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The quest for measuring myelin with MRI – An interactive meta-analysis of quantitative comparisons with histology

View ORCID ProfileMatteo Mancini, Agah Karakuzu, View ORCID ProfileThomas Nichols, View ORCID ProfileJulien Cohen-Adad, View ORCID ProfileMara Cercignani, View ORCID ProfileNikola Stikov
doi: https://doi.org/10.1101/2020.07.13.200972
Matteo Mancini
1Department of Neuroscience, Brighton and Sussex Medical School, University of Sussex, Brighton, United Kingdom
2NeuroPoly Lab, Polytechnique Montreal, Montreal, Quebec, Canada
3CUBRIC, Cardiff University, Cardiff, United Kingdom
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  • ORCID record for Matteo Mancini
  • For correspondence: ingmatteomancini@gmail.com
Agah Karakuzu
2NeuroPoly Lab, Polytechnique Montreal, Montreal, Quebec, Canada
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Thomas Nichols
4Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
5Big Data Institute, University of Oxford, Oxford, United Kingdom
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Julien Cohen-Adad
2NeuroPoly Lab, Polytechnique Montreal, Montreal, Quebec, Canada
6Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, Quebec, Canada
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Mara Cercignani
1Department of Neuroscience, Brighton and Sussex Medical School, University of Sussex, Brighton, United Kingdom
7Neuroimaging Laboratory, Fondazione Santa Lucia, Rome, Italy
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Nikola Stikov
2NeuroPoly Lab, Polytechnique Montreal, Montreal, Quebec, Canada
8Montreal Heart Institute, Université de Montréal, Montreal, Quebec, Canada
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Abstract

Several MRI measures have been proposed as in vivo biomarkers of myelin content, each with a concrete application ranging from plasticity to pathology. Despite the broad availability of these myelin-sensitive MRI modalities, specificity and sensitivity have been a matter of discussion. Debate about which MRI measure is the most suitable one for quantifying myelin is still ongoing.

In this study, we performed a systematic review of published quantitative validation studies, and used meta-analysis tools to clarify how different these measures are when compared to the underlying histology, controlling for the study sample size and using interactive visualization tools (https://neurolibre.github.io/myelin-meta-analysis). A first qualitative selection of 58 studies proposed 35 different measures to characterize myelin content. However, a quantitative analysis showed that most of these measures have a limited coefficient of determination and provide little information to inform future studies, because of the large prediction intervals and high heterogeneity. These results indicate that most measures are statistically equivalent regarding their relationship with histology and that future work should take inter-study variability into consideration.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Links to interactive figures and PRISMA checklist have been added.

  • https://neurolibre.github.io/myelin-meta-analysis

  • Abbreviations and mathematical symbols

    AD
    axial diffusivity
    AK
    axial kurtosis
    AWF
    axonal water fraction
    FA
    fraction anisotropy
    ihMTR
    inhomogeneous magnetization transfer ratio
    k_fm
    free water-macromolecular exchange rate
    k_mf
    macromolecular-free water exchange rate
    M0m
    macromolecular pool magnetization fraction
    MD
    mean diffusivity
    MK
    mean kurtosis
    MPF
    macromolecular pool fraction
    MT
    magnetization transfer
    MTR
    magnetization transfer ratio
    MTR-UTE
    magnetization transfer ratio (using ultra-short echo time)
    MTV
    macromolecular tissue volume
    MVF-MT
    myelin volume fraction (estimated from MT)
    MVF-T2
    myelin volume fraction (estimated from T2)
    MWF
    myelin water fraction
    PD
    proton density
    PN
    peripheral nerve
    PRISMA
    Preferred Reporting Items for Systematic Reviews and Meta-Analyses
    QSM
    quantitative susceptibility mapping
    R1f
    free water pool longitudinal relaxation rate
    R2*
    apparent transverse relaxation rate
    RAFF
    relaxation along a fictitious field
    RD
    radial diffusivity
    RD-DBSI
    radial diffusivity (from diffusion basis spectrum imaging)
    RDe
    extra-cellular compartment radial diffusivity
    RK
    radial kurtosis
    rSPF
    relative semi-solid proton fraction
    SC
    spinal cord
    T1
    longitudinal relaxation time
    T1p
    adiabatic longitudinal relaxation time
    T1sat
    longitudinal relaxation time under magnetization transfer irradiation
    T2
    transverse relaxation time
    T2f
    free water pool transverse relaxation time
    T2int
    transverse relaxation intermediate component
    T2m
    macromolecular pool transverse relaxation rate
    T2p
    adiabatic transverse relaxation time
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    The quest for measuring myelin with MRI – An interactive meta-analysis of quantitative comparisons with histology
    Matteo Mancini, Agah Karakuzu, Thomas Nichols, Julien Cohen-Adad, Mara Cercignani, Nikola Stikov
    bioRxiv 2020.07.13.200972; doi: https://doi.org/10.1101/2020.07.13.200972
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    The quest for measuring myelin with MRI – An interactive meta-analysis of quantitative comparisons with histology
    Matteo Mancini, Agah Karakuzu, Thomas Nichols, Julien Cohen-Adad, Mara Cercignani, Nikola Stikov
    bioRxiv 2020.07.13.200972; doi: https://doi.org/10.1101/2020.07.13.200972

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