Explicit Modeling of White Matter Axonal Fiber Tracts in a Finite Element Brain Model

Wu, Taotao; Alshareef, Ahmed; Giudice, J. Sebastian; Panzer, Matthew B.

doi:10.1007/s10439-019-02239-8

Cited by 81 publications

(83 citation statements)

References 56 publications

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Section: Finite Element Modelingmentioning

confidence: 99%

“…We utilized a three-dimensional finite element (FE) model developed and validated in a previous study (Wu et al, 2019b) to estimate the brain deformation that occurred in response to a defined head impact. Details on the model development and validation can be found in the literature (Wu et al, 2019b). In this study, we do not include axonal fiber strain as has been done by other groups (e.g., Giordano and Kleiven, 2014), and instead focused on the MPS in the gray matter.…”

Section: Finite Element Modelingmentioning

confidence: 99%

“…The prescribed six degree-of-freedom head kinematics were FIGURE 1 | Overview of methods. (A) Sanchez et al estimated the 6 degree-of-freedom kinematics of 53 NFL impact reconstructions and then used them as inputs into a three-dimensional finite element (FE) model developed by Wu et al (2019b) to estimate the regional maximum principal strain (rMPS) in each of 129 brain regions (Sanchez et al, 2019). Image taken from Sanchez et al (2019) with permission.…”

Section: Finite Element Modelingmentioning

confidence: 99%

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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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Section: Finite Element Modelingmentioning

confidence: 99%

Section: Finite Element Modelingmentioning

confidence: 99%

Section: Finite Element Modelingmentioning

confidence: 99%

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Predicting Concussion Outcome by Integrating Finite Element Modeling and Network Analysis

Anderson

Giudice

et al. 2020

Front. Bioeng. Biotechnol.

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“…Human head models with varying levels of anatomical accuracy and modeling complexity have been developed during the recent decades, e.g., WSUBIM (Ruan et al 1994;Zhang et al 2001), SUFEHM (earlier called ULP) (Kang et al 1997;Sahoo et al 2014), KTH head model (Kleiven and von Holst 2002;Kleiven 2007;Giordano and Kleiven 2014b;Zhou et al 2019a), UCDBTM (Horgan and Gilchrist 2003;Trotta et al 2020), SIMon (Takhounts et al 2003;Takhounts et al 2008), THUMS (Kimpara et al 2006;Atsumi et al 2016), GHBMC (Mao et al 2013;Wu et al 2019), WHIM (earlier called DHIM) (Ji et al 2015;Zhao and Ji 2018;Zhao and Ji 2020). Continued efforts on model enhancement, including material model improvement, incorporating diffusion tensor imaging (DTI), brain-skull interface improvement, as well as mesh refinement, have led to updated versions compared to the original.…”

Section: Introductionmentioning

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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“…7 Garimella et al and Wu et al both developed new techniques for utilizing Diffusion Tensor Imaging (DTI) data to embed axonal tract information into existing finite element brain models. 2,19 These techniques improved the biofidelity of the models, but also allowed for better correspondence between TBI simulation of axonal strain and white matter changes in clinical TBI patients. Lu et al utilized a different type of numerical technique, the material point method, to develop a model of the human brain that was validated against volunteer brain deformation data calculated from reconstructed magnetic resonance images.…”

mentioning

confidence: 99%

State-of-the-Art Modeling and Simulation of the Brain’s Response to Mechanical Loads

2019

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Explicit Modeling of White Matter Axonal Fiber Tracts in a Finite Element Brain Model

Cited by 81 publications

References 56 publications

Predicting Concussion Outcome by Integrating Finite Element Modeling and Network Analysis

Predicting Concussion Outcome by Integrating Finite Element Modeling and Network Analysis

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

State-of-the-Art Modeling and Simulation of the Brain’s Response to Mechanical Loads

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