Investigation of Cross-Species Scaling Methods for Traumatic Brain Injury Using Finite Element Analysis

Wu, Taotao; Antona‐Makoshi, Jacobo; Alshareef, Ahmed; Giudice, J. Sebastian; Panzer, Matthew B.

doi:10.1089/neu.2019.6576

Cited by 38 publications

(30 citation statements)

References 33 publications

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“…Kleiven and von Holst (2002) by globally scaling a 3D adult FE head model to six heads of varying dimensions showed brain response increased almost monotonically from the smallest to the largest head under a linear acceleration. Similarly, as revealed in this current study, a larger ICV (relating to larger brain mass) tends to have a larger strain under the same impact, which is also suggested by Holbourn's scaling principle (Ommaya et al 1967), and more recent work by Wu et al (2020) and Panzer et al (2014). Of interest would be to investigate whether brain strains predicted from FE models follow or can be predicted by the acceleration-mass scaling law, which indeed has been studied by Prange et al (1999).…”

Section: Head Size Influence On Brain Strain Responsesupporting

confidence: 87%

“…For example, by using principal component analysis (PCA) (Wold et al 1987) with more brain images, which may also allow identifying the characteristics of brain shape and WM morphologies that are most vulnerable to impact. Percentile strain (e.g., 95th percentile MPS) have been used in previous studies both for adults (Miller et al 2019;Panzer et al 2012;Wu et al 2020;Wu et al 2020;Beckwith et al 2018) and children head models (Li and Kleiven 2018) to avoid potential numerical issues (e.g., strain concentration). This study uses MPS (i.e., 100th percentile MPS) to compare between subjects.…”

Section: Head Size Influence On Brain Strain Responsementioning

confidence: 99%

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An anatomically detailed and personalizable head injury model: Significance of brain and white matter tract morphological variability on strain

Zhou

Kleiven

2020

Biomech Model Mechanobiol

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Finite element head (FE) models are important numerical tools to study head injuries and develop protection systems. The generation of anatomically accurate and subject-specific head models with conforming hexahedral meshes remains a significant challenge. The focus of this study is to present two developmental works: first, an anatomically detailed FE head model with conforming hexahedral meshes that has smooth interfaces between the brain and the cerebrospinal fluid, embedded with white matter (WM) fiber tracts; second, a morphing approach for subject-specific head model generation via a new hierarchical image registration pipeline integrating Demons and Dramms deformable registration algorithms. The performance of the head model is evaluated by comparing model predictions with experimental data of brain–skull relative motion, brain strain, and intracranial pressure. To demonstrate the applicability of the head model and the pipeline, six subject-specific head models of largely varying intracranial volume and shape are generated, incorporated with subject-specific WM fiber tracts. DICE similarity coefficients for cranial, brain mask, local brain regions, and lateral ventricles are calculated to evaluate personalization accuracy, demonstrating the efficiency of the pipeline in generating detailed subject-specific head models achieving satisfactory element quality without further mesh repairing. The six head models are then subjected to the same concussive loading to study the sensitivity of brain strain to inter-subject variability of the brain and WM fiber morphology. The simulation results show significant differences in maximum principal strain and axonal strain in local brain regions (one-way ANOVA test, p < 0.001), as well as their locations also vary among the subjects, demonstrating the need to further investigate the significance of subject-specific models. The techniques developed in this study may contribute to better evaluation of individual brain injury and the development of individualized head protection systems in the future. This study also contains general aspects the research community may find useful: on the use of experimental brain strain close to or at injury level for head model validation; the hierarchical image registration pipeline can be used to morph other head models, such as smoothed-voxel models.

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Section: Head Size Influence On Brain Strain Responsesupporting

confidence: 87%

Section: Head Size Influence On Brain Strain Responsementioning

confidence: 99%

An anatomically detailed and personalizable head injury model: Significance of brain and white matter tract morphological variability on strain

Zhou

Kleiven

2020

Biomech Model Mechanobiol

View full text Add to dashboard Cite

show abstract

“…Kleiven and von Holst (2002) by globally scaling a 3D adult FE head model to six heads of varying dimensions, showed brain response increased almost monotonically from the smallest to the largest head under a linear acceleration. Similarly, as revealed in this current study, a larger ICV (relating to larger brain mass) tends to have a larger strain under the same impact, which is also suggested by Holbourn's scaling principle (Ommaya et al 1967), and more recent work by Wu et al (2020) and Panzer et al (2014). Of interest would be to investigate whether brain strains predicted from FE models follow or can be predicted by the acceleration-mass scaling law, which indeed has been studied by Prange et al (1999).…”

Section: Head Size Influence On Brain Strain Responsesupporting

confidence: 88%

“…Percentile strain (e.g., 95 th percentile MPS) have been used in previous studies both for adults (Miller et al 2019;Panzer et al 2012;Wu et al 2020;Wu et al 2020;Beckwith et al 2018) and child head models (Li and Kleiven 2018) to avoid potential numerical issues (e.g., strain concentration). This study uses MPS (i.e., 100 th percentile MPS) to compare between subjects.…”

Section: Head Size Influence On Brain Strain Responsementioning

confidence: 99%

An anatomically accurate and personalizable head injury model: Significance of brain and white matter tract morphological variability on strain

Zhou

Kleiven

2020

Preprint

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Finite element head (FE) models are important numerical tools to study head injuries and develop protection systems. The generation of anatomically accurate and subject-specific head models with conforming hexahedral meshes remains a significant challenge. The focus of this study is to present two developmental work: First, an anatomically detailed FE head model with conforming hexahedral meshes that has smooth interfaces between the brain and the cerebrospinal fluid, embedded with white matter (WM) fiber tracts; Second, a morphing approach for subject-specific head model generation via a new hierarchical image registration pipeline integrating Demons and Dramms deformable registration algorithms. The performance of the head model is evaluated by comparing model predictions with experimental data of brain-skull relative motion, brain strain, and intracranial pressure. To demonstrate the applicability of the head model and the pipeline, six subject-specific head models of largely varying intracranial volume and shape are generated, incorporated with subject-specific WM fiber tracts. DICE similarity coefficients for cranial, brain mask, local brain regions, and lateral ventricles are calculated to evaluate personalization accuracy, demonstrating the efficiency of the pipeline in generating detailed subject-specific head models achieving satisfactory element quality without further mesh repairing. The six head models are then subjected to the same concussive loading to study sensitivity of brain strain to inter-subject variability of the brain and WM fiber morphology. The simulation results show significant differences in maximum principal strain (MPS) and axonal strain (MAS) in local brain regions (one-way ANOVA test, p<0.001), as well as their locations also vary among the subjects, demonstrating the need to further investigate the significance of subject-specific models. The techniques developed in this study may contribute to better evaluation of individual brain injury and development of individualized head protection systems in the future. This study also contains general aspects the research community may find useful: on the use of experimental brain strain close to or at injury level for head model validation; the hierarchical image registration pipeline can be used to morph other head models, such as smoothed-voxel models.

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“…From these, the 95th percentile value was recorded, resulting in a single metric of brain deformation for each of the 129 parcellated brain regions in a given impact case. The global 95th percentile MPS (MPS95) was also calculated, considering all elements in the brain, as is commonly done in FE brain injury analysis to avoid potential numerical instabilities (Panzer et al, 2012;Beckwith et al, 2018;Gabler et al, 2019;Miller et al, 2019;Sanchez et al, 2019;Wu et al, 2019a). Betzel et al (2016) performed diffusion spectrum imaging (DSI) for a total of 30 subjects along with T1-weighted anatomical scans.…”

Section: Finite Element Modelingmentioning

confidence: 99%

Predicting Concussion Outcome by Integrating Finite Element Modeling and Network Analysis

Anderson

Giudice

et al. 2020

Front. Bioeng. Biotechnol.

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Concussion is a significant public health problem affecting 1.6-2.4 million Americans annually. An alternative to reducing the burden of concussion is to reduce its incidence with improved protective equipment and injury mitigation systems. Finite element (FE) models of the brain response to blunt trauma are often used to estimate injury potential and can lead to improved helmet designs. However, these models have yet to incorporate how the patterns of brain connectivity disruption after impact affects the relay of information in the injured brain. Furthermore, FE brain models typically do not consider the differences in individual brain structural connectivities and their purported role in concussion risk. Here, we use graph theory techniques to integrate brain deformations predicted from FE modeling with measurements of network efficiency to identify brain regions whose connectivity characteristics may influence concussion risk. We computed maximum principal strain in 129 brain regions using head kinematics measured from 53 professional football impact reconstructions that included concussive and non-concussive cases. In parallel, using diffusion spectrum imaging data from 30 healthy subjects, we simulated structural lesioning of each of the same 129 brain regions. We simulated lesioning by removing each region one at a time along with all its connections. In turn, we computed the resultant change in global efficiency to identify regions important for network communication. We found that brain regions that deformed the most during an impact did not overlap with regions most important for network communication (Pearson's correlation, ρ = 0.07; p = 0.45). Despite this dissimilarity, we found that predicting concussion incidence was equally accurate when considering either areas of high strain or of high importance to global efficiency. Interestingly, accuracy for concussion prediction varied considerably across the 30 healthy connectomes. These results suggest that individual network structure is an important confounding variable in concussion prediction and that further investigation of its role may improve concussion prediction and lead to the development of more effective protective equipment.

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Investigation of Cross-Species Scaling Methods for Traumatic Brain Injury Using Finite Element Analysis

Cited by 38 publications

References 33 publications

An anatomically detailed and personalizable head injury model: Significance of brain and white matter tract morphological variability on strain

An anatomically detailed and personalizable head injury model: Significance of brain and white matter tract morphological variability on strain

An anatomically accurate and personalizable head injury model: Significance of brain and white matter tract morphological variability on strain

Predicting Concussion Outcome by Integrating Finite Element Modeling and Network Analysis

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