1
|
Zhan X, Liu Y, Cecchi NJ, Gevaert O, Zeineh MM, Grant GA, Camarillo DB. Brain Deformation Estimation With Transfer Learning for Head Impact Datasets Across Impact Types. IEEE Trans Biomed Eng 2024; 71:1853-1863. [PMID: 38224520 DOI: 10.1109/tbme.2024.3354192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2024]
Abstract
OBJECTIVE The machine-learning head model (MLHM) to accelerate the calculation of brain strain and strain rate, which are the predictors for traumatic brain injury (TBI), but the model accuracy was found to decrease sharply when the training/test datasets were from different head impacts types (i.e., car crash, college football), which limits the applicability of MLHMs to different types of head impacts and sports. Particularly, small sizes of target dataset for specific impact types with tens of impacts may not be enough to train an accurate impact-type-specific MLHM. METHODS To overcome this, we propose data fusion and transfer learning to develop a series of MLHMs to predict the maximum principal strain (MPS) and maximum principal strain rate (MPSR). RESULTS The strategies were tested on American football (338), mixed martial arts (457), reconstructed car crash (48) and reconstructed American football (36) and we found that the MLHMs developed with transfer learning are significantly more accurate in estimating MPS and MPSR than other models, with a mean absolute error (MAE) smaller than 0.03 in predicting MPS and smaller than [Formula: see text] in predicting MPSR on all target impact datasets. High performance in concussion detection was observed based on the MPS and MPSR estimated by the transfer-learning-based models. CONCLUSION The MLHMs can be applied to various head impact types for rapidly and accurately calculating brain strain and strain rate. SIGNIFICANCE This study enables developing MLHMs for the head impact type with limited availability of data, and will accelerate the applications of MLHMs.
Collapse
|
2
|
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 2024:10.1007/s10439-024-03525-w. [PMID: 38739210 DOI: 10.1007/s10439-024-03525-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [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
|
3
|
Upadhyay K, Jagani R, Giovanis DG, Alshareef A, Knutsen AK, Johnson CL, Carass A, Bayly PV, Shields MD, Ramesh KT. Effect of Human Head Shape on the Risk of Traumatic Brain Injury: A Gaussian Process Regression-based Machine Learning Approach. Mil Med 2024:usae199. [PMID: 38739497 DOI: 10.1093/milmed/usae199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 03/06/2024] [Accepted: 04/02/2024] [Indexed: 05/16/2024] Open
Abstract
INTRODUCTION Computational head injury models are promising tools for understanding and predicting traumatic brain injuries. However, most available head injury models are "average" models that employ a single set of head geometry (e.g., 50th-percentile U.S. male) without considering variability in these parameters across the human population. A significant variability of head shapes exists in U.S. Army soldiers, evident from the Anthropometric Survey of U.S. Army Personnel (ANSUR II). The objective of this study is to elucidate the effects of head shape on the predicted risk of traumatic brain injury from computational head injury models. MATERIALS AND METHODS Magnetic resonance imaging scans of 25 human subjects are collected. These images are registered to the standard MNI152 brain atlas, and the resulting transformation matrix components (called head shape parameters) are used to quantify head shapes of the subjects. A generative machine learning model is used to generate 25 additional head shape parameter datasets to augment our database. Head injury models are developed for these head shapes, and a rapid injurious head rotation event is simulated to obtain several brain injury predictor variables (BIPVs): Peak cumulative maximum principal strain (CMPS), average CMPS, and the volume fraction of brain exceeding an injurious CMPS threshold. A Gaussian process regression model is trained between head shape parameters and BIPVs, which is then used to study the relative sensitivity of the various BIPVs on individual head shape parameters. We distinguish head shape parameters into 2 types: Scaling components ${T_{xx}}$, ${T_{yy}}$, and ${T_{zz}}$ that capture the breadth, length, and height of the head, respectively, and shearing components (${T_{xy}},{T_{xz}},{T_{yx}},{T_{yz}},{T_{zx}}$, and ${T_{zy}}$) that capture the relative skewness of the head shape. RESULTS An overall positive correlation is evident between scaling components and BIPVs. Notably, a very high, positive correlation is seen between the BIPVs and the head volume. As an example, a 57% increase in peak CMPS was noted between the smallest and the largest investigated head volume parameters. The variation in shearing components ${T_{xy}},{T_{xz}},{T_{yx}},{T_{yz}},{T_{zx}}$, and ${T_{zy}}$ on average does not cause notable changes in the BIPVs. From the Gaussian process regression model, all 3 BIPVs showed an increasing trend with each of the 3 scaling components, but the BIPVs are found to be most sensitive to the height dimension of the head. From the Sobol sensitivity analysis, the ${T_{zz}}$ scaling parameter contributes nearly 60% to the total variance in peak and average CMPS; ${T_{yy}}$ contributes approximately 20%, whereas ${T_{xx}}$ contributes less than 5%. The remaining contribution is from the 6 shearing components. Unlike peak and average CMPS, the VF-CMPS BIPV is associated with relatively evenly distributed Sobol indices across the 3 scaling parameters. Furthermore, the contribution of shearing components on the total variance in this case is negligible. CONCLUSIONS Head shape has a considerable influence on the injury predictions of computational head injury models. Available "average" head injury models based on a 50th-percentile U.S. male are likely associated with considerable uncertainty. In general, larger head sizes correspond to greater BIPV magnitudes, which point to potentially a greater injury risk under rapid neck rotation for people with larger heads.
Collapse
Affiliation(s)
- Kshitiz Upadhyay
- Department of Mechanical and Industrial Engineering, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Roshan Jagani
- 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 Mechanical Engineering, University of South Carolina, Columbia, SC 29208, USA
| | - Andrew K Knutsen
- Center for Neuroscience and Regenerative Medicine, Henry M. Jackson Foundation, Bethesda, MD 20817, USA
| | - Curtis L Johnson
- Department of Biomedical Engineering, University of Delaware, Newark, DE 19713, 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
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| |
Collapse
|
4
|
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
|
5
|
Campana S, Cecchetti L, Venturi M, Buemi F, Foti C, Cerasa A, Vicario CM, Carboncini MC, Tomaiuolo F. Evolution of Severe Closed Head Injury: Assessing Ventricular Volume and Behavioral Measures at 30 and 90 Days Post-Injury. J Clin Med 2024; 13:874. [PMID: 38337568 PMCID: PMC10856794 DOI: 10.3390/jcm13030874] [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: 12/18/2023] [Revised: 01/22/2024] [Accepted: 01/30/2024] [Indexed: 02/12/2024] Open
Abstract
Background: Assessing functional outcomes in Severe Closed Head Injury (SCHI) is complex due to brain parenchymal changes. This study examines the Ventricles to Intracranial Volume Ratio (VBR) as a metric for these changes and its correlation with behavioral scales. Methods: Thirty-one SCHI patients were included. VBR was derived from CT scans at 3, 30, and 90 days post-injury and compared with Levels of Cognitive Functioning (LCF), Disability Rating Scale (DRS), and Early Rehabilitation Barthel Index (ERBI) assessments at 30 and 90 days. Results: Ten patients were excluded post-decompressive craniectomy or ventriculoperitoneal shunt. Findings indicated a VBR decrease at 3 days, suggesting acute phase compression, followed by an increase from 30 to 90 days, indicative of post-acute brain atrophy. VBR correlated positively with the Marshall score in the initial 72 h, positioning it as an early indicator of subsequent brain atrophy. Nevertheless, in contrast to the Marshall score, VBR had stronger associations with DRS and ERBI at 90 days. Conclusions: VBR, alongside behavioral assessments, presents a robust framework for evaluating SCHI progression. It supports early functional outcome correlations informing therapeutic approaches. VBR's reliability underscores its utility in neurorehabilitation for ongoing SCHI assessment and aiding clinical decisions.
Collapse
Affiliation(s)
- Serena Campana
- Neurorehabilitation Unit, Auxilium Vitae Volterra, Via Borgo San Lazzero 5, 56048 Volterra, Italy;
| | - Luca Cecchetti
- Social and Affective Neuroscience (SANe) Group, MoMiLab, IMT School for Advanced Studies Lucca, 55100 Lucca, Italy
| | - Martina Venturi
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy;
| | - Francesco Buemi
- Department of Diagnostic and Interventional Radiology, Azienda Ospedaliera Papardo, 98158 Messina, Italy;
| | - Cristina Foti
- Department of Clinical and Experimental Medicine, University of Messina, 98122 Messina, Italy;
| | - Antonio Cerasa
- Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy, 98164 Messina, Italy;
- S. Anna Institute, 88900 Crotone, Italy
- Pharmacotechnology Documentation and Transfer Unit, Preclinical and Translational Pharmacology, Department of Pharmacy, Health Science and Nutrition, University of Calabria, 87036 Rende, Italy
| | - Carmelo Mario Vicario
- Department of Cognitive Sciences, Psychology, Education and Cultural Studies, University of Messina, 98125 Messina, Italy;
| | - Maria Chiara Carboncini
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy;
| | - Francesco Tomaiuolo
- Department of Clinical and Experimental Medicine, University of Messina, 98122 Messina, Italy;
| |
Collapse
|
6
|
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
|
7
|
Lin N, Wu S, Ji S. A Morphologically Individualized Deep Learning Brain Injury Model. J Neurotrauma 2023; 40:2233-2247. [PMID: 37212255 DOI: 10.1089/neu.2022.0413] [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: 05/23/2023] Open
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
|
8
|
Zhan X, Li Y, Liu Y, Cecchi NJ, Raymond SJ, Zhou Z, Vahid Alizadeh H, Ruan J, Barbat S, Tiernan S, Gevaert O, Zeineh MM, Grant GA, Camarillo DB. Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematics. JOURNAL OF SPORT AND HEALTH SCIENCE 2023; 12:619-629. [PMID: 36921692 PMCID: PMC10466194 DOI: 10.1016/j.jshs.2023.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 12/06/2022] [Accepted: 02/16/2023] [Indexed: 05/26/2023]
Abstract
BACKGROUND Traumatic brain injury can be caused by head impacts, but many brain injury risk estimation models are not equally accurate across the variety of impacts that patients may undergo, and the characteristics of different types of impacts are not well studied. We investigated the spectral characteristics of different head impact types with kinematics classification. METHODS Data were analyzed from 3262 head impacts from lab reconstruction, American football, mixed martial arts, and publicly available car crash data. A random forest classifier with spectral densities of linear acceleration and angular velocity was built to classify head impact types (e.g., football, car crash, mixed martial arts). To test the classifier robustness, another 271 lab-reconstructed impacts were obtained from 5 other instrumented mouthguards. Finally, with the classifier, type-specific, nearest-neighbor regression models were built for brain strain. RESULTS The classifier reached a median accuracy of 96% over 1000 random partitions of training and test sets. The most important features in the classification included both low- and high-frequency features, both linear acceleration features and angular velocity features. Different head impact types had different distributions of spectral densities in low- and high-frequency ranges (e.g., the spectral densities of mixed martial arts impacts were higher in the high-frequency range than in the low-frequency range). The type-specific regression showed a generally higher R2 value than baseline models without classification. CONCLUSION The machine-learning-based classifier enables a better understanding of the impact kinematics spectral density in different sports, and it can be applied to evaluate the quality of impact-simulation systems and on-field data augmentation.
Collapse
Affiliation(s)
- Xianghao Zhan
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Yiheng Li
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Yuzhe Liu
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
| | - Nicholas J Cecchi
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Samuel J Raymond
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Zhou Zhou
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | | | - Jesse Ruan
- Ford Motor Company, 3001 Miller Rd, Dearborn, MI 48120, USA
| | - Saeed Barbat
- Ford Motor Company, 3001 Miller Rd, Dearborn, MI 48120, USA
| | | | - Olivier Gevaert
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Michael M Zeineh
- Department of Radiology, Stanford University, Stanford, CA 94305, USA
| | - Gerald A Grant
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA
| | - David B Camarillo
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| |
Collapse
|
9
|
Amendolara A, Pfister D, Settelmayer M, Shah M, Wu V, Donnelly S, Johnston B, Peterson R, Sant D, Kriak J, Bills K. An Overview of Machine Learning Applications in Sports Injury Prediction. Cureus 2023; 15:e46170. [PMID: 37905265 PMCID: PMC10613321 DOI: 10.7759/cureus.46170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/28/2023] [Indexed: 11/02/2023] Open
Abstract
Use injuries, i.e., injuries caused by repetitive strain on the body, represent a serious problem in athletics that has traditionally relied on historic datasets and human experience for prevention. Existing methodologies have been frustratingly slow at developing higher precision prevention practices. Technological advancements have permitted the emergence of artificial intelligence and machine learning (ML) as promising toolsets to enhance both injury mitigation and rehabilitation protocols. This article provides a comprehensive overview of recent advances in ML techniques as they have been applied to sports injury prediction and prevention. A comprehensive literature review was conducted searching PubMed/Medline, Institute of Electrical and Electronics Engineers (IEEE)/Institute of Engineering and Technology (IET), and ScienceDirect. Ovid Discovery and Google Scholar were used to provide additional aggregate results and a grey literature search. A focus was placed on papers published from 2017 to 2022. Algorithms of interest were limited to K-Nearest Neighbor (KNN), K-means, decision tree, random forest, gradient boosting and AdaBoost, and neural networks. A total of 42 original research papers were included, and their results were summarized. We conclude that given the current lack of open source, uniform data sets, as well as a reliance on dated regression models, no strong conclusions about the real-world efficacy of ML as it applies to sports injury prediction can be made. However, it is suggested that addressing these two issues will allow powerful, novel ML architectures to be deployed, thus rapidly advancing the state of this field, and providing validated clinical tools.
Collapse
Affiliation(s)
- Alfred Amendolara
- Federated Department of Biology, New Jersey Institute of Technology, Newark, USA
- Department of Biomedical Sciences, Noorda College of Osteopathic Medicine, Provo, USA
| | - Devin Pfister
- Department of Biomedical Sciences, Noorda College of Osteopathic Medicine, Provo, USA
| | - Marina Settelmayer
- Department of Biomedical Sciences, Noorda College of Osteopathic Medicine, Provo, USA
| | - Mujtaba Shah
- Department of Biomedical Sciences, Noorda College of Osteopathic Medicine, Provo, USA
| | - Veronica Wu
- Department of Biomedical Sciences, Noorda College of Osteopathic Medicine, Provo, USA
| | - Sean Donnelly
- Department of Biomedical Sciences, Noorda College of Osteopathic Medicine, Provo, USA
| | - Brooke Johnston
- Department of Biomedical Sciences, Noorda College of Osteopathic Medicine, Provo, USA
| | - Race Peterson
- Department of Biomedical Sciences, Noorda College of Osteopathic Medicine, Provo, USA
| | - David Sant
- Department of Biomedical Sciences, Noorda College of Osteopathic Medicine, Provo, USA
| | - John Kriak
- Department of Biomedical Sciences, Noorda College of Osteopathic Medicine, Provo, USA
| | - Kyle Bills
- Department of Biomedical Sciences, Noorda College of Osteopathic Medicine, Provo, USA
| |
Collapse
|
10
|
Lin N, Wu S, Wu Z, Ji S. Efficient Generation of Pretraining Samples for Developing a Deep Learning Brain Injury Model via Transfer Learning. Ann Biomed Eng 2023:10.1007/s10439-023-03354-3. [PMID: 37642795 DOI: 10.1007/s10439-023-03354-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 08/18/2023] [Indexed: 08/31/2023]
Abstract
The large amount of training samples required to develop a deep learning brain injury model demands enormous computational resources. Here, we study how a transformer neural network (TNN) of high accuracy can be used to efficiently generate pretraining samples for a convolutional neural network (CNN) brain injury model to reduce computational cost. The samples use synthetic impacts emulating real-world events or augmented impacts generated from limited measured impacts. First, we verify that the TNN remains highly accurate for the two impact types (N = 100 each; [Formula: see text] of 0.948-0.967 with root mean squared error, RMSE, ~ 0.01, for voxelized peak strains). The TNN-estimated samples (1000-5000 for each data type) are then used to pretrain a CNN, which is further finetuned using directly simulated training samples (250-5000). An independent measured impact dataset considered of complete capture of impact event is used to assess estimation accuracy (N = 191). We find that pretraining can significantly improve CNN accuracy via transfer learning compared to a baseline CNN without pretraining. It is most effective when the finetuning dataset is relatively small (e.g., 2000-4000 pretraining synthetic or augmented samples improves success rate from 0.72 to 0.81 with 500 finetuning samples). When finetuning samples reach 3000 or more, no obvious improvement occurs from pretraining. These results support using the TNN to rapidly generate pretraining samples to facilitate a more efficient training strategy for future deep learning brain models, by limiting the number of costly direct simulations from an alternative baseline model. This study could contribute to a wider adoption of deep learning brain injury models for large-scale predictive modeling and ultimately, enhancing safety protocols and protective equipment.
Collapse
Affiliation(s)
- Nan Lin
- Department of Biomedical Engineering, Worcester Polytechnic Institute, 60 Prescott Street, Worcester, MA, 01605, USA
| | - Shaoju Wu
- Department of Biomedical Engineering, Worcester Polytechnic Institute, 60 Prescott Street, Worcester, MA, 01605, USA
| | - Zheyang Wu
- Mathematical Sciences, Worcester Polytechnic Institute, Worcester, MA, 01609, USA
| | - Songbai Ji
- Department of Biomedical Engineering, Worcester Polytechnic Institute, 60 Prescott Street, Worcester, MA, 01605, USA.
- Department of Mechanical Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA, 01609, USA.
| |
Collapse
|
11
|
Zimmerman KA, Cournoyer J, Lai H, Snider SB, Fischer D, Kemp S, Karton C, Hoshizaki TB, Ghajari M, Sharp DJ. The biomechanical signature of loss of consciousness: computational modelling of elite athlete head injuries. Brain 2023; 146:3063-3078. [PMID: 36546554 PMCID: PMC10316777 DOI: 10.1093/brain/awac485] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 11/17/2022] [Accepted: 12/02/2022] [Indexed: 08/27/2023] Open
Abstract
Sports related head injuries can cause transient neurological events including loss of consciousness and dystonic posturing. However, it is unknown why head impacts that appear similar produce distinct neurological effects. The biomechanical effect of impacts can be estimated using computational models of strain within the brain. Here, we investigate the strain and strain rates produced by professional American football impacts that led to loss of consciousness, posturing or no neurological signs. We reviewed 1280 National Football League American football games and selected cases where the team's medical personnel made a diagnosis of concussion. Videos were then analysed for signs of neurological events. We identified 20 head impacts that showed clear video signs of loss of consciousness and 21 showing clear abnormal posturing. Forty-one control impacts were selected where there was no observable evidence of neurological signs, resulting in 82 videos of impacts for analysis. Video analysis was used to guide physical reconstructions of these impacts, allowing us to estimate the impact kinematics. These were then used as input to a detailed 3D high-fidelity finite element model of brain injury biomechanics to estimate strain and strain rate within the brain. We tested the hypotheses that impacts producing loss of consciousness would be associated with the highest biomechanical forces, that loss of consciousness would be associated with high forces in brainstem nuclei involved in arousal and that dystonic posturing would be associated with high forces in motor regions. Impacts leading to loss of consciousness compared to controls produced higher head acceleration (linear acceleration; 81.5 g ± 39.8 versus 47.9 ± 21.4; P = 0.004, rotational acceleration; 5.9 krad/s2 ± 2.4 versus 3.5 ± 1.6; P < 0.001) and in voxel-wise analysis produced larger brain deformation in many brain regions, including parts of the brainstem and cerebellum. Dystonic posturing was also associated with higher deformation compared to controls, with brain deformation observed in cortical regions that included the motor cortex. Loss of consciousness was specifically associated with higher strain rates in brainstem regions implicated in maintenance of consciousness, including following correction for the overall severity of impact. These included brainstem nuclei including the locus coeruleus, dorsal raphé and parabrachial complex. The results show that in head impacts producing loss of consciousness, brain deformation is disproportionately seen in brainstem regions containing nuclei involved in arousal, suggesting that head impacts produce loss of consciousness through a biomechanical effect on key brainstem nuclei involved in the maintenance of consciousness.
Collapse
Affiliation(s)
- Karl A Zimmerman
- UK Dementia Research Institute, Care Research & Technology Centre, Imperial College London, London, UK
- Department of Brain Sciences, Hammersmith Hospital, Imperial College London, London, UK
- HEAD Lab, Dyson School of Design Engineering, Imperial College London, London, UK
| | - Janie Cournoyer
- Neurotrauma Impact Science Laboratory, University of Ottawa, Ottawa, ON, Canada
| | - Helen Lai
- UK Dementia Research Institute, Care Research & Technology Centre, Imperial College London, London, UK
- Department of Brain Sciences, Hammersmith Hospital, Imperial College London, London, UK
| | - Samuel B Snider
- Division of Neurocritical care, Department of Neurology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - David Fischer
- Division of Neurocritical Care, Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Simon Kemp
- Rugby Football Union, Twickenham, UK
- London School of Hygiene and Tropical Medicine, London, UK
| | - Clara Karton
- Neurotrauma Impact Science Laboratory, University of Ottawa, Ottawa, ON, Canada
| | - Thomas B Hoshizaki
- Neurotrauma Impact Science Laboratory, University of Ottawa, Ottawa, ON, Canada
| | - Mazdak Ghajari
- HEAD Lab, Dyson School of Design Engineering, Imperial College London, London, UK
| | - David J Sharp
- UK Dementia Research Institute, Care Research & Technology Centre, Imperial College London, London, UK
- Department of Brain Sciences, Hammersmith Hospital, Imperial College London, London, UK
- The Royal British Legion Centre for Blast Injury Studies and the Department of Bioengineering, Imperial College London, London, UK
| |
Collapse
|
12
|
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
|
13
|
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
|
14
|
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: 15] [Impact Index Per Article: 7.5] [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
|
15
|
Zhan X, Liu Y, Cecchi NJ, Gevaert O, Zeineh MM, Grant GA, Camarillo DB. Finding the Spatial Co-Variation of Brain Deformation With Principal Component Analysis. IEEE Trans Biomed Eng 2022; 69:3205-3215. [PMID: 35349430 PMCID: PMC9580615 DOI: 10.1109/tbme.2022.3163230] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE Strain and strain rate are effective traumatic brain injury metrics. In finite element (FE) head model, thousands of elements were used to represent the spatial distribution of these metrics. Owing that these metrics are resulted from brain inertia, their spatial distribution can be represented in more concise pattern. Since head kinematic features and brain deformation vary largely across head impact types (Zhan et al., 2021), we applied principal component analysis (PCA) to find the spatial co-variation of injury metrics (maximum principal strain (MPS), MPS rate (MPSR) and MPS × MPSR) in four impact types: simulation, football, mixed martial arts and car crashes, and used the PCA to find patterns in these metrics and improve the machine learning head model (MLHM). METHODS We applied PCA to decompose the injury metrics for all impacts in each impact type, and investigate the spatial co-variation using the first principal component (PC1). Furthermore, we developed a MLHM to predict PC1 and then inverse-transform to predict for all brain elements. The accuracy, the model complexity and the size of training dataset of PCA-MLHM are compared with previous MLHM (Zhan et al., 2021). RESULTS PC1 explained variance on the datasets. Based on PC1 coefficients, the corpus callosum and midbrain exhibit high variance on all datasets. Finally, the PCA-MLHM reduced model parameters by 74% with a similar MPS estimation accuracy. CONCLUSION The brain injury metric in a dataset can be decomposed into mean components and PC1 with high explained variance. SIGNIFICANCE The spatial co-variation analysis enables better interpretation of the patterns in brain injury metrics. It also improves the efficiency of MLHM.
Collapse
|
16
|
Wang F, Huang J, Hu L, Hu S, Wang M, Yin J, Zou T, Li Q. Numerical investigation of the rider's head injury in typical single-electric self-balancing scooter accident scenarios. J R Soc Interface 2022; 19:20220495. [PMID: 36128701 PMCID: PMC9490341 DOI: 10.1098/rsif.2022.0495] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 08/24/2022] [Indexed: 11/12/2022] Open
Abstract
As the use of electric self-balancing scooters (ESSs) increases, so does the number of related traffic accidents. Because of the special control method, mechanical structure and driving posture, ESSs are prone to various single-vehicle accidents, such as collisions with fixed obstacles and falls due to mechanical failures. In various ESS accident scenarios, the rider's head injury is the most frequent injury type. In this study, several typical single-ESS accident scenarios are reconstructed via computational methods, and the risk of riders' head/brain injury is assessed in depth using various injury criteria. Results showed that two types of ESSs (solo- and two-wheeler) do not have clear differences in head kinematics and head injury risks; the head kinematics (or falling posture) and ESS accident scenario exhibit a distinct effect on the head injury responses; half of the analysed ESS riders have a 50% probability of skull fracture, a few riders have a 50% risk of abbreviated injury scale (AIS) 4+ brain injury, and none has a diffuse axonal injury; the ESS speed plays an important role in producing the head/brain injury in ESS riders, and generally, higher ESS speed generates higher level of predicted head injury parameters. These findings will provide theoretical support for preventing head injury among ESS riders and data support for developing and legislating ESSs.
Collapse
Affiliation(s)
- Fang Wang
- School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha, Hunan People's Republic of China
| | - Jiaxian Huang
- School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen, Fujian, People's Republic of China
| | - Lin Hu
- School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha, Hunan People's Republic of China
| | - Shenghui Hu
- School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha, Hunan People's Republic of China
| | - Mingliang Wang
- School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha, Hunan People's Republic of China
| | - Jiajie Yin
- School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha, Hunan People's Republic of China
| | - Tiefang Zou
- School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha, Hunan People's Republic of China
| | - Qiqi Li
- School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha, Hunan People's Republic of China
| |
Collapse
|
17
|
Stitt D, Kabaliuk N, Alexander K, Draper N. Drop Test Kinematics Using Varied Impact Surfaces and Head/Neck Configurations for Rugby Headgear Testing. Ann Biomed Eng 2022; 50:1633-1647. [PMID: 36002780 DOI: 10.1007/s10439-022-03045-5] [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: 06/16/2022] [Accepted: 08/03/2022] [Indexed: 11/28/2022]
Abstract
World Rugby employs a specific drop test method to evaluate headgear performance, but almost all researchers use a different variation of this method. The aim of this study was, therefore, to quantify the differences between variations of the drop testing method using a Hybrid III headform and neck in the following impact setups: (1) headform only, with a flat steel impact surface, approximating the World Rugby method, (2 and 3) headform with and without a neck, respectively, onto a flat MEP pad impact surface, and (4) headform and neck, dropped onto an angled MEP pad impact surface. Each variation was subject to drop heights of 75-600 mm across three orientations (forehead, side, and rear boss). Comparisons were limited to the linear and rotational acceleration and rotational velocity for simplicity. Substantial differences in kinematic profile shape manifested between all drop test variations. Peak accelerations varied highly between variations, but the peak rotational velocities did not. Drop test variation also significantly changed the ratios of the peak kinematics to each other. This information can be compared to kinematic data from field head impacts and could inform more realistic impact testing methods for assessing headgear.
Collapse
Affiliation(s)
- Danyon Stitt
- Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch, 8140, New Zealand.,Sport Health and Rehabilitation Research Centre (SHARRC), University of Canterbury, Christchurch, New Zealand
| | - Natalia Kabaliuk
- Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch, 8140, New Zealand. .,Sport Health and Rehabilitation Research Centre (SHARRC), University of Canterbury, Christchurch, New Zealand.
| | - Keith Alexander
- Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch, 8140, New Zealand.,Sport Health and Rehabilitation Research Centre (SHARRC), University of Canterbury, Christchurch, New Zealand
| | - Nick Draper
- Faculty of Health, University of Canterbury, Christchurch, New Zealand.,Sport Health and Rehabilitation Research Centre (SHARRC), University of Canterbury, Christchurch, New Zealand
| |
Collapse
|
18
|
Zhan X, Li Y, Liu Y, Cecchi NJ, Gevaert O, Zeineh MM, Grant GA, Camarillo DB. Piecewise Multivariate Linearity Between Kinematic Features and Cumulative Strain Damage Measure (CSDM) Across Different Types of Head Impacts. Ann Biomed Eng 2022; 50:1596-1607. [PMID: 35922726 DOI: 10.1007/s10439-022-03020-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 07/12/2022] [Indexed: 11/28/2022]
Abstract
In a previous study, we found that the relationship between brain strain and kinematic features cannot be described by a generalized linear model across different types of head impacts. In this study, we investigate if such a linear relationship exists when partitioning head impacts using a data-driven approach. We applied the K-means clustering method to partition 3161 impacts from various sources including simulation, college football, mixed martial arts, and car crashes. We found piecewise multivariate linearity between the cumulative strain damage (CSDM; assessed at the threshold of 0.15) and head kinematic features. Compared with the linear regression models without partition and the partition according to the types of head impacts, K-means-based data-driven partition showed significantly higher CSDM regression accuracy, which suggested the presence of piecewise multivariate linearity across types of head impacts. Additionally, we compared the piecewise linearity with the partitions based on individual features used in clustering. We found that the partition with maximum angular acceleration magnitude at 4706 rad/s2 led to the highest piecewise linearity. This study may contribute to an improved method for the rapid prediction of CSDM in the future.
Collapse
Affiliation(s)
- Xianghao Zhan
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA
| | - Yiheng Li
- Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA
| | - Yuzhe Liu
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA.
| | - Nicholas J Cecchi
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA
| | - Olivier Gevaert
- Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA.,Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, 94305, USA
| | - Michael M Zeineh
- Department of Radiology, Stanford University, Stanford, CA, 94305, USA
| | - Gerald A Grant
- Department of Neurosurgery, Stanford University, Stanford, CA, 94305, USA
| | - David B Camarillo
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA
| |
Collapse
|
19
|
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
|
20
|
Wu S, Zhao W, Ji S. Real-time dynamic simulation for highly accurate spatiotemporal brain deformation from impact. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2022; 394:114913. [PMID: 35572209 PMCID: PMC9097909 DOI: 10.1016/j.cma.2022.114913] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Real-time dynamic simulation remains a significant challenge for spatiotemporal data of high dimension and resolution. In this study, we establish a transformer neural network (TNN) originally developed for natural language processing and a separate convolutional neural network (CNN) to estimate five-dimensional (5D) spatiotemporal brain-skull relative displacement resulting from impact (isotropic spatial resolution of 4 mm with temporal resolution of 1 ms). Sequential training is applied to train (N = 5184 samples) the two neural networks for estimating the complete 5D displacement across a temporal duration of 60 ms. We find that TNN slightly but consistently outperforms CNN in accuracy for both displacement and the resulting voxel-wise four-dimensional (4D) maximum principal strain (e.g., root mean squared error (RMSE) of ~1.0% vs. ~1.6%, with coefficient of determination, R 2 >0.99 vs. >0.98, respectively, and normalized RMSE (NRMSE) at peak displacement of 2%-3%, based on an independent testing dataset; N = 314). Their accuracies are similar for a range of real-world impacts drawn from various published sources (dummy, helmet, football, soccer, and car crash; average RMSE/NRMSE of ~0.3 mm/~4%-5% and average R 2 of ~0.98 at peak displacement). Sequential training is effective for allowing instantaneous estimation of 5D displacement with high accuracy, although TNN poses a heavier computational burden in training. This work enables efficient characterization of the intrinsically dynamic brain strain in impact critical for downstream multiscale axonal injury model simulation. This is also the first application of TNN in biomechanics, which offers important insight into how real-time dynamic simulations can be achieved across diverse engineering fields.
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
- Correspondence to: Department of Biomedical Engineering, Worcester Polytechnic Institute, 60 Prescott Street, Worcester, MA 01506, USA., (S. Ji)
| |
Collapse
|
21
|
Deep learning methodology for predicting time history of head angular kinematics from simulated crash videos. Sci Rep 2022; 12:6526. [PMID: 35444174 PMCID: PMC9021239 DOI: 10.1038/s41598-022-10480-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 03/30/2022] [Indexed: 11/12/2022] Open
Abstract
Head kinematics information is important as it is used to measure brain injury risk. Currently, head kinematics are measured using wearable devices or instrumentation mounted on the head. This paper evaluates the deep learning approach in predicting time history of head angular kinematics directly from videos without any instrumentation. To prove the concept, a deep learning model was developed for predicting time history of head angular velocities using finite element (FE) based crash simulation videos. This FE dataset was split into training, validation, and test datasets. A combined convolutional neural network and recurrent neural network based deep learning model was developed using the training and validations sets. The test (unseen) dataset was used to evaluate the predictive capability of the deep learning model. On the test dataset, correlation coefficient obtained between the actual and predicted peak angular velocities was 0.73, 0.85, and 0.92 for X, Y, and Z components respectively.
Collapse
|
22
|
Mojahed A, Abderezaei J, Ozkaya E, Bergman L, Vakakis A, Kurt M. Predictive Helmet Optimization Framework Based on Reduced-Order Modeling of the Brain Dynamics. Ann Biomed Eng 2022; 50:1661-1673. [DOI: 10.1007/s10439-022-02908-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 01/01/2022] [Indexed: 11/25/2022]
|
23
|
|
24
|
Tierney G. Concussion biomechanics, head acceleration exposure and brain injury criteria in sport: a review. Sports Biomech 2021:1-29. [PMID: 34939531 DOI: 10.1080/14763141.2021.2016929] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 12/05/2021] [Indexed: 10/19/2022]
Abstract
There are mounting concerns surrounding the risk of neurodegenerative diseases and complications associated with concussion incidence and repetitive head acceleration events (HAE) in sport. The aim of this review is to provide an overview of concussion biomechanics, head acceleration exposure and brain injury criteria in sport. Rotational head motion appears to be the primary contributor to brain injury risk due to the unique mechanical properties of the brain and its location within the body. There is a growing evidence base of different biomechanical brain injury mechanisms, including those involving repetitive HAE. Historically, many studies on concussion biomechanics, head acceleration exposure and brain injury criteria in sport have been limited by validity of the biomechanical approaches undertaken. Biomechanical approaches such as instrumented mouthguards and subject-specific finite element (FE) brain models provide a unique opportunity to develop greater brain injury criteria and aid in on-field athlete removal. Implementing these approaches on a large-scale can gain insight into potential risk factors within sports and certain athletes/cohorts who sustain a greater number and/or severity of HAE throughout their playing career. These findings could play a key role in the development of concussion prevention strategies and techniques that mitigate the severity of HAE in sport.
Collapse
Affiliation(s)
- Gregory Tierney
- Sport and Exercise Sciences Research Institute, School of Sport, Faculty of Life and Health Sciences, Ulster University, Belfast, UK
| |
Collapse
|
25
|
Wu T, Sato F, Antona-Makoshi J, Gabler L, Giudice JS, Alshareef A, Yaguchi M, Masuda M, Margulies S, Panzer MB. Integrating Human and Non-Human Primate Data to Estimate Human Tolerances for Traumatic Brain Injury. J Biomech Eng 2021; 144:1129238. [PMID: 34897386 DOI: 10.1115/1.4053209] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Indexed: 11/08/2022]
Abstract
Traumatic brain injury (TBI) contributes to a significant portion of the injuries resulting from motor vehicle crashes, falls, and sports collisions. The development of advanced countermeasures to mitigate these injuries requires a complete understanding of the tolerance of the human brain to injury. In this study, we developed a new method to establish human injury tolerance levels using an integrated database of reconstructed football impacts, sub-injurious human volunteer data, and non-human primate data. The human tolerance levels were analyzed using tissue-level metrics determined using harmonized species-specific finite element brain models. Kinematics-based metrics involving complete characterization of angular motion (e.g., DAMAGE) showed better power of predicting tissue-level deformation in a variety of impact conditions and were subsequently used to characterize injury tolerance. The proposed human brain tolerances for mild and severe TBI were estimated and presented in the form of injury risk curves based on selected tissue-level and kinematics-based injury metrics. The application of the estimated injury tolerances was finally demonstrated using real-world automotive crash data.
Collapse
Affiliation(s)
- Taotao Wu
- Center for Applied Biomechanics, University of Virginia, Charlottesville, VA, USA
| | - Fusako Sato
- Safety Research Division, Japan Automobile Research Institute, Tsukuba, Japan
| | | | - Lee Gabler
- Center for Applied Biomechanics, University of Virginia, Charlottesville, VA, USA
| | - J Sebastian Giudice
- Center for Applied Biomechanics, University of Virginia, Charlottesville, VA, USA
| | - Ahmed Alshareef
- Center for Applied Biomechanics, University of Virginia, Charlottesville, VA, USA
| | - Masayuki Yaguchi
- Safety Research Division, Japan Automobile Research Institute, Tsukuba, Japan
| | - Mitsutoshi Masuda
- Safety Subcommittee, Japan Automobile Manufacturers Association, Inc., Tokyo, Japan
| | - Susan Margulies
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Matthew B Panzer
- Center for Applied Biomechanics, University of Virginia, Charlottesville, VA, USA
| |
Collapse
|
26
|
Blinkouskaya Y, Caçoilo A, Gollamudi T, Jalalian S, Weickenmeier J. Brain aging mechanisms with mechanical manifestations. Mech Ageing Dev 2021; 200:111575. [PMID: 34600936 PMCID: PMC8627478 DOI: 10.1016/j.mad.2021.111575] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 09/09/2021] [Accepted: 09/22/2021] [Indexed: 12/14/2022]
Abstract
Brain aging is a complex process that affects everything from the subcellular to the organ level, begins early in life, and accelerates with age. Morphologically, brain aging is primarily characterized by brain volume loss, cortical thinning, white matter degradation, loss of gyrification, and ventricular enlargement. Pathophysiologically, brain aging is associated with neuron cell shrinking, dendritic degeneration, demyelination, small vessel disease, metabolic slowing, microglial activation, and the formation of white matter lesions. In recent years, the mechanics community has demonstrated increasing interest in modeling the brain's (bio)mechanical behavior and uses constitutive modeling to predict shape changes of anatomically accurate finite element brain models in health and disease. Here, we pursue two objectives. First, we review existing imaging-based data on white and gray matter atrophy rates and organ-level aging patterns. This data is required to calibrate and validate constitutive brain models. Second, we review the most critical cell- and tissue-level aging mechanisms that drive white and gray matter changes. We focuse on aging mechanisms that ultimately manifest as organ-level shape changes based on the idea that the integration of imaging and mechanical modeling may help identify the tipping point when normal aging ends and pathological neurodegeneration begins.
Collapse
Affiliation(s)
- Yana Blinkouskaya
- Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States
| | - Andreia Caçoilo
- Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States
| | - Trisha Gollamudi
- Department of Biomedical Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States
| | - Shima Jalalian
- Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States
| | - Johannes Weickenmeier
- Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States.
| |
Collapse
|
27
|
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
|
28
|
Menichetti A, Bartsoen L, Depreitere B, Vander Sloten J, Famaey N. A Machine Learning Approach to Investigate the Uncertainty of Tissue-Level Injury Metrics for Cerebral Contusion. Front Bioeng Biotechnol 2021; 9:714128. [PMID: 34692652 PMCID: PMC8531645 DOI: 10.3389/fbioe.2021.714128] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 08/10/2021] [Indexed: 11/13/2022] Open
Abstract
Controlled cortical impact (CCI) on porcine brain is often utilized to investigate the pathophysiology and functional outcome of focal traumatic brain injury (TBI), such as cerebral contusion (CC). Using a finite element (FE) model of the porcine brain, the localized brain strain and strain rate resulting from CCI can be computed and compared to the experimentally assessed cortical lesion. This way, tissue-level injury metrics and corresponding thresholds specific for CC can be established. However, the variability and uncertainty associated with the CCI experimental parameters contribute to the uncertainty of the provoked cortical lesion and, in turn, of the predicted injury metrics. Uncertainty quantification via probabilistic methods (Monte Carlo simulation, MCS) requires a large number of FE simulations, which results in a time-consuming process. Following the recent success of machine learning (ML) in TBI biomechanical modeling, we developed an artificial neural network as surrogate of the FE porcine brain model to predict the brain strain and the strain rate in a computationally efficient way. We assessed the effect of several experimental and modeling parameters on four FE-derived CC injury metrics (maximum principal strain, maximum principal strain rate, product of maximum principal strain and strain rate, and maximum shear strain). Next, we compared the in silico brain mechanical response with cortical damage data from in vivo CCI experiments on pig brains to evaluate the predictive performance of the CC injury metrics. Our ML surrogate was capable of rapidly predicting the outcome of the FE porcine brain undergoing CCI. The now computationally efficient MCS showed that depth and velocity of indentation were the most influential parameters for the strain and the strain rate-based injury metrics, respectively. The sensitivity analysis and comparison with the cortical damage experimental data indicate a better performance of maximum principal strain and maximum shear strain as tissue-level injury metrics for CC. These results provide guidelines to optimize the design of CCI tests and bring new insights to the understanding of the mechanical response of brain tissue to focal traumatic brain injury. Our findings also highlight the potential of using ML for computationally efficient TBI biomechanics investigations.
Collapse
Affiliation(s)
- Andrea Menichetti
- Biomechanics Section, Department of Mechanical Engineering, KU Leuven, Leuven, Belgium
| | - Laura Bartsoen
- Biomechanics Section, Department of Mechanical Engineering, KU Leuven, Leuven, Belgium
| | | | - Jos Vander Sloten
- Biomechanics Section, Department of Mechanical Engineering, KU Leuven, Leuven, Belgium
| | - Nele Famaey
- Biomechanics Section, Department of Mechanical Engineering, KU Leuven, Leuven, Belgium
| |
Collapse
|
29
|
Zhou Z, Li X, Liu Y, Fahlstedt M, Georgiadis M, Zhan X, Raymond SJ, Grant G, Kleiven S, Camarillo D, Zeineh M. Toward a Comprehensive Delineation of White Matter Tract-Related Deformation. J Neurotrauma 2021; 38:3260-3278. [PMID: 34617451 DOI: 10.1089/neu.2021.0195] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Finite element (FE) models of the human head are valuable instruments to explore the mechanobiological pathway from external loading, localized brain response, and resultant injury risks. The injury predictability of these models depends on the use of effective criteria as injury predictors. The FE-derived normal deformation along white matter (WM) fiber tracts (i.e., tract-oriented strain) recently has been suggested as an appropriate predictor for axonal injury. However, the tract-oriented strain only represents a partial depiction of the WM fiber tract deformation. A comprehensive delineation of tract-related deformation may improve the injury predictability of the FE head model by delivering new tract-related criteria as injury predictors. Thus, the present study performed a theoretical strain analysis to comprehensively characterize the WM fiber tract deformation by relating the strain tensor of the WM element to its embedded fiber tract. Three new tract-related strains with exact analytical solutions were proposed, measuring the normal deformation perpendicular to the fiber tracts (i.e., tract-perpendicular strain), and shear deformation along and perpendicular to the fiber tracts (i.e., axial-shear strain and lateral-shear strain, respectively). The injury predictability of these three newly proposed strain peaks along with the previously used tract-oriented strain peak and maximum principal strain (MPS) were evaluated by simulating 151 impacts with known outcome (concussion or non-concussion). The results preliminarily showed that four tract-related strain peaks exhibited superior performance than MPS in discriminating concussion and non-concussion cases. This study presents a comprehensive quantification of WM tract-related deformation and advocates the use of orientation-dependent strains as criteria for injury prediction, which may ultimately contribute to an advanced mechanobiological understanding and enhanced computational predictability of brain injury.
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
|
30
|
Predictive Factors of Kinematics in Traumatic Brain Injury from Head Impacts Based on Statistical Interpretation. Ann Biomed Eng 2021; 49:2901-2913. [PMID: 34244908 DOI: 10.1007/s10439-021-02813-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Accepted: 06/10/2021] [Indexed: 02/08/2023]
Abstract
Brain tissue deformation resulting from head impacts is primarily caused by rotation and can lead to traumatic brain injury. To quantify brain injury risk based on measurements of kinematics on the head, finite element (FE) models and various brain injury criteria based on different factors of these kinematics have been developed, but the contribution of different kinematic factors has not been comprehensively analyzed across different types of head impacts in a data-driven manner. To better design brain injury criteria, the predictive power of rotational kinematics factors, which are different in (1) the derivative order (angular velocity, angular acceleration, angular jerk), (2) the direction and (3) the power (e.g., square-rooted, squared, cubic) of the angular velocity, were analyzed based on different datasets including laboratory impacts, American football, mixed martial arts (MMA), NHTSA automobile crashworthiness tests and NASCAR crash events. Ordinary least squares regressions were built from kinematics factors to the 95% maximum principal strain (MPS95), and we compared zero-order correlation coefficients, structure coefficients, commonality analysis, and dominance analysis. The angular acceleration, the magnitude and the first power factors showed the highest predictive power for the majority of impacts including laboratory impacts, American football impacts, with few exceptions (angular velocity for MMA and NASCAR impacts). The predictive power of rotational kinematics about three directions (x: posterior-to-anterior, y: left-to-right, z: superior-to-inferior) of kinematics varied with different sports and types of head impacts.
Collapse
|
31
|
Identifying Factors Associated with Head Impact Kinematics and Brain Strain in High School American Football via Instrumented Mouthguards. Ann Biomed Eng 2021; 49:2814-2826. [PMID: 34549342 PMCID: PMC8906650 DOI: 10.1007/s10439-021-02853-5] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 08/13/2021] [Indexed: 01/04/2023]
Abstract
Repeated head impact exposure and concussions are common in American football. Identifying the factors associated with high magnitude impacts aids in informing sport policy changes, improvements to protective equipment, and better understanding of the brain's response to mechanical loading. Recently, the Stanford Instrumented Mouthguard (MiG2.0) has seen several improvements in its accuracy in measuring head kinematics and its ability to correctly differentiate between true head impact events and false positives. Using this device, the present study sought to identify factors (e.g., player position, helmet model, direction of head acceleration, etc.) that are associated with head impact kinematics and brain strain in high school American football athletes. 116 athletes were monitored over a total of 888 athlete exposures. 602 total impacts were captured and verified by the MiG2.0's validated impact detection algorithm. Peak values of linear acceleration, angular velocity, and angular acceleration were obtained from the mouthguard kinematics. The kinematics were also entered into a previously developed finite element model of the human brain to compute the 95th percentile maximum principal strain. Overall, impacts were (mean ± SD) 34.0 ± 24.3 g for peak linear acceleration, 22.2 ± 15.4 rad/s for peak angular velocity, 2979.4 ± 3030.4 rad/s2 for peak angular acceleration, and 0.262 ± 0.241 for 95th percentile maximum principal strain. Statistical analyses revealed that impacts resulting in Forward head accelerations had higher magnitudes of peak kinematics and brain strain than Lateral or Rearward impacts and that athletes in skill positions sustained impacts of greater magnitude than athletes in line positions. 95th percentile maximum principal strain was significantly lower in the observed cohort of high school football athletes than previous reports of collegiate football athletes. No differences in impact magnitude were observed in athletes with or without previous concussion history, in athletes wearing different helmet models, or in junior varsity or varsity athletes. This study presents novel information on head acceleration events and their resulting brain strain in high school American football from our advanced, validated method of measuring head kinematics via instrumented mouthguard technology.
Collapse
|
32
|
Ghazi K, Wu S, Zhao W, Ji S. Effective Head Impact Kinematics to Preserve Brain Strain. Ann Biomed Eng 2021; 49:2777-2790. [PMID: 34341899 DOI: 10.1007/s10439-021-02840-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 07/16/2021] [Indexed: 11/29/2022]
Abstract
Conventional kinematics-based brain injury metrics often approximate peak maximum principal strain (MPS) of the whole brain but ignore the anatomical location of occurrence. In this study, we develop effective impact kinematics consisting of peak rotational velocity and the associated rotational axis to preserve not only peak MPS but also spatially detailed MPS. A pre-computed brain response atlas (pcBRA) serves as a common reference. A training dataset (N = 3069) is used to develop a convolutional neural network (CNN) to automate impact simplification. When preserving peak MPS alone, the CNN-estimated effective peak rotational velocity achieves a coefficient of determination ([Formula: see text]) of ~ 0.96 relative to the directly identified counterpart, far outperforming nominal peak velocity from the resultant profiles ([Formula: see text] of ~ 0.34). Impacts from a subset of data (N = 1900) are also successfully matched with pcBRA idealized impacts based on elementwise MPS, where their regression slope and Pearson correlation coefficient do not deviate from 1.0 (when identical) by more than 0.1. The CNN-estimated effective peak rotation velocity and rotational axis are sufficiently accurate for ~ 73.5% of the impacts. This is not possible for the nominal peak velocity or any other conventional injury metric. The performance may be further improved by expanding the pcBRA to include deceleration and focusing on region-wise strains. This study establishes a new avenue to reduce an arbitrary head impact into an idealized but actual "impact mode" characterized by triplets of basic kinematic variables. They retain specific physical interpretations of head impact and may be an advancement over state-of-the-art kinematics-based scalar metrics for more effective impact comparison in the future.
Collapse
Affiliation(s)
- Kianoosh Ghazi
- Department of Biomedical Engineering, Worcester Polytechnic Institute, 60 Prescott Street, Worcester, MA, 01605, USA
| | - Shaoju Wu
- Department of Biomedical Engineering, Worcester Polytechnic Institute, 60 Prescott Street, Worcester, MA, 01605, USA
| | - Wei Zhao
- Department of Biomedical Engineering, Worcester Polytechnic Institute, 60 Prescott Street, Worcester, MA, 01605, USA
| | - Songbai Ji
- Department of Biomedical Engineering, Worcester Polytechnic Institute, 60 Prescott Street, Worcester, MA, 01605, USA. .,Department of Mechanical Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA, 01609, USA.
| |
Collapse
|
33
|
Zhan X, Li Y, Liu Y, Domel AG, Alizadeh HV, Raymond SJ, Ruan J, Barbat S, Tiernan S, Gevaert O, Zeineh MM, Grant GA, Camarillo DB. The relationship between brain injury criteria and brain strain across different types of head impacts can be different. J R Soc Interface 2021; 18:20210260. [PMID: 34062102 PMCID: PMC8169213 DOI: 10.1098/rsif.2021.0260] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 05/06/2021] [Indexed: 12/27/2022] Open
Abstract
Multiple brain injury criteria (BIC) are developed to quickly quantify brain injury risks after head impacts. These BIC originated from different head impact types (e.g. sports and car crashes) are widely used in risk evaluation. However, the accuracy of using the BIC on brain injury risk estimation across head impact types has not been evaluated. Physiologically, brain strain is often considered the key parameter of brain injury. To evaluate the BIC's risk estimation accuracy across five datasets comprising different head impact types, linear regression was used to model 95% maximum principal strain, 95% maximum principal strain at the corpus callosum and cumulative strain damage (15%) on 18 BIC. The results show significantly different relationships between BIC and brain strain across datasets, indicating the same BIC value may suggest different brain strain across head impact types. The accuracy of brain strain regression is generally decreasing if the BIC regression models are fitted on a dataset with a different type of head impact rather than on the dataset with the same type. Given this finding, this study raises concerns for applying BIC to estimate the brain injury risks for head impacts different from the head impacts on which the BIC was developed.
Collapse
Affiliation(s)
- Xianghao Zhan
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Yiheng Li
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Yuzhe Liu
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - August G. Domel
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | | | - Samuel J. Raymond
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Jesse Ruan
- Ford Motor Company, 3001 Miller Road, Dearborn, MI 48120, USA
| | - Saeed Barbat
- Ford Motor Company, 3001 Miller Road, Dearborn, MI 48120, USA
| | | | - Olivier Gevaert
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Michael M. Zeineh
- Department of Radiology, Stanford University, Stanford, CA 94305, USA
| | - Gerald A. Grant
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA
| | - David B. Camarillo
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| |
Collapse
|
34
|
Zhan X, Liu Y, Raymond S, Vahid Alizadeh H, Domel A, Gevaert O, Zeineh M, Grant G, Camarillo D. Rapid Estimation of Entire Brain Strain Using Deep Learning Models. IEEE Trans Biomed Eng 2021; 68:3424-3434. [PMID: 33852381 DOI: 10.1109/tbme.2021.3073380] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Many recent studies have suggested that brain deformation resulting from a head impact is linked to the corresponding clinical outcome, such as mild traumatic brain injury (mTBI). Even though several finite element (FE) head models have been developed and validated to calculate brain deformation based on impact kinematics, the clinical application of these FE head models is limited due to the time-consuming nature of FE simulations. This work aims to accelerate the process of brain deformation calculation and thus improve the potential for clinical applications. METHODS We propose a deep learning head model with a five-layer deep neural network and feature engineering, and trained and tested the model on 2511 total head impacts from a combination of head model simulations and on-field college football and mixed martial arts impacts. RESULTS The proposed deep learning head model can calculate the maximum principal strain (Green Lagrange) for every element in the entire brain in less than 0.001s with an average root mean squared error of 0.022, and with a standard deviation of 0.001 over twenty repeats with random data partition and model initialization. CONCLUSION Trained and tested using the dataset of 2511 head impacts, this model can be applied to various sports in the calculation of brain strain with accuracy, and its applicability can even further be extended by incorporating data from other types of head impacts. SIGNIFICANCE In addition to the potential clinical application in real-time brain deformation monitoring, this model will help researchers estimate the brain strain from a large number of head impacts more efficiently than using FE models.
Collapse
|
35
|
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
|