Anisotropic finite element models for brain injury prediction: the sensitivity of axonal strain to white matter tract inter-subject variability

Computational models incorporating anisotropic features of brain tissue have become a valuable tool for studying the occurrence of traumatic brain injury. The tissue deformation in the direction of white matter tracts (axonal strain) was repeatedly shown to be an appropriate mechanical parameter to predict injury. However, when assessing the reliability of axonal strain to predict injury in a population, it is important to consider the predictor sensitivity to the biological inter-subject variability of the human brain. The present study investigated the axonal strain response of 485 white matter subject-specific anisotropic finite element models of the head subjected to the same loading conditions. It was observed that the biological variability affected the orientation of the preferential directions (coefficient of variation of 39.41% for the elevation angle—coefficient of variation of 29.31% for the azimuth angle) and the determination of the mechanical fiber alignment parameter in the model (gray matter volume 55.55–70.75%). The magnitude of the maximum axonal strain showed coefficients of variation of 11.91%. On the contrary, the localization of the maximum axonal strain was consistent: the peak of strain was typically located in a 2 cm3 volume of the brain. For a sport concussive event, the predictor was capable of discerning between non-injurious and concussed populations in several areas of the brain. It was concluded that, despite its sensitivity to biological variability, axonal strain is an appropriate mechanical parameter to predict traumatic brain injury.

[1]  F. Servadei,et al.  A systematic review of brain injury epidemiology in Europe , 2006, Acta Neurochirurgica.

[2]  D. Hovda,et al.  The neurophysiology of concussion. , 2014, Progress in neurological surgery.

[3]  N. Colgan,et al.  Applying DTI white matter orientations to finite element head models to examine diffuse TBI under high rotational accelerations. , 2010, Progress in biophysics and molecular biology.

[4]  Rjh Rudy Cloots,et al.  The influence of anisotropy on brain injury prediction. , 2014, Journal of biomechanics.

[5]  Arthur W. Toga,et al.  Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template , 2008, NeuroImage.

[6]  D. Louis Collins,et al.  Symmetric Atlasing and Model Based Segmentation: An Application to the Hippocampus in Older Adults , 2006, MICCAI.

[7]  James C. Gee,et al.  Spatial transformations of diffusion tensor magnetic resonance images , 2001, IEEE Transactions on Medical Imaging.

[8]  B. Morrison,et al.  Functional tolerance to mechanical deformation developed from organotypic hippocampal slice cultures , 2015, Biomechanics and modeling in mechanobiology.

[9]  Scott T. Grafton,et al.  Combining the Finite Element Method with Structural Connectome-based Analysis for Modeling Neurotrauma: Connectome Neurotrauma Mechanics , 2012, PLoS Comput. Biol..

[10]  Guido Gerig,et al.  Towards a shape model of white matter fiber bundles using diffusion tensor MRI , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[11]  S. Mori,et al.  Principles of Diffusion Tensor Imaging and Its Applications to Basic Neuroscience Research , 2006, Neuron.

[12]  Timothy Edward John Behrens,et al.  Automated Probabilistic Reconstruction of White-Matter Pathways in Health and Disease Using an Atlas of the Underlying Anatomy , 2011, Front. Neuroinform..

[13]  D. Meaney,et al.  Tissue-level thresholds for axonal damage in an experimental model of central nervous system white matter injury. , 2000, Journal of biomechanical engineering.

[14]  James C. Ford,et al.  Parametric Comparisons of Intracranial Mechanical Responses from Three Validated Finite Element Models of the Human Head , 2013, Annals of Biomedical Engineering.

[15]  J. Olesen,et al.  The economic cost of brain disorders in Europe , 2012, European journal of neurology.

[16]  J. Langlois,et al.  Traumatic brain injury in the United States; emergency department visits, hospitalizations, and deaths , 2006 .

[17]  Rémy Willinger,et al.  Axonal strain as brain injury predictor based on real‐world head trauma simulations , 2015 .

[18]  Katrin Amunts,et al.  White matter fiber tracts of the human brain: Three-dimensional mapping at microscopic resolution, topography and intersubject variability , 2006, NeuroImage.

[19]  Guy B. Williams,et al.  Inter Subject Variability and Reproducibility of Diffusion Tensor Imaging within and between Different Imaging Sessions , 2013, PloS one.

[20]  A. Schleicher,et al.  Mapping of Histologically Identified Long Fiber Tracts in Human Cerebral Hemispheres to the MRI Volume of a Reference Brain: Position and Spatial Variability of the Optic Radiation , 1999, NeuroImage.

[21]  Scott Tashman,et al.  A study of the response of the human cadaver head to impact. , 2007, Stapp car crash journal.

[22]  King H. Yang,et al.  Application of a finite element model of the brain to study traumatic brain injury mechanisms in the rat. , 2006, Stapp car crash journal.

[23]  R. Caminiti,et al.  Diameter, Length, Speed, and Conduction Delay of Callosal Axons in Macaque Monkeys and Humans: Comparing Data from Histology and Magnetic Resonance Imaging Diffusion Tractography , 2013, The Journal of Neuroscience.

[24]  J A Newman,et al.  A proposed new biomechanical head injury assessment function - the maximum power index. , 2000, Stapp car crash journal.

[25]  King H. Yang,et al.  Investigation of Head Injury Mechanisms Using Neutral Density Technology and High-Speed Biplanar X-ray. , 2001, Stapp car crash journal.

[26]  I. Corouge,et al.  Analysis of brain white matter via fiber tract modeling , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[27]  Anders Kristoffersen,et al.  Statistical assessment of non‐Gaussian diffusion models , 2011, Magnetic resonance in medicine.

[28]  K. T. Ramesh,et al.  An axonal strain injury criterion for traumatic brain injury , 2012, Biomechanics and modeling in mechanobiology.

[29]  Carlo Pierpaoli,et al.  Analysis of the contribution of experimental bias, experimental noise, and inter-subject biological variability on the assessment of developmental trajectories in diffusion MRI studies of the brain , 2015, NeuroImage.

[30]  Chiara Giordano,et al.  Development of an Unbiased Validation Protocol to Assess the Biofidelity of Finite Element Head Models used in Prediction of Traumatic Brain Injury. , 2016, Stapp car crash journal.

[31]  S. Kleiven,et al.  Evaluation of Axonal Strain as a Predictor for Mild Traumatic Brain Injuries Using Finite Element Modeling. , 2014, Stapp car crash journal.

[32]  Steen Moeller,et al.  Advances in diffusion MRI acquisition and processing in the Human Connectome Project , 2013, NeuroImage.

[33]  S. Kleiven,et al.  Consequences of head size following trauma to the human head. , 2002, Journal of biomechanics.

[34]  D F Meaney,et al.  Dynamic stretch correlates to both morphological abnormalities and electrophysiological impairment in a model of traumatic axonal injury. , 2001, Journal of neurotrauma.

[35]  James C. Ford,et al.  Group-wise evaluation and comparison of white matter fiber strain and maximum principal strain in sports-related concussion. , 2015, Journal of neurotrauma.

[36]  S. Kleiven,et al.  Can sulci protect the brain from traumatic injury? , 2009, Journal of biomechanics.

[37]  King H. Yang,et al.  A proposed injury threshold for mild traumatic brain injury. , 2004, Journal of biomechanical engineering.

[38]  J. Armspach,et al.  Computation of axonal elongation in head trauma finite element simulation. , 2011, Journal of the mechanical behavior of biomedical materials.

[39]  Cheng Guan Koay,et al.  Investigation of anomalous estimates of tensor‐derived quantities in diffusion tensor imaging , 2006, Magnetic resonance in medicine.

[40]  Eva H. Baker,et al.  Normal regional fractional anisotropy and apparent diffusion coefficient of the brain measured on a 3 T MR scanner , 2008, Neuroradiology.

[41]  Matthew P. G. Allin,et al.  Atlasing location, asymmetry and inter-subject variability of white matter tracts in the human brain with MR diffusion tractography , 2011, NeuroImage.

[42]  Arthur W. Toga,et al.  Human brain white matter atlas: Identification and assignment of common anatomical structures in superficial white matter , 2008, NeuroImage.

[43]  S. Duma,et al.  Investigation of traumatic brain injuries using the next generation of simulated injury monitor (SIMon) finite element head model. , 2008, Stapp car crash journal.

[44]  R. Kikinis,et al.  A review of magnetic resonance imaging and diffusion tensor imaging findings in mild traumatic brain injury , 2012, Brain Imaging and Behavior.

[45]  D. Meaney,et al.  Axonal Damage in Traumatic Brain Injury , 2000 .

[46]  Thomas A. Gennarelli,et al.  Diffuse Axonal Injury: An Important Form of Traumatic Brain Damage , 1998 .

[47]  S. Kleiven Predictors for traumatic brain injuries evaluated through accident reconstructions. , 2007, Stapp car crash journal.

[48]  P. Basser,et al.  In vivo fiber tractography using DT‐MRI data , 2000, Magnetic resonance in medicine.

[49]  M. Wald,et al.  Traumatic brain injury in the United States; emergency department visits, hospitalizations, and deaths, 2002-2006 , 2010 .

[50]  B. Morrison,et al.  Region-specific tolerance criteria for the living brain. , 2007, Stapp car crash journal.

[51]  Mark Jenkinson,et al.  The minimal preprocessing pipelines for the Human Connectome Project , 2013, NeuroImage.

[52]  T. Gennarelli The Centripetal Theory of Concussion (CTC) revisited after 40 years and a proposed new Symptomcentric Concept of the Concussions , 2015 .

[53]  R. Ogden,et al.  Hyperelastic modelling of arterial layers with distributed collagen fibre orientations , 2006, Journal of The Royal Society Interface.