Brain Strain from Motion of Sparse Markers

Zhou, Zhou; Li, Xiaogai; Kleiven, Svein; Hardy, Warren N.

doi:10.4271/2019-22-0001

Cited by 24 publications

(68 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The brain FE model used in this study was evaluated with experimental brain deformation data, including a subset of the data from recent in situ studies (Alshareef et al, 2018). The fidelity of the FE models will be improved with the recent advances in in situ experimental studies (Alshareef et al, 2020;Zhou et al, 2020), but the markers used to measure brain deformation in those experiments are still sparse and rarely located in the cortical and subcortical gray matter regions, which are the regions of interest in this study.…”

Section: Discussionmentioning

confidence: 99%

Predicting Concussion Outcome by Integrating Finite Element Modeling and Network Analysis

Anderson

Giudice

et al. 2020

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Predicting Concussion Outcome by Integrating Finite Element Modeling and Network Analysis

Anderson

Giudice

et al. 2020

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

show abstract

“…The recalculated cluster brain strains of these seven selected cases presented in Zhou et al (2019b) are used as experimental strain data to evaluate brain strain performance of the model. Cluster brain strains from the above seven cases are chosen, with further motivation provided in Discussion.…”

Section: Validation Performance Of the Head Model And Cora Calculationmentioning

confidence: 99%

“…The CORA scores for the ADAPT head model on brain motion are presented in Table 3 in comparison with head models previously developed at the same research group, including the original KTH head model (Kleiven 2007) and its updated version with FSI for brain-skull interface (Zhou et al 2019a) interface (referred to as KTH-FSI model), as well as KTH detailed head model (Zhou et al 2019b). Note that, to make the validation performance of different models amenable a direct comparison, the CORA scores for previous models reported here are either newly calculated or recalculated using exactly the same approach and CORA settings described in this study.…”

Section: Brain-skull Relative Motionmentioning

confidence: 99%

“…The predicted brain strain curves from the ADAPT model are compared with the experimental brain strain data presented in Zhou et al (2019b) (Fig. 6).…”

Section: Brain Strainmentioning

confidence: 99%

“…This study attempts to address the two challenges: (1) To develop an anatomically detailed head model with conforming hexahedral meshes; (2) Takes inspiration from pioneer works on mesh morphing and develop a new image registration pipeline for morphing a detailed head model. To address the first, the meshing approach used in our previous studies (Li et al 2017;Li et al 2019;Zhou et al 2019b;Zhou et al 2020) is used. Especially the approach has been shown to generate a detailed elderly head model with a smooth interface between the brain and CSF, permitting a successful implementation of FSI at ventricle-brain interface for studying periventricular injury (Zhou et al 2020).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Zhou

Kleiven

2020

Preprint

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Brain Strain from Motion of Sparse Markers

Cited by 24 publications

References 38 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

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

Contact Info

Product

Resources

About