Review

Finite Element Analysis for Degenerative Cervical Myelopathy:
Scoping Review of the Current Findings and Design Approaches,
Including Recommendations on the Choice of Material Properties

Benjamin Davies1, BSc, MBChB (Hons), MPhil; Samuel Schaefer2, MEng; Amir Rafati Fard1, BA; Virginia

Newcombe1, MD, PhD; Michael Sutcliffe2, MA, PhD
1Department of Medicine, University of Cambridge, Cambridge, United Kingdom
2Department of Engineering, University of Cambridge, Cambridge, United Kingdom

Corresponding Author:
Benjamin Davies, BSc, MBChB (Hons), MPhil
Department of Medicine
University of Cambridge
Addenbrooke’s Hospital, Hills Road
Cambridge, CB2 0QQ
United Kingdom
Phone: 44 07766692608
Email: bd375@cam.ac.uk

Abstract

Background: Degenerative cervical myelopathy (DCM) is a slow-motion spinal cord injury caused via chronic mechanical
loading by spinal degenerative changes. A range of different degenerative changes can occur. Finite element analysis (FEA) can
predict the distribution of mechanical stress and strain on the spinal cord to help understand the implications of any mechanical
loading. One of the critical assumptions for FEA is the behavior of each anatomical element under loading (ie, its material
properties).

Objective: This scoping review aims to undertake a structured process to select the most appropriate material properties for
use in DCM FEA. In doing so, it also provides an overview of existing modeling approaches in spinal cord disease and clinical
insights into DCM.

Methods: We conducted a scoping review using qualitative synthesis. Observational studies that discussed the use of FEA
models involving the spinal cord in either health or disease (including DCM) were eligible for inclusion in the review. We followed
the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews)
guidelines. The MEDLINE and Embase databases were searched to September 1, 2021. This was supplemented with citation
searching to retrieve the literature used to define material properties. Duplicate title and abstract screening and data extraction
were performed. The quality of evidence was appraised using the quality assessment tool we developed, adapted from the
Newcastle-Ottawa Scale, and shortlisted with respect to DCM material properties, with a final recommendation provided. A
qualitative synthesis of the literature is presented according to the Synthesis Without Meta-Analysis reporting guidelines.

Results: A total of 60 papers were included: 41 (68%) “FEA articles” and 19 (32%) “source articles.” Most FEA articles (33/41,
80%) modeled the gray matter and white matter separately, with models typically based on tabulated data or, less frequently, a
hyperelastic Ogden variant or linear elastic function. Of the 19 source articles, 14 (74%) were identified as describing the material
properties of the spinal cord, of which 3 (21%) were considered most relevant to DCM. Of the 41 FEA articles, 15 (37%) focused
on DCM, of which 9 (60%) focused on ossification of the posterior longitudinal ligament. Our aggregated results of DCM FEA
indicate that spinal cord loading is influenced by the pattern of degenerative changes, with decompression alone (eg, laminectomy)
sufficient to address this as opposed to decompression combined with other procedures (eg, laminectomy and fusion).

Conclusions: FEA is a promising technique for exploring the pathobiology of DCM and informing clinical care. This review
describes a structured approach to help future investigators deploy FEA for DCM. However, there are limitations to these
recommendations and wider uncertainties. It is likely that these will need to be overcome to support the clinical translation of
FEA to DCM.

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(JMIR Biomed Eng 2024;9:e48146) doi: 10.2196/48146

KEYWORDS

scoping review; fine element analysis; cervical spine; spinal cord; degenerative cervical myelopathy

Introduction

Degenerative cervical myelopathy (DCM) occurs when arthritic
changes to the structure of the cervical spine injure the spinal
cord, causing a slowly progressive spinal cord injury (SCI) [1].
This leads to a range of different symptoms that can affect the
whole body, including loss of dexterity, imbalance, altered
sensation, bladder and bowel dysfunction, and pain [2].
Although DCM is estimated to affect 1 in 50 adults, <20% are
estimated to receive a diagnosis. This is likely, in part, as most
are only mildly affected [3,4]. Treatment is currently limited to
surgery but, due to inherent risks, is reserved for those with
progressive or moderate-to-severe disease [5]. Notably, <5%
of patients with DCM will make a complete recovery after
surgery, and instead are left with lifelong disabilities and
dependence having among the lowest quality of life scores of
any disease [6,7]. Consequently, this was recently estimated to
cost GBP £0.7 billion (approximately US $0.9 billion) per year
[8].

The etiology and pathophysiology of DCM are poorly
understood [1,9]. At a macroscopic level, this is a cohort that
displays progressive cervical myelopathy with degenerative
changes to the structure of their cervical spine, typically causing
some deformation of the spinal cord on magnetic resonance
imaging (MRI), which responds to decompressive surgery. This
led to the hypothesis that DCM is triggered by a chronic
mechanical injury, specifically compression loading.

However, this is likely to be an oversimplification. Spinal cord
compression is most commonly an incidental finding [3]; the
amount of compression visualized on the MRI poorly correlates
with the disease severity and does not predict the treatment
response [10-12]. Moreover, many other forms of mechanical
loading also occur, including stretching or shear loading. These
are recognized to be capable of causing tissue injury
independently [1]. For example, stretching is considered the
etiology of myelopathy in tethered cord syndrome and some
forms of deformity [13]. Consequently, it is more likely that
the mechanical trigger in DCM is the interaction of these
mechanical forces rather than one alone. As the structural
changes within the spine highly vary between patients, this is
likely to be a very individualized phenomenon [14]. This
presents a problem for clinical practice, as conventional
diagnostic tests such as MRI cannot measure mechanical stress;
however, the goal of surgery is to alleviate it [12,15].

Finite element analysis (FEA) is an engineering technique that
uses a computational model to derive the extent and severity of
mechanical stress from an assumed loading [16]. This has
frequently been applied to health care, including, to some extent,
SCI and, more recently, DCM [16-18]. FEA could have
important applications in DCM, both to improve our
understanding of the pathobiology and to represent an
individual’s injury and objectively inform surgical strategy.

To perform an FEA, a computer model incorporating the
geometry, motion, and material properties of each structure
must be created [17]. Geometry and motion, to a large extent,
can be defined based on an individual’s clinical imaging.
However, the material properties must be chosen from other
sources. These choices will influence the results of the FEA.
For spinal cord FEA to date, these choices have been made on
a project-by-project basis, typically informed by the experience
of the investigators, their interpretation and knowledge of the
literature, and their specific project aims. To inform the
development of FEA for DCM, we adopted an iterative approach
using a scoping review methodology with the following aims:

• To describe how FEA models have been constructed with
respect to spinal cord disease

• To identify and appraise the experimental literature that
has informed their material property choices to make
recommendations on the material properties for DCM FEA

• To aggregate the findings from studies using FEA to explore
DCM.

To the best of our knowledge, this represents a unique approach
to selecting the material properties for a clinical FEA model
and may represent an exemplar for similar initiatives.

Methods

A scoping review methodology was considered most appropriate
to meet these objectives [19]. This scoping review was reported
in accordance with the PRISMA-ScR (Preferred Reporting
Items for Systematic Reviews and Meta-Analyses extension for
Scoping Reviews) guidelines (Multimedia Appendix 1).

Search Strategy
The search was conducted using a modified population,
interventions, comparisons, and outcomes strategy, which states
that the research question for a review must include the
population, intervention, comparison, and outcome. Our research
question was, “what are the current findings and design
approaches for FEA in DCM?”, with the population being
patients with DCM, intervention being FEA, and outcomes
being current findings and design approaches. To more
comprehensively guide future decisions regarding the
application of FEA methods to DCM, we broadened our
inclusion criteria to incorporate any study that applied FEA to
the spinal cord (in either health or disease). Consequently, the
search terms were designed to capture observational studies that
had developed FEA models that included the spinal cord in
either health or disease, including DCM (Multimedia Appendix
2). Searches were conducted from inception (February 12, 2021)
to September 1, 2021, in the MEDLINE and Embase databases.
Search sensitivity was evaluated using 5 papers known to meet
the inclusion criteria; all papers were successfully captured
[18,20-23].

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Inclusion and Exclusion Criteria
Papers were considered eligible for inclusion if they were
observational studies that discussed the use of FEA models that
included the spinal cord of humans or animals in either health
or disease, including DCM.

Papers were excluded if they were written in a language other
than English, did not use FEA models, or did not include the
spinal cord in the FEA model. Furthermore, systematic reviews,
scoping reviews, editorials, and abstracts were excluded.

Study Screening and Data Extraction
Two reviewers (BMD and SS) independently performed title
and abstract screening with blinding using Rayyan (Rayyan
Systems Inc). A pilot screen of 100 publications was conducted
to ensure concordance between reviewers. Any disagreements
following unblinding were resolved by discussion between the
reviewers until mutual agreement was reached. In this review,
papers identified through our search strategy are termed “FEA
articles”.

From the included FEA articles, the references used to justify
a structure’s material properties were also screened to identify
experimental studies reporting original data acquired from
physical tissue tests. Studies exploring behavior computationally
but including their original physical experiments, even if
published elsewhere, were included. Studies that explored
properties solely on a computational basis were excluded. This
forward search continued within the references of a referenced
study if the reference did not meet this criterion and had cited
an alternative source.

Papers were retrieved for full-text screening and data extraction
using a piloted pro forma. Data extracted from the papers
included: author, year of publication, country, study objectives,
study design (eg, human or animal study), disease of interest
(if any), spinal segment (eg, cervical, thoracic, and lumbar),
reference for anatomy (eg, cadaveric specimen and imaging),
and details of how the FEA model was developed and validated
(including the material properties of the anatomical elements).

Data extraction focused on the properties specifically referenced
by the original FEA models and may not have included all the
material properties discussed in the paper. To understand an
investigator’s approach to model development, these were
distinguished as those used to define the model a priori (ie,
referenced data and the choice of material law and selected
coefficients) or those used to validate the final model (if
performed). However, for the purpose of selecting data to inform
an FEA model, these references were aggregated and termed
as “source articles” in this review.

In the absence of a standard quality assessment tool for
experimental studies of biomechanics, we developed a
classification to help appraise source articles that are most
appropriate for a DCM FEA model [24]. This included a risk
of bias assessment adapted from the Newcastle-Ottawa Scale,
focusing on selection and reporting bias (Multimedia Appendix
3) [25].

Data Analysis and Reporting
Due to significant heterogeneity between methodologies,
meta-analysis was not possible, and a qualitative Synthesis
Without Meta-Analysis (SWiM) was instead performed. Data
were aggregated, where applicable, qualitatively, quantitatively,
or using frequency statistics, as per the SWiM guidelines [26].

Given the small field size, with many papers published by single
groups, citation networks were created to graphically consider
which choices were made across the field and how they were
informed. Using this framework and our judgment, we ranked
source articles into approximate tertiles. For FEA articles that
had cited top-source articles and represented the material
properties using an equation, the performance of this equation
was further evaluated graphically by generating stress-strain
curves. These were exclusively either linear or hyperelastic. For
models using a linear elastic equation, the Young modulus was
used as the gradient of the stress-strain curve. For models using
a hyperelastic equation, a 3×3 element cube was created using
ABAQUS (Dassault Systèmes). The cube was stretched
uniaxially, with no constraint applied in the orthogonal
directions, linearly increasing the nominal strain in increments
of 0.04 to a maximum of 0.4. The outputs of this model were
then applied true stress as a function of the applied true strain.
Finally, any primary clinical papers that conducted FEA for the
investigation of DCM were aggregated separately and analyzed.

Data were displayed using a range of plots constructed using R
Studio (version 4.0.3; Posit).

Results

Overall Approach of FEA Models of Spinal Cord
Disease: Anatomy, Geometry, Motion, and Validation
The search returned 597 articles, of which 155 (25.9%) were
duplicates (Figure 1). Following screening, 41 FEA articles
were eligible for inclusion, of which 32 (78%) modeled the
human spinal cord; a further 45 (7.54%) source articles were
identified through citation search, of which 19 (42%) were
shortlisted as suitable. Of the FEA articles, approximately half
(21/41, 51%) focused on SCI [27-47]; 34% (14/41) on DCM
[18,20-22,48-57]; and 5% (2/41) each on scoliosis [58,59],
syringomyelia [60,61], and flexion myelopathy [62,63]. Most
models (25/41, 61%) included only the spinal cord, whereas
24% (10/41) included the surrounding anatomy at multiple
vertebral levels, and 17% (7/41) included the surrounding
anatomy at only 1 motion segment (ie, 2 adjacent vertebrae).
Physiological movement of the spine (flexion and extension)
was incorporated into 17% (7/41) of the models, but none
evaluated spinal cord oscillation. This was equally likely among
the DCM and SCI models (Multimedia Appendix 4).

The anatomy of each model was built using a combination of
imaging and cadaveric data in 27% (11/41) of the FEA articles.
Typically, imaging was used for bones and cadavers for soft
tissues, including the spinal cord. This included an open-source
reference library called BodyWorks [64] and a review of spinal
cord geometry [65]. MRI was used to define the spinal cord
specifically in 20% (8/41) of the FEA articles.

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Figure 1. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram. FEA: finite element analysis.

For most FEA articles (33/41, 80%), the spinal cord was
modeled as gray matter and white matter separately and had a
defined pial layer (26/41, 63%) or was encased within the dural
layer (26/41, 63%). Defined pial and dural layers were used in
combination in only half of these articles (13/41, 32%).
Cerebrospinal fluid (CSF) was specifically modeled in 41%
(17/41) of the FEA articles, while other elements were variably
included. This choice was independent of the disease and
publication date (Multimedia Appendix 4). Elements were
modeled using solid shell elements, unless specified differently
in the Material Properties of Anatomical Elements With
Recommendations for DCM FEA section.

Validation methods were specified in 63% (26/41) of the FEA
articles, with 15% (6/41) using their own experiments and 9%
(20/41) using literature (Multimedia Appendix 5). These
references pointed to 17 articles, of which 7 (42%) provided
material property data for the spinal cord in healthy
circumstances and 3 (18%) in traumatic SCI circumstances. Of
the remaining 17 articles, 4 (24%) described motion of the spine
[66-69] and 1 (6%) described the spinal cord in flexion and
extension [70]. Of the 9 articles providing information on
healthy spinal cord properties, 7 (78%) were also used in other
studies to inform the selection of material property. No
DCM-specific validation data sets were identified.

Material Properties of Anatomical Elements With
Recommendations for DCM FEA

Spinal Cord
The material properties of the whole spinal cord were defined
in 22% (9/41) of the FEA articles. This was rarely justified, but
if so, qualified by its uncertain significance [71,72]. Typically,
a hyperelastic Ogden variant (4/9, 44%) or a linear elastic (3/9,
33%) function was used.

For the remaining models, gray and white matter were modeled
separately, except for the article that explored the impact of a
range of white matter material properties, where the material
law applied to gray matter was the same as that of white matter.
The remaining 32 models were mostly based on tabulated data
from the studies by Ichihara et al [72,73], and less frequently,
Bilston and Thibault [74], Tunturi [75], and Ozawa et al [76].
Alternatively, a hyperelastic Ogden variant (10/41, 24%) or a
linear elastic (4/41, 10%) function was used.

A total of 2 studies specifically compared different material
properties with respect to a transverse contusion model of SCI.
Jannesar et al [38] explored white matter properties on the basis
that single constitutive models may not account for the dynamic
(viscoelastic) and anisotropic properties. They identified that
this could be improved by adding reinforcing functions. A
second order reduced polynomial hyperelastic function
combined with a quadratic reinforcing function in a 4-term

Prony series performed best (0.89<R2<0.99), although this was
principally in relation to the high strain rates of an SCI. Fournely
et al [45] used a first-order Ogden function but varied the
stiffness of the gray matter with respect to the white matter.
Although this fell within the range of the validation data set,
they observed differing responses to the load. When the gray
matter was stiffer than the white matter, strain distribution was
more diffuse and maximal within the white matter. When the
stiffness was equivalent, strain was localized to the impact site.
When the white matter was stiffer than the gray matter, strain
was less localized, maximal within the gray matter and involved
the contralateral gray matter. This was the principal factor
determining behavior, ahead of other factors explored, including
spinal cord diameter, curvature, and impactor angle.

A total of 2 studies similarly explored the implications of
different gray and white matter material properties with respect

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to DCM, with similar findings discussed in the Findings From
the FEA Studies of DCM section [34,50].

A total of 14 source articles were identified describing the
material properties of the spinal cord or its subcomponents
(Multimedia Appendix 6 [46,72-75,77-90]), of which 3 (21%)
were shortlisted with relevance to an FEA for DCM [72-74].
Their interpretations varied across studies (Figure 2). The choice
of material laws and values of those who directly cited the

prioritized source articles and separately distinguished gray and
white matter are listed in Tables 1-2. Broadly, these align with
the source articles; however, there are differences across the
strain range (Multimedia Appendix 6). Of these FEA articles
representing material properties with an equation, studies by
Jannesar et al [29] and Khuyagbaatar et al [53] were selected
as these were most aligned for gray matter and white matter,
respectively.

Figure 2. Network analysis of finite element analysis models, which is linked to a shortlisted source article, for the white matter (A) and gray matter
(B) or the spinal cord as a whole (C). The original finite element analysis models are represented by their choice of material law as a star (linear elastic),
square (hyperelastic), diamond (tabulated), or triangle (other) and their disease of interest as degenerative cervical myelopathy (DCM; red), spinal cord
injury (SCI; blue), or other (green). These link to the primary source articles (dots). An intermediate article, that is, the one that did not include primary
experimental data, is pale gray. A shortlisted source article is black. Each figure is additionally available as an interactive file; refer to Multimedia
Appendix 7. The higher resolution version of this figure is available in Multimedia Appendix 8.

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Table 1. Extracted material equations for the gray matter.

De (MPa−1)μd (MPa)αcνbEa (MPa)VariantLawReferencePathologyStudy, year

0.905i,j0.044510.570.49—hOgden, first
Order

HyperelasticIchihara et al
[72], 2003

SCIgJannesar et al [29], 2021f

50.50.004114.70.45—Ogden, first
order

HyperelasticIchihara et al
[73], 2001

DCMkKhuyagbaatar et al [53],
2017

6.770.03067.520.45—Ogden, first
order

HyperelasticIchihara et al
[72], 2003

SCIJannesar et al [38], 2016

50.50.004114.70.45—Ogden, first
order

HyperelasticIchihara et al
[73], 2001

SCIKhuyagbaatar et al [39],
2016

—0.2188—0.4990.656—Linear elasticIchihara et al
[72], 2003

SCICzyz et al [42], 2008

6.470.03204.70.45—Ogden, first
order

HyperelasticBilston and
Thibault [74],
1996

SCIMaikos et al [43], 2008

—0.0222—0.4990.0667—Linear elasticBilston and
Thibault [74],
1996

SCIScifert et al [44], 2002

aE: Young modulus.
bν: Poisson ratio. Where missing, the value of ν was assumed to be 0.45.
cα: material exponent parameter.
dμ: ground shear hyperelastic modulus.
eD: compressibility constant.
fThe single preferred source of the authors based on modeling (Multimedia Appendix 6), where a range of equations were put forward.
gSCI: spinal cord injury.
hNot available.
iDenotes a suspected error in original text and input value given.
jValues in italics are input based on the identity for isotropic materials, D=3(1-2ν)/(μ{1+ν}), and for linear elastic, μ=E/(2{1+ν}).
kDCM: degenerative cervical myelopathy.

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Table 2. Extracted material equations for the white matter.

De (MPa−1)μd (MPa)αcνbEa (MPa)VariantLawReferencePathologyStudy, year

—1.4483h—0.454.2—gLinear elasticIchihara et al
[73], 2001

DCMfLiang et al [48], 2021

51.70.004012.50.45—Ogden, first
order

HyperelasticIchihara et al
[73], 2001

DCMKhuyagbaatar et al [52],
2017

51.70.004012.50.45—Ogden, first
order

HyperelasticIchihara et al
[73], 2001

SCIiKhuyagbaatar et al [39],
2016

— 0.0924—0.4990.277—Linear elasticIchihara et al
[72], 2003

SCICzyz et al [42], 2008

6.470.03204.70.45—Ogden, first
order

HyperelasticBilston and
Thibault [74],
1996

SCIMaikos et al [43], 2008

—0.0222—0.4990.0667—Linear elasticBilston and
Thibault [74],
1996

SCIScifert et al [44], 2002

aE: Young modulus.
bν: Poisson ratio. Where missing, the value of ν was assumed to be 0.45.
cα: material exponent parameter.
dμ: ground shear hyperelastic modulus.
eD: compressibility constant.
fDCM: degenerative cervical myelopathy.
gNot available.
hValues in italics are input based on the identity for isotropic materials, D=3(1-2ν)/(μ{1+ν}), and for linear elastic, μ=E/(2{1+ν}).
iSCI: spinal cord injury.

Pia
Of the 26 FEA articles with defined pia, 14 (54%) used a linear
elastic function, 9 (21%) did not report their method, and 2 (5%)
used a hyperelastic Ogden variant function. The remaining study
(1/26, 4%) used tabulated data from the study by Ichihara et al
[73].

A total of 4 source articles were identified for the pia
(Multimedia Appendix 6), of which 2 (50%) were shortlisted
as suitable [75,77]. The choice of material laws and the values
of those who directly cited these shortlisted source articles are
listed in Table 3. These equations have differences in how they
represent the source article (Multimedia Appendix 6). Of the
FEA articles representing material properties with an equation,
the study by Jannesar et al [38] was selected as the most
preferred.

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Table 3. Extracted material equations for the pia.

De (MPa−1)μd

(MPa)
αcνbEa (MPa)VariantLawReferencePathologyStudy, year

—35.71g—0.4100—fLinear elasticTunturi [75], 1978OtherHenao et al [58,59], 2017

———————Tunturi [75], 1978DCMhNishida et al [91], 2016

———————Tunturi [75], 1978DCMNishida et al [54], 2015

———————Tunturi [75], 1978DCMNishida et al [55], 2014

———————Tunturi [75], 1978DCMNishida et al [22], 2012

—35.71—0.4100—Linear elasticTunturi [75], 1978OtherHenao et al [58], 2018

———————Tunturi [75], 1978DCMKato et al [56], 2010

———————Tunturi [75], 1978OtherKato et al [62], 2008

———————Tunturi [75], 1978OtherKato et al [63], 2009

—15.12—0.339.3—Linear elasticKimpara et al [77],
2006

SCIJannesar et al [38], 2016i

aE: Young modulus.
bν: Poisson ratio. Where missing, the value of ν was assumed to be 0.45.
cα: material exponent parameter.
dμ: ground shear hyperelastic modulus.
eD: compressibility constant.
fNot available.
gValues in italics are input based on the identity for isotropic materials, D=3(1-2ν)/(μ{1+ν}), and for linear elastic, μ=E/(2{1+ν}).
hDCM: degenerative cervical myelopathy.
iThe single preferred source of the authors based on modelling (Multimedia Appendix 6).

Dura
Of the 26 models with defined dura, 18 (69%) used a linear
elastic function, 5 (19%) used a hyperelastic Ogden variant, and
3 (12%) did not report their method.

Persson et al [46] compared the performance of a linear and
hyperelastic function, which is summarized in the following
CSF section.

A total of 9 source articles were referenced (Multimedia
Appendix 6), of which 4 (44%) were shortlisted [78-81]. The
choice of material laws and values of those who directly cited
these prioritized source articles are listed in Table 4. These
equations have differences in how they represent the source
article (Multimedia Appendix 6). Of the FEA articles
representing material properties with an equation, the study by
Sparrey et al [33] was selected as preferred.

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Table 4. Extracted material equations for the dura.

De (MPa−1)μd (MPa)αcνbEa (MPa)VariantLawReferencePathologyStudy, year

—1.72 h—0.455—gLinear ElasticPersson et al
[92], 2020

DCMfStoner et al [20], 2020

—26.85—0.4980—Linear ElasticPersson et al
[92], 2020

DCMKhuyagbaatar et al [49],
2018

—79.66—0.45231—Linear ElasticWilcox et al [47],
2004

OtherHenao et al [58,59], 2017

—26.85—0.4980—Linear ElasticPersson et al
[92], 2020

DCMKhuyagbaatar et al [52],
2017

0.1721.20516.20.45—Ogden,1st
Order

Hyper-elasticHong et al [78],
2011 and Zarzur
et al [79], 1996

SCISparrey et al [33], 2016i

—48.97—0.45142—Linear ElasticWilcox et al [47],
2004

SCIYan et al [36], 2012

—79.66—0.45231—Linear ElasticWilcox et al [47],
2004

OtherHenao et al [58], 2018

—26.85—0.4980—Linear ElasticPersson et al
[92], 2020

SCIKhuyagbaatar et al [39],
2016

—26.85—0.4980—Linear ElasticPersson et al
[92], 2020

DCMKhuyagbaatar et al [57],
2015

—26.85—0.4980—Linear ElasticPersson et al
[92], 2020

SCIKhuyagbaatar et al [57],
2015

—48.97—0.45142—Linear ElasticWilcox et al [47],
2004

SCICzyz et al [42], 2008

—26.85—0.4980—Linear ElasticPersson et al
[92], 2020

SCIPersson et al [46], 2011

————Young modulus
in the radial di-
rection=142,
Young modulus
in the circumfer-
ential direc-
tion=142,
Young modulus
in the longitudi-
nal direc-
tion=0.7

—Anisotropic
Elastic

Wilcox et al [47],
2004

SCIWilcox et al [47], 2004

aE: Young modulus.
bν: Poisson ratio. Where missing, the value of ν was assumed to be 0.45.
cα: material exponent parameter.
dμ: ground shear hyperelastic modulus.
eD: compressibility constant.
fDCM: degenerative cervical myelopathy.
gNot available.
hValues in italics are input based on the identity for isotropic materials, D=3(1-2ν)/(μ{1+ν}), and for linear elastic, μ=E/(2{1+ν}).
iThe single preferred source of the authors based on modelling (Multimedia Appendix 6).

Dentate Ligament
Of the 13 FEA articles that included the dentate ligament, 12
(92%) used a linear elastic function and 1 (8%) used tabulated
data. Typically, these were modeled using shell elements (6/13,

46%) with geometric properties, but 8% (1/13) used link
elements and 15% (2/13) used spring elements.

A total of 2 source articles were referenced (Multimedia
Appendix 6), of which both were shortlisted [75,82]. The choice
of material laws and values of those who directly cited these
prioritized source articles are listed in Table 5.

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Table 5. Extracted material equations for the dentate.

De (MPa−1)μd (MPa)αcνbEa (MPa)VariantLawReferencePathologyStudy, year

—35.7 g—0.4100—fLinear elasticTunturi [75], 1978OtherHenao et al [58,59], 2017

—35.7—0.4100—Linear elasticTunturi [75], 1978OtherHenao et al [58], 2018

—2.0——5.8—Linear elasticTunturi [75], 1978SCIhGreaves et al [41], 2008

—38.5—0.3100—Linear elasticTunturi [75], 1978SCICzyz et al [42], 2008

aE: Young modulus.
bν: Poisson ratio. Where missing, the value of ν was assumed to be 0.45.
cα: material exponent parameter.
dμ: ground shear hyperelastic modulus.
eD: compressibility constant.
fNot available.
gValues in italics are input based on the identity for isotropic materials, D=3(1-2ν)/(μ{1+ν}), and for linear elastic, μ=E/(2{1+ν}).
hSCI: spinal cord injury.

Cerebrospinal Fluid
Of the 17 models that included CSF, 8 (47%) modeled it as a
Newtonian fluid. Alternatives included modeling CSF as a
pressurized fluid cavity (1/17, 6%), modeling it as a polynomial
equation of state (1/17, 6%), modeling it as smoothed particular
hydrodynamics (1/17, 6%), using a hyperelastic Mooney-Rivlin
model (3/17, 18%), or using a linear elastic equation (1/17, 6%).

Persson et al [46] and Jones et al [93] specifically explored the
implications of including a CSF cavity, with or without the dura.
To measure cord deformation, Persson et al [46] used an FEA
model with reference to a transverse bovine impaction model
of SCI, whereas Jones et al [93] performed their own bovine
and surrogate cord experiments. They observed that the presence
of CSF reduced stress and strain (Persson et al [46]) on the
spinal cord and deformation (Jones et al [93]) in the spinal cord.
Persson et al [46] demonstrated this was through a greater
longitudinal distribution, particularly when the dura was

included and modeled using a hyperelastic Ogden (as opposed
to linear elastic) function. Furthermore, Persson et al [46]
observed that cord deformation occurred upon contact with the
dura (before the CSF between the spinal cord and the dura was
redistributed). Jones et al [93] observed that the inclusion of
the dura only changed behavior if CSF was also included.

Furthermore, Arhiptsov and Marom [31] explored CSF pressure,
alongside the presence or absence of epidural fat, using a
computational contusion model of SCI based on a thoracic burst
fracture. Both CSF and epidural fat were modeled using
smoothed particular hydrodynamics. In a model without epidural
fat, spinal cord stress and strain increased with increasing CSF
pressure. However, in the model with epidural fat, spinal cord
stress and strain decreased with increasing CSF pressure.

A total of 5 source articles were referenced (Multimedia
Appendix 6), of which 3 (60%) were shortlisted [46,83,84]. The
choice of material laws and values of those who directly cited
these prioritized source articles are listed in Table 6.

Table 6. Extracted material equations for the cerebrospinal fluid.

Density (kg/m3)Viscosity (Pa/s)LawReferencePathologyStudy, year

—b0.001Newtonian FluidBloomfield et al [83], 1998DCMaKhuyagbaatar et al [52], 2017

——Polynomial Equation of
State

Persson et al [46], 2011SCIcArhiptsov [31], 2021

—0.001Newtonian FluidBloomfield et al [83], 1998,
Brydon et al [84], 1995

DCMKhuyagbaatar et al [39], 2016

10000.001Newtonian FluidBloomfield et al [83], 1998,
Brydon et al [84], 1995

SCIKhuyagbaatar et al [39], 2016

—0.001Newtonian FluidBloomfield et al [83], 1998,
Brydon et al [84], 1995

DCMKhuyagbaatar et al [57], 2015

—0.001Newtonian FluidBloomfield et al [83], 1998,
Brydon et al [84], 1995

SCIKhuyagbaatar et al [57], 2015

—0.001Newtonian FluidBloomfield et al [83], 1998SCIPersson et al [46], 2011

aDCM: degenerative cervical myelopathy.
bNot available.
cSCI: spinal cord injury.

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Posterior Longitudinal Ligament and Ligamentum
Flavum
The analysis focused on the posterior longitudinal ligament and
ligamentum flavum, given their specific involvement in the
pathobiology of DCM. In all 6 instances included, they were
included together and modeled in the same manner: using
piecewise linear plasticity (2/6, 33%), linear elastic function

(2/6, 33%), hyperelastic Ogden variant (1/6, 17%), or tabulated
data (1/6, 17%).

A total of 6 source articles were referenced (Multimedia
Appendix 6), of which 3 (50%) were shortlisted [85-87]. The
choice of material laws and values of those who directly cited
these prioritized source articles are listed in Tables 7 and 8.

Table 7. Extracted material equations for the ligamentum flavum.

De (MPa−1)μd (MPa)αcνbEa (MPa)VariantLawReferencePathologyStudy, year

—1.3 h——3.8—gLinear elasticYoganandan et al 1989
and 2000 [86,87]

SCIfGreaves et al [41], 2008

aE: Young modulus.
bν: Poisson ratio. Where missing, the value of ν was assumed to be 0.45.
cα: material exponent parameter.
dμ: ground shear hyperelastic modulus.
eD: compressibility constant.
fSCI: spinal cord injury.
gNot available.
hValues in italics are input based on the identity for isotropic materials, D=3(1-2ν)/(μ{1+ν}), and for linear elastic, μ=E/(2{1+ν}).

Table 8. Extracted material equations for the posterior longitudinal ligament.

De (MPa−1)μd (MPa)αcνbEa (MPa)VariantLawReferencePathologyStudy, year

—12.3 h——35.7—gLinear elasticPrzybylski et al [85],
1996 and Yoganandan
1989 and 2000 [86,87]

SCIfGreaves et al [41], 2008

aE: Young modulus.
bν: Poisson ratio. Where missing, the value of ν was assumed to be 0.45.
cα: material exponent parameter.
dμ: ground shear hyperelastic modulus.
eD: compressibility constant.
fSCI: spinal cord injury.
gNot available.
hValues in italics are input based on the identity for isotropic materials, D=3(1-2ν)/(μ{1+ν}), and for linear elastic, μ=E/(2{1+ν}).

Spinal Roots
A total of 7 models included spinal nerve roots, of which 2
(29%) distinguished between the intradural and extradural
components. These 2 models specifically explored the nature
of C5 palsy in relation to surgery for DCM [49,57]. Nerve roots

were all modeled with spring elements, either as a spring (5/7,
71%) or with a linear elastic equation (2/7, 29%).

A total of 2 source articles of equivalent quality were referenced
(Multimedia Appendix 6) [88,89]. The choice of material laws
and values of those who directly cited these prioritized source
articles are listed in Table 9.

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Table 9. Extracted material equations for the nerve roots.

Mass (g)Spring constantνbEa (MPa)LawReferencePathologyStudy, year

0.10.133——dSpringKulkarni [88], 2007DCMcLévy et al [18], 2021

——0.31.3Linear ElasticSingh [89], 2005DCMKhuyagbaatar et al [49], 2018

—0.133——SpringKulkarni [88], 2007OtherHenao et al [58,59], 2017

——0.31.3Linear ElasticSingh [89], 2005DCMKhuyagbaatar et al [52], 2017

—0.133——SpringKulkarni [88], 2007OtherHenao et al [58], 2018

aE: Young modulus.
bν: Poisson ratio; where missing, ν was assumed to be 0.45. For Kulkarni et al [88], the unit is uncertain, with a range of different units referenced
across its citations.
cDCM: degenerative cervical myelopathy.
dNot available.

Other Elements
Other elements included in some models were bone (14/41,
34%); intervertebral disks (IVDs; 13/41, 31%); and the
remaining spinal ligaments, such as the anterior longitudinal or
interspinous ligament.

The bone was generally modeled as a rigid body (8/14, 57%).
Of the 8 models, 3 (21%) subdivided the vertebrae into
anatomical subcomponents (eg, body, laminae, and spinous
process), and 5 (36%) distinguished between cortical and
cancellous bone, of which 3 (60%) applied an equation just to
the cortical bone (linear elastic in all cases) and 2 (40%) applied
a Johnson-Cook or plastic kinematic equation. We found no
eligible source articles using our search process.

The IVD were modeled as a single entity in 54% (7/13) of the
papers, typically as a rigid body (5/7, 71%) or using a linear
elastic equation (2/7, 29%). Alternatively, they were modeled
separately as nucleus pulposus and annulus fibrosus. Techniques
for the nucleus pulposus included a Mooney-Rivlin model (3/6,
50%), Ogden second-order variant (1/6, 17%), and fluid
elements (2/6, 33%). The annulus fibrosus included a
Mooney-Rivlin model (2/6, 33%), Ogden second-order variant
(1/6, 17%), Ogden third-order variant (1/6, 17%), and linear
elastic equation (2/6, 33%).

A total of 3 source articles were found for IVD, and 1 was
shortlisted (Multimedia Appendix 6) [90]. The choice of material
laws and values of those who directly cited these prioritized
source articles are listed in Table 10.

Table 10. Extracted material equations for the intervertebral disc.

De (MPa−1)μd (MPa)αcνbEa (MPa)VariantLawReferencePathologyStudy, year

—1.2 h——3.4—gLinear elasticSpilker et al [90], 1986SCIfGreaves et al [41], 2008

aE: Young modulus.
bν: Poisson ratio. Where missing, the value of ν was assumed to be 0.45.
cα: material exponent parameter.
dμ: ground shear hyperelastic modulus.
eD: compressibility constant.
fSCI: spinal cord injury.
gNot available.
hValues in italics are input based on the identity for isotropic materials, D=3(1-2ν)/(μ{1+ν}), and for linear elastic, μ=E/(2{1+ν}).

Findings From the FEA Studies of DCM
Of the DCM models, 60% (9/15) specifically focused on
ossification of the posterior longitudinal ligament (OPLL), a
specific subtype of DCM.

Stress and Static Cord Compression
A total of 8 models explored the relationship between the
amount of static spinal cord compression and spinal cord stress.
Kato et al [56] and Kim et al [21] used parametric models of
the spinal cord to explore the implications of OPLL (anterior)
compression at 2 adjacent vertebrae. The model was constrained
posteriorly, reflecting the lamina. They found that the stress

increased with increasing cord compression, with an apparent
exponential relationship. Minimal stress was detected at <40%
but dramatically increased at ≥50%. This relationship was
replicated by Nishida et al [91] using posterior compression,
by Liang et al [48] simulating a disk prolapse, and in a
multisegmental model of OPLL by Khuyagbaatar et al [52,57].
Furthermore, it was replicated in cervical spondylosis by Levy
et al [18] (Figure 3 [18,21,52,57]).

Maximal stress was observed in the gray matter and, to a lesser
extent, in the lateral and posterior funiculus. Nishida et al [91]
observed differences in the stress distribution at low
compression rates depending on the spinal cord level related to

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differing morphology; however, beyond a compression rate of
30%, this was consistent (Figure 4).

Okazaki et al [50] explored the implications of spinal cord aging
using a parametric model of the spinal cord. The model was
given white and gray matter properties based on a young or

aged bovine spinal cord specimen. They observed that stress
increased under a low amount of anterior compression in the
aged spinal cord and was more widely distributed throughout
the gray matter and white matter. In contrast, the gray matter
was unaffected in the young specimen.

Figure 3. Spinal cord compression and spinal cord stress in degenerative cervical myelopathy models. For models tabulating the von Mises stress at
different measures of static compression or canal stenosis (n=4) [18,21,52,57], the values were plotted on a line graph with a line of best fit representing
the average value (blue).

Figure 4. Spinal cord compression and location of spinal cord stress in degenerative cervical myelopathy models. The spinal cord was partitioned, per
hemicord, as gray matter and anterior, anterolateral, posterolateral, and posterior white matter. For each study, reporting the cross-sectional distribution
of Von Mises stress (n=12) and the location of stress that fell within the top 30% of measured stress was noted. These frequencies were aggregated by
compression pattern and displayed for (A) anterior diffuse and static, (B) anterior focal and static, and (C) circumferential and dynamic distribution and
location of stress as relative proportions.

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Stress and Dynamic Cord Compression
Nishida et al [22] used a parametric model to explore the
implications of ligamentum flavum buckling in neck extension
in the context of cervical stenosis. For this, the spinal cord was
restricted posteriorly by the ligamentum flavum and then
anteriorly, either by a central curvature (representing a disk
prolapse) or a flat lateral or flat cross-sectional constraint
(representing the ligament). The amount of ligamentum flavum
buckling was measured using a kinematic MRI. Spinal cord
stress was observed in all scenarios and was maximal using the
flat cross-sectional constraint.

Later, Nishida et al [54] used a parametric model of OPLL to
demonstrate that while dynamic and static compression alone
could stress the spinal cord, they could also act together,
although it was unclear whether this was additive or
multiplicative. In dynamic compression alone, stress was more
restricted to gray matter.

Stress and Shape of Cord Compression
Khuyagbaatar et al [57] and Kim et al [21] did not identify any
difference in OPLL shape or type with respect to observed spinal
cord stress. Furthermore, in the study by Nishida et al [22], the
distribution of stress was broadly comparable across the three
scenarios affecting the gray matter and anterior and
posterolateral aspects of the white matter tracts. In unilateral
compression only, the ipsilateral gray matter was affected. Levy
et al [18] explored gradually increasing anterior diffuse
(broad-based disk), anterior lateral, and circumferential
compression using a static multilevel model. Different
phenotypes of stress were observed, including peak stress, point
of onset, and rate of increase. The highest stress was observed
with an anterior diffuse or circumferential compression (Figure
4).

Stress and Surgical Decompression
Khuyagbaatar et al [39] used a multisegmental model to explore
the implications of hemilaminectomy, laminectomy, and
laminoplasty on spinal cord stress following a 1-, 2-, 3-, or
4-level posterior decompression for continuous OPLL. Stress
remained elevated following hemilaminectomy but was low
and equivalent between laminectomy and laminoplasty. The
postoperative deformity was not modeled.

Nishida et al [55] used a parametric model to explore the
implications of alignment following posterior decompression
for OPLL. They demonstrated that although stress decreased
significantly following decompression, it slightly increased in
the anterior funiculus, increasing in the gray matter and
posterolateral funiculi with progressive deformity. They
subsequently replicated this in a separate analysis [51],
demonstrating that kyphosis and increased mobility after
decompression would elevate the observed stress.

Khuyagbaatar et al [49,52] explored the effects of laminectomy
and laminoplasty, respectively, for the treatment of OPLL using
a multisegmental static compression model. They demonstrated
that all procedures reduced spinal cord stress significantly
(>90%), whether in lordotic (K Line positive) or kyphotic
deformity (K Line negative) [94]. However, stress was elevated

within the exiting C5 nerve root following laminectomy if there
was a kyphotic deformity and lateral-type OPLL following
laminoplasty. In both instances, the amount of nerve root stress
was related to the amount of anterior compression.

Stoner et al [20] used a multisegmental dynamic model (C2-T1)
to explore the implications of multilevel C4-7 cervical
spondylosis (anterior disk prolapses and osteophyte formation)
treated with C4-7 anterior cervical discectomy and fusion
(ACDF), laminoplasty, or ACDF with laminectomy. Notably,
all procedures caused stress to increase at adjacent levels above
those of healthy controls. However, a stand-alone ACDF caused
increased stress within the spinal cord at C3 to a level above
that of the preoperative DCM model in flexion.

Where possible, these were aggregated, demonstrating that the
spinal cord tolerated significant compression before stress
increased exponentially (Figure 3 [18,21,52,57]). Aggregating
the distributions of stress observed across studies, based on the
nature of compression, demonstrated differing stress
distributions (Figure 4). For static and diffuse anterior
compression, the bilateral posterior white matter and gray matter
were the most affected. For static and focal compression, the
anterior white matter and, to a lesser extent, the gray matter
were most affected. This was observed bilaterally despite a focal
or lateral element. For circumferential compression in a dynamic
model, the bilateral gray matter and posterior white matter were
the most affected.

Stress and Tissue Injury
Notably, although differential patterns of stress were observed
throughout these DCM models, the levels remained relatively
low (<0.5 MPa). DCM FEA models did not explore the
relationship between the observed stress and tissue injury.

Discussion

Overview
FEA is a promising technique used in DCM, although there
remain uncertainties regarding the ideal approach and its clinical
interpretation. This review highlights the numerous decisions
investigators must make when performing FEA, which can
affect findings and underpin the need for a systematic approach,
as applied in this study. On the basis of current evidence, we
have shortlisted our preferred material property choices for a
DCM model and conclude that a distinction between gray and
white matter is preferable.

Principal Findings and Comparison to Prior Work
A total of 15 studies were identified applying FEA to investigate
DCM. The insights from these studies broadly align with the
current evidence base. First, the spinal cord can tolerate some
compression. This is in keeping with clinical practice, where
asymptomatic spinal cord compression is far more common [3],
and the amount of cord compression is a poor surrogate for
disease severity or progression [1]. Second, the movement of
the subaxial cervical spine can augment the stress on the spinal
cord. This is in keeping with clinical practice, including the
concept of dynamic injury and the proposed role of
flexion/extension MRI or electrophysiology [95-98]. Finally,

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it demonstrated the significant effectiveness of decompression
surgery, regardless of the technique, and the comparatively
minor gains of using one technique over the other. This is in
keeping with clinical practice, where high-quality comparisons
of anterior versus posterior surgery are equivalent, and currently,
there is no strong evidence that routine stabilization (eg,
instrumented fusion vs laminoplasty vs laminectomy or ACDF
vs ACDF with a plate) is required [99-102], all pointing toward
the need for a personalized surgical approach [15].

Furthermore, although more nuanced findings were proposed
by the identified FEA studies and this would require in vivo
corroboration, the application of FEA in DCM appears well
founded overall. More widely, it also seems potentially valuable
and timely. The pathobiology of DCM is poorly understood,
with its investigation being among the top 10 global research
priorities [1]. Current preclinical models have many limitations.
For example, common recent models use an expandable polymer
inserted behind the spinal cord and within the canal to cause
cervical myelopathy. Therefore, this does not model anterior
compression, nor does it truly represent a chronic injury
mechanism. Furthermore, in clinical practice, clinical decisions
are based on imperfect tools [103]. For example, structural MRI
in a supine position defines the nature of degenerative changes
but not if, where, or how an SCI occurs. FEA could change this,
particularly given the parallel advances in the automatic
segmentation of MRI [12].

Furthermore, while this review highlights that FEA is a versatile
technique, investigators must make many decisions regarding
how it is applied. These decisions can alter the findings and,
therefore, must be carefully considered. At this stage, there seem
to be only a few pervasive insights. First, it seems prudent to
model the white matter and gray matter separately. Ichihara et
al [73] demonstrated that these structures have differing material
properties, and how they are defined alters the observed stress
and strain. Furthermore, these structures age differently, as
shown by Ozawa et al [76]. Histological studies of DCM have
shown differing disease features among the white matter and
gray matter, with the gray matter being the focus of more
significant cellular changes [9]. Moreover, aging is an important
factor in DCM, associated with greater disease severity, a greater
rate of progression, and poorer response to treatment [104].
There are also early indicators that accelerating aging is a
pathological process [1]. Therefore, the observation that the
gray matter was unaffected in the younger spinal cord specimen
is noteworthy [34,45,50].

Second, while some models have chosen to use linear elastic
equations, time-independent hyperelastic models more closely
reflected the known material properties of the spinal cord. These,
or simply tabulated data, were generally adopted by DCM
studies and supported by a single study that evaluated different
approaches [38]. Conceptually, taking a more faithful approach
to modeling the spinal cord material properties is likely to be
more applicable to DCM and its etiology, as contrasted with
traumatic SCI, spinal cord stress may be below the limits for
tissue injury (eg, asymptomatic spinal cord compression), and
above (eg, DCM). It is worth noting that none of these
approaches considers the impact of repetitive injuries, and it is
likely that time dependence in modeling is relevant [1]. Given

the timeline of DCM pathogenesis (years), this is likely beyond
the normal material scales.

Finally, similar to DCM, as the stresses involved are well below
the elastic limit of the bone, the vertebrae can be modeled simply
as rigid bodies. The critical aspect for bones is instead the way
that their geometry and movement affect the loading on the soft
tissues.

However, there remain many uncertainties for further evaluation.
These include the role of spinal cord oscillation, the
appropriateness of the reference material properties for DCM,
and the relationship between the measured stress and tissue
injury. First, no studies specifically consider spinal cord
oscillations [105]. The spinal cord oscillates cranio-caudally
with heart rate. Recent imaging studies have indicated that this
increased in the context of symptomatic stenosis, the nature of
which may correlate with clinical measures of disease severity
[106,107]. Spinal cord oscillation would likely result in a shear
force on the spinal cord.

Second, it is uncertain how applicable the material properties
are to DCM. Most elements are based on young healthy tissue
references. In contrast, the ligaments and disks, for example, in
DCM, are often degenerated and calcified, and, as
aforementioned, the structure of the spinal cord is also
recognized to change with age.

However, most importantly, none of these studies have
specifically explored how the measured stress is related to tissue
injury. Bridging this gap is critical, not only to fully confirm
the appropriateness of FEA for DCM but also to guide its
clinical interpretation [108]. All biological systems will have
some baseline stress or strain; therefore, establishing disease
thresholds will be critical to its development. The parallel
development of in vivo techniques to measure tissue injury can
complement this, for example, microstructural MRI and the less
developed but promising serum and CSF biomarkers; however,
this requires further prospective study.

Limitations
This study has some limitations. First, the search strategy
focused on FEA models of the spinal cord and used citations
to identify the source articles for all anatomical elements.
Consequently, relevant source articles on the behavior of
anatomical elements may have been missed. This is more likely
for elements that were further removed from the spinal cord,
such as the IVD, and experiments published more recently. This
was a pragmatic decision based on the fact that existing
investigators would likely have the best perspective on the
literature, that this is a small research field, and that detailed
biomechanical data on elements such as the IVD were unlikely
to be so relevant. Consistent decisions across different research
groups and findings across source articles would endorse this.
Furthermore, due to the nature of our synthesis, we were unable
to update our search. Although this may result in the omission
of newer FEA articles, we believe that our review provides a
useful approach for future investigators aiming to use FEA in
DCM. Second, the methods used to shortlist source articles
represent a framework we developed for the purpose of building
a DCM FEA model. Again, the popularity of the shortlisted

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articles across research groups provides some external
validation, but it is possible that different investigators would
reach different conclusions. For this reason, all source articles
are listed in Multimedia Appendix 6, with their respective direct
object identifiers. Third, this review aggregates data from a
range of different experimental approaches and aims. Therefore,
the analysis is largely qualitative, adhering to the SWiM
guidelines [26]. Consequently, some conclusions, such as the
relationship between the nature of spinal cord compression and
stress distribution, remain tentative.

Conclusions
FEA has significant potential to help unlock uncertainties around
the pathophysiology of DCM and inform clinical care. Currently,

the application of FEA to DCM remains in its infancy. This
review has adopted an intensive and iterative approach to help
future investigators use FEA in DCM, including the aggregation
of experimental data reporting on material properties and how
they have been interpreted thus far. While single
recommendations have been made, they have their limitations.
The choice of material properties will influence the model
performance, and investigators should consider their decisions
carefully, particularly as new evidence emerges. More broadly,
the methodology used in this review may be relevant to future
updates and other clinical FEA initiatives when selecting
material properties.

Acknowledgments
This study aligns with the AO Spine Research Objectives and Common Data Elements for Degenerative Cervical Myelopathy
(RECODE-DCM) James Lind Alliance top research priorities, selected by people living and working with degenerative cervical
myelopathy. This includes “biological basis,” and, to a lesser extent, “individualizing surgery” and “imaging and electrophysiology.”
VN is supported by an NIHR Rosetrees Advanced Trust Fellowship. BMD was supported by a National Institute for Health
Research Clinical Doctoral Research Fellowship. The views expressed in this publication are those of the authors and not necessarily
those of the National Health Service, the National Institute for Health Research, Rosetrees Trust or the Department of Health and
Social Care. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the
manuscript.

Data Availability
The data sets generated during and analyzed during this study are available from the corresponding author on reasonable request.

Authors' Contributions
BMD designed the study, developed the search strategy, conducted the searches, screened the retrieved papers, extracted relevant
information, and drafted the manuscript. SS contributed to paper screening and data extraction. ARF contributed to the writing
of the subsequent drafts of this paper. VJFN and MPFS contributed throughout the project, starting from conceptualization to
study design, search strategy development, and editing subsequent drafts of the paper.

Conflicts of Interest
None declared.

Multimedia Appendix 1
PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist.
[DOCX File , 108 KB-Multimedia Appendix 1]

Multimedia Appendix 2
Search strategy.
[DOCX File , 13 KB-Multimedia Appendix 2]

Multimedia Appendix 3
The quality assessment tool developed by the authors.
[DOCX File , 13 KB-Multimedia Appendix 3]

Multimedia Appendix 4
Comparison of modeling decisions.
[DOCX File , 274 KB-Multimedia Appendix 4]

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Multimedia Appendix 5
Comparison of chosen equation and reference material property study.
[DOCX File , 936 KB-Multimedia Appendix 5]

Multimedia Appendix 6
Material properties of other anatomical elements.
[DOCX File , 1052 KB-Multimedia Appendix 6]

Multimedia Appendix 7
Interactive network files.
[ZIP File (Zip Archive), 561 KB-Multimedia Appendix 7]

Multimedia Appendix 8
Higher-resolution version of Figure 2.
[PNG File , 5013 KB-Multimedia Appendix 8]

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Abbreviations
ACDF: anterior cervical discectomy and fusion
CSF: cerebrospinal fluid
DCM: degenerative cervical myelopathy
FEA: finite element analysis
IVD: intervertebral disk
MRI: magnetic resonance imaging
OPLL: ossification of the posterior longitudinal ligament
PRISMA-ScR: Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping
Reviews
SCI: spinal cord injury
SWiM: Synthesis Without Meta-Analysis

Edited by T Leung; submitted 13.04.23; peer-reviewed by M Arab-Zozani, A Perez Sanpablo; comments to author 28.08.23; revised
version received 31.10.23; accepted 15.02.24; published 28.03.24

Please cite as:
Davies B, Schaefer S, Rafati Fard A, Newcombe V, Sutcliffe M
Finite Element Analysis for Degenerative Cervical Myelopathy: Scoping Review of the Current Findings and Design Approaches,
Including Recommendations on the Choice of Material Properties
JMIR Biomed Eng 2024;9:e48146
URL: https://biomedeng.jmir.org/2024/1/e48146
doi: 10.2196/48146
PMID:

©Benjamin Davies, Samuel Schaefer, Amir Rafati Fard, Virginia Newcombe, Michael Sutcliffe. Originally published in JMIR
Biomedical Engineering (http://biomsedeng.jmir.org), 28.03.2024. This is an open-access article distributed under the terms of
the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work, first published in JMIR Biomedical Engineering, is
properly cited. The complete bibliographic information, a link to the original publication on https://biomedeng.jmir.org/, as well
as this copyright and license information must be included.

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