustration, we evaluate the DAI susceptibilities of WM voxels and transcallosal fiber tracts in three idealized head impacts. Findings suggest the potential importance of the tractography-based approach for injury prediction. These efforts may enable future studies to correlate WM mechanical responses with neuroimaging, cognitive alteration, and concussion, and to reveal the relative vulnerabilities of neural pathways and identify the most vulnerable ones in real-world head impacts.
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Affiliation(s)
- Wei Zhao
- 1 Thayer School of Engineering, Dartmouth College , Hanover, New Hampshire
| | - James C Ford
- 2 Department of Psychiatry, Geisel School of Medicine at Dartmouth , Lebanon, New Hampshire
| | - Laura A Flashman
- 2 Department of Psychiatry, Geisel School of Medicine at Dartmouth , Lebanon, New Hampshire
| | - Thomas W McAllister
- 3 Department of Psychiatry, Indiana University School of Medicine , Indianapolis, Indiana
| | - Songbai Ji
- 1 Thayer School of Engineering, Dartmouth College , Hanover, New Hampshire.,4 Department of Surgery and of Orthopaedic Surgery, Geisel School of Medicine at Dartmouth , Lebanon, New Hampshire
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44
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Carlsen RW, Daphalapurkar NP. The importance of structural anisotropy in computational models of traumatic brain injury. Front Neurol 2015; 6:28. [PMID: 25745414 PMCID: PMC4333795 DOI: 10.3389/fneur.2015.00028] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2014] [Accepted: 02/02/2015] [Indexed: 11/18/2022] Open
Abstract
Understanding the mechanisms of injury might prove useful in assisting the development of methods for the management and mitigation of traumatic brain injury (TBI). Computational head models can provide valuable insight into the multi-length-scale complexity associated with the primary nature of diffuse axonal injury. It involves understanding how the trauma to the head (at the centimeter length scale) translates to the white-matter tissue (at the millimeter length scale), and even further down to the axonal-length scale, where physical injury to axons (e.g., axon separation) may occur. However, to accurately represent the development of TBI, the biofidelity of these computational models is of utmost importance. There has been a focused effort to improve the biofidelity of computational models by including more sophisticated material definitions and implementing physiologically relevant measures of injury. This paper summarizes recent computational studies that have incorporated structural anisotropy in both the material definition of the white matter and the injury criterion as a means to improve the predictive capabilities of computational models for TBI. We discuss the role of structural anisotropy on both the mechanical response of the brain tissue and on the development of injury. We also outline future directions in the computational modeling of TBI.
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Affiliation(s)
- Rika W Carlsen
- Department of Engineering, Robert Morris University , Pittsburgh, PA , USA
| | - Nitin P Daphalapurkar
- Department of Mechanical Engineering, Hopkins Extreme Materials Institute, Johns Hopkins University , Baltimore, MD , USA
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45
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Ji S, Zhao W, Ford JC, Beckwith JG, Bolander RP, Greenwald RM, Flashman LA, Paulsen KD, McAllister TW. Group-wise evaluation and comparison of white matter fiber strain and maximum principal strain in sports-related concussion. J Neurotrauma 2015; 32:441-54. [PMID: 24735430 DOI: 10.1089/neu.2013.3268] [Citation(s) in RCA: 109] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Sports-related concussion is a major public health problem in the United States and yet its biomechanical mechanisms remain unclear. In vitro studies demonstrate axonal elongation as a potential injury mechanism; however, current response-based injury predictors (e.g., maximum principal strain, ε(ep)) typically do not incorporate axonal orientations. We investigated the significance of white matter (WM) fiber orientation in strain estimation and compared fiber strain (ε(n)) with ε(ep) for 11 athletes with a clinical diagnosis of concussion. Geometrically accurate subject-specific head models with high mesh quality were created based on the Dartmouth Head Injury Model (DHIM), which was successfully validated (performance categorized as "good" to "excellent"). For WM regions estimated to be exposed to high strains using a range of injury thresholds (0.09-0.28), substantial differences existed between ε(n) and ε(ep) in both distribution (Dice coefficient of 0.13-0.33) and extent (∼ 5-10-fold differences), especially at higher threshold levels and higher rotational acceleration magnitudes. For example, an average of 3.2% vs. 29.8% of WM was predicted above an optimal threshold of 0.18 established from an in vivo animal study using ε(n) and ε(ep), respectively, with an average Dice coefficient of 0.14. The distribution of WM regions with high ε(n) was consistent with typical heterogeneous patterns of WM disruptions in diffuse axonal injury, and the group-wise extent at the optimal threshold matched well with the percentage of WM voxels experiencing significant longitudinal changes of fractional anisotropy and mean diffusivity (3.2% and 3.44%, respectively) found from a separate independent study. These results suggest the significance of incorporating WM microstructural anisotropy in future brain injury studies.
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Affiliation(s)
- Songbai Ji
- 1 Thayer School of Engineering, Dartmouth College , Hanover, New Hampshire
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46
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Bigler ED, Stern Y. Traumatic brain injury and reserve. HANDBOOK OF CLINICAL NEUROLOGY 2015; 128:691-710. [DOI: 10.1016/b978-0-444-63521-1.00043-1] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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47
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Andreotti J, Jann K, Melie-Garcia L, Giezendanner S, Abela E, Wiest R, Dierks T, Federspiel A. Validation of network communicability metrics for the analysis of brain structural networks. PLoS One 2014; 9:e115503. [PMID: 25549088 PMCID: PMC4280193 DOI: 10.1371/journal.pone.0115503] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2014] [Accepted: 11/13/2014] [Indexed: 01/21/2023] Open
Abstract
Computational network analysis provides new methods to analyze the brain's structural organization based on diffusion imaging tractography data. Networks are characterized by global and local metrics that have recently given promising insights into diagnosis and the further understanding of psychiatric and neurologic disorders. Most of these metrics are based on the idea that information in a network flows along the shortest paths. In contrast to this notion, communicability is a broader measure of connectivity which assumes that information could flow along all possible paths between two nodes. In our work, the features of network metrics related to communicability were explored for the first time in the healthy structural brain network. In addition, the sensitivity of such metrics was analysed using simulated lesions to specific nodes and network connections. Results showed advantages of communicability over conventional metrics in detecting densely connected nodes as well as subsets of nodes vulnerable to lesions. In addition, communicability centrality was shown to be widely affected by the lesions and the changes were negatively correlated with the distance from lesion site. In summary, our analysis suggests that communicability metrics that may provide an insight into the integrative properties of the structural brain network and that these metrics may be useful for the analysis of brain networks in the presence of lesions. Nevertheless, the interpretation of communicability is not straightforward; hence these metrics should be used as a supplement to the more standard connectivity network metrics.
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Affiliation(s)
- Jennifer Andreotti
- Department of Psychiatric Neurophysiology, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| | - Kay Jann
- Department of Psychiatric Neurophysiology, University Hospital of Psychiatry, University of Bern, Bern, Switzerland; Laboratory of Functional MRI Technology, Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, University of California Los Angeles, Los Angeles California, United States of America
| | - Lester Melie-Garcia
- Department of Psychiatric Neurophysiology, University Hospital of Psychiatry, University of Bern, Bern, Switzerland; Neuroimaging Research Laboratory (LREN), Department of Clinical Neurosciences, Vaud University Hospital Center (CHUV), Lausanne, Switzerland
| | - Stéphanie Giezendanner
- Department of Psychiatric Neurophysiology, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| | - Eugenio Abela
- University Institute of Diagnostic and Interventional Neuroradiology, Inselspital and University of Bern, Bern, Switzerland
| | - Roland Wiest
- University Institute of Diagnostic and Interventional Neuroradiology, Inselspital and University of Bern, Bern, Switzerland
| | - Thomas Dierks
- Department of Psychiatric Neurophysiology, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| | - Andrea Federspiel
- Department of Psychiatric Neurophysiology, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
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48
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A Pre-computed Brain Response Atlas for Instantaneous Strain Estimation in Contact Sports. Ann Biomed Eng 2014; 43:1877-95. [PMID: 25449149 DOI: 10.1007/s10439-014-1193-3] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2014] [Accepted: 11/19/2014] [Indexed: 11/27/2022]
Abstract
Finite element models of the human head play an important role in investigating the mechanisms of traumatic brain injury, including sports concussion. A critical limitation, however, is that they incur a substantial computational cost to simulate even a single impact. Therefore, current simulation schemes significantly hamper brain injury studies based on model-estimated tissue-level responses. In this study, we present a pre-computed brain response atlas (pcBRA) to substantially increase the simulation efficiency in estimating brain strains using isolated rotational acceleration impulses parameterized with four independent variables (peak magnitude and duration, and rotational axis azimuth and elevation angles) with values determined from on-field measurements. Using randomly generated testing datasets, the partially established pcBRA achieved a 100% success rate in interpolation based on element-wise differences in accumulated peak strain ([Formula: see text]) according to a "double-10%" criterion or average regional [Formula: see text] in generic regions and the corpus callosum. A similar performance was maintained in extrapolation. The pcBRA performance was further successfully validated against directly simulated responses from two independently measured typical real-world rotational profiles. The computational cost to estimate element-wise whole-brain or regional [Formula: see text] was 6 s and <0.01 s, respectively, vs. ~50 min directly simulating a 40 ms impulse. These findings suggest the pcBRA could substantially increase the throughput in impact simulation without significant loss of accuracy from the estimation itself and, thus, its potential to accelerate the exploration of the mechanisms of sports concussion in general. If successful, the pcBRA may also become a diagnostic adjunct in conjunction with sensors that measure head impact kinematics on the field to objectively monitor and identify tissue-level brain trauma in real-time for "return-to-play" decision-making on the sideline.
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49
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A finite-element reciprocity solution for EEG forward modeling with realistic individual head models. Neuroimage 2014; 103:542-551. [DOI: 10.1016/j.neuroimage.2014.08.056] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2014] [Revised: 08/27/2014] [Accepted: 08/30/2014] [Indexed: 11/21/2022] Open
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50
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Zhao W, Ruan S, Ji S. Brain pressure responses in translational head impact: a dimensional analysis and a further computational study. Biomech Model Mechanobiol 2014; 14:753-66. [PMID: 25412925 DOI: 10.1007/s10237-014-0634-0] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2014] [Accepted: 11/08/2014] [Indexed: 11/28/2022]
Abstract
Brain pressure responses resulting from translational head impact are typically related to focal injuries at the coup and contrecoup sites. Despite significant efforts characterizing brain pressure responses using experimental and modeling approaches, a thorough investigation of the key controlling parameters appears lacking. In this study, we identified three parameters specific and important for brain pressure responses induced by isolated linear acceleration a(lin) via a dimensional analysis: a(lin) itself (magnitude and directionality), brain size and shape. These findings were verified using our recently developed Dartmouth Head Injury Model (DHIM). Applying a(lin) to the rigid skull, we found that the temporal profile of the given a(lin) directly determined that of pressure. Brain pressure was also found to be linearly proportional to brain size and dependent on impact direction. In addition, we investigated perturbations to brain pressure responses as a result of non-rigid skull deformation. Finally, DHIM pressure responses were quantitatively validated against two representative cadaveric head impacts (categorized as "good" to "excellent" in performance). These results suggest that both the magnitude and directionality of a(lin) as well as brain size and shape should be considered when interpreting brain pressure responses. Further, a model validated against pressure responses alone is not sufficient to ensure its fidelity in strain-related responses. These findings provide important insights into brain pressure responses in translational head impact and the resulting risk of pressure-induced injury. In addition, they establish the feasibility of creating a pre-computed atlas for real-time tissue-level pressure responses without a direct simulation in the future.
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Affiliation(s)
- Wei Zhao
- Thayer School of Engineering, Dartmouth College, 14 Engineering Drive, Hanover, NH, 03755, USA
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