Myelin volume fraction imaging with MRI☆
Graphical abstract
Introduction
There is a long-standing effort to develop MRI methods that are not just sensitive to myelin but report on changes in myelin with specificity. Recent interest in using MRI to measure the g-ratio (Stikov et al., 2015, Stikov et al., 2010, West et al., 2016) has raised the aims of myelin imaging a step further, beyond specificity to accuracy. That is, an ideal method for g-ratio imaging includes more than just a correlative measure of myelin content, but an absolute measure of myelin volume fraction (MVF). To date, two myelin imaging techniques have been particularly well studied: myelin water imaging (MWI) via multi-exponential T2 (MET2) analysis (Mackay et al., 1994) and quantitative magnetization transfer (qMT) imaging (Sled and Pike, 2001). Both techniques have been shown to provide correlative measures of myelin content (Laule et al., 2006, Odrobina et al., 2005, Schmierer et al., 2007, Webb et al., 2003), but exactly how each relates to MVF remains unclear.
In the case of MWI, white matter is modeled as being comprised of two micro-anatomically separated water compartments with different transverse relaxation time constants (T2): 1) water trapped between the lipid bilayers of myelin (myelin water, T2=5–40 ms, depending on static field strength, B0), and 2) water in both the intra- and extra-axonal spaces (i/e water, T2=30–100 ms, depending on B0). Given sufficient signal-to-noise ratio (SNR), multiple spin-echo amplitudes can be fitted to a model that distinguishes these water pools based on T2, and the myelin water fraction (MWF) is typically reported as a measure of relative myelin content (Mackay et al., 1994, Menon et al., 1992, Whittall et al., 1997).
Measures of MWF have been shown to correlate with optical density in luxol fast blue stained sections of cadaver brain from MS patients (Laule et al., 2006) and with direct measures of myelin cross sectional area in electron microscopy of control and injured rat nerve (Odrobina et al., 2005, Webb et al., 2003). Also, Laule et al., used literature values of the composition of white matter to predict MWFs that were in close agreement with their observed values (Laule et al., 2004). However, none of these studies attempted to explicitly estimate and/or validate values of MVFs from MWF measures. The relationship between MWF and MVF depends on the relative water proton densities in the myelin and non-myelin compartments, but may also depend on the rate at which water exchanges between these compartments (Zimmerman and Brittin, 1957). Studies in rat spinal cord have indicated that variations in MWFs between different white matter tracts may be due to differences in water exchange rates, mediated by variations in axon diameter and myelin thickness (Dula et al., 2010, Harkins et al., 2012). This effect has been postulated to exist in brain (Russell-Schulz et al., 2013, Sled et al., 2004), but it remains unclear to what extent it effects observed MWF values.
Similar to MWI, the qMT method is based on a two-pool model of protons in white matter, but instead of two anatomically separated pools they are two pools of different molecular origins, water protons and protons bound to macromolecules. Although the bound proton signal is not typically measured directly, the exchange of magnetization between the bound and water protons results in contrast that depends on bound proton concentration (Henkelman et al., 1993, Wolff and Balaban, 1989). Thus, given an appropriate series of images with different MT contrast, the ratio of bound protons to total protons, or bound pool fraction (BPF), can be estimated. Note that, unlike the two-pool model used for MWI, this two-pool model: i) incorporates no anatomical information (both water and bound protons pools are assumed to be well mixed from one anatomical compartment, meaning that myelin is not explicitly part of the model), and ii) is predicated on the exchange of magnetization between the two pools (while the MWI model assumes no exchange of magnetization between the two water pools) (Gochberg and Gore, 2007, Sled and Pike, 2001). The lack of anatomy in the model presents a problem in relating BPF to MVF because bound protons will exist in both myelin and non-myelin regions of the tissue, and there is no reason to believe that all bound protons exchange magnetization with water at the same rate. As in MWI, this raises the question of whether geometric characteristics of axons/myelin contribute to the measured BPF.
Similar to literature on MWF, measures of BPF (or similar/related quantities) have been demonstrated to linearly correlate with MVF as measured by histology in both human cadaver brain (Schmierer et al., 2007) and rodent brain and nerve (Janve et al., 2013, Odrobina et al., 2005, Thiessen et al., 2013, Underhill et al., 2011). Stikov et al. have recently used such a linear correlation to estimate MVF from BPF (Stikov et al., 2015), but otherwise, there has been limited effort in explicitly estimating MVF from estimates from qMT measures.
Using literature information on the composition of white matter, this study proposes analytical expressions for computing estimates of MVF from MET2 and qMT data. These approaches are applied with high resolution 3D MRI protocols to excised and fixed mouse brains from control mice and three mouse models of abnormal myelination. MRI results are quantitatively evaluated with transmission electron microscopy.
Section snippets
Theory
To derive myelin volume measures from MRI, a model of white matter tissue that uses volumes, not just populations, of the different proton pools is presented in Fig. 1. The model includes four proton pools, with volumes of bound and water protons in the myelin (VB,M and VW,M, respectively) and non-myelin (VB,NM and VW,NM, respectively). The model assumes exchange of longitudinal magnetization between the bound and water protons, enabling qMT analysis, but no exchange of water or magnetization
Tissue preparation
The Vanderbilt University Institutional Animal Care and Use Committee approved animal studies. Fifteen adult mice were anesthetized with isoflurane and sacrificed via transcardial perfusion. The perfusion consisted of 1X phosphate-buffered saline (PBS) wash followed by 2.5% glutaraldehyde + 2% paraformaldehyde (modified Karnovsky solution). Following perfusion, brains were quickly removed from skull and immersed in the fixative solution for 1 week. Brains were then washed with 1X PBS + 0.01%
Results
Representative TEM histology in Fig. 3 demonstrates the abnormal myelination characteristics expected from these mouse models of tuberous sclerosis. The Rictor CKO and especially the Tsc2 CKO mice exhibited loss of myelinated axons, consistent with previous literature (Carson et al., 2015, Carson et al., 2013). Similarly consistent with literature (Harrington et al., 2010), the myelin in Pten CKO mice was noticeably thicker than in control mice. These variations in both myelin content and
Discussion
For the purpose of establishing MRI methods for measuring MVF, this paper presents MRI data and quantitative histology acquired in excised and fixed mouse brains with normal and abnormal myelination. One of the current goals of the authors’ lab is to develop robust MRI assays for routine use in rodent brain studies, and to that end the findings here demonstrate that both MET2 and qMT methods have the potential to provide both specific and accurate whole brain maps of MVF.
Looking beyond excised
Conclusion
Here, we assess MRI measures of myelin in control and 3 different models of white matter disease in mouse brain. Both MWF (from MET2) and BPF (from qMT) show strong correlations to quantitative histology. Using a volumetric model of white matter, MWF and BPF were converted to absolute measures of MVF, and displayed strong agreement with histologic myelin volume fraction. Using MET2, MVF measures were derived independently from histology but may be affected by inter-compartmental water exchange
Acknowledgments
The authors would like to thank Brittany Parker for assistance with tissue preparation; Vaibhav Janve for supporting data; Julien Cohen-Adad and Tanguy Duval for useful discussions relating to quantitative TEM analysis; and Janice Williams, Mary Dawes, and Maria Vinogradova for help with electron microscopy which was performed through the use of VUMC Cell Imaging Shared Resource (supported by NIH grants CA68485, DK20593, DK58404, DK59637 and EY08126).
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Grant Sponsor: NIH EB001744, NIH EB019980, NSF GRFP DGE-0909667, NIH S10 RR029523, NIH 5K08 NS050484