@article{ab3a9a0c965f4eae80eb49e9f70c80b7,
title = "Modeling the mechanics of axonal fiber tracts using the embedded finite element method",
abstract = "A subject-specific human head finite element model with embedded axonal fiber tractography obtained from diffusion tensor imaging was developed. The axonal fiber tractography finite element model was coupled with the volumetric elements in the head model using the embedded element method. This technique enables the calculation of axonal strains and real-time tracking of the mechanical response of the axonal fiber tracts. The coupled model was then verified using pressure and relative displacement-based (between skull and brain) experimental studies and was employed to analyze a head impact, demonstrating the applicability of this method in studying axonal injury. Following this, a comparison study of different injury criteria was performed. This model was used to determine the influence of impact direction on the extent of the axonal injury. The results suggested that the lateral impact loading is more dangerous compared to loading in the sagittal plane, a finding in agreement with previous studies. Through this analysis, we demonstrated the viability of the embedded element method as an alternative numerical approach for studying axonal injury in patient-specific human head models.",
author = "Garimella, {Harsha T.} and Kraft, {Reuben H.}",
note = "Funding Information: The authors gratefully acknowledge the support provided by Computational Fluid Dynamics Research Corporation (CFDRC) under a subcontract funded by the Department of Defense, Department of Health Program through contract W81XWH-14-C-0045. All the DTI/diffusion spectrum imaging data used here are being provided by The Pennsylvania State University Center for Sports Concussion Research and Service, University Park, USA. The authors thank Dr Sam Slobounov and Dr Brian D. Johnson for the data provided. This work was supported in part through an instrumentation grant funded by the National Science Foundation through grant OCI-0821527. We would also like to acknowledge The Pennsylvania State University Social, Life, and Engineering Sciences Imaging Center (SLEIC), High Field MRI Facility for providing access to the imaging equipment. The authors thank The Pennsylvania State University Institute for Cyberscience for providing the computational resources required for this work. The authors also thank the reviewers for their insightful comments which helped improve the quality of the paper. The research being reported in this paper has been conducted in an ethical and responsible manner. The authors declare that they have complied with all the relevant ethical standards. The authors declare that the results being reported here are produced without any falsification, fabrication, or manipulation. They declare that this work is original and not plagiarized and has not been published elsewhere. The authors declare that they have no affiliations with or involvement in any organization with any financial interest (such as honoraria; educational grants; participation in speakers' bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements) or non-financial interest (such as personal or professional relationships, affiliations, knowledge, or beliefs) in the subject matter or materials discussed in the paper. Publisher Copyright: Copyright {\textcopyright} 2016 John Wiley & Sons, Ltd.",
year = "2017",
month = may,
doi = "10.1002/cnm.2823",
language = "English (US)",
volume = "33",
journal = "International Journal for Numerical Methods in Biomedical Engineering",
issn = "2040-7939",
publisher = "Wiley-Blackwell",
number = "5",
}