Modeling the mechanics of axonal fiber tracts using the embedded finite element method

Garimella, Harsha T.; Kraft, Reuben H.

doi:10.1002/cnm.2823

Cited by 62 publications

(56 citation statements)

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“…Sophisticated head models continue to emerge with more anatomical details (Mao et al 2013), representing subject-specific anatomies (Ji et al 2015), and characterizing anisotropic material properties of the white matter (WM) (Sahoo et al 2014; Giordano and Kleiven 2014b). Lately, there are also efforts to integrate information from neuroimages (Fahlstedt et al 2015; Miller et al 2016), e.g., WM structural anisotropy (Wright and Ramesh 2012; Garimella and Kraft 2016), into biomechanical modeling for injury analysis. This aligns well with in vitro studies that suggest strain component along axonal longitudinal direction responsible for axonal injury (Cullen and LaPlaca 2006).…”

Section: Introductionmentioning

confidence: 99%

Injury prediction and vulnerability assessment using strain and susceptibility measures of the deep white matter

Zhao

Cai

et al. 2017

Biomech Model Mechanobiol

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Reliable prediction and diagnosis of concussion is important for its effective clinical management. Previous model- based studies largely employ peak responses from a single element in a pre-selected anatomical region of interest (ROI) and utilize a single training dataset for injury prediction. A more systematic and rigorous approach is necessary to scrutinize the entire white matter (WM) ROIs as well as ROI-constrained neural tracts. To this end, we evaluated injury prediction performances of the 50 deep WM regions using predictor variables based on strains obtained from simulating the 58 reconstructed American National Football League (NFL) head impacts. To objectively evaluate performance, repeated random subsampling was employed to split the impacts into independent training and testing datasets (39 and 19 cases, respectively, with 100 trials). Univariate logistic regressions were conducted based on training datasets to compute the area under the receiver operating characteristic curve (AUC), while accuracy, sensitivity, and specificity were reported based on testing datasets. Two tract-wise injury susceptibilities were identified as the best overall via pair-wise permutation test. They had comparable AUC, accuracy, and sensitivity, with the highest values occurring in SLF (superior longitudinal fasciculus; 0.867–0.879, 84.4–85.2%, and 84.1–84.6%, respectively). Using metrics based on WM fiber strain, the most vulnerable ROIs included genu of corpus callosum, cerebral peduncle, and uncinate fasciculus, while genu and main body of corpus callosum, and SLF were among the most vulnerable tracts. Even for one un-concussed athlete, injury susceptibility of the cingulum (hippocampus) right was elevated. These findings highlight the unique injury discriminatory potentials of computational models, and may provide important insight into how best to incorporate WM structural anisotropy for investigation of brain injury.

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Section: Introductionmentioning

confidence: 99%

Injury prediction and vulnerability assessment using strain and susceptibility measures of the deep white matter

Zhao

Cai

et al. 2017

Biomech Model Mechanobiol

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“…Unfortunately, most biomechanical head injury models incorporate generic regions of the brain but not yet targeted ROIs or the structural basis for brain function that is available from advanced neuroimaging (except, perhaps, for limited work incorporating whole-brain tractography 16,44,45 ). Existing response-based injury metrics such as ε p and CSDM do not consider the spatial distribution of impact-induced strains.…”

Section: Discussionmentioning

confidence: 99%

Performance Evaluation of a Pre-computed Brain Response Atlas in Dummy Head Impacts

Zhao

Kuo

et al. 2017

Ann Biomed Eng

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A pre-computed brain response atlas (pcBRA) may have the potential to accelerate the investigation of the biomechanical mechanisms of traumatic brain injury on a large-scale. In this study, we further enhance the technique and evaluate its performance using angular velocity profiles from dummy head impacts. Using the pcBRA to simplify profiles into acceleration-only shapes, sufficiently accurate strain estimates were obtained for impacts with a major dominating velocity peak. However, they were largely under-estimated when substantial deceleration occurred that reversed the direction of angular velocity. For these impacts, estimation accuracy was substantially improved with a biphasic profile simplification (average correlation coefficient and linear regression slope of 0.92±0.03 and 0.95±0.07 for biphasic shapes, respectively, vs. 0.80±0.06 and 0.80±0.08 for acceleration-only shapes). Peak maximum principal strain (εp) and cumulative strain damage measure (CSDM) from the estimated strains consistently correlated stronger than kinematic metrics with respect to the baseline εp and CSDM from the directly simulated responses, regardless of the brain region (e.g., correlation of 0.93 vs. 0.75 compared to Brain Injury Criterion (BrIC) for εp in the whole-brain, and 0.91 vs. 0.47 compared to BrIC for CSDM in the corpus callosum). These findings further support the pre-computation technique for accurate, real-time strain estimation, which could be important to accelerate the pace of model-based brain injury studies in the future.

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“…The following material models are based on our previous work [18]. As a result, the matrix material is modeled using the modified Mooney-Rivlin hyperelastic strain energy function [36], which is written as:…”

Section: Materials Modelsmentioning

confidence: 99%

“…biomechanical injury and allow for early medical intervention [15]. At this time, a number of FE brain studies have specifically focused on the prediction of DAI [16] [17] [18] [19] [20]. Using a variety of methods, these models predict axonal strains, which are then compared with functional and mechanical tissue thresholds [21].…”

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confidence: 99%

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Computation of history-dependent mechanical damage of axonal fiber tracts in the brain: towards tracking sub-concussive and occupational damage to the brain

Garimella

Kraft

2018

Preprint

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Finite element models are frequently used to simulate traumatic brain injuries. However, current models are unable to capture the progressive damage caused by repeated head trauma. In this work, we propose a method for computing the history-dependent mechanical damage of axonal fiber bundle tracts in the brain. Through the introduction of multiple damage models, we provide the ability to link consecutive head impact simulations, so that potential injury to the brain can be tracked over time. In addition, internal damage variables are used to degrade the mechanical response of each axonal fiber bundle element. As a result, the stiffness of the aggregate tissue decreases as damage evolves. To counteract this degenerative process, we have also introduced a preliminary healing model that reverses the accumulated damage, based on a user-specified healing duration. Using two detailed examples, we demonstrate that damage produces a significant decrease in fiber stress, which ultimately propagates to the tissue level and produces a measurable decrease in overall stiffness. These results suggest that damage modeling has the potential to enhance current brain simulation techniques and lead to new insights, especially in the study of repetitive head injuries. KEYWORDSaxonal injury, diffusion tensor imaging, traumatic brain injury, damage mechanics, finite element analysis, composites, embedded element method, occupational brain injury, sub-concussive brain injury INTRODUCTIONTraumatic brain injury (TBI) is a significant cause of death and long-term disability [1]. In the United States, there were 2.8 million TBI-related emergency department visits, hospitalizations, and deaths in 2013 [2]. The structure of the brain can be divided into the gray and white matter regions. In general, gray matter tissue forms the outer layer of the brain and contains the neuron cell bodies, while white matter tissue forms the central region of the brain and contains neuron cell extensions, known as axons. These tightly bundled axons form a dense communication network that transmits signals between the neuron cell bodies. While axons account for the majority of white matter tissue, they are surrounded by a mixture of other components, such as glial cells and the extracellular matrix (ECM). However, this surrounding host material is much softer than the axons [3] [4]. As a result, white matter tissue is commonly treated as a fiber-reinforced composite in mechanical simulations [5] [6] [7] [8]. During severe head impacts, brain deformations can result in the widespread stretching and shearing of axons. This kind of TBI is classified as diffuse axonal injury (DAI) and is typically associated with motor vehicle crashes, sports injuries, and military blast trauma [9] [10]. DAI is the primary injury mechanism in more than 40% of all hospitalized TBIs [11] and can result in both physical and cognitive impairments, which may be temporary or permanent [12].In an effort to design safer products and study the underlying mechanisms of brain injury, there h...

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Modeling the mechanics of axonal fiber tracts using the embedded finite element method

Cited by 62 publications

References 61 publications

Injury prediction and vulnerability assessment using strain and susceptibility measures of the deep white matter

Injury prediction and vulnerability assessment using strain and susceptibility measures of the deep white matter

Performance Evaluation of a Pre-computed Brain Response Atlas in Dummy Head Impacts

Computation of history-dependent mechanical damage of axonal fiber tracts in the brain: towards tracking sub-concussive and occupational damage to the brain

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