In summary, we have presented several qMRI measures or stains that are promising for characterizing WM in vivo. Diffusion MRI (including DTI), MT, and relaxometry are all sensitive measures of myelination and axons; however, each is based upon different mechanisms. Further, the specificity of these measures to specific WM properties like the degree of myelination is less clear and many questions remain. Diffusion MRI is modulated by the presence and spacing of membranes and any other barriers, which include the myelin membranes as well as any other cellular structures in the WM. MT is sensitive not only to the myelin proteins, but also to any other proteins and any other macromolecules, such as those found in regions of inflammation. The short T2 signal from the water trapped in the myelin bilayers appears to be fairly specific, though the actual quantification in terms of the amount of myelination is more challenging.
An important perspective to maintain in the interpretation of these qMRI stains is that all of these measures are modulations of the water signal. A change in the overall amount of water in a region of tissue will significantly influence the qMRI measure irrespective of the axonal properties. For example, edema will increase the extracellular water fraction, which will impact all of the qMRI measures described here. A decreased MWF
or increased DR
in a region of WM with edema does not necessarily reflect a decreased level of myelination. The size of the voxels is also very large relative to the cellular structural features that are being characterized. A 1-mm cubic voxel may contain more than a thousand axons (1–20
μm in diameter), which may have a broad range of diameters, degree of myelination, and numbers and types of glia; thus, these images are very course and blurred maps of the microstructural detail.
The qMRI stain maps are all derived from multiple contrast-weighted images, which causes these imaging methods to be sensitive to misregistration from head motion, measurement noise, and artifacts in any of the images. Thus, it is critical to carefully review individual image quality and the registration fidelity before computing quantitative maps. The calculation of the quantitative measures often uses highly nonlinear models with multiple local energy minima in the solution space, making them highly sensitive to the measurement noise. These measurement noise effects can subsequently lead to biased estimates with high variance. If the noise is too high to achieve reliable estimates, then either scan time should be increased or spatial resolution decreased. Ideally, the SNR of the original image measurements should be reported in publications to be able to assess the level of image quality. Studies are also needed to determine SNR cutoff thresholds below which the calculations are either biased or unstable. While obtaining multiple qMRI measures in a single study is appealing, the scan time can be considerable—for example, DTI is on the order of 10
min and qMT and multicomponent relaxometry are on the order of 20–30
min or more each. Thus, if imaging time is limited, it is probably preferable to spend more time on a single qMRI measure or chose simpler measures that can be estimated from smaller data sets (e.g., MD
instead of the full diffusion tensor, or T1
with B1 calibration instead of MWF
). Current and future improvements to coil sensitivity design, parallel imaging, and constrained reconstruction methods for undersampled multiparametric image data (Velikina et al., 2011
) may be used to significantly accelerate acquisition times and/or improve the measurement accuracy.
Note that there is an inherent trade-off between resolution and SNR. In general, imaging can accurately resolve signal from structures that are at least twice as large as the resolution. If the imaging resolution dimension is larger, then the minimum resolvable structure size likewise increases. For smaller structures (e.g., fornix and cingulum bundles), the measurements will have some partial volume averaging, which makes it difficult to disambiguate the microstructural properties from the macrostructure. Another consideration is that as long as the SNR is not too low (>3–4) for any of the images, the SNR can be improved by spatial smoothing so obtaining DW images at the highest possible resolution is a reasonable strategy. The concept of superresolution tractography is particularly exciting and novel and may provide details beyond the inherent image resolution; however, the quantitative measures along those pathways may still have partial volume averaging effects.
To apply these qMRI stains to multicenter clinical trials, it is necessary to develop standardized acquisition protocols that include methods to correct for errors and inhomogeneities in both B0 (static field strength/frequency) and B1 (flip angle). This is currently challenged by differences in pulse sequences on different scanner platforms. Phantom materials with specific qMRI properties may be useful for comparing measurements across scanners and sites. Further, while there are a growing number of software tools for calculating and analyzing DTI images, there are no widely available tools for either qMT or multicomponent relaxometry, which limits their application to more technically advanced research groups.
DTI has clearly been the most widely used method for investigating and describing structural connectivity properties in the brain. As the field moves forward, it is critical to also investigate MT and relaxometry measures along specific WM pathways to obtain complementary and potentially more specific information about the biological properties of these connections. Relevant to this point, there is still a lot that is not known about the mechanisms of these qMRI measures and how they are influenced by subtle variations in CNS pathology. More detailed and specific studies that relate WM histology and pathology to qMRI measures are essential to move this field forward and make the interpretation of these measurements more clear.
The recent work in mapping the global networks or connectomes of structural connectivity using tractography-based approaches is extremely exciting and has generated considerable enthusiasm in the neuroscience community. However, we must remember that these networks represent abstractions of the real structural brain connections through the modulation of water diffusion properties by the WM microstructure. Sophisticated mathematical models are being applied to characterize these networks, which hopefully reflect the structural properties of biological substrates that we are trying to characterize. How to define and/or interpret connectivity based upon structural connectomes is a work in progress. To date, connectome studies have focused on DTI/DWI properties based upon tractography properties; however, future connectome studies may also incorporate other WM measures like the FA, the BPF, or the MWF.
After these methods are standardized, qMRI stain atlases can be generated as a function of age, gender, disease, and or trait measure. Either tractography-based or morphologic-based templates of WM regions or structures can be used to characterize WM properties across populations. Integration of these atlases and tract-based measures may subsequently be compared against functional connectivity measures. This integration of qMRI stains with functional connectivity will provide a more complete picture of brain connectivity properties.