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      Use of Brain Biomechanical Models for Monitoring Impact Exposure in Contact Sports

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          Abstract

          Head acceleration measurement sensors are now widely deployed in the field to monitor head kinematic exposure in contact sports. The wealth of impact kinematics data provides valuable, yet challenging, opportunities to study the biomechanical basis of mild traumatic brain injury (mTBI) and subconcussive kinematic exposure. Head impact kinematics are translated into brain mechanical responses through physics-based computational simulations using validated brain models to study the mechanisms of injury. First, this article reviews representative legacy and contemporary brain biomechanical models primarily used for blunt impact simulation. Then, it summarizes perspectives regarding the development and validation of these models, and discusses how simulation results can be interpreted to facilitate injury risk assessment and head acceleration exposure monitoring in the context of contact sports. Recommendations and consensus statements are presented on the use of validated brain models in conjunction with kinematic sensor data to understand the biomechanics of mTBI and subconcussion. Mainly, there is general consensus that validated brain models have strong potential to improve injury prediction and interpretation of subconcussive kinematic exposure over global head kinematics alone. Nevertheless, a major roadblock to this capability is the lack of sufficient data encompassing different sports, sex, age and other factors. The authors recommend further integration of sensor data and simulations with modern data science techniques to generate large datasets of exposures and predicted brain responses along with associated clinical findings. These efforts are anticipated to help better understand the biomechanical basis of mTBI and improve the effectiveness in monitoring kinematic exposure in contact sports for risk and injury mitigation purposes.

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          Most cited references169

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          A proposed injury threshold for mild traumatic brain injury.

          Traumatic brain injuries constitute a significant portion of injury resulting from automotive collisions, motorcycle crashes, and sports collisions. Brain injuries not only represent a serious trauma for those involved but also place an enormous burden on society, often exacting a heavy economical, social, and emotional price. Development of intervention strategies to prevent or minimize these injuries requires a complete understanding of injury mechanisms, response and tolerance level. In this study, an attempt is made to delineate actual injury causation and establish a meaningful injury criterion through the use of the actual field accident data. Twenty-four head-to-head field collisions that occurred in professional football games were duplicated using a validated finite element human head model. The injury predictors and injury levels were analyzed based on resulting brain tissue responses and were correlated with the site and occurrence of mild traumatic brain injury (MTBI). Predictions indicated that the shear stress around the brainstem region could be an injury predictor for concussion. Statistical analyses were performed to establish the new brain injury tolerance level.
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            Complex brain networks: graph theoretical analysis of structural and functional systems

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              Rotational Head Kinematics in Football Impacts: An Injury Risk Function for Concussion

              Recent research has suggested a possible link between sports-related concussions and neurodegenerative processes, highlighting the importance of developing methods to accurately quantify head impact tolerance. The use of kinematic parameters of the head to predict brain injury has been suggested because they are indicative of the inertial response of the brain. The objective of this study is to characterize the rotational kinematics of the head associated with concussive impacts using a large head acceleration dataset collected from human subjects. The helmets of 335 football players were instrumented with accelerometer arrays that measured head acceleration following head impacts sustained during play, resulting in data for 300,977 sub-concussive and 57 concussive head impacts. The average sub-concussive impact had a rotational acceleration of 1230 rad/s 2 and a rotational velocity of 5.5 rad/s, while the average concussive impact had a rotational acceleration of 5022 rad/s 2 and a rotational velocity of 22.3 rad/s. An injury risk curve was developed and a nominal injury value of 6383 rad/s 2 associated with 28.3 rad/s represents 50% risk of concussion. These data provide an increased understanding of the biomechanics associated with concussion and they provide critical insight into injury mechanisms, human tolerance to mechanical stimuli, and injury prevention techniques.
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                Author and article information

                Contributors
                sji@wpi.edu
                jstitzel@wakehealth.edu
                Journal
                Ann Biomed Eng
                Ann Biomed Eng
                Annals of Biomedical Engineering
                Springer International Publishing (Cham )
                0090-6964
                1573-9686
                22 July 2022
                22 July 2022
                2022
                : 50
                : 11
                : 1389-1408
                Affiliations
                [1 ]GRID grid.268323.e, ISNI 0000 0001 1957 0327, Department of Biomedical Engineering, , Worcester Polytechnic Institute, ; Worcester, MA USA
                [2 ]GRID grid.7445.2, ISNI 0000 0001 2113 8111, Dyson School of Design Engineering, , Imperial College London, ; London, UK
                [3 ]GRID grid.39381.30, ISNI 0000 0004 1936 8884, Department of Mechanical and Materials Engineering, Faculty of Engineering, , Western University, ; London, ON N6A 5B9 Canada
                [4 ]GRID grid.29857.31, ISNI 0000 0001 2097 4281, Department of Mechanical and Nuclear Engineering, Department of Biomedical Engineering, , The Pennsylvania State University, ; University Park, PA USA
                [5 ]GRID grid.215352.2, ISNI 0000000121845633, Department of Biomedical Engineering, , The University of Texas at San Antonio, ; San Antonio, TX USA
                [6 ]GRID grid.27755.32, ISNI 0000 0000 9136 933X, Department of Mechanical and Aerospace Engineering, , University of Virginia, ; Charlottesville, VA USA
                [7 ]GRID grid.11843.3f, ISNI 0000 0001 2157 9291, University of Strasbourg, IMFS-CNRS, ; 2 rue Boussingault, 67000 Strasbourg, France
                [8 ]GRID grid.7886.1, ISNI 0000 0001 0768 2743, School of Mechanical & Materials Engineering, , University College Dublin, ; Belfield, Dublin 4, Ireland
                [9 ]GRID grid.5037.1, ISNI 0000000121581746, Division of Neuronic Engineering, , KTH Royal Institute of Technology, ; Hälsovägen 11C, 141 57 Huddinge, Sweden
                [10 ]GRID grid.241167.7, ISNI 0000 0001 2185 3318, Department of Biomedical Engineering, , Wake Forest School of Medicine, ; Winston-Salem, NC USA
                Author notes

                Associate Editor Stefan M. Duma oversaw the review of this article.

                Author information
                http://orcid.org/0000-0002-2886-5781
                Article
                2999
                10.1007/s10439-022-02999-w
                9652195
                35867314
                da3bd6c5-6d4a-49ed-9e76-5b274246ced3
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 6 May 2022
                : 22 June 2022
                Categories
                Concussions
                Custom metadata
                © The Author(s) under exclusive licence to Biomedical Engineering Society 2022

                Biomedical engineering
                brain biomechanics,concussion,subconcussion,impact kinematics,instrumentation,finite element model

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