Predictive Factors of Kinematics in Traumatic Brain Injury from Head Impacts Based on Statistical Interpretation

Zhan, Xianghao; Li, Yi‐Heng; Liu, Yuzhe; Domel, August G.; Alizadeh, Hossein Vahid; Zhou, Zhou; Cecchi, Nicholas J.; Raymond, Samuel; Tiernan, Stephen; Ruan, Jesse; Barbat, Saeed; Gevaert, Olivier; Zeineh, Michael; Grant, Gerald A.; Camarillo, David B.

doi:10.1007/s10439-021-02813-z

Cited by 21 publications

(11 citation statements)

References 52 publications

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“…have resulted in helmet, 3,9 mouthguard, 16 ear, 29 and epidermal 38 mounted systems (see O'Connor et al 28 for review). Parallel to these advances have been the implementation of video analyses, 2,23,27 rigid body reconstructions, 24 dummy reconstructions, 13,32 computer modeling, 4,18,31 and increasingly sophisticated data analytics, 10,44 paired with head acceleration measurement devices to quantify head impact exposure in sports. The result has been increased research to better understand the head kinematics of players participating in American football, 6,11,14,20,36 football/soccer, 19,21 ice hockey, 12,25,43 rugby, 17 and other sports.…”

Section: Introductionmentioning

confidence: 99%

Consensus Head Acceleration Measurement Practices (CHAMP): Origins, Methods, Transparency and Disclosure

et al. 2022

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The use of head kinematic measurement devices has recently proliferated owing to technology advances that make such measurement more feasible. In parallel, demand to understand the biomechanics of head impacts and injury in sports and the military has increased as the burden of such loading on the brain has received focused attention. As a result, the field has matured to the point of needing methodological guidelines to improve the rigor and consistency of research and reduce the risk of scientific bias. To this end, a diverse group of scientists undertook a comprehensive effort to define current best practices in head kinematic measurement, culminating in a series of manuscripts outlining consensus methodologies and companion summary statements. Summary statements were discussed, revised, and voted upon at the Consensus Head Acceleration Measurement Practices (CHAMP) Conference in March 2022. This manuscript summarizes the motivation and methods of the consensus process and introduces recommended reporting checklists to be used to increase transparency and rigor of future experimental design and publication of work in this field. The checklists provide an accessible means for researchers to apply the best practices summarized in the companion manuscripts when reporting studies utilizing head kinematic measurement in sport and military settings.

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Section: Introductionmentioning

confidence: 99%

Consensus Head Acceleration Measurement Practices (CHAMP): Origins, Methods, Transparency and Disclosure

et al. 2022

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“…Responses of both models have shown good correlation with experiments of brain strain, brainskull relative motion, and intracranial pressure (Kleiven, 2006; Li et al, 2021; Zhou et al, 2019c; Zhou et al, 2019d; Zhou et al, 2018). Both models have been used to uncover the brain injury mechanism (Kleiven and von Holst, 2002; Li, 2021; Zhou et al, 2019a), evaluate the brain injury risk (Kleiven, 2007; Zhan et al, 2021), and inform the contrivance of advanced protective devices and treatment strategies (Fahlstedt et al, 2021; Wang et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

Fiber orientation downsampling compromises the computation of white matter tract-related deformation

Zhou

Wang

2021

Preprint

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Incorporating neuroimaging-revealed structural details into finite element (FE) head models opens vast opportunities to understand brain injury mechanisms. Recently, growing efforts have been made to integrate the fiber orientation from diffusion tensor imaging into the FE models to compute white matter (WM) tract-related deformation. Commonly used approaches often downsample the spatially enriched fiber orientation to match the resolution of FE meshes, resulting in an element-wise orientation implementation. However, the validity of downsampling and the consequences on the computed tract-related strains remain elusive. To address this problem, the current study proposed a new voxel-wise approach to integrate fiber orientation into FE models without downsampling. By setting the voxel-wise orientation responses as the reference, we then evaluated the reliability of two existing downsampling approaches on tract-related strains using two FE models with varying element sizes. The results showed that, for a model with a large mesh-image resolution dismatch, the downsampling orientation exhibited an absolute difference over 30 degree across the WM/gray matter interface and pons regions and further negatively affects the computation of tract-related strains with the normalized root-mean-square error up to 20% and peaking tract-related strains underestimated by 5%. This downsampling-induced effect was lower in FE models with finer meshes. Thus, this study yields insights on integrating neuroimaging-revealed fiber orientation into FE models and may better inform the computation of WM tract-related deformation, which are crucial for advancing the etiological understanding and computational predictability of brain injury.

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“…For these five strain-based metrics, the 95 th percentile maximum values in five commonly injured sub-regions (i.e., cerebral WM, brainstem, midbrain, corpus callosum, and thalamus) and the whole WM were obtained. In light of the common practice of extracting MPS at the entire brain level, [73][74][75][76] the predictability of MPS extract from the whole brain elements was additionally examined. Thus, the injury predictability of these 5 strain-based metrics was evaluated either at the regional level or whole WM/brain level, similar to previous studies.…”

Section: Strain-based Injury Metricsmentioning

confidence: 99%

Toward a Comprehensive Delineation of White Matter Tract-Related Deformation

Zhou

Liu

et al. 2021

Journal of Neurotrauma

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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., tractoriented strain) has recently been suggested as an appropriate predictor for axonal injury. However, the tract-oriented strain only represents a partial depiction of the WM fiber tract deformation. A comprehensive delineation of tract-related deformation may improve the injury predictability of the FE head model by delivering new tract-related criteria as injury predictors. Thus, the present study performed a theoretical strain analysis to comprehensively characterize the WM fiber tract deformation by relating the strain tensor of the WM element to its embedded fiber tract. Three new tract-related strains with exact analytical solutions were proposed, measuring the normal deformation perpendicular to the fiber tracts (i.e., tract-perpendicular strain), and shear deformation along and perpendicular to the fiber tracts (i.e., axial-shear strain and lateral-shear strain, respectively). The injury predictability of these three newly-proposed strain peaks along with the previously-used tract-oriented strain peak and maximum principal strain (MPS) were evaluated by simulating 151 impacts with known outcome (concussion or non-concussion). The results preliminarily showed that four tract-related strain peaks exhibited superior performance than MPS in discriminating concussion and non-concussion cases. This study presents a comprehensive quantification of WM tract-related deformation and advocates the use of orientation-dependent strains as criteria for injury prediction, which may ultimately contribute to an advanced mechanobiological understanding and enhanced computational predictability of brain injury.

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Predictive Factors of Kinematics in Traumatic Brain Injury from Head Impacts Based on Statistical Interpretation

Cited by 21 publications

References 52 publications

Consensus Head Acceleration Measurement Practices (CHAMP): Origins, Methods, Transparency and Disclosure

Consensus Head Acceleration Measurement Practices (CHAMP): Origins, Methods, Transparency and Disclosure

Fiber orientation downsampling compromises the computation of white matter tract-related deformation

Toward a Comprehensive Delineation of White Matter Tract-Related Deformation

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