Research Article: A comparison of in vivo MRI based cortical myelin mapping using T1w/T2w and R1 mapping at 3T

Date Published: July 3, 2019

Publisher: Public Library of Science

Author(s): Zahra Shams, David G. Norris, José P. Marques, Peter Lundberg.

http://doi.org/10.1371/journal.pone.0218089

Abstract

In this manuscript, we compare two commonly used methods to perform cortical mapping based on myelination of the human neocortex. T1w/T2w and R1 maps with matched total acquisition times were obtained from a young cohort in randomized order and using a test–retest design. Both methodologies showed cortical myelin maps that enhanced similar anatomical features, namely primary sensory regions known to be myelin rich. T1w/T2w maps showed increased robustness to movement artifacts in comparison to R1 maps, while the test re-test reproducibility of both methods was comparable. Based on Brodmann parcellation, both methods showed comparable variability within each region. Having parcellated cortical myelin maps into VDG11b areas of 4a, 4p, 3a, 3b, 1, 2, V2, and MT, both methods behave identically with R1 showing an increased variability between subjects. In combination with the test re-test evaluation, we concluded that this increased variability between subjects reflects relevant tissue variability. A high level of correlation was found between the R1 and T1w/T2w regions with regions of higher deviations being co-localized with those where the transmit RF field deviated most from its nominal value. We conclude that R1 mapping strategies might be preferable when studying different population cohorts where cortical properties are expected to be altered while T1w/T2w mapping will have advantages when performing cortical based segmentation.

Partial Text

The identification of the spatial organization of the myelinated fibres throughout the cortex was the basis of myeloarchitectonic studies of the cerebral cortex, which became of interest in the early 20th century [1]. Recently, various in vivo MRI-based methods have been developed for studying the cerebral cortex on the basis of its “myelin” content to help achieve the goal of identifying regions on the basis of their myeloarchitecture which has been suggested to be predictive of brain connectivity [2]. These have used three general approaches for cortical myelin mapping in MRI: longitudinal relaxation rate, R1 (inverse of the longitudinal relaxation time, T1) [3–8]; apparent transverse relaxation rate R2* (inverse of the apparent transverse relaxation time, T2*) [9,10]; T1w/ T2*w or T1w/T2w mapping [11,12]. The motivation to use such metrics, particularly longitudinal relaxation rate maps to study myelin distribution, originates from various ex-vivo studies that have shown a direct relationship between R1 and myelin content [13–15]. Particularly in the cortex, R1 variations reflect water mobility [16,17], even if the impact of iron content on R1 tissue contrast should not be fully neglected [18]. Exploring the high SNR offered by high field imaging, Geyer et al. (2011) obtained high resolution ex-vivo (0.6 mm isotropic) quantitative T1 maps, using the MP2RAGE sequence [19], to delineate the border between Brodmann area 3a and area 4. Its location was in good agreement with their observations from high-resolution post-mortem histological studies [5] of the same tissue section. The same group extended this work to ex-vivo samples of primary visual cortex and used proton induced X-ray imaging to quantify iron and myelin. A dependence of the measured longitudinal relaxation rate on the local concentrations of both myelin and iron was observed [8]. Although the term “myelin mapping” is commonly used in literature, there is growing evidence that the high sensitivity of these methods to myelin does not make them very specific. Other methods, including Myelin Water Fraction Imaging using multi-compartment T2 fitting approaches [20] and Magnetisation Transfer [21] included in multi-compartment relaxometry [22] could be more specific to myelin concentration. Yet, this higher specificity comes at the cost of a decrease in sensitivity which does not allow whole brain acquisitions at the resolution needed to study cortical myelin distributions. Furthermore, it has been shown that the correlation between myelin water fraction and T1w/T2w imaging is low across subcortical white matter suggesting these two methodologies could be sensitive to different microstructural properties [23]. Due to these nuances, in this manuscript the maps obtained will be referred to as cortical- rather than myelin maps.

In this manuscript we have thoroughly compared two of the most commonly used protocols to perform cortical mapping. We have tried to address various aspects of their performance: the robustness to artifacts; reproducibility between and within subjects; ability to distinguish neighbouring cortical regions; the correlation between the two methods as well as the left vs right hemisphere symmetry. It should be noted that many of these findings, might only be valid at 3T and with the specific imaging protocols used, including also the calibration B1 and B0 maps.

Both cortical mapping methods show similar and highly replicable cortical maps enhancing regions traditionally found as being highly myelinated. T1w/T2w maps were shown to be more robust to motion related artifacts cohort, which was attributed to relying on two separate acquisitions. Removing the residual bias field from T1w/T2w maps is an essential step for within subject reproducibility and reliable cortical segmentation, at the cost of removing natural variations that exist between subjects. R1 maps showed more reproducibility in test re-test experiments within the same subject. The ability to differentiate across neighbouring cortical regions was found to be comparable with R1 maps having stronger differentiation in occipital areas and T1w/T2w being superior in the central sulcus. This could make R1 a preferable metric in longitudinal studies while T1w/T2w maps (bias corrected) showed a higher reproducibility of the average myelination across subjects for different Brodmann areas which could make it a preferable metric for cortical segmentation.

 

Source:

http://doi.org/10.1371/journal.pone.0218089