Cervical spine morphology and ligament property variations: A finite element study of their influence on sagittal bending characteristics.


Journal

Journal of biomechanics
ISSN: 1873-2380
Titre abrégé: J Biomech
Pays: United States
ID NLM: 0157375

Informations de publication

Date de publication:
06 03 2019
Historique:
received: 25 08 2018
revised: 07 12 2018
accepted: 26 12 2018
pubmed: 2 2 2019
medline: 25 3 2020
entrez: 2 2 2019
Statut: ppublish

Résumé

Cervical spine finite element models reported in biomechanical literature usually represent a static morphology. Not considering morphology as a model parameter limits the predictive capabilities for applications in personalized medicine, a growing trend in modern clinical practice. The objective of the study was to investigate the influence of variations in spinal morphology on the flexion-extension responses, utilizing mesh-morphing-based parametrization and metamodel-based sensitivity analysis. A C5-C6 segment was used as the baseline model. Variations of intervertebral disc height, facet joint slope, facet joint articular processes height, vertebral body anterior-posterior depth, and segment size were parametrized. In addition, material property variations of ligaments were considered for sensitivity analysis. The influence of these variations on vertebral rotation and forces in the ligaments were analyzed. The disc height, segmental size, and body depth were found to be the most influential (in the cited order) morphology variations; while among the ligament material property variations, capsular ligament and ligamentum flavum influenced vertebral rotation the most. Changes in disc height influenced forces in the posterior ligaments, indicating that changes in the anterior load-bearing column of the spine could have consequences on the posterior column. A method to identify influential morphology variations is presented in this work, which will help automation efforts in modeling to focus on variations that matter. This study underscores the importance of incorporating influential morphology parameters, easily obtained through computed tomography/magnetic resonance images, to better predict subject-specific biomechanical responses for applications in personalized medicine.

Identifiants

pubmed: 30704760
pii: S0021-9290(19)30007-7
doi: 10.1016/j.jbiomech.2018.12.044
pii:
doi:

Types de publication

Journal Article Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

18-26

Informations de copyright

Copyright © 2019 Elsevier Ltd. All rights reserved.

Auteurs

Jobin D John (JD)

Department of Engineering Design, Indian Institute of Technology Madras, India; Center for NeuroTrauma Research, Department of Neurosurgery, Medical College of Wisconsin, USA.

Gurunathan Saravana Kumar (G)

Department of Engineering Design, Indian Institute of Technology Madras, India. Electronic address: gsaravana@iitm.ac.in.

Narayan Yoganandan (N)

Center for NeuroTrauma Research, Department of Neurosurgery, Medical College of Wisconsin, USA.

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Classifications MeSH