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Cited by 316Advanced Neuroimaging Techniques and ApplicationsAdvanced MRI Techniques and ApplicationsNMR spectroscopy and applications

Degeneracy in model parameter estimation for multi‐compartmental diffusion in neuronal tissue

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Jelescu, Ileana O., Veraart, Jelle, Fieremans, Els, Novikov, Dmitry S.·2016·NMR in Biomedicine
DOI 10.1002/nbm.3450PMC PMC4920129
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Abstract

The ultimate promise of diffusion MRI (dMRI) models is specificity to neuronal microstructure, which may lead to distinct clinical biomarkers using noninvasive imaging. While multi‐compartment models are a common approach to interpret water diffusion in the brain in vivo, the estimation of their parameters from the dMRI signal remains an unresolved problem. Practically, even when q space is highly oversampled, nonlinear fit outputs suffer from heavy bias and poor precision. So far, this has been alleviated by fixing some of the model parameters to a priori values, for improved precision at the expense of accuracy. Here we use a representative two‐compartment model to show that fitting fails to determine the five model parameters from over 60 measurement points. For the first time, we identify the reasons for this poor performance. The first reason is the existence of two local minima in the parameter space for the objective function of the fitting procedure. These minima correspond to qualitatively different sets of parameters, yet they both lie within biophysically plausible ranges. We show that, at realistic signal‐to‐noise ratio values, choosing between the two minima based on the associated objective function values is essentially impossible. Second, there is an ensemble of very low objective function values around each of these minima in the form of a pipe. The existence of such a direction in parameter space, along which the objective function profile is very flat, explains the bias and large uncertainty in parameter estimation, and the spurious parameter correlations: in the presence of noise, the minimum can be randomly displaced by a very large amount along each pipe. Our results suggest that the biophysical interpretation of dMRI model parameters crucially depends on establishing which of the minima is closer to the biophysical reality and the size of the uncertainty associated with each parameter. Copyright © 2015 John Wiley & Sons, Ltd.

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  • Pathology changes at different MS stages may be attributed to decoupling between intra-axonal and extra-axonal diffusivity.

    “The pathology changes at different MS stages may also be attributed to the decoupling between intra-axonal and extra-axonal diffusivity, which may also affect the calculated NODDI metrics and need substantial investigation in further studies (Jelescu et al. 2015; Jelescu et al. 2016).”

    From Neurite orientation dispersion and density imaging of mouse brain microstructure (2019) · cites this paper as Jelescu et al. 2016

    The Discussion (page not provided) says diffusivities may change with pathology and reports that intra- and extra-axonal axial diffusivities may differ. The Theory section (page not provided) says NODDI instead fixes them equal, supporting concern about its estimates. The paper does not link their decoupling to pathology changes at different MS stages. (LLM, conf=0.85)

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  • Diffusion MRI can provide a mesoscopic assay of tissue geometry, provided that model complexity is matched to the acquisition protocol and that the resulting parameters are interpreted as model-dependent estimates rather than direct histological measurements.

    “These observations reinforce the broader view that diffusion MRI can provide a mesoscopic assay of tissue geometry, provided that model complexity is matched to the acquisition protocol and that the resulting parameters are interpreted as model-dependent estimates rather than direct histological measurements (Jelescu & Budde, 2017; Jelescu et al., 2016; Novikov et al., 2019).”

    From 40 Years of diffusion MRI in the brain: From history to emerging frontiers (2026) · cites this paper as Jelescu et al., 2016

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  • Calculated NODDI metrics are parametrically dependent on the Din value.

    “Since obtaining the ground truth of Din value is still challenging, the calculated NODDI metrics are parametrically dependent on this value (Jelescu et al. 2016; Lampinen et al. 2017).”

    From Neurite orientation dispersion and density imaging of mouse brain microstructure (2019) · cites this paper as Jelescu et al. 2016

  • Decoupling between intra-axonal and extra-axonal diffusivity may affect calculated NODDI metrics.

    “The pathology changes at different MS stages may also be attributed to the decoupling between intra-axonal and extra-axonal diffusivity, which may also affect the calculated NODDI metrics and need substantial investigation in further studies (Jelescu et al. 2015; Jelescu et al. 2016).”

    From Neurite orientation dispersion and density imaging of mouse brain microstructure (2019) · cites this paper as Jelescu et al. 2016

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Cited by

Sorted by citations

Authors
Jelescu, I. O., Veraart, J., Fieremans, E., & Novikov, D. S.
Affiliations
NYU Langone Health (US)
Corresponding author
Jelescu, Ileana O.
ORCID
Venue
NMR in Biomedicine
Publisher
Wiley
Published
2015-11-29
Volume
29
Issue
1
Pages
33-47
ISSN
  • 0952-3480 (ISSN-L)
  • 1099-1492
DOI
10.1002/nbm.3450
PMC
PMC4920129
OpenAlex
W2177835590
Cited by
316
References
5
Citations by year
2026: 20 · 2025: 37 · 2024: 29 · 2023: 24 · 2022: 26 · 2021: 38 · 2020: 40 · 2019: 32 · 2018: 27 · 2017: 29 · 2016: 13
FWCI
18.62
Type
journal article
Language
en
Open access
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations
  • NMR in Biomedicine (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • National Institute on Aging (P30AG008051)
  • National Institute of Neurological Disorders and Stroke (R01NS088040)
  • National Institute of Biomedical Imaging and Bioengineering (P41EB017183)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
  • Physical Sciences › Physics and Astronomy › Nuclear and High Energy Physics › NMR spectroscopy and applications
Keywords
diffusion MRI, parameter estimation, degeneracy, local minima, two-compartment model, parameter uncertainty, signal-to-noise ratio, q-space imaging
MeSH terms
Aged, Female, Humans, Neurons, Diffusion Magnetic Resonance Imaging

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