<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD v1.1 20151215//EN" "JATS-archivearticle1.dtd"> <article xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" article-type="research-article" dtd-version="1.1"><front><journal-meta><journal-id journal-id-type="nlm-ta">elife</journal-id><journal-id journal-id-type="publisher-id">eLife</journal-id><journal-title-group><journal-title>eLife</journal-title></journal-title-group><issn pub-type="epub" publication-format="electronic">2050-084X</issn><publisher><publisher-name>eLife Sciences Publications, Ltd</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">61523</article-id><article-id pub-id-type="doi">10.7554/eLife.61523</article-id><article-categories><subj-group subj-group-type="display-channel"><subject>Research Article</subject></subj-group><subj-group subj-group-type="heading"><subject>Neuroscience</subject></subj-group></article-categories><title-group><article-title>An interactive meta-analysis of MRI biomarkers of myelin</article-title></title-group><contrib-group><contrib contrib-type="author" corresp="yes" id="author-175058"><name><surname>Mancini</surname><given-names>Matteo</given-names></name><contrib-id authenticated="true" contrib-id-type="orcid">https://orcid.org/0000-0001-7194-4568</contrib-id><email>ingmatteomancini@gmail.com</email><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="aff" rid="aff2">2</xref><xref ref-type="aff" rid="aff3">3</xref><xref ref-type="other" rid="fund1"/><xref ref-type="fn" rid="con1"/><xref ref-type="fn" rid="conf1"/></contrib><contrib contrib-type="author" id="author-199790"><name><surname>Karakuzu</surname><given-names>Agah</given-names></name><contrib-id authenticated="true" contrib-id-type="orcid">https://orcid.org/0000-0001-7283-271X</contrib-id><xref ref-type="aff" rid="aff2">2</xref><xref ref-type="fn" rid="con2"/><xref ref-type="fn" rid="conf1"/></contrib><contrib contrib-type="author" id="author-10951"><name><surname>Cohen-Adad</surname><given-names>Julien</given-names></name><contrib-id authenticated="true" contrib-id-type="orcid">https://orcid.org/0000-0003-3662-9532</contrib-id><xref ref-type="aff" rid="aff2">2</xref><xref ref-type="aff" rid="aff4">4</xref><xref ref-type="fn" rid="con3"/><xref ref-type="fn" rid="conf1"/></contrib><contrib contrib-type="author" id="author-199791"><name><surname>Cercignani</surname><given-names>Mara</given-names></name><contrib-id authenticated="true" contrib-id-type="orcid">https://orcid.org/0000-0002-4550-2456</contrib-id><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="aff" rid="aff5">5</xref><xref ref-type="fn" rid="con4"/><xref ref-type="fn" rid="conf1"/></contrib><contrib contrib-type="author" equal-contrib="yes" id="author-42661"><name><surname>Nichols</surname><given-names>Thomas E</given-names></name><contrib-id authenticated="true" contrib-id-type="orcid">https://orcid.org/0000-0002-4516-5103</contrib-id><xref ref-type="aff" rid="aff6">6</xref><xref ref-type="aff" rid="aff7">7</xref><xref ref-type="fn" rid="equal-contrib1">†</xref><xref ref-type="other" rid="fund2"/><xref ref-type="fn" rid="con5"/><xref ref-type="fn" rid="conf1"/></contrib><contrib contrib-type="author" equal-contrib="yes" id="author-132700"><name><surname>Stikov</surname><given-names>Nikola</given-names></name><contrib-id authenticated="true" contrib-id-type="orcid">http://orcid.org/0000-0002-8480-5230</contrib-id><xref ref-type="aff" rid="aff2">2</xref><xref ref-type="aff" rid="aff8">8</xref><xref ref-type="fn" rid="equal-contrib1">†</xref><xref ref-type="fn" rid="con6"/><xref ref-type="fn" rid="conf1"/></contrib><aff id="aff1"><label>1</label><institution>Department of Neuroscience, Brighton and Sussex Medical School, University of Sussex</institution><addr-line><named-content content-type="city">Brighton</named-content></addr-line><country>United Kingdom</country></aff><aff id="aff2"><label>2</label><institution>NeuroPoly Lab, Polytechnique Montreal</institution><addr-line><named-content content-type="city">Montreal</named-content></addr-line><country>Canada</country></aff><aff id="aff3"><label>3</label><institution>CUBRIC, Cardiff University</institution><addr-line><named-content content-type="city">Cardiff</named-content></addr-line><country>United Kingdom</country></aff><aff id="aff4"><label>4</label><institution>Functional Neuroimaging Unit, CRIUGM, Université de Montréal</institution><addr-line><named-content content-type="city">Montreal</named-content></addr-line><country>Canada</country></aff><aff id="aff5"><label>5</label><institution>Neuroimaging Laboratory, Fondazione Santa Lucia</institution><addr-line><named-content content-type="city">Rome</named-content></addr-line><country>Italy</country></aff><aff id="aff6"><label>6</label><institution>Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford</institution><addr-line><named-content content-type="city">Oxford</named-content></addr-line><country>United Kingdom</country></aff><aff id="aff7"><label>7</label><institution>Big Data Institute, University of Oxford</institution><addr-line><named-content content-type="city">Oxford</named-content></addr-line><country>United Kingdom</country></aff><aff id="aff8"><label>8</label><institution>Montreal Heart Institute, Université de Montréal</institution><addr-line><named-content content-type="city">Montreal</named-content></addr-line><country>Canada</country></aff></contrib-group><contrib-group content-type="section"><contrib contrib-type="editor"><name><surname>Jbabdi</surname><given-names>Saad</given-names></name><role>Reviewing Editor</role><aff><institution>University of Oxford</institution><country>United Kingdom</country></aff></contrib><contrib contrib-type="senior_editor"><name><surname>Baker</surname><given-names>Chris I</given-names></name><role>Senior Editor</role><aff><institution>National Institute of Mental Health, National Institutes of Health</institution><country>United States</country></aff></contrib></contrib-group><author-notes><fn fn-type="con" id="equal-contrib1"><label>†</label><p>These authors contributed equally to this work</p></fn></author-notes><pub-date date-type="publication" publication-format="electronic"><day>21</day><month>10</month><year>2020</year></pub-date><pub-date pub-type="collection"><year>2020</year></pub-date><volume>9</volume><elocation-id>e61523</elocation-id><history><date date-type="received" iso-8601-date="2020-07-28"><day>28</day><month>07</month><year>2020</year></date><date date-type="accepted" iso-8601-date="2020-10-20"><day>20</day><month>10</month><year>2020</year></date></history><permissions><copyright-statement>© 2020, Mancini et al</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Mancini et al</copyright-holder><ali:free_to_read/><license xlink:href="http://creativecommons.org/licenses/by/4.0/"><ali:license_ref>http://creativecommons.org/licenses/by/4.0/</ali:license_ref><license-p>This article is distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License</ext-link>, which permits unrestricted use and redistribution provided that the original author and source are credited.</license-p></license></permissions><self-uri content-type="pdf" xlink:href="elife-61523-v2.pdf"/><abstract><p>Several MRI measures have been proposed as in vivo biomarkers of myelin, each with applications ranging from plasticity to pathology. Despite the availability of these myelin-sensitive modalities, specificity and sensitivity have been a matter of discussion. Debate about which MRI measure is the most suitable for quantifying myelin is still ongoing. In this study, we performed a systematic review of published quantitative validation studies to clarify how different these measures are when compared to the underlying histology. We analyzed the results from 43 studies applying meta-analysis tools, controlling for study sample size and using interactive visualization (<ext-link ext-link-type="uri" xlink:href="https://neurolibre.github.io/myelin-meta-analysis">https://neurolibre.github.io/myelin-meta-analysis</ext-link>). We report the overall estimates and the prediction intervals for the coefficient of determination and find that MT and relaxometry-based measures exhibit the highest correlations with myelin content. We also show which measures are, and which measures are not statistically different regarding their relationship with histology.</p></abstract><kwd-group kwd-group-type="author-keywords"><kwd>myelin</kwd><kwd>MRI</kwd><kwd>histology</kwd><kwd>meta-analysis</kwd><kwd>central nervous system</kwd><kwd>brain</kwd></kwd-group><kwd-group kwd-group-type="research-organism"><title>Research organism</title><kwd>Human</kwd><kwd>Mouse</kwd><kwd>Rat</kwd></kwd-group><funding-group><award-group id="fund1"><funding-source><institution-wrap><institution-id institution-id-type="FundRef">http://dx.doi.org/10.13039/100004440</institution-id><institution>Wellcome Trust</institution></institution-wrap></funding-source><award-id>213722/Z/18/Z</award-id><principal-award-recipient><name><surname>Mancini</surname><given-names>Matteo</given-names></name></principal-award-recipient></award-group><award-group id="fund2"><funding-source><institution-wrap><institution-id institution-id-type="FundRef">http://dx.doi.org/10.13039/100000002</institution-id><institution>National Institutes of Health</institution></institution-wrap></funding-source><award-id>R01MH096906</award-id><principal-award-recipient><name><surname>Nichols</surname><given-names>Thomas E</given-names></name></principal-award-recipient></award-group><funding-statement>The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.</funding-statement></funding-group><custom-meta-group><custom-meta specific-use="meta-only"><meta-name>Author impact statement</meta-name><meta-value>Most MRI-based myelin biomarkers are not statistically different regarding their relationship with histology.</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id="s1" sec-type="intro"><title>Introduction</title><p>Myelin is a key component of the central nervous system. The myelin sheaths insulate axons with a triple effect: allowing fast electrical conduction, protecting the axon, and providing trophic support (<xref ref-type="bibr" rid="bib50">Nave and Werner, 2014</xref>). The conduction velocity regulation has become an important research topic, with evidence of activity-dependent myelination as an additional mechanism of plasticity (<xref ref-type="bibr" rid="bib20">Fields, 2015</xref>; <xref ref-type="bibr" rid="bib61">Sampaio-Baptista and Johansen-Berg, 2017</xref>). Myelin is also relevant from a clinical perspective, given that demyelination is often observed in several neurological diseases such as multiple sclerosis (<xref ref-type="bibr" rid="bib29">Höftberger and Lassmann, 2018</xref>).</p><p>Given this important role in pathology and plasticity, measuring myelin in vivo has been an ambitious goal for magnetic resonance imaging (MRI) for more than two decades (<xref ref-type="bibr" rid="bib46">MacKay et al., 1994</xref>; <xref ref-type="bibr" rid="bib60">Rooney et al., 2007</xref>; <xref ref-type="bibr" rid="bib70">Stanisz et al., 1999</xref>). Even though the thickness of the myelin sheath is in the order of micrometres, well beyond the MRI spatial resolution, its presence influences several physical properties that can be probed with MRI, from longitudinal and transversal relaxation phenomena to water molecule diffusion processes.</p><p>However, being sensitive to myelin is not enough: to study how and why myelin content changes, it is necessary to define a specific biomarker. Interestingly, the quest for measuring myelin has evolved in parallel with an important paradigm shift in MRI research, where MRI data are no longer treated as just ‘pictures’, but as actual 3D distributions of quantitative measures. This perspective has breathed new life into an important field of research, quantitative MRI (qMRI), that encompasses the study of how to measure the relevant electromagnetic properties that influence magnetic resonance phenomena in biological tissues (<xref ref-type="bibr" rid="bib7">Cercignani et al., 2018</xref>; <xref ref-type="bibr" rid="bib12">Cohen-Adad and Wheeler-Kingshott, 2014</xref>). From the very definition of qMRI, it is clear that its framework applies to any approach for non-invasive myelin quantification.</p><p>Similarly to other qMRI biomarkers, MRI-based myelin measurements are indirect, and might be affected by other microstructural features, making the relationship between these indices and myelination noisy. Assessing the accuracy of such measurements, and their sensitivity to change, is essential for their translation into clinical applications. Validation is therefore a fundamental aspect of their development (<xref ref-type="bibr" rid="bib11">Cohen-Adad, 2018</xref>). The most common approach is based on acquiring MR data from in vivo or ex vivo tissue and then comparing those data with the related samples analyzed using histological techniques. Despite being the most realistic approach, this comparison involves several methodological choices, from the specific technique used as a reference to the quantitative measure used to describe the relationship between MRI and histology. So far, a long list of studies have looked at MRI-histology comparisons (<xref ref-type="bibr" rid="bib11">Cohen-Adad, 2018</xref>; <xref ref-type="bibr" rid="bib42">Laule and Moore, 2018</xref>; <xref ref-type="bibr" rid="bib47">MacKay and Laule, 2016</xref>; <xref ref-type="bibr" rid="bib53">Petiet et al., 2019</xref>), each of them focusing on a specific pathology and a few MRI measures.</p><p>Despite these numerous studies, there is still an ongoing debate on what MRI measure should be used to quantify myelin and as a consequence there is a constant methodological effort to propose new measures. This debate would benefit from a quantitative analysis of all the findings published so far, specifically addressing inter-study variations and prospects for future studies, something that is currently missing from the literature.</p><p>In this study, we systematically reviewed quantitative MRI-histology comparisons and we used meta-analysis tools to address the following question: how different are the modalities for myelin quantification in terms of their relationship with the underlying histology?</p></sec><sec id="s2" sec-type="results"><title>Results</title><sec id="s2-1"><title>Literature survey</title><p>The screening process is summarized in the flowcharts in <xref ref-type="fig" rid="fig1">Figure 1</xref> and <xref ref-type="fig" rid="app1fig1">Appendix 1—figure 1</xref>. The keywords as reported in the appendix returned 688 results on PubMed (last search on 03/06/2020). These results included 50 review articles. From the 50 review articles, six were selected as relevant for both the topics of myelin and related MRI-histology comparisons (<xref ref-type="bibr" rid="bib11">Cohen-Adad, 2018</xref>; <xref ref-type="bibr" rid="bib42">Laule and Moore, 2018</xref>; <xref ref-type="bibr" rid="bib39">Laule et al., 2007</xref>; <xref ref-type="bibr" rid="bib47">MacKay and Laule, 2016</xref>; <xref ref-type="bibr" rid="bib53">Petiet et al., 2019</xref>; <xref ref-type="bibr" rid="bib77">Turner, 2019</xref>). After the assessment, 58 original research studies were considered eligible, as shown in <xref ref-type="table" rid="app1table1">Appendix 1—table 1</xref> (in the appendix) and Figure S2. All the data collected are available in the supplementary materials (<xref ref-type="supplementary-material" rid="sdata1">Source data 1</xref>).</p><fig id="fig1" position="float"><label>Figure 1.</label><caption><title>Sankey diagram representing the screening procedure (PRISMA flow chart provided in the appendix).</title><p>To see the interactive figure: <ext-link ext-link-type="uri" xlink:href="https://neurolibre.github.io/myelin-meta-analysis/01/selection.html#figure-1">https://neurolibre.github.io/myelin-meta-analysis/01/selection.html#figure-1</ext-link>.</p></caption><graphic mime-subtype="jpeg" mimetype="image" xlink:href="elife-61523.xml.media/fig1.jpg"/></fig><p>In terms of specific modalities, the survey shows that the most common MRI approach compared with histology was diffusion-weighted imaging (used in 28 studies), followed by magnetization transfer (MT, 27 studies), T2 relaxometry (19 studies) and T1 relaxometry (10 studies). Only 20 studies considered more than one approach: among the others, 20 focused exclusively on diffusion, 12 on MT, and six on T2 relaxometry.</p><p>From these 58 studies, we then focused only on brain studies and we further excluded studies not reporting either the number of subjects or the number of ROIs per subject. We also excluded one single-subject study that relied on voxels as distinct samples, whereas the other studies in this review are based on ROIs (i.e. including more than one voxel). In the end, 43 suitable studies were identified for the subsequent analyses.</p></sec><sec id="s2-2"><title>Meta-analysis</title><p>To compare the studies of interest, we first organized them according to the MRI measure used. <xref ref-type="fig" rid="fig2">Figure 2</xref> and <xref ref-type="fig" rid="fig3">Figure 3</xref> (and also Figure S3-S4) show the R<sup>2</sup> values for the selected studies across measures: the highest values (R<sup>2</sup> >0.8) are obtained mostly from MT measures, but they are associated with small sample sizes (with an average of 32 sample points). The studies with largest sample sizes are associated with R<sup>2</sup> values between 0.6 and 0.8 for MT and T2 relaxometry, but with lower values for T1 relaxometry and other approaches.</p><fig id="fig2" position="float"><label>Figure 2.</label><caption><title>Bubble chart of R<sup>2</sup> values between a given MRI measure and histology for each study across MRI measures, with the area proportional to the number of samples.</title><p>To see the interactive figure: <ext-link ext-link-type="uri" xlink:href="https://neurolibre.github.io/myelin-meta-analysis/02/closer_look.html#figure-3">https://neurolibre.github.io/myelin-meta-analysis/02/closer_look.html#figure-3</ext-link>.</p></caption><graphic mime-subtype="jpeg" mimetype="image" xlink:href="elife-61523.xml.media/fig2.jpg"/></fig><fig id="fig3" position="float"><label>Figure 3.</label><caption><title>Treemap chart of the studies considered for the meta-analysis, organized by MRI measure.</title><p>The color of each box represents the reported R<sup>2</sup> value while the size box is proportional to the sample size. To see the interactive figure: <ext-link ext-link-type="uri" xlink:href="https://neurolibre.github.io/myelin-meta-analysis/02/closer_look.html#figure-4">https://neurolibre.github.io/myelin-meta-analysis/02/closer_look.html#figure-4</ext-link>.</p></caption><graphic mime-subtype="jpeg" mimetype="image" xlink:href="elife-61523.xml.media/fig3.jpg"/></fig><p>To combine the results for each measure, we then used a mixed-effect model: in this way we were able to express the overall effect size in terms of a range of R<sup>2</sup> values within a confidence interval, but also to assess prediction intervals and inter-study differences. The results are shown as forest plots in <xref ref-type="fig" rid="fig4">Figure 4</xref> (and also Figure S5).</p><fig id="fig4" position="float"><label>Figure 4.</label><caption><title>Forest plots showing the R<sup>2</sup> values reported by the studies and estimated from the mixed-effect model for each measure.</title><p>The hourglasses and the dotted lines in the mixed-effect model outcomes represent the prediction intervals. To see the interactive figure: <ext-link ext-link-type="uri" xlink:href="https://neurolibre.github.io/myelin-meta-analysis/03/meta_analysis.html#figure-5">https://neurolibre.github.io/myelin-meta-analysis/03/meta_analysis.html#figure-5</ext-link>.</p></caption><graphic mime-subtype="jpeg" mimetype="image" xlink:href="elife-61523.xml.media/fig4.jpg"/></fig><p>Apart from MPF and MWF, all the measures showed R<sup>2</sup> overall estimates in the range 0.21–0.53. To investigate the significance of the differences between measures, we conducted a repeated measures meta-regression on every R<sup>2</sup> estimate recorded (98 in total over 43 studies). As shown in <xref ref-type="fig" rid="fig5">Figure 5</xref> (and also Figure S6), the measures can be roughly subdivided in two groups: MT- and relaxometry-based measures gave significantly higher R<sup>2</sup> estimates compared to diffusion-based measures. Within the diffusion-based measures, FA shows slightly higher estimates than the others, with marginal significance over RD and AD or no significance in case of MD.</p><fig id="fig5" position="float"><label>Figure 5.</label><caption><title>Results from the repeated measures meta-regression, displayed in terms of z-scores (left) and p-values (right) for each pairwise comparison across all the MRI measures.</title><p>In the z-score heatmap, each element refers to the comparison between the measure on the x axis with the one on the y axis. For example, MPF and FA (z-score = 7.14; p-value<0.0001) are statistically different, while MPF and T1 (z-score = 2.51; p-value=0.43) are not statistically different. To see the interactive figure: <ext-link ext-link-type="uri" xlink:href="https://neurolibre.github.io/myelin-meta-analysis/03/meta_analysis.html#figure-6">https://neurolibre.github.io/myelin-meta-analysis/03/meta_analysis.html#figure-6</ext-link>.</p></caption><graphic mime-subtype="jpeg" mimetype="image" xlink:href="elife-61523.xml.media/fig5.jpg"/></fig><p>Within MT- and relaxometry-based measures, the trends follow those in the forest plots (<xref ref-type="fig" rid="fig4">Figure 4</xref>), but most differences are not significant (<xref ref-type="fig" rid="fig5">Figure 5</xref>). However, the results in terms of z-score give a measure of distance between the R<sup>2</sup> distributions. From this perspective, MPF has higher R<sup>2</sup> estimates compared to all the other measures, but it is only marginally higher than MWF (z-score = 0.77; p-value=1) so we cannot claim that one is superior to the other. Following the same reasoning, MTR and T1 are not statistically different (z-score = 0.47; p-value=1).</p><p>When considering the prediction intervals calculated using τ<sup>2</sup> (the variance of the effect size parameters across the population of studies), for most measures the interval spanned from 0.1 to 0.9 (<xref ref-type="fig" rid="fig4">Figure 4</xref> and Figure S5). This implies that future studies relying on such measures can expect, on the basis of these studies, to obtain any R<sup>2</sup> value in this broad interval. The only exceptions were MPF (0.49–1) and MWF (0.45–0.95), whose intervals were narrower than the alternatives. Finally, I<sup>2</sup> (a measure of how much of the variability in a typical study is due to heterogeneity in the experimental design) was generally quite high (<xref ref-type="table" rid="table1">Table 1</xref>). MWF showed the lowest I<sup>2</sup> across measures (I<sup>2</sup> = 73.19%), but this may be misleading considering that it was based on only four studies, while the other measures included around 10 studies. Excluding MWF, MPF also showed a relatively low I<sup>2</sup> (I<sup>2</sup> = 83.18%). Qualitative comparisons across experimental conditions and methodological choices highlighted differences across pathology models, targeted tissue types and reference techniques (<xref ref-type="fig" rid="fig6">Figure 6</xref> and Figure S7). Other factors such as magnetic field, co-registration, specific tissue and the related conditions (Figure S8) showed comparable distributions.</p><fig id="fig6" position="float"><label>Figure 6.</label><caption><title>Experimental conditions and methodological choices influencing the R<sup>2</sup> values (top: reference techniques; middle: pathology model; bottom: tissue types).</title><p>To see the interactive figure: <ext-link ext-link-type="uri" xlink:href="https://neurolibre.github.io/myelin-meta-analysis/04/other_factors.html#figure-7">https://neurolibre.github.io/myelin-meta-analysis/04/other_factors.html#figure-7</ext-link>.</p></caption><graphic mime-subtype="jpeg" mimetype="image" xlink:href="elife-61523.xml.media/fig6.jpg"/></fig><table-wrap id="table1" position="float"><label>Table 1.</label><caption><title>Results from the mixed-effect models: for each measure we reported the number of studies, the estimate and standard error of the overall R<sup>2</sup> distribution, the τ<sup>2</sup> and the I<sup>2</sup>.</title></caption><table frame="hsides" rules="groups"><thead><tr><th valign="top">Measure</th><th valign="top">Number of studies</th><th valign="top">Estimate</th><th valign="top">Standard error</th><th valign="top">Tau<sup>2</sup></th><th valign="top">I<sup>2</sup></th></tr></thead><tbody><tr><td valign="top">MTR</td><td valign="top">16</td><td valign="top">0.508</td><td valign="top">0.0691</td><td valign="top">0.07</td><td valign="top">96.03%</td></tr><tr><td valign="top">MPF</td><td valign="top">10</td><td valign="top">0.7657</td><td valign="top">0.0455</td><td valign="top">0.0128</td><td valign="top">83.18%</td></tr><tr><td valign="top">FA</td><td valign="top">17</td><td valign="top">0.3766</td><td valign="top">0.0663</td><td valign="top">0.0652</td><td valign="top">87.49%</td></tr><tr><td valign="top">RD</td><td valign="top">15</td><td valign="top">0.3364</td><td valign="top">0.0679</td><td valign="top">0.0615</td><td valign="top">92.30%</td></tr><tr><td valign="top">MD</td><td valign="top">12</td><td valign="top">0.2639</td><td valign="top">0.0679</td><td valign="top">0.044</td><td valign="top">87.35%</td></tr><tr><td valign="top">T1</td><td valign="top">8</td><td valign="top">0.5321</td><td valign="top">0.0692</td><td valign="top">0.0328</td><td valign="top">86.51%</td></tr><tr><td valign="top">AD</td><td valign="top">9</td><td valign="top">0.2095</td><td valign="top">0.0802</td><td valign="top">0.048</td><td valign="top">97.69%</td></tr><tr><td valign="top">T2</td><td valign="top">7</td><td valign="top">0.3938</td><td valign="top">0.1023</td><td valign="top">0.0651</td><td valign="top">84.49%</td></tr><tr><td valign="top">MWF</td><td valign="top">4</td><td valign="top">0.6997</td><td valign="top">0.0432</td><td valign="top">0.0041</td><td valign="top">73.19%</td></tr></tbody></table></table-wrap></sec></sec><sec id="s3" sec-type="discussion"><title>Discussion</title><sec id="s3-1"><title>Indirect measures are the most popular (for better or worse)</title><p>The literature survey offers an interesting perspective on popular research trends (Figure S2). The first consideration one can make is that every myelin imaging technique achieves myelin sensitivity through different means. A clear example is offered by the two most common approaches in this meta-analysis, DWI and MT: the MT effect is driven by saturation pulses interacting with myelin macromolecules that transfer their magnetization to water, whereas in diffusion experiments myelin is just not part of the picture. Diffusion acquisitions are blind to direct myelin measurement because the TEs used are too long (~100 ms) to be influenced by the actual macromolecules – with T2 of ~ 10 us (<xref ref-type="bibr" rid="bib70">Stanisz et al., 1999</xref>) – or even the water molecules trapped in the myelin sheath – with T2 of ~ 30 ms (<xref ref-type="bibr" rid="bib46">MacKay et al., 1994</xref>). To infer myelin content, one needs to rely on the interaction between intracellular and extracellular water compartments. The majority of diffusion studies included in this analysis used tensor-based measures (with fractional anisotropy being the most common), but some also used kurtosis-based analysis. The main issue with this approach is that other factors affect those measures (<xref ref-type="bibr" rid="bib3">Beaulieu, 2002</xref>; <xref ref-type="bibr" rid="bib4">Beaulieu, 2009</xref>), making it difficult to specifically relate changes in water compartments to changes in myelin.</p><p>Despite this issue, the use of diffusion as a proxy for myelin is quite widespread, specifically outside the field of quantitative MRI. This is probably a consequence of how popular DWI has become and how widely available are the related acquisition sequences. MT, the second most popular technique for quantifying myelin, estimates myelin by acquiring data with and without saturating the macromolecular proton pool. The simplest MT measure, MT ratio (MTR), incorporates non-myelin contributions in the final measurement. Recent acquisition variations include computing MTR from acquisitions with ultra-short echo times (<xref ref-type="bibr" rid="bib15">Du et al., 2009</xref>; <xref ref-type="bibr" rid="bib23">Guglielmetti et al., 2020</xref>; <xref ref-type="bibr" rid="bib85">Wei et al., 2018</xref>) or relying on inhomogeneous MT (<xref ref-type="bibr" rid="bib16">Duhamel et al., 2019</xref>; <xref ref-type="bibr" rid="bib80">Varma et al., 2015</xref>). More complex experiments, for example quantitative MT, are based on fitting two compartments to the data, the free water and the macromolecular compartments, or pools. In this way, one is able to assess myelin through MPF with higher specificity, although still potentially including contributions from other macromolecules. Additional measures have also been considered (including the T2 of each pool, the exchange rate between the pools). The drawback of qMT is the requirement for a longer and more complex acquisition. Recently, there have been alternative techniques to estimate only MPF, resulting in faster acquisitions with similar results (<xref ref-type="bibr" rid="bib35">Khodanovich et al., 2019</xref>; <xref ref-type="bibr" rid="bib34">Khodanovich et al., 2017</xref>; <xref ref-type="bibr" rid="bib90">Yarnykh, 2012</xref>). Despite being focused on macromolecular contributions, these approaches are not strictly specific to myelin (<xref ref-type="bibr" rid="bib68">Sled, 2018</xref>): in this sense, an important limitation is that MT effects are sensitive to the pH of the targeted tissue and therefore changes in the pH (caused for example by inflammation processes) will affect MT-based measures of myelin (<xref ref-type="bibr" rid="bib71">Stanisz et al., 2004</xref>).</p><p>Following diffusion and MT, the most popular approach is T2 relaxometry. Unlike diffusion and MT, in T2 relaxometry experiments one can directly observe the contribution from the water trapped between the myelin bilayers, and can therefore estimate the myelin water fraction. A simpler but less specific approach consists in estimating the transverse relaxation time considering the decay to be mono-exponential. A historical and practical drawback of these approaches is that they require longer acquisitions, although faster alternatives have been developed (<xref ref-type="bibr" rid="bib14">Does and Gore, 2000</xref>; <xref ref-type="bibr" rid="bib56">Prasloski et al., 2012</xref>). A more subtle but nevertheless important limitation lies in the multi-compartment model used in multi-exponential T2 relaxometry (<xref ref-type="bibr" rid="bib13">Does, 2018</xref>): this model generally assumes slow water exchange between compartments, but it has been showed that water exchange actually contributes to T2 spectra variations (<xref ref-type="bibr" rid="bib17">Dula et al., 2010</xref>; <xref ref-type="bibr" rid="bib26">Harkins et al., 2012</xref>).</p><p>Finally, other studies used a diverse collection of other measures, including T1 relaxometry, apparent transversal relaxation rate (R2*), proton density (PD), macromolecular tissue volume (MTV), relaxation along a fictitious field (RAFF), and quantitative susceptibility mapping (QSM).</p><p>After this general overview, it is clear that each modality could be a suitable candidate for a quantitative myelin biomarker. To then make a choice informed by the studies here reported, it becomes necessary to consider not only effect sizes in terms of correlation, but also sample sizes and acquisition times.</p></sec><sec id="s3-2"><title>There is no myelin MRI measure true to histology</title><p>When looking at the R<sup>2</sup> values across the different measures, the first detail that catches one’s eye is how most measures present a broad range of values (<xref ref-type="fig" rid="fig2">Figure 2</xref> and <xref ref-type="fig" rid="fig3">Figure 3</xref>). When taking into account the sample size, the largest studies show higher correlations for MT and T2 relaxometry studies than any other approach (Figure S3 and Figure S4). In quantitative terms, the meta-analysis corroborates this idea, showing that MPF and MWF tend to be more specific to myelin compared to the other measures (respectively with R<sup>2</sup> = 0.7657 and R<sup>2</sup> = 0.6997), in line with the underlying theory. Notably, diffusion-based measures show the lowest overall estimates (with values between R<sup>2</sup> = 0.3766 for FA and R<sup>2</sup> = 0.2095 for AD): this could be due to the fact, as already mentioned, that DWI does not specifically measure myelin properties, and despite FA and RD being influenced by the myelin content, they are also influenced by other factors that make them unsuitable as measures of myelin. The repeated measure meta-regression confirms this overall picture, clearly distinguishing MT- and relaxometry-based measures from diffusion-based ones (<xref ref-type="fig" rid="fig5">Figure 5</xref>).</p><p>Despite these considerations on the advantages of MPF and MWF, one should refrain from concluding that they are the ‘true’ MRI measures of myelin. The reason for this caution is given not by the overall effect sizes observed here, but by the collateral outcomes of the meta-analysis. The first one is given by the prediction intervals: most measures exhibit large intervals (<xref ref-type="fig" rid="fig4">Figure 4</xref>), not supporting the idea of them being robust biomarkers. MPF and MWF seem to be again the most suitable choices for future studies, but a range between 0.5 and 1 is still quite large.</p><p>The second important aspect to consider is given by the differences across studies: the meta-analysis showed how such differences strongly limit inter-study comparisons for a given measure (<xref ref-type="fig" rid="fig6">Figure 6</xref>). This result should be expected, given that the studies here examined are inevitably influenced by the specific experimental constraints and methodological choices. Given the limited number of studies, it is not possible to quantitatively study interactions between MRI measures and the other factors (e.g. modality used as a reference, tissue types, magnetic field strength). For further qualitative insights, we invite the reader to explore the interactive figures S7-S8. A first important factor to consider is the validation modality used as a reference, which will be dictated by the equipment availability and cost. However, such a choice has an impact on the actual comparison: histology and immunochemistry, despite being specific to myelin, do not offer a volumetric measure of myelin, but rather a proxy based on the transmittance of the histological sections. So far, the only modality able to give a volumetric measure would be electron microscopy, which is an expensive and resource-consuming approach. Also, electron microscopy has several limitations, including tissue shrinkage, degradation of the myelin sheath structure due to imperfect fixation, imperfect penetration of the osmium stain, polishing, keeping focus over large imaging regions. All these effects contribute to the lack of precision and accuracy when quantifying myelin content with EM-based histology (<xref ref-type="bibr" rid="bib11">Cohen-Adad, 2018</xref>). Another important observation is that none of the studies here reviewed considered histology reproducibility, which is hard to quantify as a whole given that a sample can be processed only once: collateral factors affecting tissue processing (e.g. sectioning distortions, mounting and staining issues) constitute an actual limitation for histology-based validation. A further example of influential factor often dictated by equipment availability is the magnetic field strength of the MRI scanner: figure S8 shows that most studies were conducted at 7T and 9.4T, with some pioneering studies at 1.5T and even fewer ones at other field strengths.</p><p>In addition to differences in experimental and methodological designs, there are also several considerations that arise out of the lack of shared practices in MRI validation studies. The first evident one is the use of correlations: despite being a simple measure that serves well the purpose of roughly characterizing a relationship, Pearson correlation is not the right tool for quantitative biomarkers, as it does not characterize the actual relationship between histology and MRI. Linear regression is a step forward but has the disadvantage of assuming a linear relationship. Despite Pearson correlation and linear regression being the most common measures used in the studies here reviewed, it is still not clear if the relationship is actually linear. Only one study among the considered ones computed both Pearson and Spearman correlation values (<xref ref-type="bibr" rid="bib73">Tardif et al., 2012</xref>), and reported higher Spearman correlations, pointing out that non-linear relationships should actually be considered. One last consideration regarding the use of correlation measures for validating quantitative biomarkers is about the intercept in the MRI-histology relationship. Notably, only MWF is expected to assume a value equal to zero when myelin is absent (<xref ref-type="bibr" rid="bib87">West et al., 2018</xref>). For the other measures, it would be necessary to estimate the intercept, which leads to the calibration problem in the estimate of myelin volume fraction. Notably, calculating Pearson correlation does not provide any information for such calibration. Another arbitrary practice that would benefit from some harmonization is the choice of ROIs. The studies reported here examined a diverse list of ROIs, in most cases hand-drawn on each modality, encompassing different types of tissue, and the most common approach is to report a single, pooled correlation. This is problematic, as different types of tissue (e.g. grey matter and white matter) will show different values for MRI-based measures but also for histology-based ones, making linearity assumptions about the two modalities. However, with this approach gross differences between tissues drive the observed correlation, without actually showing if the MRI-based measure under analysis is sensitive to subtle differences and therefore a suitable quantitative biomarker for myelin. The effect of considering different types of tissues is showed in <xref ref-type="fig" rid="fig6">Figure 6</xref> and Figure S7, where correlation ranges change when considering different types of tissue. However, the large correlation range in white matter, the most common tissue studied, suggests that other factors also affect the correlation.</p><p>It should be clear at this point that any debate about a universal MRI-based measure of myelin is pointless, at least at the moment, as the overall picture provided by previous studies does not point to any such ideal measure. Nevertheless, is debating about a universal measure helpful for future studies?</p></sec><sec id="s3-3"><title>Better biomarkers require more reproducibility studies</title><p>We hope this meta-analysis convinces the reader that a holy grail of myelin imaging does not exist, at least as long as we consider histology to be the ground truth. Given that we all have to pick our poison, the upside is that measures based on MT and relaxometry are not statistically different, and therefore, future studies have an actual choice among candidate measures. For further progress, rather than debating about a perfect measure, we would argue that what is missing at the moment is a clear picture of what can be achieved with each specific MRI modality. The studies examined here focus on a large set of different measures, and more than half of them considered at most two measures, highlighting how the field is mostly focused on formulating new measures. While it is understood that novel measures can provide new perspectives, it is also fundamentally important to understand the concrete capabilities and limitations of current measures. From this meta-analysis, what the literature clearly lacks is reproducibility studies, specifically answering two main questions: (1) what is the specificity of each measure? We should have a practical validation of our theoretical understanding of the relevant confounds; (2) what is the ‘parameter sensitivity’ of each measure? Here, we refer to parameter sensitivity in a broad sense, that includes also experimental conditions and methodological choices. The results here presented show how certain conditions (e.g. pathology) seem to affect the coefficient of determination more than others but given the limited number of studies for each modality, we refrained from additional analyses to avoid speculation. A warning message that is evident from these results is the inherent limitation of DWI for estimating myelin content: this is not by any means a novel result (<xref ref-type="bibr" rid="bib3">Beaulieu, 2002</xref>; <xref ref-type="bibr" rid="bib4">Beaulieu, 2009</xref>), but it is nevertheless worth reiterating given the outcomes of our analysis. If estimating myelin content is relevant in a diffusion study, it is important to consider complementing the diffusion measure with one of the modalities here reviewed; in this way, it would be possible to decouple the influence of myelin content from the many other factors that come into play when considering diffusion phenomena.</p><p>Finally, an important factor to take into account when choosing a biomarker of myelin is the actual application. For animal research, long acquisitions are not a major issue. However, when considering biomarkers for potential clinical use, the acquisition time can become a relevant issue. An example is the well-established multi-echo spin-echo implementation of MWF, that can only be used for a specific slice in a hypothetical clinical scenario. Faster techniques have been proposed for estimating it with gradient- and spin-echo (GRASE) sequences (<xref ref-type="bibr" rid="bib14">Does and Gore, 2000</xref>; <xref ref-type="bibr" rid="bib19">Feinberg and Oshio, 1991</xref>; <xref ref-type="bibr" rid="bib56">Prasloski et al., 2012</xref>). Even in this case, the acquisition time still reaches 15 min for acquiring roughly the whole brain with an isotropic resolution of 2 mm. Complex MT acquisitions such as qMT suffer from the same problem, although it is possible to use optimized and faster protocols to focus specifically on MPF (<xref ref-type="bibr" rid="bib35">Khodanovich et al., 2019</xref>; <xref ref-type="bibr" rid="bib34">Khodanovich et al., 2017</xref>; <xref ref-type="bibr" rid="bib90">Yarnykh, 2012</xref>).</p></sec><sec id="s3-4"><title>Conclusions</title><p>Several MRI measures are sensitive to myelin content and the current literature suggests that most of them are not statistically different in terms of their relationship with the underlying histology. Measures highly correlated with histology are also the ones with a higher expected specificity. This suggests that future studies should try to better address how specific each measure is, for the sake of clarifying suitable applications.</p></sec></sec><sec id="s4" sec-type="materials|methods"><title>Materials and methods</title><sec id="s4-1"><title>Review methodology</title><p>The Medline database (<ext-link ext-link-type="uri" xlink:href="https://pubmed.ncbi.nlm.nih.gov">https://pubmed.ncbi.nlm.nih.gov</ext-link>) was used to retrieve the articles. The keywords used are specified in the appendix. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for record screening and study selection. The results were first screened to remove unrelated work. Specifically we discarded: work relying only on MRI; work relying only on histology or equivalent approaches; work reporting only qualitative comparisons. After this first screening, the remaining papers were assessed. At this stage, we discarded: studies using MRI-based measures in arbitrary units (e.g. T1-weighted or T2-weighted data); studies using measures of variation in myelin content (defined either as the difference between normal and abnormal myelin content) either for MRI or for histology; studies using arbitrary assessment scales; studies comparing MRI-based absolute measures of myelin with histology-based relative measures (e.g. g-ratio); studies reporting other quantitative measures than correlation or R<sup>2</sup> values; studies comparing histology from one dataset and MRI from a different one. As an additional source for potential candidate studies, we screened the review articles in the initial results, and we selected the relevant studies that were not already present in the studies already selected.</p><p>From the final papers, we collected first the following details: the DOI; which approach was used (diffusion, MT, T1 relaxometry, T2 relaxometry, or other); which specific MRI measures were compared to histology or equivalent techniques; the magnetic field; the technique used as a reference (histology, immunochemistry, microscopy, electron microscopy); the focus of the study in terms of brain, spinal cord or peripheral nerve; if the subjects were humans or animals, and if the latter which animal; if the tissue under exam was in vivo, in situ or ex vivo, and in the latter case if the tissue was fixed or not; if the tissue was healthy or pathological, and if the latter which pathology; the specific structures examined for correlation purposes; which comparison technique was used (e.g. Pearson correlation, Spearman correlation, linear regression); the number of subjects; the number of ROIs per subject; the male/female ratio; if registration procedures were performed to align MRI and histology; in case of pathological tissue, if control tissue was considered as well; other relevant notes. If before calculating the correlations the data were averaged across subjects, the number of subjects was considered to be one. The same consideration was made for averaging across ROIs. This is because the numbers of subjects and ROIs were used to take into account how many sample points were used when computing the correlation. We set each of those numbers to one for all the studies where the data were averaged respectively across subjects and across ROIs. Finally, in those cases where the number of ROIs or the number of subjects were given as a range rather than specific values, we used the most conservative value and added the related details to the notes.</p><p>We then proceeded to collect the quantitative results reported for each measure and for each study in the form of R<sup>2</sup>. Given that different studies may rely on a different strategy when reporting correlations, we adopted the following reasoning to limit discrepancies across studies while still objectively representing each of them. In case of multiple correlation values reported, for our analysis we selected the ones referring to the whole dataset and the entire brain if available, and considering each ROI in a given subject as a sample if possible; if only correlation values for specific ROIs were reported, the one for the most common reported structure would be chosen. In the case of multiple subjects, if data were provided separately for each group, the correlation for the control group was used. When different comparison methods were reported (e.g. both Pearson and Spearman correlation) or if the MRI data was compared with multiple references (e.g. both histology and immunohistochemistry), the correlations used were chosen on the basis of the following priority orders (from the most preferable to the least): for multiple comparison methods, linear regression, Spearman correlation, Pearson correlation; for multiple reference techniques, electron microscopy, immunohistochemistry, histology. Finally, in any other case where more than one correlation value was available, the most conservative value was used. Any other additional value was in any case mentioned in the notes of the respective study.</p></sec><sec id="s4-2"><title>Meta-analysis</title><p>For the quantitative analysis, we restricted our focus on brain studies and only on the ones providing an indication of both the number of subjects and the number of ROIs. For each study, we computed the sample size as the product between the number of subjects and the number of ROIs per subject. In this way, we were able to compare the reported R<sup>2</sup> values across measures taking into account the related number of points actually used for correlation purposes. We note that correlation or regression analyses run on multiple ROIs and subjects represents a repeated measures analysis, for which the degrees of freedom computation can be complex; however, most papers neglected the repeated measures structure of the data and thus the sample size computation here represents a very approximate and optimistic view of the precision of each R<sup>2</sup> value.</p><p>To estimate the variance of each R<sup>2</sup> value, we relied on the correlation properties and the delta method (<xref ref-type="bibr" rid="bib43">Lehman, 1999</xref>). Let us consider the Pearson’s correlation r of two variables X and Y with population correlation ρ. If r is calculated from N random samples, the sampling variance is (1-ρ<sup>2</sup>)<sup>2</sup>/N. Applying the delta method, we then approximated the variance of R<sup>2</sup> as 4 R<sup>2</sup>(1 R<sup>2</sup>)<sup>2</sup>/N, assuming R<sup>2</sup>≈ρ<sup>2</sup>. As we recognise that some papers computed Spearman correlation, this calculation is again optimistic and may underestimate the sampling variability of the squared Spearman correlation.</p><p>To estimate the overall effect size in terms of R<sup>2</sup>, we have to choose how to model the distribution of true effects given by the data collected from the literature. The two most common approaches are fixed-effects and mixed-effects models. While the underlying mathematical model is the same as the one used for linear regression (more details in the appendix), the assumptions are different: fixed-effects models assume that all the studies share a common effect size, while mixed-effects models assume that the effect size across studies is similar but not identical (<xref ref-type="bibr" rid="bib58">Raudenbush, 2009</xref>). In our case, as the studies have several factors that influence the R<sup>2</sup> values (e.g. histology/microscopy reference, magnetic field strength, pathology model), we expect a distribution of effect sizes due to inter-study differences. This is why we proceeded to fit a mixed-effects model to each measure that was featured in more than two studies. Apart from the effect size distributions, we reported two additional measures, I<sup>2</sup> and τ<sup>2</sup>: the former expresses as a percentage how much of variability in a typical study is due to heterogeneity (i.e. the variation in study outcomes between studies) rather than chance (<xref ref-type="bibr" rid="bib28">Higgins and Thompson, 2002</xref>), while the latter can be used to calculate the prediction interval (<xref ref-type="bibr" rid="bib58">Raudenbush, 2009</xref>), which gives the expected range for the measure of interest in future studies. We used forest plots to represent the outcomes, and both the mixed effects estimate of the population estimated R<sup>2</sup>, with both a 95% confidence and a (larger) 95% prediction interval.</p><p>For the explicit purpose of comparing the effect sizes between different MRI measures, we conducted a repeated measures meta-regression on every R<sup>2</sup> value recorded. We associated each R<sup>2</sup> value with three additional details: (i) the related variance, as done in the measure-specific mixed-effects models; (ii) the related study, used as the random intercept (i.e. random variable) to incorporate potential inter-study variability; and (iii) the related MRI measure, used as the moderator (i.e. categorical variable) to estimate the differences between measures. In this way, the meta-regression leads to R<sup>2</sup> intervals for each MRI measure, with the same trend as measure-specific mixed-effects models but with subtle differences. This is because the meta-regression makes two additional assumptions: first, R<sup>2</sup> estimates within the same study share the same random effects and second, the between-study variance is the same for all observations. We then used the meta-regression R<sup>2</sup> estimates to compute every possible pairwise comparison between MRI measures and to identify significantly different pairs using Tukey's test, while controlling the error rate over all the possible comparisons (Bonferroni correction).</p><p>This additional model is necessary, as direct comparisons are not possible with measure-specific analyses. While the repeated measures meta-regression makes direct comparisons straightforward, we reported the main R<sup>2</sup> estimates based on the measure-specific mixed-effects models, as they make weaker assumptions.</p><p>For visual comparisons, we used the Jupyter notebook provided in the supplementary materials. For model fitting, we used the Metafor package, version 2.4–0 (<xref ref-type="bibr" rid="bib81">Viechtbauer, 2010</xref>).</p></sec></sec></body><back><ack id="ack"><title>Acknowledgements</title><p>MM was funded by the Wellcome Trust through a Sir Henry Wellcome Postdoctoral Fellowship [213722/Z/18/Z]. TEN was supported by NIH grant R01MH096906.</p></ack><sec id="s5" sec-type="additional-information"><title>Additional information</title><fn-group content-type="competing-interest"><title>Competing interests</title><fn fn-type="COI-statement" id="conf1"><p>No competing interests declared</p></fn></fn-group><fn-group content-type="author-contribution"><title>Author contributions</title><fn fn-type="con" id="con1"><p>Conceptualization, Data curation, Formal analysis, Visualization, Methodology, Writing - original draft, Writing - review and editing</p></fn><fn fn-type="con" id="con2"><p>Visualization, Writing - review and editing</p></fn><fn fn-type="con" id="con3"><p>Conceptualization, Writing - review and editing</p></fn><fn fn-type="con" id="con4"><p>Conceptualization, Writing - review and editing</p></fn><fn fn-type="con" id="con5"><p>Conceptualization, Methodology, Writing - review and editing</p></fn><fn fn-type="con" id="con6"><p>Conceptualization, Methodology, Writing - review and editing</p></fn></fn-group></sec><sec id="s6" sec-type="supplementary-material"><title>Additional files</title><supplementary-material id="scode1"><label>Source code 1.</label><caption><title>A Jupyter notebook in ipynb format containing the Python code used to process the data, run the analyses and generate all the figures.</title><p>In order to execute the notebook, the Python (3.7) and R (3.6) interpreters are required, as well as the R packages metafor (2.4) and multcomp (1.4), and the following Python packages: numpy (1.18.4); pandas (0.25.3); plotly (4.8.1); rpy2 (3.3.4); xlrd (1.2.0). The notebook assumes that the spreadsheet is in the same path as the notebook itself. More details are provided here: <ext-link ext-link-type="uri" xlink:href="https://github.com/matteomancini/myelin-meta-analysis">https://github.com/matteomancini/myelin-meta-analysis</ext-link> (<xref ref-type="bibr" rid="bib48">Mancini, 2020</xref>; copy archived at <ext-link ext-link-type="uri" xlink:href="https://archive.softwareheritage.org/swh:1:rev:17ca8673c9e15c54ad0b814248b69232b63c3a38/">swh:1:rev:17ca8673c9e15c54ad0b814248b69232b63c3a38</ext-link>). </p></caption><media mime-subtype="zip" mimetype="application" xlink:href="elife-61523-code1-v2.zip"/></supplementary-material><supplementary-material id="sdata1"><label>Source data 1.</label><caption><title>A spreadsheet in xlsx format containing all the data and details collected for the studies considered in this systematic review.</title></caption><media mime-subtype="xlsx" mimetype="application" xlink:href="elife-61523-data1-v2.xlsx"/></supplementary-material><supplementary-material id="supp1"><label>Supplementary file 1.</label><caption><title>A multimedia file in HTML format containing an interactive version of the figures in this manuscript plus additional ones.</title></caption><media mime-subtype="zip" mimetype="application" xlink:href="elife-61523-supp1-v2.zip"/></supplementary-material><supplementary-material id="transrepform"><label>Transparent reporting form</label><media mime-subtype="pdf" mimetype="application" xlink:href="elife-61523-transrepform-v2.pdf"/></supplementary-material></sec><sec id="s7" sec-type="data-availability"><title>Data availability</title><p>All the data collected from the selected studies for this meta-analysis are provided in the spreadsheet file Source data 1.</p></sec><ref-list><title>References</title><ref id="bib1"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Abe</surname> <given-names>Y</given-names></name><name><surname>Komaki</surname> <given-names>Y</given-names></name><name><surname>Seki</surname> <given-names>F</given-names></name><name><surname>Shibata</surname> <given-names>S</given-names></name><name><surname>Okano</surname> <given-names>H</given-names></name><name><surname>Tanaka</surname> <given-names>KF</given-names></name></person-group><year iso-8601-date="2019">2019</year><article-title>Correlative study using structural MRI and super-resolution microscopy to detect structural alterations induced by long-term optogenetic stimulation of striatal medium spiny neurons</article-title><source>Neurochemistry International</source><volume>125</volume><fpage>163</fpage><lpage>174</lpage><pub-id pub-id-type="doi">10.1016/j.neuint.2019.02.017</pub-id><pub-id pub-id-type="pmid">30825601</pub-id></element-citation></ref><ref id="bib2"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Aojula</surname> <given-names>A</given-names></name><name><surname>Botfield</surname> <given-names>H</given-names></name><name><surname>McAllister</surname> <given-names>JP</given-names></name><name><surname>Gonzalez</surname> <given-names>AM</given-names></name><name><surname>Abdullah</surname> <given-names>O</given-names></name><name><surname>Logan</surname> <given-names>A</given-names></name><name><surname>Sinclair</surname> <given-names>A</given-names></name></person-group><year iso-8601-date="2016">2016</year><article-title>Diffusion tensor imaging with direct cytopathological validation: characterisation of decorin treatment in experimental juvenile communicating hydrocephalus</article-title><source>Fluids and Barriers of the CNS</source><volume>13</volume><elocation-id>9</elocation-id><pub-id pub-id-type="doi">10.1186/s12987-016-0033-2</pub-id></element-citation></ref><ref id="bib3"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Beaulieu</surname> <given-names>C</given-names></name></person-group><year iso-8601-date="2002">2002</year><article-title>The basis of anisotropic water diffusion in the nervous system - a technical review</article-title><source>NMR in Biomedicine</source><volume>15</volume><fpage>435</fpage><lpage>455</lpage><pub-id pub-id-type="doi">10.1002/nbm.782</pub-id><pub-id pub-id-type="pmid">12489094</pub-id></element-citation></ref><ref id="bib4"><element-citation publication-type="book"><person-group person-group-type="author"><name><surname>Beaulieu</surname> <given-names>C</given-names></name></person-group><year iso-8601-date="2009">2009</year><chapter-title>CHAPTER 6 - The Biological Basis of Diffusion Anisotropy</chapter-title><person-group person-group-type="editor"><name><surname>Johansen-Berg</surname> <given-names>H</given-names></name><name><surname>Behrens</surname> <given-names>T. E. J</given-names></name></person-group><source>Diffusion MRI</source><publisher-name>Academic Press</publisher-name><fpage>105</fpage><lpage>126</lpage></element-citation></ref><ref id="bib5"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Beckmann</surname> <given-names>N</given-names></name><name><surname>Giorgetti</surname> <given-names>E</given-names></name><name><surname>Neuhaus</surname> <given-names>A</given-names></name><name><surname>Zurbruegg</surname> <given-names>S</given-names></name><name><surname>Accart</surname> <given-names>N</given-names></name><name><surname>Smith</surname> <given-names>P</given-names></name><name><surname>Perdoux</surname> <given-names>J</given-names></name><name><surname>Perrot</surname> <given-names>L</given-names></name><name><surname>Nash</surname> <given-names>M</given-names></name><name><surname>Desrayaud</surname> <given-names>S</given-names></name><name><surname>Wipfli</surname> <given-names>P</given-names></name><name><surname>Frieauff</surname> <given-names>W</given-names></name><name><surname>Shimshek</surname> <given-names>DR</given-names></name></person-group><year iso-8601-date="2018">2018</year><article-title>Brain region-specific enhancement of remyelination and prevention of demyelination by the CSF1R kinase inhibitor BLZ945</article-title><source>Acta Neuropathologica Communications</source><volume>6</volume><elocation-id>9</elocation-id><pub-id pub-id-type="doi">10.1186/s40478-018-0510-8</pub-id><pub-id pub-id-type="pmid">29448957</pub-id></element-citation></ref><ref id="bib6"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Berman</surname> <given-names>S</given-names></name><name><surname>West</surname> <given-names>KL</given-names></name><name><surname>Does</surname> <given-names>MD</given-names></name><name><surname>Yeatman</surname> <given-names>JD</given-names></name><name><surname>Mezer</surname> <given-names>AA</given-names></name></person-group><year iso-8601-date="2018">2018</year><article-title>Evaluating g-ratio weighted changes in the corpus callosum as a function of age and sex</article-title><source>NeuroImage</source><volume>182</volume><fpage>304</fpage><lpage>313</lpage><pub-id pub-id-type="doi">10.1016/j.neuroimage.2017.06.076</pub-id><pub-id pub-id-type="pmid">28673882</pub-id></element-citation></ref><ref id="bib7"><element-citation publication-type="book"><person-group person-group-type="author"><name><surname>Cercignani</surname> <given-names>M</given-names></name><name><surname>Dowell</surname> <given-names>NG</given-names></name><name><surname>Tofts</surname> <given-names>PS</given-names></name></person-group><year iso-8601-date="2018">2018</year><source>Quantitative MRI of the Brain</source><publisher-name>CRC Press</publisher-name></element-citation></ref><ref id="bib8"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Chandran</surname> <given-names>P</given-names></name><name><surname>Upadhyay</surname> <given-names>J</given-names></name><name><surname>Markosyan</surname> <given-names>S</given-names></name><name><surname>Lisowski</surname> <given-names>A</given-names></name><name><surname>Buck</surname> <given-names>W</given-names></name><name><surname>Chin</surname> <given-names>CL</given-names></name><name><surname>Fox</surname> <given-names>G</given-names></name><name><surname>Luo</surname> <given-names>F</given-names></name><name><surname>Day</surname> <given-names>M</given-names></name></person-group><year iso-8601-date="2012">2012</year><article-title>Magnetic resonance imaging and histological evidence for the blockade of cuprizone-induced demyelination in C57BL/6 mice</article-title><source>Neuroscience</source><volume>202</volume><fpage>446</fpage><lpage>453</lpage><pub-id pub-id-type="doi">10.1016/j.neuroscience.2011.10.051</pub-id><pub-id pub-id-type="pmid">22119061</pub-id></element-citation></ref><ref id="bib9"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Chang</surname> <given-names>EH</given-names></name><name><surname>Argyelan</surname> <given-names>M</given-names></name><name><surname>Aggarwal</surname> <given-names>M</given-names></name><name><surname>Chandon</surname> <given-names>TS</given-names></name><name><surname>Karlsgodt</surname> <given-names>KH</given-names></name><name><surname>Mori</surname> <given-names>S</given-names></name><name><surname>Malhotra</surname> <given-names>AK</given-names></name></person-group><year iso-8601-date="2017">2017</year><article-title>Diffusion tensor imaging measures of white matter compared to myelin basic protein immunofluorescence in tissue cleared intact brains</article-title><source>Data in Brief</source><volume>10</volume><fpage>438</fpage><lpage>443</lpage><pub-id pub-id-type="doi">10.1016/j.dib.2016.12.018</pub-id><pub-id pub-id-type="pmid">28054004</pub-id></element-citation></ref><ref id="bib10"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Chen</surname> <given-names>HS</given-names></name><name><surname>Holmes</surname> <given-names>N</given-names></name><name><surname>Liu</surname> <given-names>J</given-names></name><name><surname>Tetzlaff</surname> <given-names>W</given-names></name><name><surname>Kozlowski</surname> <given-names>P</given-names></name></person-group><year iso-8601-date="2017">2017</year><article-title>Validating myelin water imaging with transmission electron microscopy in a rat spinal cord injury model</article-title><source>NeuroImage</source><volume>153</volume><fpage>122</fpage><lpage>130</lpage><pub-id pub-id-type="doi">10.1016/j.neuroimage.2017.03.065</pub-id><pub-id pub-id-type="pmid">28377211</pub-id></element-citation></ref><ref id="bib11"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Cohen-Adad</surname> <given-names>J</given-names></name></person-group><year iso-8601-date="2018">2018</year><article-title>Microstructural imaging in the spinal cord and validation strategies</article-title><source>NeuroImage</source><volume>182</volume><fpage>169</fpage><lpage>183</lpage><pub-id pub-id-type="doi">10.1016/j.neuroimage.2018.04.009</pub-id><pub-id pub-id-type="pmid">29635029</pub-id></element-citation></ref><ref id="bib12"><element-citation publication-type="book"><person-group person-group-type="author"><name><surname>Cohen-Adad</surname> <given-names>J</given-names></name><name><surname>Wheeler-Kingshott</surname> <given-names>CA</given-names></name></person-group><year iso-8601-date="2014">2014</year><source>Quantitative MRI of the Spinal Cord</source><publisher-name>Academic Press</publisher-name></element-citation></ref><ref id="bib13"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Does</surname> <given-names>MD</given-names></name></person-group><year iso-8601-date="2018">2018</year><article-title>Inferring brain tissue composition and microstructure via MR relaxometry</article-title><source>NeuroImage</source><volume>182</volume><fpage>136</fpage><lpage>148</lpage><pub-id pub-id-type="doi">10.1016/j.neuroimage.2017.12.087</pub-id><pub-id pub-id-type="pmid">29305163</pub-id></element-citation></ref><ref id="bib14"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Does</surname> <given-names>MD</given-names></name><name><surname>Gore</surname> <given-names>JC</given-names></name></person-group><year iso-8601-date="2000">2000</year><article-title>Rapid acquisition transverse relaxometric imaging</article-title><source>Journal of Magnetic Resonance</source><volume>147</volume><fpage>116</fpage><lpage>120</lpage><pub-id pub-id-type="doi">10.1006/jmre.2000.2168</pub-id><pub-id pub-id-type="pmid">11042054</pub-id></element-citation></ref><ref id="bib15"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Du</surname> <given-names>J</given-names></name><name><surname>Takahashi</surname> <given-names>AM</given-names></name><name><surname>Bydder</surname> <given-names>M</given-names></name><name><surname>Chung</surname> <given-names>CB</given-names></name><name><surname>Bydder</surname> <given-names>GM</given-names></name></person-group><year iso-8601-date="2009">2009</year><article-title>Ultrashort TE imaging with off-resonance saturation contrast (UTE-OSC)</article-title><source>Magnetic Resonance in Medicine</source><volume>62</volume><fpage>527</fpage><lpage>531</lpage><pub-id pub-id-type="doi">10.1002/mrm.22007</pub-id><pub-id pub-id-type="pmid">19449436</pub-id></element-citation></ref><ref id="bib16"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Duhamel</surname> <given-names>G</given-names></name><name><surname>Prevost</surname> <given-names>VH</given-names></name><name><surname>Cayre</surname> <given-names>M</given-names></name><name><surname>Hertanu</surname> <given-names>A</given-names></name><name><surname>Mchinda</surname> <given-names>S</given-names></name><name><surname>Carvalho</surname> <given-names>VN</given-names></name><name><surname>Varma</surname> <given-names>G</given-names></name><name><surname>Durbec</surname> <given-names>P</given-names></name><name><surname>Alsop</surname> <given-names>DC</given-names></name><name><surname>Girard</surname> <given-names>OM</given-names></name></person-group><year iso-8601-date="2019">2019</year><article-title>Validating the sensitivity of inhomogeneous magnetization transfer (ihMT) MRI to myelin with fluorescence microscopy</article-title><source>NeuroImage</source><volume>199</volume><fpage>289</fpage><lpage>303</lpage><pub-id pub-id-type="doi">10.1016/j.neuroimage.2019.05.061</pub-id><pub-id pub-id-type="pmid">31141736</pub-id></element-citation></ref><ref id="bib17"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Dula</surname> <given-names>AN</given-names></name><name><surname>Gochberg</surname> <given-names>DF</given-names></name><name><surname>Valentine</surname> <given-names>HL</given-names></name><name><surname>Valentine</surname> <given-names>WM</given-names></name><name><surname>Does</surname> <given-names>MD</given-names></name></person-group><year iso-8601-date="2010">2010</year><article-title>Multiexponential <italic>T2,</italic> magnetization transfer, and quantitative histology in white matter tracts of rat spinal cord</article-title><source>Magnetic Resonance in Medicine</source><volume>63</volume><fpage>902</fpage><lpage>909</lpage><pub-id pub-id-type="doi">10.1002/mrm.22267</pub-id><pub-id pub-id-type="pmid">20373391</pub-id></element-citation></ref><ref id="bib18"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Fatemi</surname> <given-names>A</given-names></name><name><surname>Wilson</surname> <given-names>MA</given-names></name><name><surname>Phillips</surname> <given-names>AW</given-names></name><name><surname>McMahon</surname> <given-names>MT</given-names></name><name><surname>Zhang</surname> <given-names>J</given-names></name><name><surname>Smith</surname> <given-names>SA</given-names></name><name><surname>Arauz</surname> <given-names>EJ</given-names></name><name><surname>Falahati</surname> <given-names>S</given-names></name><name><surname>Gummadavelli</surname> <given-names>A</given-names></name><name><surname>Bodagala</surname> <given-names>H</given-names></name><name><surname>Mori</surname> <given-names>S</given-names></name><name><surname>Johnston</surname> <given-names>MV</given-names></name></person-group><year iso-8601-date="2011">2011</year><article-title><italic>In vivo</italic> magnetization transfer MRI shows dysmyelination in an ischemic mouse model of periventricular leukomalacia</article-title><source>Journal of Cerebral Blood Flow & Metabolism</source><volume>31</volume><fpage>2009</fpage><lpage>2018</lpage><pub-id pub-id-type="doi">10.1038/jcbfm.2011.68</pub-id><pub-id pub-id-type="pmid">21540870</pub-id></element-citation></ref><ref id="bib19"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Feinberg</surname> <given-names>DA</given-names></name><name><surname>Oshio</surname> <given-names>K</given-names></name></person-group><year iso-8601-date="1991">1991</year><article-title>GRASE (gradient- and spin-echo) MR imaging: a new fast clinical imaging technique</article-title><source>Radiology</source><volume>181</volume><fpage>597</fpage><lpage>602</lpage><pub-id pub-id-type="doi">10.1148/radiology.181.2.1924811</pub-id><pub-id pub-id-type="pmid">1924811</pub-id></element-citation></ref><ref id="bib20"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Fields</surname> <given-names>RD</given-names></name></person-group><year iso-8601-date="2015">2015</year><article-title>A new mechanism of nervous system plasticity: activity-dependent myelination</article-title><source>Nature Reviews Neuroscience</source><volume>16</volume><fpage>756</fpage><lpage>767</lpage><pub-id pub-id-type="doi">10.1038/nrn4023</pub-id><pub-id pub-id-type="pmid">26585800</pub-id></element-citation></ref><ref id="bib21"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Fjær</surname> <given-names>S</given-names></name><name><surname>Bo</surname> <given-names>L</given-names></name><name><surname>Lundervold</surname> <given-names>A</given-names></name><name><surname>Myhr</surname> <given-names>KM</given-names></name><name><surname>Pavlin</surname> <given-names>T</given-names></name><name><surname>Torkildsen</surname> <given-names>O</given-names></name><name><surname>Wergeland</surname> <given-names>S</given-names></name></person-group><year iso-8601-date="2013">2013</year><article-title>Deep gray matter demyelination detected by magnetization transfer ratio in the cuprizone model</article-title><source>PLOS ONE</source><volume>8</volume><elocation-id>e84162</elocation-id><pub-id pub-id-type="doi">10.1371/journal.pone.0084162</pub-id><pub-id pub-id-type="pmid">24386344</pub-id></element-citation></ref><ref id="bib22"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Fjær</surname> <given-names>S</given-names></name><name><surname>Bo</surname> <given-names>L</given-names></name><name><surname>Myhr</surname> <given-names>KM</given-names></name><name><surname>Torkildsen</surname> <given-names>Ø</given-names></name><name><surname>Wergeland</surname> <given-names>S</given-names></name></person-group><year iso-8601-date="2015">2015</year><article-title>Magnetization transfer ratio does not correlate to myelin content in the brain in the MOG-EAE mouse model</article-title><source>Neurochemistry International</source><volume>83-84</volume><fpage>28</fpage><lpage>40</lpage><pub-id pub-id-type="doi">10.1016/j.neuint.2015.02.006</pub-id><pub-id pub-id-type="pmid">25744931</pub-id></element-citation></ref><ref id="bib23"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Guglielmetti</surname> <given-names>C</given-names></name><name><surname>Boucneau</surname> <given-names>T</given-names></name><name><surname>Cao</surname> <given-names>P</given-names></name><name><surname>Van der Linden</surname> <given-names>A</given-names></name><name><surname>Larson</surname> <given-names>PEZ</given-names></name><name><surname>Chaumeil</surname> <given-names>MM</given-names></name></person-group><year iso-8601-date="2020">2020</year><article-title>Longitudinal evaluation of demyelinated lesions in a multiple sclerosis model using ultrashort Echo time magnetization transfer (UTE-MT) imaging</article-title><source>NeuroImage</source><volume>208</volume><elocation-id>116415</elocation-id><pub-id pub-id-type="doi">10.1016/j.neuroimage.2019.116415</pub-id><pub-id pub-id-type="pmid">31811900</pub-id></element-citation></ref><ref id="bib24"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Hakkarainen</surname> <given-names>H</given-names></name><name><surname>Sierra</surname> <given-names>A</given-names></name><name><surname>Mangia</surname> <given-names>S</given-names></name><name><surname>Garwood</surname> <given-names>M</given-names></name><name><surname>Michaeli</surname> <given-names>S</given-names></name><name><surname>Gröhn</surname> <given-names>O</given-names></name><name><surname>Liimatainen</surname> <given-names>T</given-names></name></person-group><year iso-8601-date="2016">2016</year><article-title>MRI relaxation in the presence of fictitious fields correlates with myelin content in normal rat brain</article-title><source>Magnetic Resonance in Medicine</source><volume>75</volume><fpage>161</fpage><lpage>168</lpage><pub-id pub-id-type="doi">10.1002/mrm.25590</pub-id></element-citation></ref><ref id="bib25"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Hametner</surname> <given-names>S</given-names></name><name><surname>Endmayr</surname> <given-names>V</given-names></name><name><surname>Deistung</surname> <given-names>A</given-names></name><name><surname>Palmrich</surname> <given-names>P</given-names></name><name><surname>Prihoda</surname> <given-names>M</given-names></name><name><surname>Haimburger</surname> <given-names>E</given-names></name><name><surname>Menard</surname> <given-names>C</given-names></name><name><surname>Feng</surname> <given-names>X</given-names></name><name><surname>Haider</surname> <given-names>T</given-names></name><name><surname>Leisser</surname> <given-names>M</given-names></name><name><surname>Köck</surname> <given-names>U</given-names></name><name><surname>Kaider</surname> <given-names>A</given-names></name><name><surname>Höftberger</surname> <given-names>R</given-names></name><name><surname>Robinson</surname> <given-names>S</given-names></name><name><surname>Reichenbach</surname> <given-names>JR</given-names></name><name><surname>Lassmann</surname> <given-names>H</given-names></name><name><surname>Traxler</surname> <given-names>H</given-names></name><name><surname>Trattnig</surname> <given-names>S</given-names></name><name><surname>Grabner</surname> <given-names>G</given-names></name></person-group><year iso-8601-date="2018">2018</year><article-title>The influence of brain iron and myelin on magnetic susceptibility and effective transverse relaxation - A biochemical and histological validation study</article-title><source>NeuroImage</source><volume>179</volume><fpage>117</fpage><lpage>133</lpage><pub-id pub-id-type="doi">10.1016/j.neuroimage.2018.06.007</pub-id><pub-id pub-id-type="pmid">29890327</pub-id></element-citation></ref><ref id="bib26"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Harkins</surname> <given-names>KD</given-names></name><name><surname>Dula</surname> <given-names>AN</given-names></name><name><surname>Does</surname> <given-names>MD</given-names></name></person-group><year iso-8601-date="2012">2012</year><article-title>Effect of intercompartmental water exchange on the apparent myelin water fraction in multiexponential <italic>T2</italic> measurements of rat spinal cord</article-title><source>Magnetic Resonance in Medicine</source><volume>67</volume><fpage>793</fpage><lpage>800</lpage><pub-id pub-id-type="doi">10.1002/mrm.23053</pub-id><pub-id pub-id-type="pmid">21713984</pub-id></element-citation></ref><ref id="bib27"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Harkins</surname> <given-names>KD</given-names></name><name><surname>Valentine</surname> <given-names>WM</given-names></name><name><surname>Gochberg</surname> <given-names>DF</given-names></name><name><surname>Does</surname> <given-names>MD</given-names></name></person-group><year iso-8601-date="2013">2013</year><article-title>In-vivo multi-exponential T2, magnetization transfer and quantitative histology in a rat model of intramyelinic edema</article-title><source>NeuroImage: Clinical</source><volume>2</volume><fpage>810</fpage><lpage>817</lpage><pub-id pub-id-type="doi">10.1016/j.nicl.2013.06.007</pub-id></element-citation></ref><ref id="bib28"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Higgins</surname> <given-names>JP</given-names></name><name><surname>Thompson</surname> <given-names>SG</given-names></name></person-group><year iso-8601-date="2002">2002</year><article-title>Quantifying heterogeneity in a meta-analysis</article-title><source>Statistics in Medicine</source><volume>21</volume><fpage>1539</fpage><lpage>1558</lpage><pub-id pub-id-type="doi">10.1002/sim.1186</pub-id><pub-id pub-id-type="pmid">12111919</pub-id></element-citation></ref><ref id="bib29"><element-citation publication-type="book"><person-group person-group-type="author"><name><surname>Höftberger</surname> <given-names>R</given-names></name><name><surname>Lassmann</surname> <given-names>H</given-names></name></person-group><year iso-8601-date="2018">2018</year><chapter-title>Chapter 19 - Inflammatory demyelinating diseases of the central nervous system</chapter-title><person-group person-group-type="editor"><name><surname>Kovacs</surname> <given-names>G. G</given-names></name><name><surname>Alafuzoff</surname> <given-names>I</given-names></name></person-group><source>Handbook of Clinical Neurology</source><publisher-name>Elsevier</publisher-name><fpage>263</fpage><lpage>283</lpage></element-citation></ref><ref id="bib30"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Janve</surname> <given-names>VA</given-names></name><name><surname>Zu</surname> <given-names>Z</given-names></name><name><surname>Yao</surname> <given-names>SY</given-names></name><name><surname>Li</surname> <given-names>K</given-names></name><name><surname>Zhang</surname> <given-names>FL</given-names></name><name><surname>Wilson</surname> <given-names>KJ</given-names></name><name><surname>Ou</surname> <given-names>X</given-names></name><name><surname>Does</surname> <given-names>MD</given-names></name><name><surname>Subramaniam</surname> <given-names>S</given-names></name><name><surname>Gochberg</surname> <given-names>DF</given-names></name></person-group><year iso-8601-date="2013">2013</year><article-title>The radial diffusivity and magnetization transfer pool size ratio are sensitive markers for demyelination in a rat model of type III multiple sclerosis (MS) lesions</article-title><source>NeuroImage</source><volume>74</volume><fpage>298</fpage><lpage>305</lpage><pub-id pub-id-type="doi">10.1016/j.neuroimage.2013.02.034</pub-id><pub-id pub-id-type="pmid">23481461</pub-id></element-citation></ref><ref id="bib31"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Jelescu</surname> <given-names>IO</given-names></name><name><surname>Zurek</surname> <given-names>M</given-names></name><name><surname>Winters</surname> <given-names>KV</given-names></name><name><surname>Veraart</surname> <given-names>J</given-names></name><name><surname>Rajaratnam</surname> <given-names>A</given-names></name><name><surname>Kim</surname> <given-names>NS</given-names></name><name><surname>Babb</surname> <given-names>JS</given-names></name><name><surname>Shepherd</surname> <given-names>TM</given-names></name><name><surname>Novikov</surname> <given-names>DS</given-names></name><name><surname>Kim</surname> <given-names>SG</given-names></name><name><surname>Fieremans</surname> <given-names>E</given-names></name></person-group><year iso-8601-date="2016">2016</year><article-title>In vivo quantification of demyelination and recovery using compartment-specific diffusion MRI metrics validated by electron microscopy</article-title><source>NeuroImage</source><volume>132</volume><fpage>104</fpage><lpage>114</lpage><pub-id pub-id-type="doi">10.1016/j.neuroimage.2016.02.004</pub-id><pub-id pub-id-type="pmid">26876473</pub-id></element-citation></ref><ref id="bib32"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Jito</surname> <given-names>J</given-names></name><name><surname>Nakasu</surname> <given-names>S</given-names></name><name><surname>Ito</surname> <given-names>R</given-names></name><name><surname>Fukami</surname> <given-names>T</given-names></name><name><surname>Morikawa</surname> <given-names>S</given-names></name><name><surname>Inubushi</surname> <given-names>T</given-names></name></person-group><year iso-8601-date="2008">2008</year><article-title>Maturational changes in diffusion anisotropy in the rat corpus callosum: comparison with quantitative histological evaluation</article-title><source>Journal of Magnetic Resonance Imaging</source><volume>28</volume><fpage>847</fpage><lpage>854</lpage><pub-id pub-id-type="doi">10.1002/jmri.21496</pub-id><pub-id pub-id-type="pmid">18821626</pub-id></element-citation></ref><ref id="bib33"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Kelm</surname> <given-names>ND</given-names></name><name><surname>West</surname> <given-names>KL</given-names></name><name><surname>Carson</surname> <given-names>RP</given-names></name><name><surname>Gochberg</surname> <given-names>DF</given-names></name><name><surname>Ess</surname> <given-names>KC</given-names></name><name><surname>Does</surname> <given-names>MD</given-names></name></person-group><year iso-8601-date="2016">2016</year><article-title>Evaluation of diffusion kurtosis imaging in ex vivo hypomyelinated mouse brains</article-title><source>NeuroImage</source><volume>124</volume><fpage>612</fpage><lpage>626</lpage><pub-id pub-id-type="doi">10.1016/j.neuroimage.2015.09.028</pub-id><pub-id pub-id-type="pmid">26400013</pub-id></element-citation></ref><ref id="bib34"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Khodanovich</surname> <given-names>MY</given-names></name><name><surname>Sorokina</surname> <given-names>IV</given-names></name><name><surname>Glazacheva</surname> <given-names>VY</given-names></name><name><surname>Akulov</surname> <given-names>AE</given-names></name><name><surname>Nemirovich-Danchenko</surname> <given-names>NM</given-names></name><name><surname>Romashchenko</surname> <given-names>AV</given-names></name><name><surname>Tolstikova</surname> <given-names>TG</given-names></name><name><surname>Mustafina</surname> <given-names>LR</given-names></name><name><surname>Yarnykh</surname> <given-names>VL</given-names></name></person-group><year iso-8601-date="2017">2017</year><article-title>Histological validation of fast macromolecular proton fraction mapping as a quantitative myelin imaging method in the cuprizone demyelination model</article-title><source>Scientific Reports</source><volume>7</volume><elocation-id>46686</elocation-id><pub-id pub-id-type="doi">10.1038/srep46686</pub-id><pub-id pub-id-type="pmid">28436460</pub-id></element-citation></ref><ref id="bib35"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Khodanovich</surname> <given-names>M</given-names></name><name><surname>Pishchelko</surname> <given-names>A</given-names></name><name><surname>Glazacheva</surname> <given-names>V</given-names></name><name><surname>Pan</surname> <given-names>E</given-names></name><name><surname>Akulov</surname> <given-names>A</given-names></name><name><surname>Svetlik</surname> <given-names>M</given-names></name><name><surname>Tyumentseva</surname> <given-names>Y</given-names></name><name><surname>Anan’ina</surname> <given-names>T</given-names></name><name><surname>Yarnykh</surname> <given-names>V</given-names></name></person-group><year iso-8601-date="2019">2019</year><article-title>Quantitative imaging of white and gray matter remyelination in the cuprizone demyelination model using the macromolecular proton fraction</article-title><source>Cells</source><volume>8</volume><elocation-id>1204</elocation-id><pub-id pub-id-type="doi">10.3390/cells8101204</pub-id></element-citation></ref><ref id="bib36"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Kozlowski</surname> <given-names>P</given-names></name><name><surname>Raj</surname> <given-names>D</given-names></name><name><surname>Liu</surname> <given-names>J</given-names></name><name><surname>Lam</surname> <given-names>C</given-names></name><name><surname>Yung</surname> <given-names>AC</given-names></name><name><surname>Tetzlaff</surname> <given-names>W</given-names></name></person-group><year iso-8601-date="2008">2008</year><article-title>Characterizing white matter damage in rat spinal cord with quantitative MRI and histology</article-title><source>Journal of Neurotrauma</source><volume>25</volume><fpage>653</fpage><lpage>676</lpage><pub-id pub-id-type="doi">10.1089/neu.2007.0462</pub-id><pub-id pub-id-type="pmid">18578635</pub-id></element-citation></ref><ref id="bib37"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Kozlowski</surname> <given-names>P</given-names></name><name><surname>Rosicka</surname> <given-names>P</given-names></name><name><surname>Liu</surname> <given-names>J</given-names></name><name><surname>Yung</surname> <given-names>AC</given-names></name><name><surname>Tetzlaff</surname> <given-names>W</given-names></name></person-group><year iso-8601-date="2014">2014</year><article-title>In vivo longitudinal myelin water imaging in rat spinal cord following dorsal column transection injury</article-title><source>Magnetic Resonance Imaging</source><volume>32</volume><fpage>250</fpage><lpage>258</lpage><pub-id pub-id-type="doi">10.1016/j.mri.2013.12.006</pub-id><pub-id pub-id-type="pmid">24462106</pub-id></element-citation></ref><ref id="bib38"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Laule</surname> <given-names>C</given-names></name><name><surname>Leung</surname> <given-names>E</given-names></name><name><surname>Lis</surname> <given-names>DK</given-names></name><name><surname>Traboulsee</surname> <given-names>AL</given-names></name><name><surname>Paty</surname> <given-names>DW</given-names></name><name><surname>MacKay</surname> <given-names>AL</given-names></name><name><surname>Moore</surname> <given-names>GR</given-names></name></person-group><year iso-8601-date="2006">2006</year><article-title>Myelin water imaging in multiple sclerosis: quantitative correlations with histopathology</article-title><source>Multiple Sclerosis Journal</source><volume>12</volume><fpage>747</fpage><lpage>753</lpage><pub-id pub-id-type="doi">10.1177/1352458506070928</pub-id><pub-id pub-id-type="pmid">17263002</pub-id></element-citation></ref><ref id="bib39"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Laule</surname> <given-names>C</given-names></name><name><surname>Vavasour</surname> <given-names>IM</given-names></name><name><surname>Kolind</surname> <given-names>SH</given-names></name><name><surname>Li</surname> <given-names>DK</given-names></name><name><surname>Traboulsee</surname> <given-names>TL</given-names></name><name><surname>Moore</surname> <given-names>GR</given-names></name><name><surname>MacKay</surname> <given-names>AL</given-names></name></person-group><year iso-8601-date="2007">2007</year><article-title>Magnetic resonance imaging of myelin</article-title><source>Neurotherapeutics</source><volume>4</volume><fpage>460</fpage><lpage>484</lpage><pub-id pub-id-type="doi">10.1016/j.nurt.2007.05.004</pub-id><pub-id pub-id-type="pmid">17599712</pub-id></element-citation></ref><ref id="bib40"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Laule</surname> <given-names>C</given-names></name><name><surname>Kozlowski</surname> <given-names>P</given-names></name><name><surname>Leung</surname> <given-names>E</given-names></name><name><surname>Li</surname> <given-names>DK</given-names></name><name><surname>Mackay</surname> <given-names>AL</given-names></name><name><surname>Moore</surname> <given-names>GR</given-names></name></person-group><year iso-8601-date="2008">2008</year><article-title>Myelin water imaging of multiple sclerosis at 7 T: correlations with histopathology</article-title><source>NeuroImage</source><volume>40</volume><fpage>1575</fpage><lpage>1580</lpage><pub-id pub-id-type="doi">10.1016/j.neuroimage.2007.12.008</pub-id><pub-id pub-id-type="pmid">18321730</pub-id></element-citation></ref><ref id="bib41"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Laule</surname> <given-names>C</given-names></name><name><surname>Vavasour</surname> <given-names>IM</given-names></name><name><surname>Leung</surname> <given-names>E</given-names></name><name><surname>Li</surname> <given-names>DK</given-names></name><name><surname>Kozlowski</surname> <given-names>P</given-names></name><name><surname>Traboulsee</surname> <given-names>AL</given-names></name><name><surname>Oger</surname> <given-names>J</given-names></name><name><surname>Mackay</surname> <given-names>AL</given-names></name><name><surname>Moore</surname> <given-names>GR</given-names></name></person-group><year iso-8601-date="2011">2011</year><article-title>Pathological basis of diffusely abnormal white matter: insights from magnetic resonance imaging and histology</article-title><source>Multiple Sclerosis Journal</source><volume>17</volume><fpage>144</fpage><lpage>150</lpage><pub-id pub-id-type="doi">10.1177/1352458510384008</pub-id><pub-id pub-id-type="pmid">20965961</pub-id></element-citation></ref><ref id="bib42"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Laule</surname> <given-names>C</given-names></name><name><surname>Moore</surname> <given-names>GRW</given-names></name></person-group><year iso-8601-date="2018">2018</year><article-title>Myelin water imaging to detect demyelination and remyelination and its validation in pathology</article-title><source>Brain Pathology</source><volume>28</volume><fpage>750</fpage><lpage>764</lpage><pub-id pub-id-type="doi">10.1111/bpa.12645</pub-id><pub-id pub-id-type="pmid">30375119</pub-id></element-citation></ref><ref id="bib43"><element-citation publication-type="book"><person-group person-group-type="author"><name><surname>Lehman</surname> <given-names>EL</given-names></name></person-group><year iso-8601-date="1999">1999</year><source>Elements of Large-Sample Theory</source><publisher-name>Springer</publisher-name></element-citation></ref><ref id="bib44"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Lehto</surname> <given-names>LJ</given-names></name><name><surname>Albors</surname> <given-names>AA</given-names></name><name><surname>Sierra</surname> <given-names>A</given-names></name><name><surname>Tolppanen</surname> <given-names>L</given-names></name><name><surname>Eberly</surname> <given-names>LE</given-names></name><name><surname>Mangia</surname> <given-names>S</given-names></name><name><surname>Nurmi</surname> <given-names>A</given-names></name><name><surname>Michaeli</surname> <given-names>S</given-names></name><name><surname>Gröhn</surname> <given-names>O</given-names></name></person-group><year iso-8601-date="2017">2017a</year><article-title>Lysophosphatidyl choline induced demyelination in rat probed by relaxation along a fictitious field in high rank rotating frame</article-title><source>Frontiers in Neuroscience</source><volume>11</volume><elocation-id>433</elocation-id><pub-id pub-id-type="doi">10.3389/fnins.2017.00433</pub-id><pub-id pub-id-type="pmid">28824359</pub-id></element-citation></ref><ref id="bib45"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Lehto</surname> <given-names>LJ</given-names></name><name><surname>Sierra</surname> <given-names>A</given-names></name><name><surname>Gröhn</surname> <given-names>O</given-names></name></person-group><year iso-8601-date="2017">2017b</year><article-title>Magnetization transfer SWIFT MRI consistently detects histologically verified myelin loss in the thalamocortical pathway after a traumatic brain injury in rat</article-title><source>NMR in Biomedicine</source><volume>30</volume><elocation-id>e3678</elocation-id><pub-id pub-id-type="doi">10.1002/nbm.3678</pub-id></element-citation></ref><ref id="bib46"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>MacKay</surname> <given-names>A</given-names></name><name><surname>Whittall</surname> <given-names>K</given-names></name><name><surname>Adler</surname> <given-names>J</given-names></name><name><surname>Li</surname> <given-names>D</given-names></name><name><surname>Paty</surname> <given-names>D</given-names></name><name><surname>Graeb</surname> <given-names>D</given-names></name></person-group><year iso-8601-date="1994">1994</year><article-title>In vivo visualization of myelin water in brain by magnetic resonance</article-title><source>Magnetic Resonance in Medicine</source><volume>31</volume><fpage>673</fpage><lpage>677</lpage><pub-id pub-id-type="doi">10.1002/mrm.1910310614</pub-id><pub-id pub-id-type="pmid">8057820</pub-id></element-citation></ref><ref id="bib47"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>MacKay</surname> <given-names>AL</given-names></name><name><surname>Laule</surname> <given-names>C</given-names></name></person-group><year iso-8601-date="2016">2016</year><article-title>Magnetic resonance of myelin water: an in vivo Marker for Myelin</article-title><source>Brain Plasticity</source><volume>2</volume><fpage>71</fpage><lpage>91</lpage><pub-id pub-id-type="doi">10.3233/BPL-160033</pub-id></element-citation></ref><ref id="bib48"><element-citation publication-type="software"><person-group person-group-type="author"><name><surname>Mancini</surname> <given-names>M</given-names></name></person-group><year iso-8601-date="2020">2020</year><data-title>myelin-meta-analysis</data-title><source>GitHub</source><version designator="17ca867">17ca867</version><ext-link ext-link-type="uri" xlink:href="https://github.com/matteomancini/myelin-meta-analysis">https://github.com/matteomancini/myelin-meta-analysis</ext-link></element-citation></ref><ref id="bib49"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Mollink</surname> <given-names>J</given-names></name><name><surname>Hiemstra</surname> <given-names>M</given-names></name><name><surname>Miller</surname> <given-names>KL</given-names></name><name><surname>Huszar</surname> <given-names>IN</given-names></name><name><surname>Jenkinson</surname> <given-names>M</given-names></name><name><surname>Raaphorst</surname> <given-names>J</given-names></name><name><surname>Wiesmann</surname> <given-names>M</given-names></name><name><surname>Ansorge</surname> <given-names>O</given-names></name><name><surname>Pallebage-Gamarallage</surname> <given-names>M</given-names></name><name><surname>van Cappellen van Walsum</surname> <given-names>AM</given-names></name></person-group><year iso-8601-date="2019">2019</year><article-title>White matter changes in the perforant path area in patients with amyotrophic lateral sclerosis</article-title><source>Neuropathology and Applied Neurobiology</source><volume>45</volume><fpage>570</fpage><lpage>585</lpage><pub-id pub-id-type="doi">10.1111/nan.12555</pub-id><pub-id pub-id-type="pmid">31002412</pub-id></element-citation></ref><ref id="bib50"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Nave</surname> <given-names>KA</given-names></name><name><surname>Werner</surname> <given-names>HB</given-names></name></person-group><year iso-8601-date="2014">2014</year><article-title>Myelination of the nervous system: mechanisms and functions</article-title><source>Annual Review of Cell and Developmental Biology</source><volume>30</volume><fpage>503</fpage><lpage>533</lpage><pub-id pub-id-type="doi">10.1146/annurev-cellbio-100913-013101</pub-id><pub-id pub-id-type="pmid">25288117</pub-id></element-citation></ref><ref id="bib51"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Odrobina</surname> <given-names>EE</given-names></name><name><surname>Lam</surname> <given-names>TY</given-names></name><name><surname>Pun</surname> <given-names>T</given-names></name><name><surname>Midha</surname> <given-names>R</given-names></name><name><surname>Stanisz</surname> <given-names>GJ</given-names></name></person-group><year iso-8601-date="2005">2005</year><article-title>MR properties of excised neural tissue following experimentally induced demyelination</article-title><source>NMR in Biomedicine</source><volume>18</volume><fpage>277</fpage><lpage>284</lpage><pub-id pub-id-type="doi">10.1002/nbm.951</pub-id><pub-id pub-id-type="pmid">15948233</pub-id></element-citation></ref><ref id="bib52"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Peters</surname> <given-names>JM</given-names></name><name><surname>Struyven</surname> <given-names>RR</given-names></name><name><surname>Prohl</surname> <given-names>AK</given-names></name><name><surname>Vasung</surname> <given-names>L</given-names></name><name><surname>Stajduhar</surname> <given-names>A</given-names></name><name><surname>Taquet</surname> <given-names>M</given-names></name><name><surname>Bushman</surname> <given-names>JJ</given-names></name><name><surname>Lidov</surname> <given-names>H</given-names></name><name><surname>Singh</surname> <given-names>JM</given-names></name><name><surname>Scherrer</surname> <given-names>B</given-names></name><name><surname>Madsen</surname> <given-names>JR</given-names></name><name><surname>Prabhu</surname> <given-names>SP</given-names></name><name><surname>Sahin</surname> <given-names>M</given-names></name><name><surname>Afacan</surname> <given-names>O</given-names></name><name><surname>Warfield</surname> <given-names>SK</given-names></name></person-group><year iso-8601-date="2019">2019</year><article-title>White matter mean diffusivity correlates with myelination in tuberous sclerosis complex</article-title><source>Annals of Clinical and Translational Neurology</source><volume>6</volume><fpage>1178</fpage><lpage>1190</lpage><pub-id pub-id-type="doi">10.1002/acn3.793</pub-id><pub-id pub-id-type="pmid">31353853</pub-id></element-citation></ref><ref id="bib53"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Petiet</surname> <given-names>A</given-names></name><name><surname>Adanyeguh</surname> <given-names>I</given-names></name><name><surname>Aigrot</surname> <given-names>MS</given-names></name><name><surname>Poirion</surname> <given-names>E</given-names></name><name><surname>Nait-Oumesmar</surname> <given-names>B</given-names></name><name><surname>Santin</surname> <given-names>M</given-names></name><name><surname>Stankoff</surname> <given-names>B</given-names></name></person-group><year iso-8601-date="2019">2019</year><article-title>Ultrahigh field imaging of myelin disease models: toward specific markers of myelin integrity?</article-title><source>Journal of Comparative Neurology</source><volume>527</volume><fpage>2179</fpage><lpage>2189</lpage><pub-id pub-id-type="doi">10.1002/cne.24598</pub-id><pub-id pub-id-type="pmid">30520034</pub-id></element-citation></ref><ref id="bib54"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Pol</surname> <given-names>S</given-names></name><name><surname>Sveinsson</surname> <given-names>M</given-names></name><name><surname>Sudyn</surname> <given-names>M</given-names></name><name><surname>Babek</surname> <given-names>N</given-names></name><name><surname>Siebert</surname> <given-names>D</given-names></name><name><surname>Bertolino</surname> <given-names>N</given-names></name><name><surname>Modica</surname> <given-names>CM</given-names></name><name><surname>Preda</surname> <given-names>M</given-names></name><name><surname>Schweser</surname> <given-names>F</given-names></name><name><surname>Zivadinov</surname> <given-names>R</given-names></name></person-group><year iso-8601-date="2019">2019</year><article-title>Teriflunomide's Effect on Glia in Experimental Demyelinating Disease: A Neuroimaging and Histologic Study</article-title><source>Journal of Neuroimaging</source><volume>29</volume><fpage>52</fpage><lpage>61</lpage><pub-id pub-id-type="doi">10.1111/jon.12561</pub-id><pub-id pub-id-type="pmid">30232810</pub-id></element-citation></ref><ref id="bib55"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Praet</surname> <given-names>J</given-names></name><name><surname>Manyakov</surname> <given-names>NV</given-names></name><name><surname>Muchene</surname> <given-names>L</given-names></name><name><surname>Mai</surname> <given-names>Z</given-names></name><name><surname>Terzopoulos</surname> <given-names>V</given-names></name><name><surname>de Backer</surname> <given-names>S</given-names></name><name><surname>Torremans</surname> <given-names>A</given-names></name><name><surname>Guns</surname> <given-names>PJ</given-names></name><name><surname>Van De Casteele</surname> <given-names>T</given-names></name><name><surname>Bottelbergs</surname> <given-names>A</given-names></name><name><surname>Van Broeck</surname> <given-names>B</given-names></name><name><surname>Sijbers</surname> <given-names>J</given-names></name><name><surname>Smeets</surname> <given-names>D</given-names></name><name><surname>Shkedy</surname> <given-names>Z</given-names></name><name><surname>Bijnens</surname> <given-names>L</given-names></name><name><surname>Pemberton</surname> <given-names>DJ</given-names></name><name><surname>Schmidt</surname> <given-names>ME</given-names></name><name><surname>Van der Linden</surname> <given-names>A</given-names></name><name><surname>Verhoye</surname> <given-names>M</given-names></name></person-group><year iso-8601-date="2018">2018</year><article-title>Diffusion kurtosis imaging allows the early detection and longitudinal follow-up of amyloid-β-induced pathology</article-title><source>Alzheimer's Research & Therapy</source><volume>10</volume><elocation-id>8</elocation-id><pub-id pub-id-type="doi">10.1186/s13195-017-0329-8</pub-id><pub-id pub-id-type="pmid">29370870</pub-id></element-citation></ref><ref id="bib56"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Prasloski</surname> <given-names>T</given-names></name><name><surname>Rauscher</surname> <given-names>A</given-names></name><name><surname>MacKay</surname> <given-names>AL</given-names></name><name><surname>Hodgson</surname> <given-names>M</given-names></name><name><surname>Vavasour</surname> <given-names>IM</given-names></name><name><surname>Laule</surname> <given-names>C</given-names></name><name><surname>Mädler</surname> <given-names>B</given-names></name></person-group><year iso-8601-date="2012">2012</year><article-title>Rapid whole cerebrum myelin water imaging using a 3D GRASE sequence</article-title><source>NeuroImage</source><volume>63</volume><fpage>533</fpage><lpage>539</lpage><pub-id pub-id-type="doi">10.1016/j.neuroimage.2012.06.064</pub-id><pub-id pub-id-type="pmid">22776448</pub-id></element-citation></ref><ref id="bib57"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Pun</surname> <given-names>TW</given-names></name><name><surname>Odrobina</surname> <given-names>E</given-names></name><name><surname>Xu</surname> <given-names>QG</given-names></name><name><surname>Lam</surname> <given-names>TY</given-names></name><name><surname>Munro</surname> <given-names>CA</given-names></name><name><surname>Midha</surname> <given-names>R</given-names></name><name><surname>Stanisz</surname> <given-names>GJ</given-names></name></person-group><year iso-8601-date="2005">2005</year><article-title>Histological and magnetic resonance analysis of sciatic nerves in the tellurium model of neuropathy</article-title><source>Journal of the Peripheral Nervous System : JPNS</source><volume>10</volume><fpage>38</fpage><lpage>46</lpage><pub-id pub-id-type="doi">10.1111/j.1085-9489.2005.10107.x</pub-id><pub-id pub-id-type="pmid">15703017</pub-id></element-citation></ref><ref id="bib58"><element-citation publication-type="book"><person-group person-group-type="author"><name><surname>Raudenbush</surname> <given-names>SW</given-names></name></person-group><year iso-8601-date="2009">2009</year><chapter-title>Analyzing effect sizes: Random-effects models</chapter-title><person-group person-group-type="editor"><name><surname>Hedges</surname> <given-names>L. V</given-names></name></person-group><source>The Handbook of Research Synthesis and Meta-Analysis,</source><publisher-name>Russell Sage Foundation</publisher-name><fpage>295</fpage><lpage>315</lpage></element-citation></ref><ref id="bib59"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Reeves</surname> <given-names>C</given-names></name><name><surname>Tachrount</surname> <given-names>M</given-names></name><name><surname>Thomas</surname> <given-names>D</given-names></name><name><surname>Michalak</surname> <given-names>Z</given-names></name><name><surname>Liu</surname> <given-names>J</given-names></name><name><surname>Ellis</surname> <given-names>M</given-names></name><name><surname>Diehl</surname> <given-names>B</given-names></name><name><surname>Miserocchi</surname> <given-names>A</given-names></name><name><surname>McEvoy</surname> <given-names>AW</given-names></name><name><surname>Eriksson</surname> <given-names>S</given-names></name><name><surname>Yousry</surname> <given-names>T</given-names></name><name><surname>Thom</surname> <given-names>M</given-names></name></person-group><year iso-8601-date="2016">2016</year><article-title>Combined <italic>ex vivo</italic> 9.4 T MRI and Quantitative Histopathological Study in Normal and Pathological Neocortical Resections in Focal Epilepsy</article-title><source>Brain Pathology</source><volume>26</volume><fpage>319</fpage><lpage>333</lpage><pub-id pub-id-type="doi">10.1111/bpa.12298</pub-id><pub-id pub-id-type="pmid">26268959</pub-id></element-citation></ref><ref id="bib60"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Rooney</surname> <given-names>WD</given-names></name><name><surname>Johnson</surname> <given-names>G</given-names></name><name><surname>Li</surname> <given-names>X</given-names></name><name><surname>Cohen</surname> <given-names>ER</given-names></name><name><surname>Kim</surname> <given-names>SG</given-names></name><name><surname>Ugurbil</surname> <given-names>K</given-names></name><name><surname>Springer</surname> <given-names>CS</given-names></name></person-group><year iso-8601-date="2007">2007</year><article-title>Magnetic field and tissue dependencies of human brain longitudinal 1h2o relaxation in vivo</article-title><source>Magnetic Resonance in Medicine</source><volume>57</volume><fpage>308</fpage><lpage>318</lpage><pub-id pub-id-type="doi">10.1002/mrm.21122</pub-id><pub-id pub-id-type="pmid">17260370</pub-id></element-citation></ref><ref id="bib61"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Sampaio-Baptista</surname> <given-names>C</given-names></name><name><surname>Johansen-Berg</surname> <given-names>H</given-names></name></person-group><year iso-8601-date="2017">2017</year><article-title>White matter plasticity in the adult brain</article-title><source>Neuron</source><volume>96</volume><fpage>1239</fpage><lpage>1251</lpage><pub-id pub-id-type="doi">10.1016/j.neuron.2017.11.026</pub-id><pub-id pub-id-type="pmid">29268094</pub-id></element-citation></ref><ref id="bib62"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Schmierer</surname> <given-names>K</given-names></name><name><surname>Scaravilli</surname> <given-names>F</given-names></name><name><surname>Altmann</surname> <given-names>DR</given-names></name><name><surname>Barker</surname> <given-names>GJ</given-names></name><name><surname>Miller</surname> <given-names>DH</given-names></name></person-group><year iso-8601-date="2004">2004</year><article-title>Magnetization transfer ratio and myelin in postmortem multiple sclerosis brain</article-title><source>Annals of Neurology</source><volume>56</volume><fpage>407</fpage><lpage>415</lpage><pub-id pub-id-type="doi">10.1002/ana.20202</pub-id><pub-id pub-id-type="pmid">15349868</pub-id></element-citation></ref><ref id="bib63"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Schmierer</surname> <given-names>K</given-names></name><name><surname>Tozer</surname> <given-names>DJ</given-names></name><name><surname>Scaravilli</surname> <given-names>F</given-names></name><name><surname>Altmann</surname> <given-names>DR</given-names></name><name><surname>Barker</surname> <given-names>GJ</given-names></name><name><surname>Tofts</surname> <given-names>PS</given-names></name><name><surname>Miller</surname> <given-names>DH</given-names></name></person-group><year iso-8601-date="2007">2007a</year><article-title>Quantitative magnetization transfer imaging in postmortem multiple sclerosis brain</article-title><source>Journal of Magnetic Resonance Imaging</source><volume>26</volume><fpage>41</fpage><lpage>51</lpage><pub-id pub-id-type="doi">10.1002/jmri.20984</pub-id><pub-id pub-id-type="pmid">17659567</pub-id></element-citation></ref><ref id="bib64"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Schmierer</surname> <given-names>K</given-names></name><name><surname>Wheeler-Kingshott</surname> <given-names>CA</given-names></name><name><surname>Boulby</surname> <given-names>PA</given-names></name><name><surname>Scaravilli</surname> <given-names>F</given-names></name><name><surname>Altmann</surname> <given-names>DR</given-names></name><name><surname>Barker</surname> <given-names>GJ</given-names></name><name><surname>Tofts</surname> <given-names>PS</given-names></name><name><surname>Miller</surname> <given-names>DH</given-names></name></person-group><year iso-8601-date="2007">2007b</year><article-title>Diffusion tensor imaging of post mortem multiple sclerosis brain</article-title><source>NeuroImage</source><volume>35</volume><fpage>467</fpage><lpage>477</lpage><pub-id pub-id-type="doi">10.1016/j.neuroimage.2006.12.010</pub-id><pub-id pub-id-type="pmid">17258908</pub-id></element-citation></ref><ref id="bib65"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Schmierer</surname> <given-names>K</given-names></name><name><surname>Wheeler-Kingshott</surname> <given-names>CA</given-names></name><name><surname>Tozer</surname> <given-names>DJ</given-names></name><name><surname>Boulby</surname> <given-names>PA</given-names></name><name><surname>Parkes</surname> <given-names>HG</given-names></name><name><surname>Yousry</surname> <given-names>TA</given-names></name><name><surname>Scaravilli</surname> <given-names>F</given-names></name><name><surname>Barker</surname> <given-names>GJ</given-names></name><name><surname>Tofts</surname> <given-names>PS</given-names></name><name><surname>Miller</surname> <given-names>DH</given-names></name></person-group><year iso-8601-date="2008">2008</year><article-title>Quantitative magnetic resonance of postmortem multiple sclerosis brain before and after fixation</article-title><source>Magnetic Resonance in Medicine</source><volume>59</volume><fpage>268</fpage><lpage>277</lpage><pub-id pub-id-type="doi">10.1002/mrm.21487</pub-id><pub-id pub-id-type="pmid">18228601</pub-id></element-citation></ref><ref id="bib66"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Schmierer</surname> <given-names>K</given-names></name><name><surname>Parkes</surname> <given-names>HG</given-names></name><name><surname>So</surname> <given-names>PW</given-names></name><name><surname>An</surname> <given-names>SF</given-names></name><name><surname>Brandner</surname> <given-names>S</given-names></name><name><surname>Ordidge</surname> <given-names>RJ</given-names></name><name><surname>Yousry</surname> <given-names>TA</given-names></name><name><surname>Miller</surname> <given-names>DH</given-names></name></person-group><year iso-8601-date="2010">2010</year><article-title>High field (9.4 tesla) magnetic resonance imaging of cortical grey matter lesions in multiple sclerosis</article-title><source>Brain</source><volume>133</volume><fpage>858</fpage><lpage>867</lpage><pub-id pub-id-type="doi">10.1093/brain/awp335</pub-id><pub-id pub-id-type="pmid">20123726</pub-id></element-citation></ref><ref id="bib67"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Seehaus</surname> <given-names>A</given-names></name><name><surname>Roebroeck</surname> <given-names>A</given-names></name><name><surname>Bastiani</surname> <given-names>M</given-names></name><name><surname>Fonseca</surname> <given-names>L</given-names></name><name><surname>Bratzke</surname> <given-names>H</given-names></name><name><surname>Lori</surname> <given-names>N</given-names></name><name><surname>Vilanova</surname> <given-names>A</given-names></name><name><surname>Goebel</surname> <given-names>R</given-names></name><name><surname>Galuske</surname> <given-names>R</given-names></name></person-group><year iso-8601-date="2015">2015</year><article-title>Histological validation of high-resolution DTI in human post mortem tissue</article-title><source>Frontiers in Neuroanatomy</source><volume>9</volume><elocation-id>98</elocation-id><pub-id pub-id-type="doi">10.3389/fnana.2015.00098</pub-id><pub-id pub-id-type="pmid">26257612</pub-id></element-citation></ref><ref id="bib68"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Sled</surname> <given-names>JG</given-names></name></person-group><year iso-8601-date="2018">2018</year><article-title>Modelling and interpretation of magnetization transfer imaging in the brain</article-title><source>NeuroImage</source><volume>182</volume><fpage>128</fpage><lpage>135</lpage><pub-id pub-id-type="doi">10.1016/j.neuroimage.2017.11.065</pub-id><pub-id pub-id-type="pmid">29208570</pub-id></element-citation></ref><ref id="bib69"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Soustelle</surname> <given-names>L</given-names></name><name><surname>Antal</surname> <given-names>MC</given-names></name><name><surname>Lamy</surname> <given-names>J</given-names></name><name><surname>Rousseau</surname> <given-names>F</given-names></name><name><surname>Armspach</surname> <given-names>JP</given-names></name><name><surname>Loureiro de Sousa</surname> <given-names>P</given-names></name></person-group><year iso-8601-date="2019">2019</year><article-title>Correlations of quantitative MRI metrics with myelin basic protein (MBP) staining in a murine model of demyelination</article-title><source>NMR in Biomedicine</source><volume>32</volume><elocation-id>e4116</elocation-id><pub-id pub-id-type="doi">10.1002/nbm.4116</pub-id><pub-id pub-id-type="pmid">31225675</pub-id></element-citation></ref><ref id="bib70"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Stanisz</surname> <given-names>GJ</given-names></name><name><surname>Kecojevic</surname> <given-names>A</given-names></name><name><surname>Bronskill</surname> <given-names>MJ</given-names></name><name><surname>Henkelman</surname> <given-names>RM</given-names></name></person-group><year iso-8601-date="1999">1999</year><article-title>Characterizing white matter with magnetization transfer and T(2)</article-title><source>Magnetic Resonance in Medicine</source><volume>42</volume><fpage>1128</fpage><lpage>1136</lpage><pub-id pub-id-type="doi">10.1002/(SICI)1522-2594(199912)42:6<1128::AID-MRM18>3.0.CO;2-9</pub-id><pub-id pub-id-type="pmid">10571935</pub-id></element-citation></ref><ref id="bib71"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Stanisz</surname> <given-names>GJ</given-names></name><name><surname>Webb</surname> <given-names>S</given-names></name><name><surname>Munro</surname> <given-names>CA</given-names></name><name><surname>Pun</surname> <given-names>T</given-names></name><name><surname>Midha</surname> <given-names>R</given-names></name></person-group><year iso-8601-date="2004">2004</year><article-title>MR properties of excised neural tissue following experimentally induced inflammation</article-title><source>Magnetic Resonance in Medicine</source><volume>51</volume><fpage>473</fpage><lpage>479</lpage><pub-id pub-id-type="doi">10.1002/mrm.20008</pub-id><pub-id pub-id-type="pmid">15004787</pub-id></element-citation></ref><ref id="bib72"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Takagi</surname> <given-names>T</given-names></name><name><surname>Nakamura</surname> <given-names>M</given-names></name><name><surname>Yamada</surname> <given-names>M</given-names></name><name><surname>Hikishima</surname> <given-names>K</given-names></name><name><surname>Momoshima</surname> <given-names>S</given-names></name><name><surname>Fujiyoshi</surname> <given-names>K</given-names></name><name><surname>Shibata</surname> <given-names>S</given-names></name><name><surname>Okano</surname> <given-names>HJ</given-names></name><name><surname>Toyama</surname> <given-names>Y</given-names></name><name><surname>Okano</surname> <given-names>H</given-names></name></person-group><year iso-8601-date="2009">2009</year><article-title>Visualization of peripheral nerve degeneration and regeneration: monitoring with diffusion tensor tractography</article-title><source>NeuroImage</source><volume>44</volume><fpage>884</fpage><lpage>892</lpage><pub-id pub-id-type="doi">10.1016/j.neuroimage.2008.09.022</pub-id><pub-id pub-id-type="pmid">18948210</pub-id></element-citation></ref><ref id="bib73"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Tardif</surname> <given-names>CL</given-names></name><name><surname>Bedell</surname> <given-names>BJ</given-names></name><name><surname>Eskildsen</surname> <given-names>SF</given-names></name><name><surname>Collins</surname> <given-names>DL</given-names></name><name><surname>Pike</surname> <given-names>GB</given-names></name></person-group><year iso-8601-date="2012">2012</year><article-title>Quantitative magnetic resonance imaging of cortical multiple sclerosis pathology</article-title><source>Multiple Sclerosis International</source><volume>2012</volume><fpage>1</fpage><lpage>13</lpage><pub-id pub-id-type="doi">10.1155/2012/742018</pub-id><pub-id pub-id-type="pmid">23213531</pub-id></element-citation></ref><ref id="bib74"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Thiessen</surname> <given-names>JD</given-names></name><name><surname>Zhang</surname> <given-names>Y</given-names></name><name><surname>Zhang</surname> <given-names>H</given-names></name><name><surname>Wang</surname> <given-names>L</given-names></name><name><surname>Buist</surname> <given-names>R</given-names></name><name><surname>Del Bigio</surname> <given-names>MR</given-names></name><name><surname>Kong</surname> <given-names>J</given-names></name><name><surname>Li</surname> <given-names>X-M</given-names></name><name><surname>Martin</surname> <given-names>M</given-names></name></person-group><year iso-8601-date="2013">2013</year><article-title>Quantitative MRI and ultrastructural examination of the cuprizone mouse model of demyelination</article-title><source>NMR in Biomedicine</source><volume>26</volume><fpage>1562</fpage><lpage>1581</lpage><pub-id pub-id-type="doi">10.1002/nbm.2992</pub-id></element-citation></ref><ref id="bib75"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Tu</surname> <given-names>T-W</given-names></name><name><surname>Williams</surname> <given-names>RA</given-names></name><name><surname>Lescher</surname> <given-names>JD</given-names></name><name><surname>Jikaria</surname> <given-names>N</given-names></name><name><surname>Turtzo</surname> <given-names>LC</given-names></name><name><surname>Frank</surname> <given-names>JA</given-names></name></person-group><year iso-8601-date="2016">2016</year><article-title>Radiological-pathological correlation of diffusion tensor and magnetization transfer imaging in a closed head traumatic brain injury model</article-title><source>Annals of Neurology</source><volume>79</volume><fpage>907</fpage><lpage>920</lpage><pub-id pub-id-type="doi">10.1002/ana.24641</pub-id></element-citation></ref><ref id="bib76"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Turati</surname> <given-names>L</given-names></name><name><surname>Moscatelli</surname> <given-names>M</given-names></name><name><surname>Mastropietro</surname> <given-names>A</given-names></name><name><surname>Dowell</surname> <given-names>NG</given-names></name><name><surname>Zucca</surname> <given-names>I</given-names></name><name><surname>Erbetta</surname> <given-names>A</given-names></name><name><surname>Cordiglieri</surname> <given-names>C</given-names></name><name><surname>Brenna</surname> <given-names>G</given-names></name><name><surname>Bianchi</surname> <given-names>B</given-names></name><name><surname>Mantegazza</surname> <given-names>R</given-names></name><name><surname>Cercignani</surname> <given-names>M</given-names></name><name><surname>Baggi</surname> <given-names>F</given-names></name><name><surname>Minati</surname> <given-names>L</given-names></name></person-group><year iso-8601-date="2015">2015</year><article-title><italic>In vivo</italic> quantitative magnetization transfer imaging correlates with histology during de- and remyelination in cuprizone-treated mice</article-title><source>NMR in Biomedicine</source><volume>28</volume><fpage>327</fpage><lpage>337</lpage><pub-id pub-id-type="doi">10.1002/nbm.3253</pub-id><pub-id pub-id-type="pmid">25639498</pub-id></element-citation></ref><ref id="bib77"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Turner</surname> <given-names>R</given-names></name></person-group><year iso-8601-date="2019">2019</year><article-title>Myelin and modeling: bootstrapping cortical microcircuits</article-title><source>Frontiers in Neural Circuits</source><volume>13</volume><elocation-id>34</elocation-id><pub-id pub-id-type="doi">10.3389/fncir.2019.00034</pub-id><pub-id pub-id-type="pmid">31133821</pub-id></element-citation></ref><ref id="bib78"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Underhill</surname> <given-names>HR</given-names></name><name><surname>Rostomily</surname> <given-names>RC</given-names></name><name><surname>Mikheev</surname> <given-names>AM</given-names></name><name><surname>Yuan</surname> <given-names>C</given-names></name><name><surname>Yarnykh</surname> <given-names>VL</given-names></name></person-group><year iso-8601-date="2011">2011</year><article-title>Fast bound pool fraction imaging of the in vivo rat brain: association with myelin content and validation in the C6 glioma model</article-title><source>NeuroImage</source><volume>54</volume><fpage>2052</fpage><lpage>2065</lpage><pub-id pub-id-type="doi">10.1016/j.neuroimage.2010.10.065</pub-id><pub-id pub-id-type="pmid">21029782</pub-id></element-citation></ref><ref id="bib79"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>van Tilborg</surname> <given-names>E</given-names></name><name><surname>Achterberg</surname> <given-names>EJM</given-names></name><name><surname>van Kammen</surname> <given-names>CM</given-names></name><name><surname>van der Toorn</surname> <given-names>A</given-names></name><name><surname>Groenendaal</surname> <given-names>F</given-names></name><name><surname>Dijkhuizen</surname> <given-names>RM</given-names></name><name><surname>Heijnen</surname> <given-names>CJ</given-names></name><name><surname>Vanderschuren</surname> <given-names>L</given-names></name><name><surname>Benders</surname> <given-names>M</given-names></name><name><surname>Nijboer</surname> <given-names>CHA</given-names></name></person-group><year iso-8601-date="2018">2018</year><article-title>Combined fetal inflammation and postnatal hypoxia causes myelin deficits and autism-like behavior in a rat model of diffuse white matter injury</article-title><source>Glia</source><volume>66</volume><fpage>78</fpage><lpage>93</lpage><pub-id pub-id-type="doi">10.1002/glia.23216</pub-id><pub-id pub-id-type="pmid">28925578</pub-id></element-citation></ref><ref id="bib80"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Varma</surname> <given-names>G</given-names></name><name><surname>Duhamel</surname> <given-names>G</given-names></name><name><surname>de Bazelaire</surname> <given-names>C</given-names></name><name><surname>Alsop</surname> <given-names>DC</given-names></name></person-group><year iso-8601-date="2015">2015</year><article-title>Magnetization transfer from Inhomogeneously broadened lines: a potential marker for myelin</article-title><source>Magnetic Resonance in Medicine</source><volume>73</volume><fpage>614</fpage><lpage>622</lpage><pub-id pub-id-type="doi">10.1002/mrm.25174</pub-id><pub-id pub-id-type="pmid">24604578</pub-id></element-citation></ref><ref id="bib81"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Viechtbauer</surname> <given-names>W</given-names></name></person-group><year iso-8601-date="2010">2010</year><article-title>Conducting Meta-Analyses in R with the metafor Package</article-title><source>Journal of Statistical Software</source><volume>36</volume><elocation-id>i03</elocation-id><pub-id pub-id-type="doi">10.18637/jss.v036.i03</pub-id></element-citation></ref><ref id="bib82"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Wang</surname> <given-names>S</given-names></name><name><surname>Wu</surname> <given-names>EX</given-names></name><name><surname>Cai</surname> <given-names>K</given-names></name><name><surname>Lau</surname> <given-names>HF</given-names></name><name><surname>Cheung</surname> <given-names>PT</given-names></name><name><surname>Khong</surname> <given-names>PL</given-names></name></person-group><year iso-8601-date="2009">2009</year><article-title>Mild hypoxic-ischemic injury in the neonatal rat brain: longitudinal evaluation of white matter using diffusion tensor MR imaging</article-title><source>American Journal of Neuroradiology</source><volume>30</volume><fpage>1907</fpage><lpage>1913</lpage><pub-id pub-id-type="doi">10.3174/ajnr.A1697</pub-id><pub-id pub-id-type="pmid">19749219</pub-id></element-citation></ref><ref id="bib83"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Wang</surname> <given-names>X</given-names></name><name><surname>Cusick</surname> <given-names>MF</given-names></name><name><surname>Wang</surname> <given-names>Y</given-names></name><name><surname>Sun</surname> <given-names>P</given-names></name><name><surname>Libbey</surname> <given-names>JE</given-names></name><name><surname>Trinkaus</surname> <given-names>K</given-names></name><name><surname>Fujinami</surname> <given-names>RS</given-names></name><name><surname>Song</surname> <given-names>SK</given-names></name></person-group><year iso-8601-date="2014">2014</year><article-title>Diffusion basis spectrum imaging detects and distinguishes coexisting subclinical inflammation, demyelination and axonal injury in experimental autoimmune encephalomyelitis mice</article-title><source>NMR in Biomedicine</source><volume>27</volume><fpage>843</fpage><lpage>852</lpage><pub-id pub-id-type="doi">10.1002/nbm.3129</pub-id><pub-id pub-id-type="pmid">24816651</pub-id></element-citation></ref><ref id="bib84"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Wang</surname> <given-names>Y</given-names></name><name><surname>Sun</surname> <given-names>P</given-names></name><name><surname>Wang</surname> <given-names>Q</given-names></name><name><surname>Trinkaus</surname> <given-names>K</given-names></name><name><surname>Schmidt</surname> <given-names>RE</given-names></name><name><surname>Naismith</surname> <given-names>RT</given-names></name><name><surname>Cross</surname> <given-names>AH</given-names></name><name><surname>Song</surname> <given-names>SK</given-names></name></person-group><year iso-8601-date="2015">2015</year><article-title>Differentiation and quantification of inflammation, demyelination and axon injury or loss in multiple sclerosis</article-title><source>Brain</source><volume>138</volume><fpage>1223</fpage><lpage>1238</lpage><pub-id pub-id-type="doi">10.1093/brain/awv046</pub-id><pub-id pub-id-type="pmid">25724201</pub-id></element-citation></ref><ref id="bib85"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Wei</surname> <given-names>H</given-names></name><name><surname>Cao</surname> <given-names>P</given-names></name><name><surname>Bischof</surname> <given-names>A</given-names></name><name><surname>Henry</surname> <given-names>RG</given-names></name><name><surname>Larson</surname> <given-names>PEZ</given-names></name><name><surname>Liu</surname> <given-names>C</given-names></name></person-group><year iso-8601-date="2018">2018</year><article-title>MRI gradient-echo phase contrast of the brain at ultra-short TE with off-resonance saturation</article-title><source>NeuroImage</source><volume>175</volume><fpage>1</fpage><lpage>11</lpage><pub-id pub-id-type="doi">10.1016/j.neuroimage.2018.03.066</pub-id><pub-id pub-id-type="pmid">29604452</pub-id></element-citation></ref><ref id="bib86"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Wendel</surname> <given-names>KM</given-names></name><name><surname>Lee</surname> <given-names>JB</given-names></name><name><surname>Affeldt</surname> <given-names>BM</given-names></name><name><surname>Hamer</surname> <given-names>M</given-names></name><name><surname>Harahap-Carrillo</surname> <given-names>IS</given-names></name><name><surname>Pardo</surname> <given-names>AC</given-names></name><name><surname>Obenaus</surname> <given-names>A</given-names></name></person-group><year iso-8601-date="2018">2018</year><article-title>Corpus callosum vasculature predicts white matter microstructure abnormalities after pediatric mild traumatic brain injury</article-title><source>Journal of Neurotrauma</source><volume>23</volume><elocation-id>e5670</elocation-id><pub-id pub-id-type="doi">10.1089/neu.2018.5670</pub-id></element-citation></ref><ref id="bib87"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>West</surname> <given-names>KL</given-names></name><name><surname>Kelm</surname> <given-names>ND</given-names></name><name><surname>Carson</surname> <given-names>RP</given-names></name><name><surname>Gochberg</surname> <given-names>DF</given-names></name><name><surname>Ess</surname> <given-names>KC</given-names></name><name><surname>Does</surname> <given-names>MD</given-names></name></person-group><year iso-8601-date="2018">2018</year><article-title>Myelin volume fraction imaging with MRI</article-title><source>NeuroImage</source><volume>182</volume><fpage>511</fpage><lpage>521</lpage><pub-id pub-id-type="doi">10.1016/j.neuroimage.2016.12.067</pub-id><pub-id pub-id-type="pmid">28025129</pub-id></element-citation></ref><ref id="bib88"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Wu</surname> <given-names>QZ</given-names></name><name><surname>Yang</surname> <given-names>Q</given-names></name><name><surname>Cate</surname> <given-names>HS</given-names></name><name><surname>Kemper</surname> <given-names>D</given-names></name><name><surname>Binder</surname> <given-names>M</given-names></name><name><surname>Wang</surname> <given-names>HX</given-names></name><name><surname>Fang</surname> <given-names>K</given-names></name><name><surname>Quick</surname> <given-names>MJ</given-names></name><name><surname>Marriott</surname> <given-names>M</given-names></name><name><surname>Kilpatrick</surname> <given-names>TJ</given-names></name><name><surname>Egan</surname> <given-names>GF</given-names></name></person-group><year iso-8601-date="2008">2008</year><article-title>MRI identification of the rostral-caudal pattern of pathology within the corpus callosum in the cuprizone mouse model</article-title><source>Journal of Magnetic Resonance Imaging : JMRI</source><volume>27</volume><fpage>446</fpage><lpage>453</lpage><pub-id pub-id-type="doi">10.1002/jmri.21111</pub-id><pub-id pub-id-type="pmid">17968901</pub-id></element-citation></ref><ref id="bib89"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Yano</surname> <given-names>R</given-names></name><name><surname>Hata</surname> <given-names>J</given-names></name><name><surname>Abe</surname> <given-names>Y</given-names></name><name><surname>Seki</surname> <given-names>F</given-names></name><name><surname>Yoshida</surname> <given-names>K</given-names></name><name><surname>Komaki</surname> <given-names>Y</given-names></name><name><surname>Okano</surname> <given-names>H</given-names></name><name><surname>Tanaka</surname> <given-names>KF</given-names></name></person-group><year iso-8601-date="2018">2018</year><article-title>Quantitative temporal changes in DTI values coupled with histological properties in cuprizone-induced demyelination and remyelination</article-title><source>Neurochemistry International</source><volume>119</volume><fpage>151</fpage><lpage>158</lpage><pub-id pub-id-type="doi">10.1016/j.neuint.2017.10.004</pub-id></element-citation></ref><ref id="bib90"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Yarnykh</surname> <given-names>VL</given-names></name></person-group><year iso-8601-date="2012">2012</year><article-title>Fast macromolecular proton fraction mapping from a single off-resonance magnetization transfer measurement</article-title><source>Magnetic Resonance in Medicine</source><volume>68</volume><fpage>166</fpage><lpage>178</lpage><pub-id pub-id-type="doi">10.1002/mrm.23224</pub-id><pub-id pub-id-type="pmid">22190042</pub-id></element-citation></ref><ref id="bib91"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Zaaraoui</surname> <given-names>W</given-names></name><name><surname>Deloire</surname> <given-names>M</given-names></name><name><surname>Merle</surname> <given-names>M</given-names></name><name><surname>Girard</surname> <given-names>C</given-names></name><name><surname>Raffard</surname> <given-names>G</given-names></name><name><surname>Biran</surname> <given-names>M</given-names></name><name><surname>Inglese</surname> <given-names>M</given-names></name><name><surname>Petry</surname> <given-names>KG</given-names></name><name><surname>Gonen</surname> <given-names>O</given-names></name><name><surname>Brochet</surname> <given-names>B</given-names></name><name><surname>Franconi</surname> <given-names>J-M</given-names></name><name><surname>Dousset</surname> <given-names>V</given-names></name></person-group><year iso-8601-date="2008">2008</year><article-title>Monitoring demyelination and remyelination by magnetization transfer imaging in the mouse brain at 9.4 T</article-title><source>Magnetic Resonance Materials in Physics, Biology and Medicine</source><volume>21</volume><fpage>357</fpage><lpage>362</lpage><pub-id pub-id-type="doi">10.1007/s10334-008-0141-3</pub-id></element-citation></ref><ref id="bib92"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Zhang</surname> <given-names>J</given-names></name><name><surname>Jones</surname> <given-names>M</given-names></name><name><surname>DeBoy</surname> <given-names>CA</given-names></name><name><surname>Reich</surname> <given-names>DS</given-names></name><name><surname>Farrell</surname> <given-names>JA</given-names></name><name><surname>Hoffman</surname> <given-names>PN</given-names></name><name><surname>Griffin</surname> <given-names>JW</given-names></name><name><surname>Sheikh</surname> <given-names>KA</given-names></name><name><surname>Miller</surname> <given-names>MI</given-names></name><name><surname>Mori</surname> <given-names>S</given-names></name><name><surname>Calabresi</surname> <given-names>PA</given-names></name></person-group><year iso-8601-date="2009">2009</year><article-title>Diffusion tensor magnetic resonance imaging of wallerian degeneration in rat spinal cord after dorsal root axotomy</article-title><source>Journal of Neuroscience</source><volume>29</volume><fpage>3160</fpage><lpage>3171</lpage><pub-id pub-id-type="doi">10.1523/JNEUROSCI.3941-08.2009</pub-id><pub-id pub-id-type="pmid">19279253</pub-id></element-citation></ref></ref-list><app-group><app id="appendix-1"><title>Appendix 1</title><sec id="s8" sec-type="appendix"><title>Search keywords</title><boxed-text><p>(myelin[Title/Abstract] AND ((magnetic[Title/Abstract] AND resonance[Title/Abstract]) OR mr[Title/Abstract] OR mri[Title/Abstract])) AND (histology[Title/Abstract] OR histopathology[Title/Abstract] OR microscopy[Title/Abstract] OR immunohistochemistry[Title/Abstract] OR histological[Title/Abstract] OR histologically[Title/Abstract] OR histologic[Title/Abstract] OR histopathological[Title/Abstract] OR histopathologically[Title/Abstract] OR histopathologic[Title/Abstract]).</p><p>Results obtained from the Medline database: 688 (03/06/2020).</p><fig id="app1fig1" position="float"><label>Appendix 1—figure 1.</label><caption><title>PRISMA flowchart for the meta-analysis.</title></caption><graphic mime-subtype="jpeg" mimetype="image" xlink:href="elife-61523.xml.media/app1-fig1.jpg"/></fig><sec id="s9"><title>Fixed- and mixed-effects models</title><p>While a traditional linear regression model estimates the error variance from residuals, in a fixed effects meta-analysis model, each paper’s response and standard errors, as well as the error variance of the regression model can be directly computed from the supplied response standard deviations. Specifically, for a (non-meta) regression model we have the <inline-formula><mml:math id="inf1"><mml:mi>i</mml:mi></mml:math></inline-formula>-th response <inline-formula><mml:math id="inf2"><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:math></inline-formula> modeled with covariate values <inline-formula><mml:math id="inf3"><mml:msub><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:math></inline-formula>, <inline-formula><mml:math id="inf4"><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mi>β</mml:mi><mml:mo>+</mml:mo><mml:msub><mml:mrow><mml:mi>ε</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:math></inline-formula>, where random error has unknown variance <inline-formula><mml:math id="inf5"><mml:mtext>Var</mml:mtext><mml:mfenced separators="|"><mml:mrow><mml:msub><mml:mrow><mml:mi>ε</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfenced><mml:mo>=</mml:mo><mml:msup><mml:mrow><mml:mi>σ</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>. In a fixed-effects meta-analysis, we are given <inline-formula><mml:math id="inf6"><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:math></inline-formula> but also <inline-formula><mml:math id="inf7"><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:math></inline-formula>, the standard error of <inline-formula><mml:math id="inf8"><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:math></inline-formula>, and the regression model has the same form except the variance is known, <inline-formula><mml:math id="inf9"><mml:mtext>Var</mml:mtext><mml:mfenced separators="|"><mml:mrow><mml:msub><mml:mrow><mml:mi>ε</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfenced><mml:mo>=</mml:mo><mml:msubsup><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:math></inline-formula>, and the weighted least squares regression can be computed, estimating beta and its standard error. A mixed-effects meta-analysis accounts for more variance than what can be ascribed to the sampling error of the reported outcome. The regression model has again the same form, except now the variance is <inline-formula><mml:math id="inf10"><mml:mtext>Var</mml:mtext><mml:mfenced separators="|"><mml:mrow><mml:msub><mml:mrow><mml:mi>ε</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfenced><mml:mo>=</mml:mo><mml:msubsup><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:mo>+</mml:mo><mml:msup><mml:mrow><mml:mi>τ</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>, the sum of the reported squared standard error and the unknown between-study variance <inline-formula><mml:math id="inf11"><mml:msup><mml:mrow><mml:mi>τ</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>. Iterative methods are used to estimate <inline-formula><mml:math id="inf12"><mml:msup><mml:mrow><mml:mi>τ</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> and, once estimated, a weighted least squares regression can be computed. The parameter <inline-formula><mml:math id="inf13"><mml:msup><mml:mrow><mml:mi>τ</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> can be interpreted as the variance of <italic>noise-free</italic> (hypothetical, zero standard error) results from the population of all possible studies. The importance of <inline-formula><mml:math id="inf14"><mml:msup><mml:mrow><mml:mi>τ</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> can also be gauged by <inline-formula><mml:math id="inf15"><mml:msup><mml:mrow><mml:mi>I</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>, the proportion of variance due to random inter-study differences (i.e. <inline-formula><mml:math id="inf16"><mml:mn>1</mml:mn><mml:mo>-</mml:mo><mml:msup><mml:mrow><mml:mi>I</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> is the proportion attributable to random sampling error of each study) (<xref ref-type="bibr" rid="bib28">Higgins and Thompson, 2002</xref>).</p></sec><sec id="s10"><title>Abbreviations and mathematical symbols</title><list list-type="simple"><list-item><p>AD – axial diffusivity</p></list-item><list-item><p>AK – axial kurtosis</p></list-item><list-item><p>AWF – axonal water fraction</p></list-item><list-item><p>FA – fraction anisotropy</p></list-item><list-item><p>ihMTR – inhomogeneous magnetization transfer ratio</p></list-item><list-item><p>k_fm – free water-macromolecular exchange rate</p></list-item><list-item><p>k_mf – macromolecular-free water exchange rate</p></list-item><list-item><p>M0m – macromolecular pool magnetization fraction</p></list-item><list-item><p>MD – mean diffusivity</p></list-item><list-item><p>MK – mean kurtosis</p></list-item><list-item><p>MPF – macromolecular pool fraction</p></list-item><list-item><p>MT – magnetization transfer</p></list-item><list-item><p>MTR – magnetization transfer ratio</p></list-item><list-item><p>MTR-UTE – magnetization transfer ratio (using ultra-short echo time)</p></list-item><list-item><p>MTV – macromolecular tissue volume</p></list-item><list-item><p>MVF-MT – myelin volume fraction (estimated from MT)</p></list-item><list-item><p>MVF-T2 – myelin volume fraction (estimated from T2)</p></list-item><list-item><p>MWF – myelin water fraction</p></list-item><list-item><p>PD – proton density</p></list-item><list-item><p>PN – peripheral nerve</p></list-item><list-item><p>PRISMA – Preferred Reporting Items for Systematic Reviews and Meta-Analyses</p></list-item><list-item><p>QSM – quantitative susceptibility mapping</p></list-item><list-item><p>R1f – free water pool longitudinal relaxation rate</p></list-item><list-item><p>R2* – apparent transverse relaxation rate</p></list-item><list-item><p>RAFF – relaxation along a fictitious field</p></list-item><list-item><p>RD – radial diffusivity</p></list-item><list-item><p>RD-DBSI – radial diffusivity (from diffusion basis spectrum imaging)</p></list-item><list-item><p>RDe – extra-cellular compartment radial diffusivity</p></list-item><list-item><p>RK – radial kurtosis</p></list-item><list-item><p>rSPF – relative semi-solid proton fraction</p></list-item><list-item><p>SC – spinal cord</p></list-item><list-item><p>T1 – longitudinal relaxation time</p></list-item><list-item><p>T1p – adiabatic longitudinal relaxation time</p></list-item><list-item><p>T1sat – longitudinal relaxation time under magnetization transfer irradiation</p></list-item><list-item><p>T2 – transverse relaxation time</p></list-item><list-item><p>T2f – free water pool transverse relaxation time</p></list-item><list-item><p>T2int – transverse relaxation intermediate component</p></list-item><list-item><p>T2m – macromolecular pool transverse relaxation rate</p></list-item><list-item><p>T2p – adiabatic transverse relaxation time</p></list-item></list><table-wrap id="app1table1" position="float"><label>Appendix 1—table 1.</label><caption><title>Selected studies for qualitative analysis.</title></caption><table frame="hsides" rules="groups"><thead><tr><th valign="top">Study</th><th valign="top">MRI measure(s)</th><th valign="top">Histology/microscopy measure</th><th valign="top">Tissue</th><th valign="top">Condition</th><th valign="top">Focus</th></tr></thead><tbody><tr><td valign="top"><xref ref-type="bibr" rid="bib62">Schmierer et al., 2004</xref></td><td valign="top">T1, MTR</td><td valign="top">Histology - LFB</td><td valign="top">Human</td><td valign="top">Multiple sclerosis</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib51">Odrobina et al., 2005</xref></td><td valign="top">T1, T2, T2int, MWF, M0m, MTR</td><td valign="top">Microscopy - Myelin fraction</td><td valign="top">Animal - Rat</td><td valign="top">Demyelination - Tellurium</td><td valign="top">PN</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib57">Pun et al., 2005</xref></td><td valign="top">T1, T2int, MWF</td><td valign="top">Microscopy - Myelin fraction</td><td valign="top">Animal - Rat</td><td valign="top">Demyelination - Tellurium</td><td valign="top">PN</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib38">Laule et al., 2006</xref></td><td valign="top">MWF</td><td valign="top">Histology - LFB</td><td valign="top">Human</td><td valign="top">Multiple sclerosis</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib63">Schmierer et al., 2007a</xref></td><td valign="top">T1, MTR, MPF, T2m</td><td valign="top">Histology - LFB</td><td valign="top">Human</td><td valign="top">Multiple sclerosis</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib64">Schmierer et al., 2007b</xref></td><td valign="top">FA, MD</td><td valign="top">Histology - LFB</td><td valign="top">Human</td><td valign="top">Multiple sclerosis</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib32">Jito et al., 2008</xref></td><td valign="top">FA</td><td valign="top">Microscopy - Myelin sheath area</td><td valign="top">Animal - Mouse</td><td valign="top">Healthy</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib36">Kozlowski et al., 2008</xref></td><td valign="top">MWF, FA, AD, RD, MD</td><td valign="top">Immunohistochemistry - MBP</td><td valign="top">Animal - Rat</td><td valign="top">Injury - Dorsal columnar transection</td><td valign="top">SC</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib40">Laule et al., 2008</xref></td><td valign="top">MWF</td><td valign="top">Histology - LFB</td><td valign="top">Human</td><td valign="top">Multiple sclerosis</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib65">Schmierer et al., 2008</xref></td><td valign="top">T1, T2, MTR, MPF, MD, FA, AD, RD</td><td valign="top">Histology - LFB</td><td valign="top">Human</td><td valign="top">Multiple sclerosis</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib88">Wu et al., 2008</xref></td><td valign="top">T2</td><td valign="top">Histology - LFB</td><td valign="top">Animal - Mouse</td><td valign="top">Demyelination - Cuprizone</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib91">Zaaraoui et al., 2008</xref></td><td valign="top">MTR</td><td valign="top">Immunohistochemistry - MBP</td><td valign="top">Animal - Mouse</td><td valign="top">Demyelination - Cuprizone</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib72">Takagi et al., 2009</xref></td><td valign="top">FA, AD</td><td valign="top">EM - Myelin thickness</td><td valign="top">Animal - Rat</td><td valign="top">Degeneration - Contusive injury</td><td valign="top">PN</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib82">Wang et al., 2009</xref></td><td valign="top">FA, RD</td><td valign="top">Histology - LFB</td><td valign="top">Animal - Rat</td><td valign="top">Ischemia - Induced hypoxia</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib92">Zhang et al., 2009</xref></td><td valign="top">RD</td><td valign="top">Histology - LFB</td><td valign="top">Animal - Rat</td><td valign="top">Injury - Dorsal columnar transection</td><td valign="top">SC</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib66">Schmierer et al., 2010</xref></td><td valign="top">MTR, T2</td><td valign="top">Histology - LFB</td><td valign="top">Human</td><td valign="top">Multiple sclerosis</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib18">Fatemi et al., 2011</xref></td><td valign="top">MTR</td><td valign="top">Immunohistochemistry - MBP</td><td valign="top">Animal - Mouse</td><td valign="top">Ischemia - Induced hypoxia</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib41">Laule et al., 2011</xref></td><td valign="top">MWF</td><td valign="top">Immunohistochemistry - MBP</td><td valign="top">Human</td><td valign="top">Multiple sclerosis</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib78">Underhill et al., 2011</xref></td><td valign="top">MPF</td><td valign="top">Histology - LFB</td><td valign="top">Animal - Mouse</td><td valign="top">Healthy</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib8">Chandran et al., 2012</xref></td><td valign="top">FA, RD</td><td valign="top">Immunohistochemistry - MBP</td><td valign="top">Animal - Mouse</td><td valign="top">Demyelination - Cuprizone</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib73">Tardif et al., 2012</xref></td><td valign="top">T1, T2, MTR, PD</td><td valign="top">Immunohistochemistry - MBP</td><td valign="top">Human</td><td valign="top">Multiple sclerosis</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib21">Fjær et al., 2013</xref></td><td valign="top">MTR</td><td valign="top">Immunohistochemistry - PLP</td><td valign="top">Animal - Mouse</td><td valign="top">Demyelination - Cuprizone</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib27">Harkins et al., 2013</xref></td><td valign="top">MWF, MPF</td><td valign="top">Microscopy - Myelin fraction</td><td valign="top">Animal - Rat</td><td valign="top">Edema - Hexaclorophene</td><td valign="top">SC</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib30">Janve et al., 2013</xref></td><td valign="top">MPF, R1a, k_ba, FA, RD, MD, AD</td><td valign="top">Histology - LFB</td><td valign="top">Animal - Rat</td><td valign="top">Demyelination - Lipopolysaccharide</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib74">Thiessen et al., 2013</xref></td><td valign="top">MPF, R1f, k_fm, k_mf, T2f, T2m, MD, RD, AD, FA, T1, T2</td><td valign="top">EM - Myelin thickness</td><td valign="top">Animal - Mouse</td><td valign="top">Demyelination - Cuprizone</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib37">Kozlowski et al., 2014</xref></td><td valign="top">MWF</td><td valign="top">Immunohistochemistry - MBP</td><td valign="top">Animal - Rat</td><td valign="top">Injury - Dorsal columnar transection</td><td valign="top">SC</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib83">Wang et al., 2014</xref></td><td valign="top">RD, RD-DBSI</td><td valign="top">Immunohistochemistry - MBP</td><td valign="top">Animal - Mouse</td><td valign="top">Demyelination - Autoimmune encephalomyelitis</td><td valign="top">SC</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib22">Fjær et al., 2015</xref></td><td valign="top">MTR</td><td valign="top">Immunohistochemistry - PLP</td><td valign="top">Animal - Mouse</td><td valign="top">Demyelination - Autoimmune encephalomyelitis</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib67">Seehaus et al., 2015</xref></td><td valign="top">FA, RD, MD</td><td valign="top">Histology - Silver</td><td valign="top">Human</td><td valign="top">Healthy</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib76">Turati et al., 2015</xref></td><td valign="top">MPF</td><td valign="top">Immunohistochemistry - MBP</td><td valign="top">Animal - Mouse</td><td valign="top">Demyelination - Cuprizone</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib84">Wang et al., 2015</xref></td><td valign="top">RD-DBSI</td><td valign="top">Histology - LFB</td><td valign="top">Human</td><td valign="top">Multiple sclerosis</td><td valign="top">SC</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib2">Aojula et al., 2016</xref></td><td valign="top">FA, AD, RD, MD</td><td valign="top">Immunohistochemistry - MBP</td><td valign="top">Animal - Rat</td><td valign="top">Hydrocephalus</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib24">Hakkarainen et al., 2016</xref></td><td valign="top">T1, T2, MTR, T1p, T2p, RAFF</td><td valign="top">Histology - Gold chloride</td><td valign="top">Animal - Rat</td><td valign="top">Healthy</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib31">Jelescu et al., 2016</xref></td><td valign="top">RD, RK, AWF, Rde, T2, MTR</td><td valign="top">EM - Myelin fraction</td><td valign="top">Animal - Mouse</td><td valign="top">Demyelination - Cuprizone</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib33">Kelm et al., 2016</xref></td><td valign="top">MD, RD, MK, RK, AWF</td><td valign="top">EM - Myelin fraction</td><td valign="top">Animal - Mouse</td><td valign="top">Demyelination - Knockout</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib59">Reeves et al., 2016</xref></td><td valign="top">T1, T2</td><td valign="top">Immunohistochemistry - MBP</td><td valign="top">Human</td><td valign="top">Epilepsy</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib75">Tu et al., 2016</xref></td><td valign="top">FA, AD, RD, MD, MTR</td><td valign="top">Immunohistochemistry - MBP</td><td valign="top">Animal - Rat</td><td valign="top">Traumatic brain injury</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib9">Chang et al., 2017</xref></td><td valign="top">FA, AD, RD, MD</td><td valign="top">Immunohistochemistry - MBP</td><td valign="top">Animal - Mouse</td><td valign="top">Healthy</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib10">Chen et al., 2017</xref></td><td valign="top">MWF</td><td valign="top">EM - Myelin fraction</td><td valign="top">Animal - Rat</td><td valign="top">Injury - Dorsal columnar transection</td><td valign="top">SC</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib34">Khodanovich et al., 2017</xref></td><td valign="top">MPF</td><td valign="top">Histology - LFB</td><td valign="top">Animal - Mouse</td><td valign="top">Demyelination - Cuprizone</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib44">Lehto et al., 2017a</xref></td><td valign="top">RAFF, MTR, T1sat, FA, MD, AD, RD</td><td valign="top">Histology - Gold chloride</td><td valign="top">Animal - Rat</td><td valign="top">Demyelination - Lipopolysaccharide</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib45">Lehto et al., 2017b</xref></td><td valign="top">MTR</td><td valign="top">Histology - Gold chloride</td><td valign="top">Animal - Rat</td><td valign="top">Traumatic brain injury</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib79">van Tilborg et al., 2018</xref></td><td valign="top">FA</td><td valign="top">Immunohistochemistry - MBP</td><td valign="top">Animal - Rat</td><td valign="top">White matter injury</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib5">Beckmann et al., 2018</xref></td><td valign="top">MTR</td><td valign="top">Histology - LFB</td><td valign="top">Animal - Mouse</td><td valign="top">Demyelination - Cuprizone</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib6">Berman et al., 2018</xref></td><td valign="top">MTV</td><td valign="top">EM - Myelin fraction</td><td valign="top">Animal - Mouse</td><td valign="top">Demyelination - Knockout</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib25">Hametner et al., 2018</xref></td><td valign="top">R2*, T1, QSM</td><td valign="top">Histology - LFB</td><td valign="top">Human</td><td valign="top">Vascular diseases</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib55">Praet et al., 2018</xref></td><td valign="top">MK, RK, AK, FA, MD, RD, AD</td><td valign="top">Immunohistochemistry - MBP</td><td valign="top">Animal - Mouse</td><td valign="top">Amyloidosis</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib86">Wendel et al., 2018</xref></td><td valign="top">FA, AD, RD, MD</td><td valign="top">Immunohistochemistry - MBP</td><td valign="top">Animal - Mouse</td><td valign="top">Traumatic brain injury</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib87">West et al., 2018</xref></td><td valign="top">MPF, MWF, MVF-T2, MVF-MT</td><td valign="top">EM - Myelin fraction</td><td valign="top">Animal - Mouse</td><td valign="top">Demyelination - Knockout</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib89">Yano et al., 2018</xref></td><td valign="top">FA, RD, MD</td><td valign="top">Immunohistochemistry - PLP</td><td valign="top">Animal - Mouse</td><td valign="top">Demyelination - Cuprizone</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib1">Abe et al., 2019</xref></td><td valign="top">FA, RD, AD</td><td valign="top">Microscopy - Myelin thickness</td><td valign="top">Animal - Mouse</td><td valign="top">Optogenetic stimulation</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib16">Duhamel et al., 2019</xref></td><td valign="top">ihMTR, MTR</td><td valign="top">Microscopy - Fluorescence</td><td valign="top">Animal - Mouse</td><td valign="top">Healthy</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib35">Khodanovich et al., 2019</xref></td><td valign="top">MPF</td><td valign="top">Immunohistochemistry - MBP</td><td valign="top">Animal - Mouse</td><td valign="top">Demyelination - Cuprizone</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib49">Mollink et al., 2019</xref></td><td valign="top">FA</td><td valign="top">Immunohistochemistry - MBP</td><td valign="top">Human</td><td valign="top">Amyotrophic lateral sclerosis</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib52">Peters et al., 2019</xref></td><td valign="top">FA, MD</td><td valign="top">Histology - LFB</td><td valign="top">Human</td><td valign="top">Tuberous sclerosis complex</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib54">Pol et al., 2019</xref></td><td valign="top">QSM, FA, MD</td><td valign="top">Histology - Solochrome</td><td valign="top">Animal - Mouse</td><td valign="top">Healthy</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib69">Soustelle et al., 2019</xref></td><td valign="top">MPF, RD, MWF, rSPF</td><td valign="top">Immunohistochemistry - MBP</td><td valign="top">Animal - Mouse</td><td valign="top">Demyelination - Cuprizone</td><td valign="top">Brain</td></tr><tr><td valign="top"><xref ref-type="bibr" rid="bib23">Guglielmetti et al., 2020</xref></td><td valign="top">MTR, MTR-UTE</td><td valign="top">Immunohistochemistry - MBP</td><td valign="top">Animal - Mouse</td><td valign="top">Healthy</td><td valign="top">Brain</td></tr></tbody></table></table-wrap></sec></boxed-text></sec></app></app-group></back><sub-article article-type="decision-letter" id="sa1"><front-stub><article-id pub-id-type="doi">10.7554/eLife.61523.sa1</article-id><title-group><article-title>Decision letter</article-title></title-group><contrib-group><contrib contrib-type="editor"><name><surname>Jbabdi</surname><given-names>Saad</given-names></name><role>Reviewing Editor</role><aff><institution>University of Oxford</institution><country>United Kingdom</country></aff></contrib></contrib-group><contrib-group><contrib contrib-type="reviewer"><name><surname>Jbabdi</surname><given-names>Saad</given-names> </name><role>Reviewer</role><aff><institution>University of Oxford</institution><country>United Kingdom</country></aff></contrib><contrib contrib-type="reviewer"><name><surname>Does</surname><given-names>Mark</given-names> </name><role>Reviewer</role><aff><institution/></aff></contrib></contrib-group></front-stub><body><boxed-text><p>In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.</p></boxed-text><p><bold>Acceptance summary:</bold></p><p>This meta-analysis is a great contribution to efforts in the neuroimaging field to better understand the biological underpinnings of MRI techniques. The work looks at published quantitative relationships between multiple MRI measures that are thought to be sensitive to myelin, and multiple measures from histology. The results are presented in an original, highly interactive manner, which will be a great resource for future studies of myelin in the brain.</p><p><bold>Decision letter after peer review:</bold></p><p>Thank you for submitting your article "An interactive meta-analysis of MRI biomarkers of myelin" for consideration by <italic>eLife</italic>. Your article has been reviewed by three peer reviewers, including Saad Jbabdi as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Chris Baker as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Mark Does (Reviewer #3).</p><p>The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.</p><p>Below is a compiled summary of all points raised by the three reviewers.</p><p>This article presents a meta-analysis of experimental comparisons between MRI and histological measures of myelin. The three reviewers agree that while this is not a particularly novel piece of work, it is well conducted and is presented in a highly original way using interactive visualisation, which is very useful for this type of complex meta analyses.</p><p>The reviewers agreed on the following points that need addressing:</p><p>1) Relating to previous literature</p><p>The primary shortcoming of the manuscript is that while it does a good job of citing prior experimental MRI-histology studies, it does a relatively poor job of providing references for other claims/assertions. We realize that it is not the authors' intention to review the physics or experimental history of myelin imaging methods, but when the authors provide a reference to support a statement, they ought to make it a suitable one (which may require looking more than a couple years back in history). We've listed a few examples below, but the authors should review the entire manuscript with this in mind.</p><p>Examples of questionable referencing:</p><p>"demyelination is often observed in several neurological diseases such as multiple sclerosis", cite: Wang Y et al., 2015, a paper reporting diffusion spectrum imaging evaluations MS</p><p>"measuring myelin in vivo has been an ambitious goal for magnetic resonance imaging (MRI) for almost two decades", cite: Petiet et al., 2019, a review of ultra-high field MRI measures of myelin.</p><p>"Diffusion acquisitions are blind to direct myelin measurement (Campbell et al., 2018)"</p><p>"A warning message that is evident from these results is the inherent limitation of DWI for estimating myelin content" again, this was studied extensively more than 20 years ago and has been discussed many time since. It's a good to reiterate, but don't make it sound like a novel finding.</p><p>"Faster techniques have been proposed for estimating it with gradient- and spin-echo (GRASE) sequences", cite: Faizy et al., 2018. This approach dates back 20 years and was used by Prasloski in 2012 to generate whole cerebrum MWF imaging.</p><p>2) Statistical analyses:</p><p>– Are the authors able to assess whether the correlations that they report are driven by tissue type differences or finer changes in the degree of myelination?</p><p>– Were there interactions between MR technique used and microscopy technique used in the literature? e.g. in Figure 5, are the R<sup>2</sup> values for "myelin thickness" low because they happened to use diffusion measures rather than MT etc.?</p><p>– In general there was not a lot of information on interactions between the variables. Another example: were some techniques more likely to have been done at lower field (there was a strong correlation between field-strength and R<sup>2</sup>)?</p><p>– Given that the posed question is "how different are the modalities in their relationship to histology", is there a way to quantify or statistically test the mixed-model findings between modalities to effectively identify if any are better?</p><p>– It would be useful to identify the different types of histological techniques alongside the studies for each modality in Figure 4. While this is just one of many factors that is driving the high I<sup>2</sup>, it would allow for the visualisation of the heterogeneity of histological assessments for each modality. Not all histological techniques are born equal and despite the limitations, that the authors have already discussed, electron microscopy might be arguably the best assessment. I suspect due to the high number of MRI modalities and histological techniques and relatively small number of studies, it's not possible to quantify if any modality has a particularly good correlation with any of the two electron microscopy metrics. Still, if possible might be worth doing as EM is the gold-standard for cellular neuroscientists in the myelin field.</p><p>3) Overall message:</p><p>– Although the authors' discussion and conclusions present a more nuanced view of the findings, this is not clear from the Abstract. At least two MRI markers (MWF and MPF) have fairly high correlations and moderate prediction intervals. We think this could come across a bit more in the Abstract, as it does in their conclusion. Whether these are a true representation of histologically measured myelin is a different question. The authors have discussed this distinction effectively in their manuscript.</p><p>– We encourage the authors to consider leaving the reader on a somewhat more positive note. That is, while there is room for more study, this meta-analysis supports the view that myelin imaging with MRI is, in fact, relatively well substantiated by comparisons to histology. In fact, I would say that myelination is one characteristic of tissue that MRI has proven to be able to measure with a good level of specificity!</p></body></sub-article><sub-article article-type="reply" id="sa2"><front-stub><article-id pub-id-type="doi">10.7554/eLife.61523.sa2</article-id><title-group><article-title>Author response</article-title></title-group></front-stub><body><disp-quote content-type="editor-comment"><p>This article presents a meta-analysis of experimental comparisons between MRI and histological measures of myelin. The three reviewers agree that while this is not a particularly novel piece of work, it is well conducted and is presented in a highly original way using interactive visualisation, which is very useful for this type of complex meta analyses.</p></disp-quote><p>We appreciate the reviewers’ enthusiasm. While we acknowledge that there are already several literature reviews on the topic of MRI-based measures of myelin (as reported in the paper, during the survey we found 50 review articles, with 6 of them specifically discussing comparisons between MRI and histology), to the best of our knowledge no systematic study has been published yet. A search for “myelin meta-analysis” and “myelin systematic review” on PubMed leads respectively to 93 and 91 results (last search: 23/09/20), yet none of these studies focus on MRI validation through histology. We believe that both the systematic and statistical aspects of our work are important, because they give overall quantitative measures of agreement and variability across studies.</p><disp-quote content-type="editor-comment"><p>The reviewers agreed on the following points that need addressing:</p><p>1) Relating to previous literature</p><p>The primary shortcoming of the manuscript is that while it does a good job of citing prior experimental MRI-histology studies, it does a relatively poor job of providing references for other claims/assertions. We realize that it is not the authors' intention to review the physics or experimental history of myelin imaging methods, but when the authors provide a reference to support a statement, they ought to make it a suitable one (which may require looking more than a couple years back in history). We've listed a few examples below, but the authors should review the entire manuscript with this in mind.</p></disp-quote><p>We thank the reviewers for pointing out this issue in the manuscript. We revised the whole manuscript and provided more suitable references where the previous ones did not properly support our statements (marked through the whole manuscript). We also specifically annotated the manuscript based on the reviewers’ comments 2-6.</p><disp-quote content-type="editor-comment"><p>Examples of questionable referencing:</p><p>"demyelination is often observed in several neurological diseases such as multiple sclerosis", cite: Wang Y et al., 2015, a paper reporting diffusion spectrum imaging evaluations MS</p></disp-quote><p>We substituted this reference with an updated review on demyelinating diseases from the Clinical Handbook of Neurology (Höftberger and Lassmann, 2018).</p><disp-quote content-type="editor-comment"><p>"measuring myelin in vivo has been an ambitious goal for magnetic resonance imaging (MRI) for almost two decades", cite: Petiet et al., 2019, a review of ultra-high field MRI measures of myelin.</p></disp-quote><p>We substituted this reference with three landmark studies respectively on myelin water imaging, magnetization transfer and T1 mapping (Mackay et al., 1994; Rooney et al., 2007; Stanisz, Kecojevic, Bronskill, and Henkelman, 1999).</p><disp-quote content-type="editor-comment"><p>"Diffusion acquisitions are blind to direct myelin measurement (Campbell et al., 2018)"</p></disp-quote><p>We substituted this reference with two seminal studies that estimated the transverse relaxation times of myelin water molecules and macromolecules (Mackay et al., 1994; Stanisz et al., 1999).</p><disp-quote content-type="editor-comment"><p>"A warning message that is evident from these results is the inherent limitation of DWI for estimating myelin content" again, this was studied extensively more than 20 years ago and has been discussed many times since. It's a good to reiterate, but don't make it sound like a novel finding.</p></disp-quote><p>We agree that this sentence may be misleading, so we clarified it and added relevant references (Beaulieu, 2002, 2009):</p><p>“A warning message that is evident from these results is the inherent limitation of DWI for estimating myelin content: this is not by any means a novel result (Beaulieu, 2002, 2009), but it is nevertheless worth reiterating given the outcomes of our analysis.”</p><p>We would still like to keep that sentence, as there are still studies using DWI-based measures for estimating myelin content without addressing the related limitations.</p><disp-quote content-type="editor-comment"><p>"Faster techniques have been proposed for estimating it with gradient- and spin-echo (GRASE) sequences", cite: Faizy et al., 2018. This approach dates back 20 years and was used by Prasloski in 2012 to generate whole cerebrum MWF imaging.</p></disp-quote><p>We substituted the previous reference with the original paper presenting the GRASE sequence, its first application to T2 relaxometry and the suggested study by Prasloski and colleagues (Does and Gore, 2000; Feinberg and Oshio, 1991; Prasloski et al., 2012).</p><disp-quote content-type="editor-comment"><p>2) Statistical analyses:</p><p>– Are the authors able to assess whether the correlations that they report are driven by tissue type differences or finer changes in the degree of myelination?</p></disp-quote><p>As we elaborate more in the next comment, we are not able to quantitatively answer this question given the number of potentially different conditions to consider and the limited number of studies. In more qualitative terms, Figure 6 and the interactive version Figure S7 (<ext-link ext-link-type="uri" xlink:href="https://neurolibre.github.io/myelin-meta-analysis/04/other_factors.html#figure-7">https://neurolibre.github.io/myelin-meta-analysis/04/other_factors.html#figure-7</ext-link>) show different correlation ranges depending on the types of tissues considered and on the specific pathology model, but in any case the range observed for white matter, the most common tissue studied, is particularly large, suggesting that tissue type differences are not the main factor affecting correlation. We added this consideration in the text:</p><p>“The effect of considering different types of tissues is showed in Figure 6 and Figure S7, where correlation ranges change when considering different types of tissue. However, the large correlation range in white matter, the most common tissue studied, suggests that other factors also affect the correlation.”</p><disp-quote content-type="editor-comment"><p>– Were there interactions between MR technique used and microscopy technique used in the literature? E.g. in Figure 5, are the R<sup>2</sup> values for "myelin thickness" low because they happened to use diffusion measures rather than MT etc.?</p></disp-quote><p>As noted by the reviewers in this and the following comment, interactions are key elements in this kind of analysis. Unfortunately, given the limited number of studies we are not able to fit a model to study those interactions: specifically, we do not have enough studies to represent each possible MRI/microscopy combination. Despite not being able to tackle this question quantitatively, we believe that the provided interactive visualization is an effective way to qualitatively explore this kind of questions. We added these considerations:</p><p>“Given the limited number of studies, it is not possible to quantitatively study interactions between MRI measures and the other factors (e.g. modality used as a reference, tissue types, magnetic field strength). For further qualitative insights, we invite the reader to explore the interactive Figures S7-S8. A first important factor to consider is the validation modality used as a reference, which will be dictated by the equipment availability and cost.”</p><p>Regarding the specific issue raised by the reviewers, Figure S7 (<ext-link ext-link-type="uri" xlink:href="https://neurolibre.github.io/myelin-meta-analysis/04/other_factors.html#figure-7">https://neurolibre.github.io/myelin-meta-analysis/04/other_factors.html#figure-7</ext-link>) allows the reader to hover over each point in the top plot and get a sense of which MRI measures were investigated for each microscopy technique (screenshot attached). Myelin thickness was used as reference for twelve measures that included diffusion, relaxometry and magnetization transfer.</p><p>We believe that having access to the interactive figures directly in an executable research article on the Stencila platform will allow a more immediate exploration of our results.</p><disp-quote content-type="editor-comment"><p>– In general there was not a lot of information on interactions between the variables. Another example: were some techniques more likely to have been done at lower field (there was a strong correlation between field-strength and R<sup>2</sup>)?</p></disp-quote><p>Following up on the previous comment, Figure S8 (<ext-link ext-link-type="uri" xlink:href="https://neurolibre.github.io/myelin-meta-analysis/04/other_factors.html#figure-8">https://neurolibre.github.io/myelin-meta-analysis/04/other_factors.html#figure-8</ext-link>) shows the MRI measures as a function of the related magnetic field strengths, using the same colour coding as Figure 2. One can notice how most studies have been done at 7T and 9.4T, while the first studies (chronologically) were performed at 1.5T. Few measures were studied at other field strengths. We added this consideration in the text:</p><p>“A further example of influential factor often dictated by equipment availability is the magnetic field strength of the MRI scanner: Figure S8 shows that most studies were conducted at 7T and 9.4T, with some pioneering studies at 1.5T and even fewer ones at other field strengths.”</p><disp-quote content-type="editor-comment"><p>– Given that the posed question is "how different are the modalities in their relationship to histology", is there a way to quantify or statistically test the mixed-model findings between modalities to effectively identify if any are better?</p></disp-quote><p>Following the reviewers’ suggestion, we performed an additional analysis using a repeated measures meta-regression, explained in the text:</p><p>“For the explicit purpose of comparing the effect sizes between different MRI measures, we conducted a repeated measures meta-regression on every R<sup>2</sup> value recorded. […] While the repeated measures meta-regression makes direct comparisons straightforward, we reported the main R<sup>2</sup> estimates based on the measure-specific mixed-effects models, as they make weaker assumptions.”</p><p>From this analysis, we observed both significant differences and comparable R<sup>2</sup> estimates, overall subdividing the MRI measures in two groups, with magnetization- and relaxometry-based ones providing higher estimates and diffusion-based measures providing lower estimates. The new results are reported in the text and discussed in subsection “Meta-analysis”:</p><p>“To investigate the significance of the differences between measures, we conducted a repeated measures meta-regression on every R<sup>2</sup> estimate recorded (98 in total over 43 studies). […] From this perspective, MPF has higher R<sup>2</sup> estimates compared to all the other measures, but it is only marginally higher than MWF (z-score=0.77; p-value=1) so we cannot claim that one is superior to the other. Following the same reasoning, MTR and T1 are not statistically different (z-score=0.47; p-value=1).”</p><p>The repeated measure meta-regression confirms this overall picture, clearly distinguishing MT- and relaxometry-based measures from diffusion-based ones (Figure 5).</p><disp-quote content-type="editor-comment"><p>– It would be useful to identify the different types of histological techniques alongside the studies for each modality in Figure 4. While this is just one of many factors that is driving the high I<sup>2</sup>, it would allow for the visualisation of the heterogeneity of histological assessments for each modality. Not all histological techniques are born equal and despite the limitations, that the authors have already discussed, electron microscopy might be arguably the best assessment. I suspect due to the high number of MRI modalities and histological techniques and relatively small number of studies, it's not possible to quantify if any modality has a particularly good correlation with any of the two electron microscopy metrics. Still, if possible might be worth doing as EM is the gold-standard for cellular neuroscientists in the myelin field.</p></disp-quote><p>We agree that it is useful to have a sense of which histological techniques were used for each MRI measure: we added this information in Figure S5 (<ext-link ext-link-type="uri" xlink:href="https://neurolibre.github.io/myelin-meta-analysis/03/meta_analysis.html#figure-5">https://neurolibre.github.io/myelin-meta-analysis/03/meta_analysis.html#figure-5</ext-link>), when hovering on each point (screenshot attached). Although this information is currently not visible in the static figure in the manuscript, it will be immediately accessible in Stencila.</p><p>As mentioned in comment 8 and as the reviewers already acknowledge, unfortunately the number of studies using EM is not sufficient to make more definitive statements for each MRI modality.</p><disp-quote content-type="editor-comment"><p>3) Overall message:</p><p>– Although the authors' discussion and conclusions present a more nuanced view of the findings, this is not clear from the Abstract. At least two MRI markers (MWF and MPF) have fairly high correlations and moderate prediction intervals. We think this could come across a bit more in the Abstract, as it does in their conclusion. Whether these are a true representation of histologically measured myelin is a different question. The authors have discussed this distinction effectively in their manuscript.</p></disp-quote><p>We agree that the Abstract was not able to present the overall picture as in the discussion. We rephrased part of the Abstract taking into account both this comment and the journal’s word count limit of 150 words:</p><p>“We report the overall estimates and the prediction intervals for the coefficient of determination and find that MT and relaxometry-based measures exhibit the highest correlations with myelin content. We also show which measures are, and which measures are not statistically different regarding their relationship with histology.”</p><disp-quote content-type="editor-comment"><p>– We encourage the authors to consider leaving the reader on a somewhat more positive note. That is, while there is room for more study, this meta-analysis supports the view that myelin imaging with MRI is, in fact, relatively well substantiated by comparisons to histology. In fact, I would say that myelination is one characteristic of tissue that MRI has proven to be able to measure with a good level of specificity!</p></disp-quote><p>We added a brief paragraph towards the end of the Discussion to emphasize the positive message of our results:</p><p>“We hope this meta-analysis convinces the reader that a holy grail of myelin imaging does not exist, at least as long as we consider histology to be the ground truth. Given that we all have to pick our poison, the upside is that measures based on MT and relaxometry are not statistically different, and therefore future studies have an actual choice among candidate measures.”</p></body></sub-article></article>