1
|
Zhou Z, Olsson C, Gasser TC, Li X, Kleiven S. The White Matter Fiber Tract Deforms Most in the Perpendicular Direction During In Vivo Volunteer Impacts. J Neurotrauma 2024. [PMID: 39212616 DOI: 10.1089/neu.2024.0183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/04/2024] Open
Abstract
White matter (WM) tract-related strains are increasingly used to quantify brain mechanical responses, but their dynamics in live human brains during in vivo impact conditions remain largely unknown. Existing research primarily looked into the normal strain along the WM fiber tracts (i.e., tract-oriented normal strain), but it is rarely the case that the fiber tract only endures tract-oriented normal strain during impacts. In this study, we aim to extend the in vivo measurement of WM fiber deformation by quantifying the normal strain perpendicular to the fiber tract (i.e., tract-perpendicular normal strain) and the shear strain along and perpendicular to the fiber tract (i.e., tract-oriented shear strain and tract-perpendicular shear strain, respectively). To achieve this, we combine the three-dimensional strain tensor from the tagged magnetic resonance imaging with the diffusion tensor imaging (DTI) from an open-access dataset, including 44 volunteer impacts under two head loading modes, i.e., neck rotations (N = 30) and neck extensions (N = 14). The strain tensor is rotated to the coordinate system with one axis aligned with DTI-revealed fiber orientation, and then four tract-related strain measures are calculated. The results show that tract-perpendicular normal strain peaks are the largest among the four strain types (p < 0.05, Friedman's test). The distribution of tract-related strains is affected by the head loading mode, of which laterally symmetric patterns with respect to the midsagittal plane are noted under neck extensions, but not under neck rotations. Our study presents a comprehensive in vivo strain quantification toward a multifaceted understanding of WM dynamics. We find that the WM fiber tract deforms most in the perpendicular direction, illuminating new fundamentals of brain mechanics. The reported strain images can be used to evaluate the fidelity of computational head models, especially those intended to predict fiber deformation under noninjurious conditions.
Collapse
Affiliation(s)
- Zhou Zhou
- Division of Neuronic Engineering, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Christoffer Olsson
- Division of Biomedical Imaging, KTH Royal Institute of Technology, Stockholm, Sweden
| | - T Christian Gasser
- Material and Structural Mechanics, Department of Engineering Mechanics, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Xiaogai Li
- Division of Neuronic Engineering, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Svein Kleiven
- Division of Neuronic Engineering, KTH Royal Institute of Technology, Stockholm, Sweden
| |
Collapse
|
2
|
Kwiatkowski A, Weidler C, Habel U, Coverdale NS, Hirad AA, Manning KY, Rauscher A, Bazarian JJ, Cook DJ, Li DKB, Mahon BZ, Menon RS, Taunton J, Reetz K, Romanzetti S, Huppertz C. Uncovering the hidden effects of repetitive subconcussive head impact exposure: A mega-analytic approach characterizing seasonal brain microstructural changes in contact and collision sports athletes. Hum Brain Mapp 2024; 45:e26811. [PMID: 39185683 PMCID: PMC11345636 DOI: 10.1002/hbm.26811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 07/16/2024] [Accepted: 07/20/2024] [Indexed: 08/27/2024] Open
Abstract
Repetitive subconcussive head impacts (RSHI) are believed to induce sub-clinical brain injuries, potentially resulting in cumulative, long-term brain alterations. This study explores patterns of longitudinal brain white matter changes across sports with RSHI-exposure. A systematic literature search identified 22 datasets with longitudinal diffusion magnetic resonance imaging data. Four datasets were centrally pooled to perform uniform quality control and data preprocessing. A total of 131 non-concussed active athletes (American football, rugby, ice hockey; mean age: 20.06 ± 2.06 years) with baseline and post-season data were included. Nonparametric permutation inference (one-sample t tests, one-sided) was applied to analyze the difference maps of multiple diffusion parameters. The analyses revealed widespread lateralized patterns of sports-season-related increases and decreases in mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) across spatially distinct white matter regions. Increases were shown across one MD-cluster (3195 voxels; mean change: 2.34%), one AD-cluster (5740 voxels; mean change: 1.75%), and three RD-clusters (817 total voxels; mean change: 3.11 to 4.70%). Decreases were shown across two MD-clusters (1637 total voxels; mean change: -1.43 to -1.48%), two RD-clusters (1240 total voxels; mean change: -1.92 to -1.93%), and one AD-cluster (724 voxels; mean change: -1.28%). The resulting pattern implies the presence of strain-induced injuries in central and brainstem regions, with comparatively milder physical exercise-induced effects across frontal and superior regions of the left hemisphere, which need further investigation. This article highlights key considerations that need to be addressed in future work to enhance our understanding of the nature of observed white matter changes, improve the comparability of findings across studies, and promote data pooling initiatives to allow more detailed investigations (e.g., exploring sex- and sport-specific effects).
Collapse
Affiliation(s)
- Anna Kwiatkowski
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical FacultyRWTH Aachen UniversityAachenGermany
| | - Carmen Weidler
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical FacultyRWTH Aachen UniversityAachenGermany
| | - Ute Habel
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical FacultyRWTH Aachen UniversityAachenGermany
- Institute of Neuroscience and Medicine 10, Research Centre JülichJülichGermany
- JARA‐BRAIN Institute Brain Structure Function Relationship, Research Center Jülich and RWTH Aachen UniversityAachenGermany
| | | | - Adnan A. Hirad
- Department of SurgeryUniversity of Rochester Medical CenterRochesterNew YorkUSA
- Department of NeuroscienceUniversity of Rochester Medical CenterRochesterNew YorkUSA
- Del Monte Neuroscience Institute, University of RochesterNew YorkUSA
| | - Kathryn Y. Manning
- Department of RadiologyUniversity of Calgary and Alberta Children's Hospital Research InstituteCalgaryAlbertaCanada
| | - Alexander Rauscher
- Department of Radiology, Faculty of MedicineUniversity of British ColumbiaVancouverBritish ColumbiaCanada
- Department of Pediatrics, Division of NeurologyUniversity of British ColumbiaVancouverBritish ColumbiaCanada
- Department of Physics and AstronomyUniversity of British ColumbiaVancouverBritish ColumbiaCanada
- UBC MRI Research Centre, University of British ColumbiaVancouverBritish ColumbiaCanada
| | - Jeffrey J. Bazarian
- Department of Emergency MedicineUniversity of Rochester School of Medicine and DentistryRochesterNew YorkUSA
| | - Douglas J. Cook
- Centre for Neuroscience Studies, Queen's UniversityKingstonOntarioCanada
- Division of Neurosurgery, Department of SurgeryQueen's UniversityKingstonOntarioCanada
| | - David K. B. Li
- Department of Radiology, Faculty of MedicineUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Bradford Z. Mahon
- Department of PsychologyCarnegie Mellon UniversityPittsburghPennsylvaniaUSA
- Carnegie Mellon Neuroscience InstitutePittsburghPennsylvaniaUSA
- Department of NeurosurgeryUniversity of Rochester Medical CenterRochesterNew YorkUSA
| | - Ravi S. Menon
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western OntarioLondonOntarioCanada
| | - Jack Taunton
- Allan McGavin Sports Medicine Centre, Faculty of MedicineUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Kathrin Reetz
- Department of Neurology, Medical FacultyRWTH Aachen UniversityAachenGermany
- JARA‐BRAIN Institute Molecular Neuroscience and Neuroimaging, Research Center Jülich and RWTH Aachen UniversityAachenGermany
| | - Sandro Romanzetti
- Department of Neurology, Medical FacultyRWTH Aachen UniversityAachenGermany
- JARA‐BRAIN Institute Molecular Neuroscience and Neuroimaging, Research Center Jülich and RWTH Aachen UniversityAachenGermany
| | - Charlotte Huppertz
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical FacultyRWTH Aachen UniversityAachenGermany
| |
Collapse
|
3
|
Clansey AC, Bondi D, Kenny R, Luke D, Masood Z, Gao Y, Elez M, Ji S, Rauscher A, van Donkelaar P, Wu LC. On-field Head Acceleration Exposure Measurements Using Instrumented Mouthguards: Multi-stage Screening to Optimize Data Quality. Ann Biomed Eng 2024:10.1007/s10439-024-03592-z. [PMID: 39097541 DOI: 10.1007/s10439-024-03592-z] [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: 04/20/2024] [Accepted: 07/25/2024] [Indexed: 08/05/2024]
Abstract
Instrumented mouthguards (iMGs) are widely applied to measure head acceleration event (HAE) exposure in sports. Despite laboratory validation, on-field factors including potential sensor skull-decoupling and spurious recordings limit data accuracy. Video analysis can provide complementary information to verify sensor data but lacks quantitative kinematics reference information and suffers from subjectivity. The purpose of this study was to develop a rigorous multi-stage screening procedure, combining iMG and video as independent measurements, aimed at improving the quality of on-field HAE exposure measurements. We deployed iMGs and gathered video recordings in a complete university men's ice hockey varsity season. We developed a four-stage process that involves independent video and sensor data collection (Stage I), general screening (Stage II), cross verification (Stage III), and coupling verification (Stage IV). Stage I yielded 24,596 iMG acceleration events (AEs) and 17,098 potential video HAEs from all games. Approximately 2.5% of iMG AEs were categorized as cross-verified and coupled iMG HAEs after Stage IV, and less than 1/5 of confirmed or probable video HAEs were cross-verified with iMG data during stage III. From Stage I to IV, we observed lower peak kinematics (median peak linear acceleration from 36.0 to 10.9 g; median peak angular acceleration from 3922 to 942 rad/s2) and reduced high-frequency signals, indicative of potential reduction in kinematic noise. Our study proposes a rigorous process for on-field data screening and provides quantitative evidence of data quality improvements using this process. Ensuring data quality is critical in further investigation of potential brain injury risk using HAE exposure data.
Collapse
Affiliation(s)
- Adam C Clansey
- Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Daniel Bondi
- Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Rebecca Kenny
- Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, Canada
| | - David Luke
- Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, Canada
- School of Biomedical Engineering, The University of British Columbia, Vancouver, BC, Canada
| | - Zaryan Masood
- Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Yuan Gao
- Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Marko Elez
- Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Songbai Ji
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Alexander Rauscher
- Department of Paediatrics, University of British Columbia, Vancouver, BC, Canada
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
| | - Paul van Donkelaar
- School of Health and Exercise Sciences, University of British Columbia, Okanagan, Kelowna, BC, Canada
| | - Lyndia C Wu
- Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, Canada.
- School of Biomedical Engineering, The University of British Columbia, Vancouver, BC, Canada.
| |
Collapse
|
4
|
Sutar S, Ganpule SG. In Silico Investigation of Biomechanical Response of a Human Brain Subjected to Primary Blast. J Biomech Eng 2024; 146:081007. [PMID: 38421339 DOI: 10.1115/1.4064968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 02/23/2024] [Indexed: 03/02/2024]
Abstract
The brain response to the explosion-induced primary blast waves is actively sought. Over the past decade, reasonable progress has been made in the fundamental understanding of blast traumatic brain injury (bTBI) using head surrogates and animal models. Yet, the current understanding of how blast waves interact with human is in nascent stages, primarily due to the lack of data in human. The biomechanical response in human is critically required to faithfully establish the connection to the aforementioned bTBI models. In this work, the biomechanical cascade of the brain under a primary blast has been elucidated using a detailed, full-body human model. The full-body model allowed us to holistically probe short- (<5 ms) and long-term (200 ms) brain responses. The full-body model has been extensively validated against impact loading in the past. We have further validated the head model against blast loading. We have also incorporated the structural anisotropy of the brain white matter. The blast wave transmission, and linear and rotational motion of the head were dominant pathways for the loading of the brain, and these loading paradigms generated distinct biomechanical fields within the brain. Blast transmission and linear motion of the head governed the volumetric response, whereas the rotational motion of the head governed the deviatoric response. Blast induced head rotation alone produced diffuse injury pattern in white matter fiber tracts. The biomechanical response under blast was comparable to the impact event. These insights will augment laboratory and clinical investigations of bTBI and help devise better blast mitigation strategies.
Collapse
Affiliation(s)
- Sunil Sutar
- Department of Mechanical and Industrial Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, Uttarakhand, India
| | - S G Ganpule
- Department of Mechanical and Industrial Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, Uttarakhand, India
| |
Collapse
|
5
|
Bradfield C, Voo L, Bhaduri A, Ramesh KT. Validation of a computational biomechanical mouse brain model for rotational head acceleration. Biomech Model Mechanobiol 2024; 23:1347-1367. [PMID: 38662175 DOI: 10.1007/s10237-024-01843-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 03/17/2024] [Indexed: 04/26/2024]
Abstract
Recent mouse brain injury experiments examine diffuse axonal injury resulting from accelerative head rotations. Evaluating brain deformation during these events would provide valuable information on tissue level thresholds for brain injury, but there are many challenges to imaging the brain's mechanical response during dynamic loading events, such as a blunt head impact. To address this shortcoming, we present an experimentally validated computational biomechanics model of the mouse brain that predicts tissue deformation, given the motion of the mouse head during laboratory experiments. First, we developed a finite element model of the mouse brain that computes tissue strains, given the same head rotations as previously conducted in situ hemicephalic mouse brain experiments. Second, we calibrated the model using a single brain segment, and then validated the model based on the spatial and temporal strain responses of other regions. The result is a computational tool that will provide researchers with the ability to predict brain tissue strains that occur during mouse laboratory experiments, and to link the experiments to the resulting neuropathology, such as diffuse axonal injury.
Collapse
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
| |
Collapse
|
6
|
Stilwell G, Stitt D, Alexander K, Draper N, Kabaliuk N. The Impact of Drop Test Conditions on Brain Strain Location and Severity: A Novel Approach Using a Deep Learning Model. Ann Biomed Eng 2024; 52:2234-2246. [PMID: 38739210 PMCID: PMC11247052 DOI: 10.1007/s10439-024-03525-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 04/23/2024] [Indexed: 05/14/2024]
Abstract
In contact sports such as rugby, players are at risk of sustaining traumatic brain injuries (TBI) due to high-intensity head impacts that generate high linear and rotational accelerations of the head. Previous studies have established a clear link between high-intensity head impacts and brain strains that result in concussions. This study presents a novel approach to investigating the effect of a range of laboratory controlled drop test parameters on regional peak and mean maximum principal strain (MPS) predictions within the brain using a trained convolutional neural network (CNN). The CNN is publicly available at https://github.com/Jilab-biomechanics/CNN-brain-strains . The results of this study corroborate previous findings that impacts to the side of the head result in significantly higher regional MPS than forehead impacts. Forehead impacts tend to result in the lowest region-averaged MPS values for impacts where the surface angle was at 0° and 45°, while side impacts tend to result in higher regional peak and mean MPS. The absence of a neck in drop tests resulted in lower regional peak and mean MPS values. The results indicated that the relationship between drop test parameters and resulting regional peak and mean MPS predictions is complex. The study's findings offer valuable insights into how deep learning models can be used to provide more detailed insights into how drop test conditions impact regional MPS. The novel approach used in this paper to predict brain strains can be applied in the development of better methods to reduce the brain strain resulting from head accelerations such as protective sports headgear.
Collapse
Affiliation(s)
- George Stilwell
- Department of Mechanical Engineering, University of Canterbury, Christchurch, 8041, New Zealand
| | - Danyon Stitt
- Department of Mechanical Engineering, University of Canterbury, Christchurch, 8041, New Zealand
| | - Keith Alexander
- Department of Mechanical Engineering, University of Canterbury, Christchurch, 8041, New Zealand
| | - Nick Draper
- Faculty of Health, University of Canterbury, Christchurch, 8041, New Zealand
| | - Natalia Kabaliuk
- Department of Mechanical Engineering, University of Canterbury, Christchurch, 8041, New Zealand.
| |
Collapse
|
7
|
Henningsen MJ, Lindgren N, Kleiven S, Li X, Jacobsen C, Villa C. Subject-specific finite element head models for skull fracture evaluation-a new tool in forensic pathology. Int J Legal Med 2024; 138:1447-1458. [PMID: 38386034 PMCID: PMC11164801 DOI: 10.1007/s00414-024-03186-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 02/09/2024] [Indexed: 02/23/2024]
Abstract
Post-mortem computed tomography (PMCT) enables the creation of subject-specific 3D head models suitable for quantitative analysis such as finite element analysis (FEA). FEA of proposed traumatic events is an objective and repeatable numerical method for assessing whether an event could cause a skull fracture such as seen at autopsy. FEA of blunt force skull fracture in adults with subject-specific 3D models in forensic pathology remains uninvestigated. This study aimed to assess the feasibility of FEA for skull fracture analysis in routine forensic pathology. Five cases with blunt force skull fracture and sufficient information on the kinematics of the traumatic event to enable numerical reconstruction were chosen. Subject-specific finite element (FE) head models were constructed by mesh morphing based on PMCT 3D models and A Detailed and Personalizable Head Model with Axons for Injury Prediction (ADAPT) FE model. Morphing was successful in maintaining subject-specific 3D geometry and quality of the FE mesh in all cases. In three cases, the simulated fracture patterns were comparable in location and pattern to the fractures seen at autopsy/PMCT. In one case, the simulated fracture was in the parietal bone whereas the fracture seen at autopsy/PMCT was in the occipital bone. In another case, the simulated fracture was a spider-web fracture in the frontal bone, whereas a much smaller fracture was seen at autopsy/PMCT; however, the fracture in the early time steps of the simulation was comparable to autopsy/PMCT. FEA might be feasible in forensic pathology in cases with a single blunt force impact and well-described event circumstances.
Collapse
Affiliation(s)
- Mikkel Jon Henningsen
- Section of Forensic Pathology, Department of Forensic Medicine, University of Copenhagen, Copenhagen, Denmark.
| | - Natalia Lindgren
- Division of Neuronic Engineering, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Svein Kleiven
- Division of Neuronic Engineering, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Xiaogai Li
- Division of Neuronic Engineering, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Christina Jacobsen
- Section of Forensic Pathology, Department of Forensic Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Chiara Villa
- Section of Forensic Pathology, Department of Forensic Medicine, University of Copenhagen, Copenhagen, Denmark
| |
Collapse
|
8
|
Zhang C, Bartels L, Clansey A, Kloiber J, Bondi D, van Donkelaar P, Wu L, Rauscher A, Ji S. A computational pipeline towards large-scale and multiscale modeling of traumatic axonal injury. Comput Biol Med 2024; 171:108109. [PMID: 38364663 DOI: 10.1016/j.compbiomed.2024.108109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 01/26/2024] [Accepted: 02/04/2024] [Indexed: 02/18/2024]
Abstract
Contemporary biomechanical modeling of traumatic brain injury (TBI) focuses on either the global brain as an organ or a representative tiny section of a single axon. In addition, while it is common for a global brain model to employ real-world impacts as input, axonal injury models have largely been limited to inputs of either tension or compression with assumed peak strain and strain rate. These major gaps between global and microscale modeling preclude a systematic and mechanistic investigation of how tissue strain from impact leads to downstream axonal damage throughout the white matter. In this study, a unique subject-specific multimodality dataset from a male ice-hockey player sustaining a diagnosed concussion is used to establish an efficient and scalable computational pipeline. It is then employed to derive voxelized brain deformation, maximum principal strains and white matter fiber strains, and finally, to produce diverse fiber strain profiles of various shapes in temporal history necessary for the development and application of a deep learning axonal injury model in the future. The pipeline employs a structured, voxelized representation of brain deformation with adjustable spatial resolution independent of model mesh resolution. The method can be easily extended to other head impacts or individuals. The framework established in this work is critical for enabling large-scale (i.e., across the entire white matter region, head impacts, and individuals) and multiscale (i.e., from organ to cell length scales) modeling for the investigation of traumatic axonal injury (TAI) triggering mechanisms. Ultimately, these efforts could enhance the assessment of concussion risks and design of protective headgear. Therefore, this work contributes to improved strategies for concussion detection, mitigation, and prevention.
Collapse
Affiliation(s)
- Chaokai Zhang
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Lara Bartels
- Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada
| | - Adam Clansey
- Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Julian Kloiber
- Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada
| | - Daniel Bondi
- Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Paul van Donkelaar
- School of Health and Exercise Sciences, University of British Columbia, Kelowna, BC, Canada
| | - Lyndia Wu
- Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Alexander Rauscher
- Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada
| | - Songbai Ji
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, USA; Department of Mechanical Engineering, Worcester Polytechnic Institute, Worcester, MA, USA.
| |
Collapse
|
9
|
Singh A, Kumar D, Ganpule S. Biomechanical Response of Head Surrogate With and Without the Helmet. J Biomech Eng 2024; 146:031001. [PMID: 37470487 DOI: 10.1115/1.4062968] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 07/12/2023] [Indexed: 07/21/2023]
Abstract
Measurements of brain deformations under injurious loading scenarios are actively sought. In this work, we report experimentally measured head kinematics and corresponding dynamic, two-dimensional brain simulant deformations in head surrogates under a blunt impact, with and without a helmet. Head surrogates used in this work consisted of skin, skull, dura, falx, tentorium, and brain stimulants. The head surrogate geometry was based on the global human body models consortium's head model. A base head surrogate consisting of skin-skull-brain was considered. In addition, the response of two other head surrogates, skin-skull-dura-brain, and skin-skull-dura-brain-falx-tentorium, was investigated. Head surrogate response was studied for sagittal and coronal plane rotations for impactor velocities of 1 and 3 m/s. Response of head surrogates was compared against strain measurements in PMHS. The strain pattern in the brain simulant was heterogenous, and peak strains were established within ∼30 ms. The choice of head surrogate affect the spatiotemporal evolution of strain. For no helmet case, peak MPS of ∼50-60% and peak MSS of ∼35-50% were seen in brain simulant corresponding to peak rotational accelerations of ∼5000-7000 rad/s2. Peak head kinematics and peak MPS have been reduced by up to 75% and 45%, respectively, with the conventional helmet and by up to 90% and 85%, respectively, with the helmet with antirotational pads. Overall, these results provide important, new data on brain simulant strains under a variety of loading scenarios-with and without the helmets.
Collapse
Affiliation(s)
- Abhilash Singh
- Department of Mechanical and Industrial Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India
| | - Devendra Kumar
- Department of Mechanical and Industrial Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India
| | - Shailesh Ganpule
- Department of Mechanical and Industrial Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India; Department of Design, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India
| |
Collapse
|
10
|
Grijalva C, Mullins VA, Michael BR, Hale D, Wu L, Toosizadeh N, Chilton FH, Laksari K. Neuroimaging, wearable sensors, and blood-based biomarkers reveal hyperacute changes in the brain after sub-concussive impacts. BRAIN MULTIPHYSICS 2023; 5:100086. [PMID: 38292249 PMCID: PMC10827333 DOI: 10.1016/j.brain.2023.100086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2024] Open
Abstract
Impacts in mixed martial arts (MMA) have been studied mainly in regard to the long-term effects of concussions. However, repetitive sub-concussive head impacts at the hyperacute phase (minutes after impact), are not understood. The head experiences rapid acceleration similar to a concussion, but without clinical symptoms. We utilize portable neuroimaging technology - transcranial Doppler (TCD) ultrasound and functional near infrared spectroscopy (fNIRS) - to estimate the extent of pre- and post-differences following contact and non-contact sparring sessions in nine MMA athletes. In addition, the extent of changes in neurofilament light (NfL) protein biomarker concentrations, and neurocognitive/balance parameters were determined following impacts. Athletes were instrumented with sensor-based mouth guards to record head kinematics. TCD and fNIRS results demonstrated significantly increased blood flow velocity (p = 0.01) as well as prefrontal (p = 0.01) and motor cortex (p = 0.04) oxygenation, only following the contact sparring sessions. This increase after contact was correlated with the cumulative angular acceleration experienced during impacts (p = 0.01). In addition, the NfL biomarker demonstrated positive correlations with angular acceleration (p = 0.03), and maximum principal and fiber strain (p = 0.01). On average athletes experienced 23.9 ± 2.9 g peak linear acceleration, 10.29 ± 1.1 rad/s peak angular velocity, and 1,502.3 ± 532.3 rad/s2 angular acceleration. Balance parameters were significantly increased following contact sparring for medial-lateral (ML) center of mass (COM) sway, and ML ankle angle (p = 0.01), illustrating worsened balance. These combined results reveal significant changes in brain hemodynamics and neurophysiological parameters that occur immediately after sub-concussive impacts and suggest that the physical impact to the head plays an important role in these changes.
Collapse
Affiliation(s)
- Carissa Grijalva
- University of Arizona, Department of Biomedical Engineering, Tucson, AZ, United States
| | - Veronica A. Mullins
- University of Arizona, School of Nutritional Sciences and Wellness, Tucson, AZ, United States
| | - Bryce R. Michael
- University of Arizona, School of Nutritional Sciences and Wellness, Tucson, AZ, United States
| | - Dallin Hale
- University of Arizona, Department of Physiology, Tucson, AZ, United States
| | - Lyndia Wu
- Univerisity of British Columbia, Department of Mechanical Engineering, Vancouver, BC, Canada
| | - Nima Toosizadeh
- University of Arizona, Department of Biomedical Engineering, Tucson, AZ, United States
- University of Arizona, Department of Medicine, Arizona Center for Aging, Tucson, AZ, United States
| | - Floyd H. Chilton
- University of Arizona, School of Nutritional Sciences and Wellness, Tucson, AZ, United States
| | - Kaveh Laksari
- University of Arizona, Department of Biomedical Engineering, Tucson, AZ, United States
- University of Arizona, Department of Aerospace and Mechanical Engineering, Tucson, AZ, United States
- University of California Riverside, Department of Mechanical Engineering, Riverside, CA, United States
| |
Collapse
|
11
|
Rovt J, Xu S, Dutrisac S, Ouellet S, Petel O. A technique for in situ intracranial strain measurement within a helmeted deformable headform. J Mech Behav Biomed Mater 2023; 147:106140. [PMID: 37778168 DOI: 10.1016/j.jmbbm.2023.106140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 05/03/2023] [Accepted: 09/20/2023] [Indexed: 10/03/2023]
Abstract
Despite the broad use of helmets, incidence of concussion remains high. Current methods for helmet evaluation focus on the measurement of head kinematics as the primary tool for quantifying risk of brain injury. Though the primary cause of mild Traumatic Brain Injury (mTBI) is thought to be intracranial strain, helmet testing methodologies are not able to directly resolve these parameters. Computational injury models and impact severity measures are currently used to approximate intracranial strains from head kinematics and predict injury outcomes. Advancing new methodologies that enable experimental intracranial strain measurements in a physical model would be useful in the evaluation of helmet performance. This study presents a proof-of-concept head surrogate and novel helmet evaluation platform that allows for the measurement of intracranial strain using high-speed X-ray digital image correlation (XDIC). In the present work, the head surrogate was subjected to a series of bare and helmeted impacts using a pneumatically-driven linear impactor. Impacts were captured at 5,000 fps using a high-speed X-ray cineradiography system, and strain fields were computed using digital image correlation. This test platform, once validated, will open the door to using brain tissue-level measurements to evaluate helmet performance, providing a tool that can be translated to represent mTBI injury mechanisms, benefiting the helmet design processes.
Collapse
Affiliation(s)
- Jennifer Rovt
- Carleton University, Department of Mechanical and Aerospace Engineering, Ottawa, K1S 5B6, ON, Canada
| | - Sheng Xu
- Carleton University, Department of Mechanical and Aerospace Engineering, Ottawa, K1S 5B6, ON, Canada
| | - Scott Dutrisac
- Carleton University, Department of Mechanical and Aerospace Engineering, Ottawa, K1S 5B6, ON, Canada
| | - Simon Ouellet
- Defence Research and Development Canada Valcartier, Québec, C3J 1X5, QC, Canada
| | - Oren Petel
- Carleton University, Department of Mechanical and Aerospace Engineering, Ottawa, K1S 5B6, ON, Canada.
| |
Collapse
|
12
|
Lustig M, Epstein Y, Gefen A. An anatomically-realistic computational framework for evaluating the efficacy of protective plates in mitigating non-penetrating ballistic impacts. Comput Biol Med 2023; 166:107490. [PMID: 37738897 DOI: 10.1016/j.compbiomed.2023.107490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 09/03/2023] [Accepted: 09/15/2023] [Indexed: 09/24/2023]
Abstract
BACKGROUND A major threat in combat scenarios is the 'behind armor blunt trauma' (BABT) of a non-penetrating ballistic impact with a ballistic protective plate (BPP). This impact results in pressure waves that propagate through tissues, potentially causing life-threatening damage. To date, there is no standardized procedure for rapid virtual testing of the effectiveness of BPP designs. The objective of this study was to develop a novel, anatomically-accurate, finite element modeling framework, as a decision-making tool to evaluate and rate the biomechanical efficacy of BPPs in protecting the torso from battlefield-acquired non-penetrating impacts. METHODS To simulate a blunt impact with a BPP, two types of BPPs representing generic designs of threat-level III and IV plates, and a generic 5.56 mm bullet were modeled, based on their real dimensions, physical and mechanical characteristics (plate level-III is smaller, thinner, and lighter than plate level-IV). The model was validated by phantom testing. RESULTS Plate level-IV induced greater strains and stresses in the superficial tissues post the ballistic impact, due to the fact that it is larger, thicker and heavier than plate level-III; the shock wave which is transferred to the superficial tissues behind the BPP is greater in the case of a non-penetrating impact. For example - the area under volumetric tissue exposure histograms of strains and stresses for the skin and adipose tissues were 16.6-19.2% and 17.3-20.3% greater in the case of plate level-IV, for strains and stresses, respectively. The validation demonstrates a strong agreement between the physical phantom experiment and the simulation, with only a 6.37% difference between them. CONCLUSIONS Our modelling provides a versatile, powerful testing framework for both industry and clients of BPPs at the prototype design phase, or for quantitative standardized evaluations of candidate products in purchasing decisions and bids.
Collapse
Affiliation(s)
- Maayan Lustig
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Yoram Epstein
- School of Public Health, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Amit Gefen
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel.
| |
Collapse
|
13
|
Morrison O, Destrade M, Tripathi BB. An atlas of the heterogeneous viscoelastic brain with local power-law attenuation synthesised using Prony-series. Acta Biomater 2023; 169:66-87. [PMID: 37507033 DOI: 10.1016/j.actbio.2023.07.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 07/16/2023] [Accepted: 07/24/2023] [Indexed: 07/30/2023]
Abstract
This review addresses the acute need to acknowledge the mechanical heterogeneity of brain matter and to accurately calibrate its local viscoelastic material properties accordingly. Specifically, it is important to compile the existing and disparate literature on attenuation power-laws and dispersion to make progress in wave physics of brain matter, a field of research that has the potential to explain the mechanisms at play in diffuse axonal injury and mild traumatic brain injury in general. Currently, viscous effects in the brain are modelled using Prony-series, i.e., a sum of decaying exponentials at different relaxation times. Here we collect and synthesise the Prony-series coefficients appearing in the literature for twelve regions: brainstem, basal ganglia, cerebellum, corona radiata, corpus callosum, cortex, dentate gyrus, hippocampus, thalamus, grey matter, white matter, homogeneous brain, and for eight different mammals: pig, rat, human, mouse, cow, sheep, monkey and dog. Using this data, we compute the fractional-exponent attenuation power-laws for different tissues of the brain, the corresponding dispersion laws resulting from causality, and the averaged Prony-series coefficients. STATEMENT OF SIGNIFICANCE: Traumatic brain injuries are considered a silent epidemic and finite element methods (FEMs) are used in modelling brain deformation, requiring access to viscoelastic properties of brain. To the best of our knowledge, this work presents 1) the first multi-frequency viscoelastic atlas of the heterogeneous brain, 2) the first review focusing on viscoelastic modelling in both FEMs and experimental works, 3) the first attempt to conglomerate the disparate existing literature on the viscoelastic modelling of the brain and 4) the largest collection of viscoelastic parameters for the brain (212 different Prony-series spanning 12 different tissues and 8 different animal surrogates). Furthermore, this work presents the first brain atlas of attenuation power-laws essential for modelling shear waves in brain.
Collapse
Affiliation(s)
- Oisín Morrison
- School of Mathematical and Statistical Sciences, University of Galway, University Road, Galway, Ireland
| | - Michel Destrade
- School of Mathematical and Statistical Sciences, University of Galway, University Road, Galway, Ireland
| | - Bharat B Tripathi
- School of Mathematical and Statistical Sciences, University of Galway, University Road, Galway, Ireland.
| |
Collapse
|
14
|
Abstract
The brain injury modeling community has recommended improving model subject specificity and simulation efficiency. Here, we extend an instantaneous (< 1 sec) convolutional neural network (CNN) brain model based on the anisotropic Worcester Head Injury Model (WHIM) V1.0 to account for strain differences due to individual morphological variations. Linear scaling factors relative to the generic WHIM along the three anatomical axes are used as additional CNN inputs. To generate training samples, the WHIM is randomly scaled to pair with augmented head impacts randomly generated from real-world data for simulation. An estimation of voxelized peak maximum principal strain of the whole-brain is said to be successful when the linear regression slope and Pearson's correlation coefficient relative to directly simulated do not deviate from 1.0 (when identical) by more than 0.1. Despite a modest training dataset (N = 1363 vs. ∼5.7 k previously), the individualized CNN achieves a success rate of 86.2% in cross-validation for scaled model responses, and 92.1% for independent generic model testing for impacts considered as complete capture of kinematic events. Using 11 scaled subject-specific models (with scaling factors determined from pre-established regression models based on head dimensions and sex and age information, and notably, without neuroimages), the morphologically individualized CNN remains accurate for impacts that also yield successful estimations for the generic WHIM. The individualized CNN instantly estimates subject-specific and spatially detailed peak strains of the entire brain and thus, supersedes others that report a scalar peak strain value incapable of informing the location of occurrence. This tool could be especially useful for youths and females due to their anticipated greater morphological differences relative to the generic model, even without the need for individual neuroimages. It has potential for a wide range of applications for injury mitigation purposes and the design of head protective gears. The voxelized strains also allow for convenient data sharing and promote collaboration among research groups.
Collapse
Affiliation(s)
- Nan Lin
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, Massachusetts, USA
| | - Shaoju Wu
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, Massachusetts, USA
| | - Songbai Ji
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, Massachusetts, USA
- Department of Mechanical Engineering, Worcester Polytechnic Institute, Worcester, Massachusetts, USA
| |
Collapse
|
15
|
Giudice JS, Druzgal TJ, Panzer MB. Investigating the Effect of Brain Size on Deformation Magnitude Using Subject-Specific Finite Element Models. J Neurotrauma 2023; 40:1796-1807. [PMID: 37002891 DOI: 10.1089/neu.2022.0339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023] Open
Abstract
Abstract In the last decade, computational models of the brain have become the gold standard tool for investigating traumatic brain injury (TBI) mechanisms and developing novel protective equipment and other safety countermeasures. However, most studies utilizing finite element (FE) models of the brain have been conducted using models developed to represent the average neuroanatomy of a target demographic, such as the 50th percentile male. Although this is an efficient strategy, it neglects normal anatomical variations present within the population and their contributions on the brain's deformation response. As a result, the contributions of structural characteristics of the brain, such as brain volume, on brain deformation are not well understood. The objective of this study was to develop a set of statistical regression models relating measures of the size and shape of the brain to the resulting brain deformation. This was performed using a database of 125 subject-specific models, simulated under six independent head kinematic boundary conditions, spanning a range of impact modes (frontal, oblique, side), severity (non-injurious and injurious), and environments (volunteer, automotive, and American football). Two statistical regression techniques were utilized. First, simple linear regression (SLR) models were trained to relate intracranial volume (ICV) and the 95th percentile of maximum principal strain (MPS-95) for each of the impact cases. Second, a partial least squares regression model was constructed to predict MPS-95 based on the affine transformation parameters from each subject, representing the size and shape of their brain, considering the six impact conditions collectively. Both techniques indicated a strong linear relationship between ICV and MPS-95, with MPS-95 varying by approximately 5% between the smallest and largest brains. This difference represented up to 40% of the mean strain across all subjects. This study represents a comprehensive assessment of the relationships between brain anatomy and deformation, which is crucial for the development of personalized protective equipment, identifying individuals at higher risk of injury, and using computational models to aid clinical diagnostics of TBI.
Collapse
Affiliation(s)
- J Sebastian Giudice
- Center for Applied Biomechanics, Department of Mechanical and Aerospace Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - T Jason Druzgal
- Department of Radiology and Medical Imaging, University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - Matthew B Panzer
- Center for Applied Biomechanics, Department of Mechanical and Aerospace Engineering, University of Virginia, Charlottesville, Virginia, USA
| |
Collapse
|
16
|
Menghani RR, Das A, Kraft RH. A sensor-enabled cloud-based computing platform for computational brain biomechanics. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 233:107470. [PMID: 36958108 DOI: 10.1016/j.cmpb.2023.107470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 02/24/2023] [Accepted: 03/06/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVES Driven by the risk of repetitive head trauma, sensors have been integrated into mouthguards to measure head impacts in contact sports and military activities. These wearable devices, referred to as "instrumented" or "smart" mouthguards are being actively developed by various research groups and organizations. These instrumented mouthguards provide an opportunity to further study and understand the brain biomechanics due to impact. In this study, we present a brain modeling service that can use information from these sensors to predict brain injury metrics in an automated fashion. METHODS We have built a brain modeling platform using several of Amazon's Web Services (AWS) to enable cloud computing and scalability. We use a custom-built cloud-based finite element modeling code to compute the physics-based nonlinear response of the intracranial brain tissue and provide a frontend web application and an application programming interface for groups working on head impact sensor technology to include simulated injury predictions into their research pipeline. RESULTS The platform results have been validated against experimental data available in literature for brain-skull relative displacements, brain strains and intracranial pressure. The parallel processing capability of the platform has also been tested and verified. We also studied the accuracy of the custom head surfaces generated by Avatar 3D. CONCLUSION We present a validated cloud-based computational brain modeling platform that uses sensor data as input for numerical brain models and outputs a quantitative description of brain tissue strains and injury metrics. The platform is expected to generate transparent, reproducible, and traceable brain computing results.
Collapse
Affiliation(s)
- Ritika R Menghani
- Department of Mechanical Engineering, The Pennsylvania State University, University Park, 16802, USA
| | - Anil Das
- Department of Mechanical Engineering, The Pennsylvania State University, University Park, 16802, USA
| | - Reuben H Kraft
- Department of Mechanical Engineering, The Pennsylvania State University, University Park, 16802, USA; Department of Biomedical Engineering, The Pennsylvania State University, University Park, 16802, USA; Institute for Computational and Data Sciences, The Pennsylvania State University, University Park, 16802, USA.
| |
Collapse
|
17
|
Zuidema TR, Bazarian JJ, Kercher KA, Mannix R, Kraft RH, Newman SD, Ejima K, Rettke DJ, Macy JT, Steinfeldt JA, Kawata K. Longitudinal Associations of Clinical and Biochemical Head Injury Biomarkers With Head Impact Exposure in Adolescent Football Players. JAMA Netw Open 2023; 6:e2316601. [PMID: 37252737 PMCID: PMC10230318 DOI: 10.1001/jamanetworkopen.2023.16601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 04/19/2023] [Indexed: 05/31/2023] Open
Abstract
Importance Consequences of subconcussive head impacts have been recognized, yet most studies to date have included small samples from a single site, used a unimodal approach, and lacked repeated testing. Objective To examine time-course changes in clinical (near point of convergence [NPC]) and brain-injury blood biomarkers (glial fibrillary acidic protein [GFAP], ubiquitin C-terminal hydrolase-L1 [UCH-L1], and neurofilament light [NF-L]) in adolescent football players and to test whether changes in the outcomes were associated with playing position, impact kinematics, and/or brain tissue strain. Design, Setting, and Participants This multisite, prospective cohort study included male high school football players aged 13 to 18 years at 4 high schools in the Midwest during the 2021 high school football season (preseason [July] and August 2 to November 19). Exposure A single football season. Main Outcomes and Measures The main outcomes were NPC (a clinical oculomotor test) and serum levels of GFAP, UCH-L1, and NF-L. Participants' head impact exposure (frequency and peak linear and rotational accelerations) was tracked using instrumented mouthguards, and maximum principal strain was computed to reflect brain tissue strain. Players' neurological function was assessed at 5 time points (preseason, post-training camp, 2 in season, and postseason). Results Ninety-nine male players contributed to the time-course analysis (mean [SD] age, 15.8 [1.1] years), but data from 6 players (6.1%) were excluded from the association analysis due to issues related to mouthguards. Thus, 93 players yielded 9498 head impacts in a season (mean [SD], 102 [113] impacts per player). There were time-course elevations in NPC and GFAP, UCH-L1, and NF-L levels. Compared with baseline, the NPC exhibited a significant elevation over time and peaked at postseason (2.21 cm; 95% CI, 1.80-2.63 cm; P < .001). Levels of GFAP and UCH-L1 increased by 25.6 pg/mL (95% CI, 17.6-33.6 pg/mL; P < .001) and 188.5 pg/mL (95% CI, 145.6-231.4 pg/mL; P < .001), respectively, later in the season. Levels of NF-L were elevated after the training camp (0.78 pg/mL; 95% CI, 0.14-1.41 pg/mL; P = .011) and midseason (0.55 pg/mL; 95% CI, 0.13-0.99 pg/mL; P = .006) but normalized by the end of the season. Changes in UCH-L1 levels were associated with maximum principal strain later in the season (0.052 pg/mL; 95% CI, 0.015-0.088 pg/mL; P = .007) and postseason (0.069 pg/mL; 95% CI, 0.031-0.106 pg/mL; P < .001). Conclusions and Relevance The study data suggest that adolescent football players exhibited impairments in oculomotor function and elevations in blood biomarker levels associated with astrocyte activation and neuronal injury throughout a season. Several years of follow-up are needed to examine the long-term effects of subconcussive head impacts in adolescent football players.
Collapse
Affiliation(s)
- Taylor R. Zuidema
- Department of Kinesiology, Indiana University School of Public Health–Bloomington
- Program in Neuroscience, College of Arts and Sciences, Indiana University, Bloomington
| | - Jeffrey J. Bazarian
- Department of Emergency Medicine, University of Rochester School of Medicine and Dentistry, Rochester, New York
| | - Kyle A. Kercher
- Department of Kinesiology, Indiana University School of Public Health–Bloomington
| | - Rebekah Mannix
- Department of Medicine, Division of Emergency Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Reuben H. Kraft
- Department of Mechanical and Biomedical Engineering, Pennsylvania State University, University Park
- Institute of Computational and Data Sciences, Pennsylvania State University, University Park
| | | | - Keisuke Ejima
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Devin J. Rettke
- Department of Kinesiology, Indiana University School of Public Health–Bloomington
| | - Jonathan T. Macy
- Department of Applied Health Science, Indiana University School of Public Health–Bloomington
| | - Jesse A. Steinfeldt
- Department of Counseling and Educational Psychology, School of Education, Indiana University, Bloomington
| | - Keisuke Kawata
- Department of Kinesiology, Indiana University School of Public Health–Bloomington
- Program in Neuroscience, College of Arts and Sciences, Indiana University, Bloomington
| |
Collapse
|
18
|
Approximating subject-specific brain injury models via scaling based on head-brain morphological relationships. Biomech Model Mechanobiol 2023; 22:159-175. [PMID: 36201071 DOI: 10.1007/s10237-022-01638-6] [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: 03/25/2022] [Accepted: 09/07/2022] [Indexed: 11/02/2022]
Abstract
Most human head/brain models represent a generic adult male head/brain. They may suffer in accuracy when investigating traumatic brain injury (TBI) on a subject-specific basis. Subject-specific models can be developed from neuroimages; however, neuroimages are not typically available in practice. In this study, we establish simple and elegant regression models between brain outer surface morphology and head dimensions measured from neuroimages along with age and sex information (N = 191; 141 males and 50 females with age ranging 14-25 years). The regression models are then used to approximate subject-specific brain models by scaling a generic counterpart, without using neuroimages. Model geometrical accuracy is assessed using adjusted [Formula: see text] and absolute percentage error (e.g., 0.720 and 3.09 ± 2.38%, respectively, for brain volume when incorporating tragion-to-top). For a subset of 11 subjects (from smallest to largest in brain volume), impact-induced brain strains are compared with those from "morphed models" derived from neuroimage-based mesh warping. We find that regional peak strains from the scaled subject-specific models are comparable to those of the morphed counterparts but could be considerably different from those of the generic model (e.g., linear regression slope of 1.01-1.03 for gray and white matter regions versus 1.16-1.19, or up to ~ 20% overestimation for the smallest brain studied). These results highlight the importance of incorporating brain morphological variations in impact simulation and demonstrate the feasibility of approximating subject-specific brain models without neuroimages using age, sex, and easily measurable head dimensions. The scaled models may improve subject specificity for future TBI investigations.
Collapse
|
19
|
Carmo GP, Grigioni J, Fernandes FAO, Alves de Sousa RJ. Biomechanics of Traumatic Head and Neck Injuries on Women: A State-of-the-Art Review and Future Directions. BIOLOGY 2023; 12:biology12010083. [PMID: 36671775 PMCID: PMC9855362 DOI: 10.3390/biology12010083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 12/27/2022] [Accepted: 12/29/2022] [Indexed: 01/06/2023]
Abstract
The biomechanics of traumatic injuries of the human body as a consequence of road crashes, falling, contact sports, and military environments have been studied for decades. In particular, traumatic brain injury (TBI), the so-called "silent epidemic", is the traumatic insult responsible for the greatest percentage of death and disability, justifying the relevance of this research topic. Despite its great importance, only recently have research groups started to seriously consider the sex differences regarding the morphology and physiology of women, which differs from men and may result in a specific outcome for a given traumatic event. This work aims to provide a summary of the contributions given in this field so far, from clinical reports to numerical models, covering not only the direct injuries from inertial loading scenarios but also the role sex plays in the conditions that precede an accident, and post-traumatic events, with an emphasis on neuroendocrine dysfunctions and chronic traumatic encephalopathy. A review on finite element head models and finite element neck models for the study of specific traumatic events is also performed, discussing whether sex was a factor in validating them. Based on the information collected, improvement perspectives and future directions are discussed.
Collapse
Affiliation(s)
- Gustavo P. Carmo
- Centre for Mechanical Technology and Automation (TEMA), Department of Mechanical Engineering, Campus Universitário de Santiago, University of Aveiro, 3810-193 Aveiro, Portugal
| | - Jeroen Grigioni
- Centre for Mechanical Technology and Automation (TEMA), Department of Mechanical Engineering, Campus Universitário de Santiago, University of Aveiro, 3810-193 Aveiro, Portugal
| | - Fábio A. O. Fernandes
- Centre for Mechanical Technology and Automation (TEMA), Department of Mechanical Engineering, Campus Universitário de Santiago, University of Aveiro, 3810-193 Aveiro, Portugal
- LASI—Intelligent Systems Associate Laboratory, 4800-058 Guimaraes, Portugal
| | - Ricardo J. Alves de Sousa
- Centre for Mechanical Technology and Automation (TEMA), Department of Mechanical Engineering, Campus Universitário de Santiago, University of Aveiro, 3810-193 Aveiro, Portugal
- LASI—Intelligent Systems Associate Laboratory, 4800-058 Guimaraes, Portugal
- Correspondence: ; Tel.: +351-234-370-200
| |
Collapse
|
20
|
Jia L, Han X. EFFECTS OF EXERCISE TRAINING COMBINED WITH ACUPUNCTURE AND MOXIBUSTION ON FATIGUE. REV BRAS MED ESPORTE 2023. [DOI: 10.1590/1517-8692202329012022_0745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2023] Open
Abstract
ABSTRACT Introduction: Muscle fatigue bothers athletes, affecting training level and competitive performance, it also has a great impact on the physical health of athletes, predisposing them to accidents and an early termination of their career. Relieving sports fatigue is the focus of research in the field of sports health nowadays. Objective: Study the effect of acupuncture and moxibustion rehabilitation combined with physical training on sports fatigue. Methods: A controlled experiment was used. The experimental group used acupuncture and moxibustion combined with exercise training, while the control group used acupuncture and moxibustion. The same group of doctors performed the acupuncture and moxibustion treatment according to the actual situation of the patients, and they tested the changes in VAS pain score and PRI pain score. Results: The VAS pain score in the experimental group was 7.88 points before the procedure and 2.96 points after the sixth week of the procedure. The control group score was 7.67 before the start and 5.03 after training. The total PRI pain score in the experimental group was 6.52 points before training and 2.05 points in the sixth week of training. The control group scored 6.66 before the procedure and 3.89 in the sixth week. Conclusion: The combination of training and exercises can achieve a better rehabilitation effect compared to the isolated treatment of acupuncture and moxibustion. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.
Collapse
|
21
|
Translational models of mild traumatic brain injury tissue biomechanics. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2022. [DOI: 10.1016/j.cobme.2022.100422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
|
22
|
Pavan PG, Nasim M, Brasco V, Spadoni S, Paoloni F, d'Avella D, Khosroshahi SF, de Cesare N, Gupta K, Galvanetto U. Development of detailed finite element models for in silico analyses of brain impact dynamics. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 227:107225. [PMID: 36370594 DOI: 10.1016/j.cmpb.2022.107225] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 10/20/2022] [Accepted: 11/02/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVE In the last few decades, several studies have been performed to investigate traumatic brain injuries (TBIs) and to understand the biomechanical response of brain tissues, by using experimental and computational approaches. As part of computational approaches, human head finite element (FE) models show to be important tools in the analysis of TBIs, making it possible to estimate local mechanical effects on brain tissue for different accident scenarios. The present study aims to contribute to the computational approach by means of the development of three advanced FE head models for accurately describing the head tissue dynamics, the first step to predict TBIs. METHODS We have developed three detailed FE models of human heads from magnetic resonance images of three volunteers: an adult female (32 yrs), an adult male (35 yrs), and a young male (16 yrs). These models have been validated against experimental data of post mortem human subjects (PMHS) tests available in the literature. Brain tissue displacements relative to the skull, hydrostatic intracranial pressure, and head acceleration have been used as the parameters to compare the model response with the experimental response for validation. The software CORAplus (CORrelation and Analysis) has been adopted to evaluate the bio-fidelity level of FE models. RESULTS Numerical results from the three models agree with experimental data. FE models presented in this study show a good bio-fidelity for hydrostatic pressure (CORA score of 0.776) and a fair bio-fidelity brain tissue displacements relative to the skull (CORA score of 0.443 and 0.535). The comparison among numerical simulations carried out with the three models shows negligible differences in the mechanical state of brain tissue due to the different morphometry of the heads, when the same acceleration history is considered. CONCLUSIONS The three FE models, thanks to their accurate description of anatomical morphology and to their bio-fidelity, can be useful tools to investigate brain mechanics due to different impact scenarios. Therefore, they can be used for different purposes, such as the investigation of the correlation between head acceleration and tissue damage, or the effectiveness of helmet designs. This work does not address the issue to define injury thresholds for the proposed models.
Collapse
Affiliation(s)
- Piero G Pavan
- Department of Industrial Engineering, University of Padova, Padova, Italy; Fondazione Istituto di Ricerca Pediatrica Città della Speranza (IRP), Padova, Italy.
| | - Mohammed Nasim
- Department of Industrial Engineering, University of Padova, Padova, Italy
| | - Veronica Brasco
- Department of Industrial Engineering, University of Padova, Padova, Italy
| | - Silvia Spadoni
- Department of Industrial Engineering, University of Padova, Padova, Italy
| | - Francesco Paoloni
- Department of Neurosciences, Section of Neurosurgery, University of Padova, Padova, Italy
| | - Domenico d'Avella
- Department of Neurosciences, Section of Neurosurgery, University of Padova, Padova, Italy
| | | | - Niccolò de Cesare
- Department of Industrial Engineering, University of Padova, Padova, Italy
| | - Karan Gupta
- Department of Industrial Engineering, University of Padova, Padova, Italy; Center of Studies and Activities for Space (CISAS) "G. Colombo", Padova, Italy
| | - Ugo Galvanetto
- Department of Industrial Engineering, University of Padova, Padova, Italy; Center of Studies and Activities for Space (CISAS) "G. Colombo", Padova, Italy
| |
Collapse
|
23
|
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.
Collapse
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.
| |
Collapse
|
24
|
Upadhyay K, Alshareef A, Knutsen AK, Johnson CL, Carass A, Bayly PV, Pham DL, Prince JL, Ramesh KT. Development and validation of subject-specific 3D human head models based on a nonlinear visco-hyperelastic constitutive framework. J R Soc Interface 2022; 19:20220561. [PMCID: PMC9554734 DOI: 10.1098/rsif.2022.0561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
Computational head models are promising tools for understanding and predicting traumatic brain injuries. Most available head models are developed using inputs (i.e. head geometry, material properties and boundary conditions) from experiments on cadavers or animals and employ hereditary integral-based constitutive models that assume linear viscoelasticity in part of the rate-sensitive material response. This leads to high uncertainty and poor accuracy in capturing the nonlinear brain tissue response. To resolve these issues, a framework for the development of subject-specific three-dimensional head models is proposed, in which all inputs are derived in vivo from the same living human subject: head geometry via magnetic resonance imaging (MRI), brain tissue properties via magnetic resonance elastography (MRE), and full-field strain-response of the brain under rapid head rotation via tagged MRI. A nonlinear, viscous dissipation-based visco-hyperelastic constitutive model is employed to capture brain tissue response. Head models are validated using quantitative metrics that compare spatial strain distribution, temporal strain evolution, and the magnitude of strain maxima, with the corresponding experimental observations from tagged MRI. Results show that our head models accurately capture the strain-response of the brain. Further, employment of the nonlinear visco-hyperelastic constitutive framework provides improvements in the prediction of peak strains and temporal strain evolution over hereditary integral-based models.
Collapse
Affiliation(s)
- Kshitiz Upadhyay
- Hopkins Extreme Materials Institute, Johns Hopkins University, Baltimore, MD 21218, USA,Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Ahmed Alshareef
- Hopkins Extreme Materials Institute, Johns Hopkins University, Baltimore, MD 21218, USA,Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Andrew K. Knutsen
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20814, USA
| | - Curtis L. Johnson
- Department of Biomedical Engineering, University of Delaware, Newark, DE 19716, USA
| | - Aaron Carass
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Philip V. Bayly
- Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Dzung L. Pham
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20814, USA
| | - Jerry L. Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - K. T. Ramesh
- Hopkins Extreme Materials Institute, Johns Hopkins University, Baltimore, MD 21218, USA,Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| |
Collapse
|
25
|
Upadhyay K, Giovanis DG, Alshareef A, Knutsen AK, Johnson CL, Carass A, Bayly PV, Shields MD, Ramesh K. Data-driven Uncertainty Quantification in Computational Human Head Models. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2022; 398:115108. [PMID: 37994358 PMCID: PMC10664838 DOI: 10.1016/j.cma.2022.115108] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2023]
Abstract
Computational models of the human head are promising tools for estimating the impact-induced response of the brain, and thus play an important role in the prediction of traumatic brain injury. The basic constituents of these models (i.e., model geometry, material properties, and boundary conditions) are often associated with significant uncertainty and variability. As a result, uncertainty quantification (UQ), which involves quantification of the effect of this uncertainty and variability on the simulated response, becomes critical to ensure reliability of model predictions. Modern biofidelic head model simulations are associated with very high computational cost and high-dimensional inputs and outputs, which limits the applicability of traditional UQ methods on these systems. In this study, a two-stage, data-driven manifold learning-based framework is proposed for UQ of computational head models. This framework is demonstrated on a 2D subject-specific head model, where the goal is to quantify uncertainty in the simulated strain fields (i.e., output), given variability in the material properties of different brain substructures (i.e., input). In the first stage, a data-driven method based on multi-dimensional Gaussian kernel-density estimation and diffusion maps is used to generate realizations of the input random vector directly from the available data. Computational simulations of a small number of realizations provide input-output pairs for training data-driven surrogate models in the second stage. The surrogate models employ nonlinear dimensionality reduction using Grassmannian diffusion maps, Gaussian process regression to create a low-cost mapping between the input random vector and the reduced solution space, and geometric harmonics models for mapping between the reduced space and the Grassmann manifold. It is demonstrated that the surrogate models provide highly accurate approximations of the computational model while significantly reducing the computational cost. Monte Carlo simulations of the surrogate models are used for uncertainty propagation. UQ of the strain fields highlights significant spatial variation in model uncertainty, and reveals key differences in uncertainty among commonly used strain-based brain injury predictor variables.
Collapse
Affiliation(s)
- Kshitiz Upadhyay
- Hopkins Extreme Materials Institute, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Dimitris G. Giovanis
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Ahmed Alshareef
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Andrew K. Knutsen
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20814, USA
| | - Curtis L. Johnson
- Department of Biomedical Engineering, University of Delaware, Newark, DE 19716, USA
| | - Aaron Carass
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Philip V. Bayly
- Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Michael D. Shields
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - K.T. Ramesh
- Hopkins Extreme Materials Institute, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| |
Collapse
|
26
|
Fiber orientation downsampling compromises the computation of white matter tract-related deformation. J Mech Behav Biomed Mater 2022; 132:105294. [DOI: 10.1016/j.jmbbm.2022.105294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 04/13/2022] [Accepted: 05/21/2022] [Indexed: 11/18/2022]
|
27
|
American Football Helmet Effectiveness Against a Strain-Based Concussion Mechanism. Ann Biomed Eng 2022; 50:1498-1509. [PMID: 35816264 DOI: 10.1007/s10439-022-03005-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 06/30/2022] [Indexed: 12/23/2022]
Abstract
Brain strain is increasingly being used in helmet design and safety performance evaluation as it is generally considered as the primary mechanism of concussion. In this study, we investigate whether different helmet designs can meaningfully alter brain strains using two commonly used metrics, peak maximum principal strain (MPS) of the whole brain and cumulative strain damage measure (CSDM). A convolutional neural network (CNN) that instantly produces detailed brain strains is first tested for accuracy for helmeted head impacts. Based on N = 144 impacts in 12 impact conditions from three random and representative helmet models, we conclude that the CNN is sufficiently accurate for helmet testing applications, for elementwise MPS (success rate of 98.6%), whole-brain peak MPS and CSDM (coefficient of determination of 0.977 and 0.980, with root mean squared error of 0.015 and 0.029, respectively). We then apply the technique to 23 football helmet models (N = 1104 impacts) to reproduce elementwise MPS. Assuming a concussion would occur when peak MPS or CSDM exceeds a threshold, we sweep their thresholds across the value ranges to evaluate the number of predicted hypothetical concussions that different helmets sustain across the impact conditions. Relative to the 12 impact conditions tested, we find that the "best" and "worst" helmets differ by an average of 22.5% in terms of predicted concussions, ranging from 0 to 42% (the latter achieved at the threshold value of 0.28 for peak MPS and 0.4 for CSDM, respectively). Such a large variation among helmets in strain-based concussion predictions demonstrate that helmet designs can still be optimized in a clinically meaningful way. The robustness and accuracy of the CNN tool also suggest its potential for routine use for helmet design and safety performance evaluation in the future. The CNN is freely available online at https://github.com/Jilab-biomechanics/CNN-brain-strains .
Collapse
|
28
|
Wu S, Zhao W, Ji S. Real-time dynamic simulation for highly accurate spatiotemporal brain deformation from impact. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2022; 394:114913. [PMID: 35572209 PMCID: PMC9097909 DOI: 10.1016/j.cma.2022.114913] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Real-time dynamic simulation remains a significant challenge for spatiotemporal data of high dimension and resolution. In this study, we establish a transformer neural network (TNN) originally developed for natural language processing and a separate convolutional neural network (CNN) to estimate five-dimensional (5D) spatiotemporal brain-skull relative displacement resulting from impact (isotropic spatial resolution of 4 mm with temporal resolution of 1 ms). Sequential training is applied to train (N = 5184 samples) the two neural networks for estimating the complete 5D displacement across a temporal duration of 60 ms. We find that TNN slightly but consistently outperforms CNN in accuracy for both displacement and the resulting voxel-wise four-dimensional (4D) maximum principal strain (e.g., root mean squared error (RMSE) of ~1.0% vs. ~1.6%, with coefficient of determination, R 2 >0.99 vs. >0.98, respectively, and normalized RMSE (NRMSE) at peak displacement of 2%-3%, based on an independent testing dataset; N = 314). Their accuracies are similar for a range of real-world impacts drawn from various published sources (dummy, helmet, football, soccer, and car crash; average RMSE/NRMSE of ~0.3 mm/~4%-5% and average R 2 of ~0.98 at peak displacement). Sequential training is effective for allowing instantaneous estimation of 5D displacement with high accuracy, although TNN poses a heavier computational burden in training. This work enables efficient characterization of the intrinsically dynamic brain strain in impact critical for downstream multiscale axonal injury model simulation. This is also the first application of TNN in biomechanics, which offers important insight into how real-time dynamic simulations can be achieved across diverse engineering fields.
Collapse
Affiliation(s)
- Shaoju Wu
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, United States of America
| | - Wei Zhao
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, United States of America
| | - Songbai Ji
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, United States of America
- Department of Mechanical Engineering, Worcester Polytechnic Institute, Worcester, MA, United States of America
| |
Collapse
|
29
|
Zhou Z, Li X, Domel AG, Dennis EL, Georgiadis M, Liu Y, Raymond SJ, Grant G, Kleiven S, Camarillo D, Zeineh M. The Presence of the Temporal Horn Exacerbates the Vulnerability of Hippocampus During Head Impacts. Front Bioeng Biotechnol 2022; 10:754344. [PMID: 35392406 PMCID: PMC8980591 DOI: 10.3389/fbioe.2022.754344] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 01/19/2022] [Indexed: 11/13/2022] Open
Abstract
Hippocampal injury is common in traumatic brain injury (TBI) patients, but the underlying pathogenesis remains elusive. In this study, we hypothesize that the presence of the adjacent fluid-containing temporal horn exacerbates the biomechanical vulnerability of the hippocampus. Two finite element models of the human head were used to investigate this hypothesis, one with and one without the temporal horn, and both including a detailed hippocampal subfield delineation. A fluid-structure interaction coupling approach was used to simulate the brain-ventricle interface, in which the intraventricular cerebrospinal fluid was represented by an arbitrary Lagrangian-Eulerian multi-material formation to account for its fluid behavior. By comparing the response of these two models under identical loadings, the model that included the temporal horn predicted increased magnitudes of strain and strain rate in the hippocampus with respect to its counterpart without the temporal horn. This specifically affected cornu ammonis (CA) 1 (CA1), CA2/3, hippocampal tail, subiculum, and the adjacent amygdala and ventral diencephalon. These computational results suggest that the presence of the temporal horn exacerbate the vulnerability of the hippocampus, highlighting the mechanobiological dependency of the hippocampus on the temporal horn.
Collapse
Affiliation(s)
- Zhou Zhou
- Department of Bioengineering, Stanford University, Stanford, CA, United States
- Neuronic Engineering, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Xiaogai Li
- Neuronic Engineering, KTH Royal Institute of Technology, Stockholm, Sweden
| | - August G. Domel
- Department of Bioengineering, Stanford University, Stanford, CA, United States
| | - Emily L. Dennis
- TBI and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT, United States
- Department of Radiology, Stanford University, Stanford, CA, United States
| | - Marios Georgiadis
- Department of Radiology, Stanford University, Stanford, CA, United States
| | - Yuzhe Liu
- Department of Bioengineering, Stanford University, Stanford, CA, United States
| | - Samuel J. Raymond
- Department of Bioengineering, Stanford University, Stanford, CA, United States
| | - Gerald Grant
- Department of Neurosurgery, Stanford University, Stanford, CA, United States
- Department of Neurology, Stanford University, Stanford, CA, United States
| | - Svein Kleiven
- Neuronic Engineering, KTH Royal Institute of Technology, Stockholm, Sweden
| | - David Camarillo
- Department of Bioengineering, Stanford University, Stanford, CA, United States
- Department of Neurosurgery, Stanford University, Stanford, CA, United States
- Department of Mechanical Engineering, Stanford University, Stanford, CA, United States
| | - Michael Zeineh
- Department of Radiology, Stanford University, Stanford, CA, United States
| |
Collapse
|
30
|
Raymond SJ, Cecchi NJ, Alizadeh HV, Callan AA, Rice E, Liu Y, Zhou Z, Zeineh M, Camarillo DB. Physics-Informed Machine Learning Improves Detection of Head Impacts. Ann Biomed Eng 2022; 50:1534-1545. [PMID: 35303171 DOI: 10.1007/s10439-022-02911-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 01/01/2022] [Indexed: 12/26/2022]
Abstract
In this work we present a new physics-informed machine learning model that can be used to analyze kinematic data from an instrumented mouthguard and detect impacts to the head. Monitoring player impacts is vitally important to understanding and protecting from injuries like concussion. Typically, to analyze this data, a combination of video analysis and sensor data is used to ascertain the recorded events are true impacts and not false positives. In fact, due to the nature of using wearable devices in sports, false positives vastly outnumber the true positives. Yet, manual video analysis is time-consuming. This imbalance leads traditional machine learning approaches to exhibit poor performance in both detecting true positives and preventing false negatives. Here, we show that by simulating head impacts numerically using a standard Finite Element head-neck model, a large dataset of synthetic impacts can be created to augment the gathered, verified, impact data from mouthguards. This combined physics-informed machine learning impact detector reported improved performance on test datasets compared to traditional impact detectors with negative predictive value and positive predictive values of 88 and 87% respectively. Consequently, this model reported the best results to date for an impact detection algorithm for American football, achieving an F1 score of 0.95. In addition, this physics-informed machine learning impact detector was able to accurately detect true and false impacts from a test dataset at a rate of 90% and 100% relative to a purely manual video analysis workflow. Saving over 12 h of manual video analysis for a modest dataset, at an overall accuracy of 92%, these results indicate that this model could be used in place of, or alongside, traditional video analysis to allow for larger scale and more efficient impact detection in sports such as American Football.
Collapse
Affiliation(s)
- Samuel J Raymond
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA.
| | - Nicholas J Cecchi
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA
| | | | - Ashlyn A Callan
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA
| | - Eli Rice
- Stanford Center for Clinical Research, Stanford University, Stanford, CA, 94305, USA
| | - Yuzhe Liu
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA
| | - Zhou Zhou
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA
| | - Michael Zeineh
- Department of Radiology, Stanford University, Stanford, CA, 94305, USA
| | - David B Camarillo
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA.,Department of Neurosurgery, Stanford University, Stanford, CA, 94305, USA.,Department of Mechanical Engineering, Stanford University, Stanford, CA, 94305, USA
| |
Collapse
|
31
|
Pearson N, Boiczyk GM, Kote VB, Sundaramurthy A, Subramaniam DR, Rubio JE, Unnikrishnan G, Reifman J, Monson K. A Strain-Rate Dependent Constitutive Model for Göttingen Minipig Cerebral Arteries. J Biomech Eng 2022; 144:1135617. [PMID: 35147172 DOI: 10.1115/1.4053796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Indexed: 11/08/2022]
Abstract
Computational simulations of traumatic brain injury (TBI) are commonly used to advance understanding of the injury-pathology relationship, tissue damage thresholds, and design of protective equipment such as helmets. Both human and animal TBI models have developed substantially over recent decades, partially due to the inclusion of more detailed brain geometry and representation of tissues like cerebral blood vessels. Explicit incorporation of vessels dramatically affects local strain and enables researchers to investigate TBI-induced damage to the vasculature. While some studies have indicated that cerebral arteries are rate-dependent, no published experimentally based, rate-sensitive constitutive models of cerebral arteries exist. In the present work, we characterize the mechanical properties of axially failed porcine arteries, both quasi-statically (0.01 s-1) and at high rate (<100 s-1), and propose a rate-sensitive model to fit the data. We find that the quasi-static and high-rate stress-stretch curves become significantly different (p>0.05) above a stretch of 1.23. We additionally find a significant change in both failure stretch and stress as a result of strain rate. The stress-stretch curve is then modeled as a Holzapfel-Gasser-Ogden material, with a Prony series added to capture the effects of viscoelasticity. Ultimately, this paper demonstrates that rate dependence should be considered in the material properties of cerebral arteries undergoing high strain-rate deformations and provides a ready-to-use model for finite element implementation.
Collapse
Affiliation(s)
- Noah Pearson
- Department of Mechanical Engineering, The University of Utah, 1495 E 100 S, Salt Lake City, UT 84112, USA
| | - Gregory M Boiczyk
- Department of Biomedical Engineering, The University of Utah, 36 S. Wasatch Drive, Salt Lake City, UT 84112, USA
| | - Vivek B 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, Fort Detrick, MD 21702, 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, Fort Detrick, MD 21702, USA; The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., 6720A Rockledge Drive, Bethesda, MD 20817, USA
| | - 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, Fort Detrick, MD 21702, 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, Fort Detrick, MD 21702, 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, Fort Detrick, MD 21702, 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, Fort Detrick, MD 21702, USA; The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., 6720A Rockledge Drive, Bethesda, MD 20817, USA
| | - Ken Monson
- Department of Biomedical Engineering, The University of Utah, Department of Mechanical Engineering, The University of Utah, 1495 E 100 S, Salt Lake City, UT 84112, USA
| |
Collapse
|
32
|
Influence of strain Post-Processing on brain injury prediction. J Biomech 2022; 132:110940. [DOI: 10.1016/j.jbiomech.2021.110940] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 10/17/2021] [Accepted: 12/10/2021] [Indexed: 11/17/2022]
|
33
|
Zhao W, Ji S. Cerebral vascular strains in dynamic head impact using an upgraded model with brain material property heterogeneity. J Mech Behav Biomed Mater 2022; 126:104967. [PMID: 34863650 PMCID: PMC8792345 DOI: 10.1016/j.jmbbm.2021.104967] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 09/27/2021] [Accepted: 11/06/2021] [Indexed: 02/03/2023]
Abstract
Cerebral vascular injury (CVI) is a frequent consequence of traumatic brain injury but has often been neglected. Substantial experimental work exists on vascular material properties and failure/subfailure thresholds. However, little is known about vascular in vivo loading conditions in dynamic head impact, which is necessary to investigate the risk, severity, and extent of CVI. In this study, we resort to the Worcester Head Injury Model (WHIM) V2.1 for investigation. The model embeds the cerebral vasculature network and is further upgraded to incorporate brain material property heterogeneity based on magnetic resonance elastography. The brain material property is calibrated to match with the previously validated anisotropic V1.0 version in terms of whole-brain strains against six experimental datasets of a wide range of blunt impact conditions. The upgraded WHIM is finally used to simulate five representative real-world head impacts drawn from contact sports and automotive crashes. We find that peak strains in veins are considerably higher than those in arteries and that peak circumferential strains are also higher than peak axial strains. For a typical concussive head impact, cerebral vascular axial strains reach the lowest reported yield strain of ∼7-8%. For severe automotive impacts, axial strains could reach ∼20%, which is on the order of the lowest reported ultimate failure strain of ∼24%. These results suggest in vivo mechanical loading conditions of the cerebral vasculature (excluding bridging veins not assessed here) due to rapid head rotation are at the lower end of failure/subfailure thresholds established from ex vivo experiments. This study provides some first insight into the risk, severity, and extent of CVI in real-world head impacts.
Collapse
Affiliation(s)
- Wei Zhao
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA
| | - Songbai Ji
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, Department of Mechanical Engineering, Worcester Polytechnic Institute, Worcester, MA,Corresponding author: Dr. Songbai Ji, 60 Prescott Street, Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA 01506, USA, ; (508) 831-4956
| |
Collapse
|
34
|
Ji S, Zhao W. Displacement voxelization to resolve mesh-image mismatch: Application in deriving dense white matter fiber strains. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 213:106528. [PMID: 34808529 PMCID: PMC8665149 DOI: 10.1016/j.cmpb.2021.106528] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 10/01/2021] [Accepted: 11/09/2021] [Indexed: 05/19/2023]
Abstract
BACKGROUND AND OBJECTIVE It is common to combine biomechanical modeling and medical images for multimodal analyses. However, mesh-image mismatch may occur that prevents direct information exchange. To eliminate mesh-image mismatch, we develop a simple but elegant displacement voxelization technique based on image voxel corner nodes to achieve voxel-wise strain. We then apply the technique to derive dense white matter fiber strains along whole-brain tractography (∼35 k fiber tracts consisting of ∼3.3 million sampling points) resulting from head impact. METHODS Displacements at image voxel corner nodes are first obtained from model simulation via scattered interpolation. Each voxel is then scaled linearly to form a unit hexahedral element. This allows convenient and efficient voxel-wise strain tensor calculation and displacement interpolation at arbitrary fiber sampling points via shape functions. Fiber strains from displacement interpolation are then compared with those from the commonly used strain tensor projection using either voxel- or element-wise strain tensors. RESULTS Based on a synthetic displacement field, fiber strains interpolated from voxelized displacement are considerably more accurate than those from strain tensor projection relative to the prescribed ground-truth (determinant of coefficient (R2) of 1.00 and root mean squared error (RMSE) of 0.01 vs. 0.87 and 0.10, respectively). For a set of real-world reconstructed head impacts (N = 53), the strain tensor projection method performs similarly poorly (R2 of 0.80-0.90 and RMSE of 0.03-0.07), with overestimation strongly correlated with strain magnitude (Pearson correlation coefficient >0.9). Up to ∼15% of the fiber strains are overestimated by more than the lower bound of a conservative injury threshold of 0.09. The percentage increases to ∼37% when halving the threshold. Voxel interpolation is also significantly more efficient (15 s vs. 40 s for element strain tensor projection, without parallelization). CONCLUSIONS Voxelized displacement interpolation is considerably more accurate and efficient in deriving dense white matter fiber strains than strain tensor projection. The latter generally overestimates with overestimation magnitude strongly correlating with fiber strain magnitude. Displacement voxelization is an effective technique to eliminate mesh-image mismatch and generates a convenient image representation of tissue deformation. This technique can be generalized to broadly facilitate a diverse range of image-related biomechanical problems for multimodal analyses. The convenient image format may also promote and facilitate biomechanical data sharing in the future.
Collapse
Affiliation(s)
- Songbai Ji
- Department of Biomedical Engineering, Worcester Polytechnic Institute, 60 Prescott Street, Worcester, MA 01506, USA; Department of Mechanical Engineering, Worcester Polytechnic Institute, Worcester, MA, USA.
| | - Wei Zhao
- Department of Biomedical Engineering, Worcester Polytechnic Institute, 60 Prescott Street, Worcester, MA 01506, USA
| |
Collapse
|
35
|
|
36
|
Li X. Subject-Specific Head Model Generation by Mesh Morphing: A Personalization Framework and Its Applications. Front Bioeng Biotechnol 2021; 9:706566. [PMID: 34733827 PMCID: PMC8558307 DOI: 10.3389/fbioe.2021.706566] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 09/03/2021] [Indexed: 11/30/2022] Open
Abstract
Finite element (FE) head models have become powerful tools in many fields within neuroscience, especially for studying the biomechanics of traumatic brain injury (TBI). Subject-specific head models accounting for geometric variations among subjects are needed for more reliable predictions. However, the generation of such models suitable for studying TBIs remains a significant challenge and has been a bottleneck hindering personalized simulations. This study presents a personalization framework for generating subject-specific models across the lifespan and for pathological brains with significant anatomical changes by morphing a baseline model. The framework consists of hierarchical multiple feature and multimodality imaging registrations, mesh morphing, and mesh grouping, which is shown to be efficient with a heterogeneous dataset including a newborn, 1-year-old (1Y), 2Y, adult, 92Y, and a hydrocephalus brain. The generated models of the six subjects show competitive personalization accuracy, demonstrating the capacity of the framework for generating subject-specific models with significant anatomical differences. The family of the generated head models allows studying age-dependent and groupwise brain injury mechanisms. The framework for efficient generation of subject-specific FE head models helps to facilitate personalized simulations in many fields of neuroscience.
Collapse
Affiliation(s)
- Xiaogai Li
- Division of Neuronic Engineering, Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Stockholm, Sweden
| |
Collapse
|
37
|
Wu S, Zhao W, Barbat S, Ruan J, Ji S. Instantaneous Brain Strain Estimation for Automotive Head Impacts via Deep Learning. STAPP CAR CRASH JOURNAL 2021; 65:139-162. [PMID: 35512787 DOI: 10.4271/2021-22-0006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Efficient brain strain estimation is critical for routine application of a head injury model. Lately, a convolutional neural network (CNN) has been successfully developed to estimate spatially detailed brain strains instantly and accurately in contact sports. Here, we extend its application to automotive head impacts, where impact profiles are typically more complex with longer durations. Head impact kinematics (N=458) from two public databases were used to generate augmented impacts (N=2694). They were simulated using the anisotropic Worcester Head Injury Model (WHIM) V1.0, which provided baseline elementwise peak maximum principal strain (MPS). For each augmented impact, rotational velocity (vrot) and the corresponding rotational acceleration (arot) profiles were concatenated as static images to serve as CNN input. Three training strategies were evaluated: 1) "baseline", using random initial weights; 2) "transfer learning", using weight transfer from a previous CNN model trained on head impacts drawn from contact sports; and 3) "combined training", combining previous training data from contact sports (N=5661) for training. The combined training achieved the best performances. For peak MPS, the CNN achieved a coefficient of determination (R2) of 0.932 and root mean squared error (RMSE) of 0.031 for the real-world testing dataset. It also achieved a success rate of 60.5% and 94.8% for elementwise MPS, where the linear regression slope, k, and correlation coefficient, r, between estimated and simulated MPS did not deviate from 1.0 (when identical) by more than 0.1 and 0.2, respectively. Cumulative strain damage measure (CSDM) from the CNN estimation was also highly accurate compared to those from direct simulation across a range of thresholds (R2 of 0.899-0.943 with RMSE of 0.054-0.069). Finally, the CNN achieved an average k and r of 0.98±0.12 and 0.90±0.07, respectively, for six reconstructed car crash impacts drawn from two other sources independent of the training dataset. Importantly, the CNN is able to efficiently estimate elementwise MPS with sufficient accuracy while conventional kinematic injury metrics cannot. Therefore, the CNN has the potential to supersede current kinematic injury metrics that can only approximate a global peak MPS or CSDM. The CNN technique developed here may offer enhanced utility in the design and development of head protective countermeasures, including in the automotive industry. This is the first study aimed at instantly estimating spatially detailed brain strains for automotive head impacts, which employs >8.8 thousand impact simulations generated from ~1.5 years of nonstop computations on a high-performance computing platform.
Collapse
Affiliation(s)
- Shaoju Wu
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA 01605, USA
| | - Wei Zhao
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA 01605, USA
| | | | - Jesse Ruan
- Tianjin University of Science and Technology, Tianjin, 300222, China
| | - Songbai Ji
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA 01605, USA
| |
Collapse
|
38
|
Mechanical characterisation of the human dura mater, falx cerebri and superior sagittal sinus. Acta Biomater 2021; 134:388-400. [PMID: 34314888 DOI: 10.1016/j.actbio.2021.07.043] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 07/01/2021] [Accepted: 07/21/2021] [Indexed: 01/14/2023]
Abstract
The cranial meninges have been shown to play a pivotal role in traumatic brain injury mechanopathology. However, while the mechanical response of the brain and its many subregions have been studied extensively, the meninges have conventionally been overlooked. This paper presents the first comparative mechanical analysis of human dura mater, falx cerebri and superior sagittal sinus tissues. Biaxial tensile analysis identified that these tissues are mechanically heterogeneous, in contrast to the assumption that the tissues are mechanically homogeneous which is typically employed in FE model design. A thickness of 0.91 ± 0.05 (standard error) mm for the falx cerebri was also identified. This data can aid in improving the biofidelity of the influential falx structure in FE models. Additionally, the use of a collagen hybridizing peptide on the superior sagittal sinus suggests this structure is particularly susceptible to the effects of circumferential stretch, which may have important implications for clinical treatment of dural venous sinus pathologies. Collectively, this research progresses understanding of meningeal mechanical and structural characteristics and may aid in elucidating the behaviour of these tissues in healthy and diseased conditions. STATEMENT OF SIGNIFICANCE: This study presents the first evaluation of human falx cerebri and superior sagittal sinus mechanical, geometrical and structural properties, along with a comparison to cranial dura mater. To mechanically characterise the tissues, biaxial tensile testing is conducted on the tissues. This analysis identifies, for the first time, mechanical stiffness differences between these tissues. Additionally, geometrical analysis identifies that there are thickness differences between the tissues. The evaluation of human meningeal tissues allows for direct implementation of the novel data to finite element head injury models to enable improved biofidelity of these influential structures in traumatic brain injury simulations. This work also identifies that the superior sagittal sinus may be easily damaged during clinical angioplasty procedures, which may inform the treatment of dural sinus pathologies.
Collapse
|
39
|
Zhou Z, Li X, Liu Y, Fahlstedt M, Georgiadis M, Zhan X, Raymond SJ, Grant G, Kleiven S, Camarillo D, Zeineh M. Toward a Comprehensive Delineation of White Matter Tract-Related Deformation. J Neurotrauma 2021; 38:3260-3278. [PMID: 34617451 DOI: 10.1089/neu.2021.0195] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Finite element (FE) models of the human head are valuable instruments to explore the mechanobiological pathway from external loading, localized brain response, and resultant injury risks. The injury predictability of these models depends on the use of effective criteria as injury predictors. The FE-derived normal deformation along white matter (WM) fiber tracts (i.e., tract-oriented strain) recently has been suggested as an appropriate predictor for axonal injury. However, the tract-oriented strain only represents a partial depiction of the WM fiber tract deformation. A comprehensive delineation of tract-related deformation may improve the injury predictability of the FE head model by delivering new tract-related criteria as injury predictors. Thus, the present study performed a theoretical strain analysis to comprehensively characterize the WM fiber tract deformation by relating the strain tensor of the WM element to its embedded fiber tract. Three new tract-related strains with exact analytical solutions were proposed, measuring the normal deformation perpendicular to the fiber tracts (i.e., tract-perpendicular strain), and shear deformation along and perpendicular to the fiber tracts (i.e., axial-shear strain and lateral-shear strain, respectively). The injury predictability of these three newly proposed strain peaks along with the previously used tract-oriented strain peak and maximum principal strain (MPS) were evaluated by simulating 151 impacts with known outcome (concussion or non-concussion). The results preliminarily showed that four tract-related strain peaks exhibited superior performance than MPS in discriminating concussion and non-concussion cases. This study presents a comprehensive quantification of WM tract-related deformation and advocates the use of orientation-dependent strains as criteria for injury prediction, which may ultimately contribute to an advanced mechanobiological understanding and enhanced computational predictability of brain injury.
Collapse
Affiliation(s)
- Zhou Zhou
- Department of Bioengineering, Stanford University, Stanford, California, USA.,Neuronic Engineering, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Xiaogai Li
- Neuronic Engineering, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Yuzhe Liu
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Madelen Fahlstedt
- Neuronic Engineering, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Marios Georgiadis
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Xianghao Zhan
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Samuel J Raymond
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Gerald Grant
- Department of Neurosurgery, Stanford University, Stanford, California, USA.,Department of Neurology, Stanford University, Stanford, California, USA
| | - Svein Kleiven
- Neuronic Engineering, KTH Royal Institute of Technology, Stockholm, Sweden
| | - David Camarillo
- Department of Bioengineering, Stanford University, Stanford, California, USA.,Department of Neurology, Stanford University, Stanford, California, USA.,Department of Mechanical Engineering, Stanford University, Stanford, California, USA
| | - Michael Zeineh
- Department of Radiology, Stanford University, Stanford, California, USA
| |
Collapse
|
40
|
Bayly PV, Alshareef A, Knutsen AK, Upadhyay K, Okamoto RJ, Carass A, Butman JA, Pham DL, Prince JL, Ramesh KT, Johnson CL. MR Imaging of Human Brain Mechanics In Vivo: New Measurements to Facilitate the Development of Computational Models of Brain Injury. Ann Biomed Eng 2021; 49:2677-2692. [PMID: 34212235 PMCID: PMC8516723 DOI: 10.1007/s10439-021-02820-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 06/22/2021] [Indexed: 01/04/2023]
Abstract
Computational models of the brain and its biomechanical response to skull accelerations are important tools for understanding and predicting traumatic brain injuries (TBIs). However, most models have been developed using experimental data collected on animal models and cadaveric specimens, both of which differ from the living human brain. Here we describe efforts to noninvasively measure the biomechanical response of the human brain with MRI-at non-injurious strain levels-and generate data that can be used to develop, calibrate, and evaluate computational brain biomechanics models. Specifically, this paper reports on a project supported by the National Institute of Neurological Disorders and Stroke to comprehensively image brain anatomy and geometry, mechanical properties, and brain deformations that arise from impulsive and harmonic skull loadings. The outcome of this work will be a publicly available dataset ( http://www.nitrc.org/projects/bbir ) that includes measurements on both males and females across an age range from adolescence to older adulthood. This article describes the rationale and approach for this study, the data available, and how these data may be used to develop new computational models and augment existing approaches; it will serve as a reference to researchers interested in using these data.
Collapse
Affiliation(s)
- Philip V Bayly
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO, USA.
| | - Ahmed Alshareef
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Andrew K Knutsen
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA
| | - Kshitiz Upadhyay
- Hopkins Extreme Materials Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Ruth J Okamoto
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO, USA
| | - Aaron Carass
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - John A Butman
- Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Dzung L Pham
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA
| | - Jerry L Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - K T Ramesh
- Hopkins Extreme Materials Institute, Johns Hopkins University, Baltimore, MD, USA
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Curtis L Johnson
- Department of Biomedical Engineering, University of Delaware, Newark, DE, USA.
| |
Collapse
|
41
|
Holcomb JM, Fisicaro RA, Miller LE, Yu FF, Davenport EM, Xi Y, Urban JE, Wagner BC, Powers AK, Whitlow CT, Stitzel JD, Maldjian JA. Regional White Matter Diffusion Changes Associated with the Cumulative Tensile Strain and Strain Rate in Nonconcussed Youth Football Players. J Neurotrauma 2021; 38:2763-2771. [PMID: 34039024 PMCID: PMC8820832 DOI: 10.1089/neu.2020.7580] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
The purpose of this study is to assess the relationship between regional white matter diffusion imaging changes and finite element strain measures in nonconcussed youth football players. Pre- and post-season diffusion-weighted imaging was performed in 102 youth football subject-seasons, in which no concussions were diagnosed. The diffusion data were normalized to the IXI template. Percent change in fractional anisotropy (%ΔFA) images were generated. Using data from the head impact telemetry system, the cumulative maximum principal strain one times strain rate (CMPS1 × SR), a measure of the cumulative tensile brain strain and strain rate for one season, was calculated for each subject. Two linear regression analyses were performed to identify significant positive or inverse relationships between CMPS1 × SR and %ΔFA within the international consortium for brain mapping white matter mask. Age, body mass index, days between pre- and post-season imaging, previous brain injury, attention disorder diagnosis, and imaging protocol were included as covariates. False discovery rate correction was used with corrected alphas of 0.025 and voxel thresholds of zero. Controlling for all covariates, a significant, positive linear relationship between %ΔFA and CMPS1 × SR was identified in the bilateral cingulum, fornix, internal capsule, external capsule, corpus callosum, corona radiata, corticospinal tract, cerebral and middle cerebellar peduncle, superior longitudinal fasciculus, and right superior fronto-occipital fasciculus. Post hoc analyses further demonstrated significant %ΔFA differences between high-strain football subjects and noncollision control athletes, no significant %ΔFA differences between low-strain subjects and noncollision control athletes, and that CMPS1 × SR significantly explained more %ΔFA variance than number of head impacts alone.
Collapse
Affiliation(s)
- James M. Holcomb
- University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Ryan A. Fisicaro
- University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Logan E. Miller
- Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Fang F. Yu
- University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | | | - Yin Xi
- University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Jillian E. Urban
- Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Ben C. Wagner
- University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | | | | | - Joel D. Stitzel
- Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | | |
Collapse
|
42
|
Potential Mechanisms of Acute Standing Balance Deficits After Concussions and Subconcussive Head Impacts: A Review. Ann Biomed Eng 2021; 49:2693-2715. [PMID: 34258718 DOI: 10.1007/s10439-021-02831-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 06/29/2021] [Indexed: 01/04/2023]
Abstract
Standing balance deficits are prevalent after concussions and have also been reported after subconcussive head impacts. However, the mechanisms underlying such deficits are not fully understood. The objective of this review is to consolidate evidence linking head impact biomechanics to standing balance deficits. Mechanical energy transferred to the head during impacts may deform neural and sensory components involved in the control of standing balance. From our review of acute balance-related changes, concussions frequently resulted in increased magnitude but reduced complexity of postural sway, while subconcussive studies showed inconsistent outcomes. Although vestibular and visual symptoms are common, potential injury to these sensors and their neural pathways are often neglected in biomechanics analyses. While current evidence implies a link between tissue deformations in deep brain regions including the brainstem and common post-concussion balance-related deficits, this link has not been adequately investigated. Key limitations in current studies include inadequate balance sampling duration, varying test time points, and lack of head impact biomechanics measurements. Future investigations should also employ targeted quantitative methods to probe the sensorimotor and neural components underlying balance control. A deeper understanding of the specific injury mechanisms will inform diagnosis and management of balance deficits after concussions and subconcussive head impact exposure.
Collapse
|
43
|
Singh A, Ganpule SG, Khan MK, Iqbal MA. Measurement of brain simulant strains in head surrogate under impact loading. Biomech Model Mechanobiol 2021; 20:2319-2334. [PMID: 34455505 DOI: 10.1007/s10237-021-01509-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Accepted: 08/13/2021] [Indexed: 10/20/2022]
Abstract
Impact-induced traumatic brain injury (TBI) is a major source of disability and mortality. Knowledge of brain strains during impact (accelerative) loading is critical for the overall management of TBI, including the development of injury thresholds, personal protective equipment, and validation of computational models. Despite these needs, the current understanding of brain strains in humans or humanlike surrogates is limited, especially for injury causing loading magnitudes. Toward this end, we measured full-field, in-plane (2D) strains in a brain simulant using the hemispherical head surrogate. The hemispherical head was mounted on the Hybrid-III neck and subjected to impact loading using a linear impactor system. The resulting head kinematics was measured using a triaxial accelerometer and angular rate sensors. Dynamic, 2D strains in a brain simulant were obtained using high-speed imaging and digital image correlation. Concurrent finite element (FE) simulations of the experiment were also performed to gain additional insights. The role of stiff membranes of the head was also studied using experiments. Our results suggest that rotational modes dominate the response of the brain simulant. The wave propagation in the brain simulant as a result of impact has a timescale of ~100 ms. We obtain peak strains of ~20%, ~40%, ~60% for peak rotational accelerations of ~838, ~5170, ~11,860 rad/s2, respectively. Further, peak strains in cortical regions are higher than subcortical regions by up to ~70%. The agreement between the experiments and FE simulations is reasonable in terms of spatiotemporal evolution of strain pattern and peak strain magnitudes. Experiments with the addition of falx and tentorium indicate significant strain concentration (up to 115%) in the brain simulant near the interface of falx or tentorium and brain simulant. Overall, this work provides important insights into the biomechanics of strain in the brain simulant during impact loading.
Collapse
Affiliation(s)
- A Singh
- Department of Mechanical and Industrial Engineering, Indian Institute of Technology Roorkee, Roorkee, 247667, India
| | - S G Ganpule
- Department of Mechanical and Industrial Engineering, Indian Institute of Technology Roorkee, Roorkee, 247667, India.
| | - M K Khan
- Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee, 247667, India
| | - M A Iqbal
- Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee, 247667, India
| |
Collapse
|
44
|
Wu T, Hajiaghamemar M, Giudice JS, Alshareef A, Margulies SS, Panzer MB. Evaluation of Tissue-Level Brain Injury Metrics Using Species-Specific Simulations. J Neurotrauma 2021; 38:1879-1888. [PMID: 33446011 PMCID: PMC8219195 DOI: 10.1089/neu.2020.7445] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Traumatic brain injury (TBI) is a significant public health burden, and the development of advanced countermeasures to mitigate and prevent these injuries during automotive, sports, and military impact events requires an understanding of the intracranial mechanisms related to TBI. In this study, the efficacy of tissue-level injury metrics for predicting TBI was evaluated using finite element reconstructions from a comprehensive, multi-species TBI database. The database consisted of human volunteer tests, laboratory-reconstructed head impacts from sports, in vivo non-human primate (NHP) tests, and in vivo pig tests. Eight tissue-level metrics related to brain tissue strain, axonal strain, and strain-rate were evaluated using survival analysis for predicting mild and severe TBI risk. The correlation between TBI risk and most of the assessed metrics were statistically significant, but when injury data was analyzed by species, the best metric was often inconclusive and limited by the small datasets. When the human and animal datasets were combined, the injury analysis was able to delineate maximum axonal strain as the best predictor of injury for all species and TBI severities, with maximum principal strain as a suitable alternative metric. The current study is the first to provide evidence to support the assumption that brain strain response between human, pig, and NHP result in similar injury outcomes through a multi-species analysis. This assumption is the biomechanical foundation for translating animal brain injury findings to humans. The findings in the study provide fundamental guidelines for developing injury criteria that would contribute towards the innovation of more effective safety countermeasures.
Collapse
Affiliation(s)
- Taotao Wu
- Center for Applied Biomechanics, University of Virginia, Charlottesville, Virginia, USA
| | - Marzieh Hajiaghamemar
- Department of Biomedical Engineering, University of Texas at San Antonio, San Antonio, Texas, USA
| | - J. Sebastian Giudice
- Center for Applied Biomechanics, University of Virginia, Charlottesville, Virginia, USA
| | - Ahmed Alshareef
- Center for Applied Biomechanics, University of Virginia, Charlottesville, Virginia, USA
| | - Susan S. Margulies
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA
| | - Matthew B. Panzer
- Center for Applied Biomechanics, University of Virginia, Charlottesville, Virginia, USA
| |
Collapse
|
45
|
Benko N, Luke E, Alsanea Y, Coats B. Mechanical characterization of the human pia-arachnoid complex. J Mech Behav Biomed Mater 2021; 120:104579. [PMID: 34020233 DOI: 10.1016/j.jmbbm.2021.104579] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 04/29/2021] [Accepted: 05/06/2021] [Indexed: 11/28/2022]
Abstract
Traumatic brain injury (TBI) is a significant problem in global health that affects a wide variety of patients. Mild forms of TBI, commonly referred to as concussion, are a result of rapid accelerations of the head from either direct or indirect impacts. Kinetic energy from the impact is transferred into deformation of the brain, leading to cellular disruption. This transfer of energy is in part mediated by the pia-arachnoid complex (PAC), a layer of anatomical structures that forms the physical connection between the brain and the skull. The importance of properly quantifying the mechanics of the PAC for use in computational models of TBI has been understood for some time, but data from human subjects has been unavailable. In this study, we quantify the normal traction modulus of the PAC in five post-mortem human subjects using hydrostatic fluid pressurization in combination with optical coherence tomography. Testing at multiple locations across each brain reveals that brain-skull stiffness is heterogeneously distributed. The material response to traction loading was linear, with a mean normal traction modulus of 12.6 ± 4.8 kPa. Modulus was 21% greater in superior regions of the brain compared to inferior regions. Comparisons with regional microstructural data suggests a potential relationship between the volume fraction of arachnoid trabeculae and modulus. Comparisons to coincident measurements of microstructural properties showed a positive correlation between arachnoid membrane thickness and normal traction modulus. This study is the first to characterize the mechanics of the human pia-arachnoid complex and quantify material properties in situ. These findings suggest implementing a heterogeneous model of the brain-skull interface in computational models of TBI may lead to more realistic injury prediction.
Collapse
Affiliation(s)
- Nikolaus Benko
- Department of Mechanical Engineering, University of Utah, Salt Lake City, UT, USA
| | - Emma Luke
- Department of Biomedical Engineering, University of Rochester Rochester, NY, USA
| | - Yousef Alsanea
- Department of Mechanical Engineering, University of Utah, Salt Lake City, UT, USA
| | - Brittany Coats
- Department of Mechanical Engineering, University of Utah, Salt Lake City, UT, USA.
| |
Collapse
|
46
|
Giudice JS, Alshareef A, Wu T, Knutsen AK, Hiscox LV, Johnson CL, Panzer MB. Calibration of a Heterogeneous Brain Model Using a Subject-Specific Inverse Finite Element Approach. Front Bioeng Biotechnol 2021; 9:664268. [PMID: 34017826 PMCID: PMC8129184 DOI: 10.3389/fbioe.2021.664268] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 04/12/2021] [Indexed: 12/02/2022] Open
Abstract
Central to the investigation of the biomechanics of traumatic brain injury (TBI) and the assessment of injury risk from head impact are finite element (FE) models of the human brain. However, many existing FE human brain models have been developed with simplified representations of the parenchyma, which may limit their applicability as an injury prediction tool. Recent advances in neuroimaging techniques and brain biomechanics provide new and necessary experimental data that can improve the biofidelity of FE brain models. In this study, the CAB-20MSym template model was developed, calibrated, and extensively verified. To implement material heterogeneity, a magnetic resonance elastography (MRE) template image was leveraged to define the relative stiffness gradient of the brain model. A multi-stage inverse FE (iFE) approach was used to calibrate the material parameters that defined the underlying non-linear deviatoric response by minimizing the error between model-predicted brain displacements and experimental displacement data. This process involved calibrating the infinitesimal shear modulus of the material using low-severity, low-deformation impact cases and the material non-linearity using high-severity, high-deformation cases from a dataset of in situ brain displacements obtained from cadaveric specimens. To minimize the geometric discrepancy between the FE models used in the iFE calibration and the cadaveric specimens from which the experimental data were obtained, subject-specific models of these cadaveric brain specimens were developed and used in the calibration process. Finally, the calibrated material parameters were extensively verified using independent brain displacement data from 33 rotational head impacts, spanning multiple loading directions (sagittal, coronal, axial), magnitudes (20–40 rad/s), durations (30–60 ms), and severity. Overall, the heterogeneous CAB-20MSym template model demonstrated good biofidelity with a mean overall CORA score of 0.63 ± 0.06 when compared to in situ brain displacement data. Strains predicted by the calibrated model under non-injurious rotational impacts in human volunteers (N = 6) also demonstrated similar biofidelity compared to in vivo measurements obtained from tagged magnetic resonance imaging studies. In addition to serving as an anatomically accurate model for further investigations of TBI biomechanics, the MRE-based framework for implementing material heterogeneity could serve as a foundation for incorporating subject-specific material properties in future models.
Collapse
Affiliation(s)
- J Sebastian Giudice
- Center for Applied Biomechanics, University of Virginia, Charlottesville, VA, United States
| | - Ahmed Alshareef
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Taotao Wu
- Center for Applied Biomechanics, University of Virginia, Charlottesville, VA, United States
| | - Andrew K Knutsen
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, United States
| | - Lucy V Hiscox
- Department of Biomedical Engineering, University of Delaware, Newark, DE, United States
| | - Curtis L Johnson
- Department of Biomedical Engineering, University of Delaware, Newark, DE, United States
| | - Matthew B Panzer
- Center for Applied Biomechanics, University of Virginia, Charlottesville, VA, United States
| |
Collapse
|
47
|
Abstract
Head injury models are notoriously time consuming and resource demanding in simulations, which prevents routine application. Here, we extend a convolutional neural network (CNN) to instantly estimate element-wise distribution of peak maximum principal strain (MPS) of the entire brain (>36 k speedup accomplished on a low-end computing platform). To achieve this, head impact rotational velocity and acceleration temporal profiles are combined into two-dimensional images to serve as CNN input for training and prediction of MPS. Compared with the directly simulated counterparts, the CNN-estimated responses (magnitude and distribution) are sufficiently accurate for 92.1% of the cases via 10-fold cross-validation using impacts drawn from the real world (n = 5661; range of peak rotational velocity in augmented data extended to 2-40 rad/sec). The success rate further improves to 97.1% for "in-range" impacts (n = 4298). When using the same CNN architecture to train (n = 3064) and test on an independent, reconstructed National Football League (NFL) impact dataset (n = 53; 20 concussions and 33 non-injuries), 51 out of 53, or 96.2% of the cases, are sufficiently accurate. The estimated responses also achieve virtually identical concussion prediction performances relative to the directly simulated counterparts, and they often outperform peak MPS of the whole brain (e.g., accuracy of 0.83 vs. 0.77 via leave-one-out cross-validation). These findings support the use of CNN for accurate and efficient estimation of spatially detailed brain strains across the vast majority of head impacts in contact sports. Our technique may hold the potential to transform traumatic brain injury (TBI) research and the design and testing standards of head protective gears by facilitating the transition from acceleration-based approximation to strain-based design and analysis. This would have broad implications in the TBI biomechanics field to accelerate new scientific discoveries. The pre-trained CNN is freely available online at https://github.com/Jilab-biomechanics/CNN-brain-strains.
Collapse
Affiliation(s)
- Kianoosh Ghazi
- Department of Biomedical Engineering and Worcester Polytechnic Institute, Worcester, Massachustts, USA
| | - Shaoju Wu
- Department of Biomedical Engineering and Worcester Polytechnic Institute, Worcester, Massachustts, USA
| | - Wei Zhao
- Department of Biomedical Engineering and Worcester Polytechnic Institute, Worcester, Massachustts, USA
| | - Songbai Ji
- Department of Biomedical Engineering and Worcester Polytechnic Institute, Worcester, Massachustts, USA
- Department of Mechanical Engineering, Worcester Polytechnic Institute, Worcester, Massachustts, USA
| |
Collapse
|
48
|
Li X, Zhou Z, Kleiven S. An anatomically detailed and personalizable head injury model: Significance of brain and white matter tract morphological variability on strain. Biomech Model Mechanobiol 2021. [PMID: 33037509 DOI: 10.1101/2020.05.20.105635] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Finite element head (FE) models are important numerical tools to study head injuries and develop protection systems. The generation of anatomically accurate and subject-specific head models with conforming hexahedral meshes remains a significant challenge. The focus of this study is to present two developmental works: first, an anatomically detailed FE head model with conforming hexahedral meshes that has smooth interfaces between the brain and the cerebrospinal fluid, embedded with white matter (WM) fiber tracts; second, a morphing approach for subject-specific head model generation via a new hierarchical image registration pipeline integrating Demons and Dramms deformable registration algorithms. The performance of the head model is evaluated by comparing model predictions with experimental data of brain-skull relative motion, brain strain, and intracranial pressure. To demonstrate the applicability of the head model and the pipeline, six subject-specific head models of largely varying intracranial volume and shape are generated, incorporated with subject-specific WM fiber tracts. DICE similarity coefficients for cranial, brain mask, local brain regions, and lateral ventricles are calculated to evaluate personalization accuracy, demonstrating the efficiency of the pipeline in generating detailed subject-specific head models achieving satisfactory element quality without further mesh repairing. The six head models are then subjected to the same concussive loading to study the sensitivity of brain strain to inter-subject variability of the brain and WM fiber morphology. The simulation results show significant differences in maximum principal strain and axonal strain in local brain regions (one-way ANOVA test, p < 0.001), as well as their locations also vary among the subjects, demonstrating the need to further investigate the significance of subject-specific models. The techniques developed in this study may contribute to better evaluation of individual brain injury and the development of individualized head protection systems in the future. This study also contains general aspects the research community may find useful: on the use of experimental brain strain close to or at injury level for head model validation; the hierarchical image registration pipeline can be used to morph other head models, such as smoothed-voxel models.
Collapse
Affiliation(s)
- Xiaogai Li
- Division of Neuronic Engineering, Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, 141 52, Huddinge, Sweden.
| | - Zhou Zhou
- Division of Neuronic Engineering, Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, 141 52, Huddinge, Sweden
| | - Svein Kleiven
- Division of Neuronic Engineering, Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, 141 52, Huddinge, Sweden
| |
Collapse
|
49
|
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.
Collapse
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
| |
Collapse
|
50
|
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.
Collapse
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;
| | | |
Collapse
|