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Meta-Analysis
. 2020 Oct 21:9:e61523.
doi: 10.7554/eLife.61523.

An interactive meta-analysis of MRI biomarkers of myelin

Matteo Mancini  1   2   3 , Agah Karakuzu  2 , Julien Cohen-Adad  2   4 , Mara Cercignani  1   5 , Thomas E Nichols #  6   7 , Nikola Stikov #  2   8
Affiliations
Meta-Analysis

An interactive meta-analysis of MRI biomarkers of myelin

Matteo Mancini et al. Elife. .

Abstract

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 (https://neurolibre.github.io/myelin-meta-analysis). 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.

Keywords: MRI; brain; central nervous system; histology; human; meta-analysis; mouse; myelin; neuroscience; rat.

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Conflict of interest statement

MM, AK, JC, MC, TN, NS No competing interests declared

Figures

Figure 1.
Figure 1.. Sankey diagram representing the screening procedure (PRISMA flow chart provided in the appendix).
To see the interactive figure: https://neurolibre.github.io/myelin-meta-analysis/01/selection.html#figure-1.
Figure 2.
Figure 2.. Bubble chart of R2 values between a given MRI measure and histology for each study across MRI measures, with the area proportional to the number of samples.
To see the interactive figure: https://neurolibre.github.io/myelin-meta-analysis/02/closer_look.html#figure-3.
Figure 3.
Figure 3.. Treemap chart of the studies considered for the meta-analysis, organized by MRI measure.
The color of each box represents the reported R2 value while the size box is proportional to the sample size. To see the interactive figure: https://neurolibre.github.io/myelin-meta-analysis/02/closer_look.html#figure-4.
Figure 4.
Figure 4.. Forest plots showing the R2 values reported by the studies and estimated from the mixed-effect model for each measure.
The hourglasses and the dotted lines in the mixed-effect model outcomes represent the prediction intervals. To see the interactive figure: https://neurolibre.github.io/myelin-meta-analysis/03/meta_analysis.html#figure-5.
Figure 5.
Figure 5.. 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.
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: https://neurolibre.github.io/myelin-meta-analysis/03/meta_analysis.html#figure-6.
Figure 6.
Figure 6.. Experimental conditions and methodological choices influencing the R2 values (top: reference techniques; middle: pathology model; bottom: tissue types).
To see the interactive figure: https://neurolibre.github.io/myelin-meta-analysis/04/other_factors.html#figure-7.
Appendix 1—figure 1.
Appendix 1—figure 1.. PRISMA flowchart for the meta-analysis.
See this image and copyright information in PMC

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