Whole brain g-ratio mapping using myelin water imaging (MWI) and neurite orientation dispersion and density imaging (NODDI)

Jung, Woojin; Lee, Jingu; Shin, Hyeong-Geol; Nam, Yoonho; Zhang, Hui; Oh, Se Hong; Lee, Jong-Ho

doi:10.1016/j.neuroimage.2017.09.053

Cited by 47 publications

(41 citation statements)

References 69 publications

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“…Other groups have explored these and other MVF and AVF sensitive contrasts for g-ratio mapping in healthy white matter. These include a study of the effects of age and gender in a population of subjects aged 20 to 76 using qMT and NODDI (Cercignani et al, 2016b), studies of healthy adults using MT sat and the TFD (Mohammadi et al, 2015) and MTV and DTI (Berman et al, 2017), and a study of healthy subjects using the ViSTa myelin water imaging technique and NODDI (Jung et al, 2016).…”

Section: The Promise: G-ratio Imaging Studiesmentioning

confidence: 99%

Promise and pitfalls of g-ratio estimation with MRI

et al. 2018

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The fiber g-ratio is the ratio of the inner to the outer diameter of the myelin sheath of a myelinated axon. It has a limited dynamic range in healthy white matter, as it is optimized for speed of signal conduction, cellular energetics, and spatial constraints. In vivo imaging of the g-ratio in health and disease would greatly increase our knowledge of the nervous system and our ability to diagnose, monitor, and treat disease. MRI based g-ratio imaging was first conceived in 2011, and expanded to be feasible in full brain white matter with preliminary results in 2013. This manuscript reviews the growing g-ratio imaging literature and speculates on future applications. It details the methodology for imaging the g-ratio with MRI, and describes the known pitfalls and challenges in doing so.

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Section: The Promise: G-ratio Imaging Studiesmentioning

confidence: 99%

Promise and pitfalls of g-ratio estimation with MRI

et al. 2018

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“…These concentrations are in good agreement with previous studies using myelin water imaging . The values are also in good agreement with a conversion between MWF and MVF presented recently . Note, however, that myelin not only affects the R 2 * of myelin water but all water in the vicinity of myelin.…”

Section: Discussionmentioning

confidence: 99%

“…[49][50][51] The values are also in good agreement with a conversion between MWF and MVF presented recently. 52 Note, however, that myelin not only affects the R 2 * of myelin water but all water in the vicinity of myelin. Even without myelin water, myelin would have an effect on R 2 * (θ), since it induces non-local field inhomogeneities that reach well beyond the space occupied by myelin water.…”

Section: Discussionmentioning

confidence: 99%

The role of iron and myelin in orientation dependent R₂^* of white matter

et al. 2019

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Brain myelin and iron content are important parameters in neurodegenerative diseases such as multiple sclerosis (MS). Both myelin and iron content influence the brain's R2* relaxation rate. However, their quantification based on R2* maps requires a realistic tissue model that can be fitted to the measured data. In structures with low myelin content, such as deep gray matter, R2* shows a linear increase with increasing iron content. In white matter, R2* is not only affected by iron and myelin but also by the orientation of the myelinated axons with respect to the external magnetic field. Here, we propose a numerical model which incorporates iron and myelin, as well as fibre orientation, to simulate R2* decay in white matter. Applying our model to fibre orientation‐dependent in vivo R2* data, we are able to determine a unique solution of myelin and iron content in global white matter. We determine an averaged myelin volume fraction of 16.02 ± 2.07% in non‐lesional white matter of patients with MS, 17.32 ± 2.20% in matched healthy controls, and 18.19 ± 2.98% in healthy siblings of patients with MS. Averaged iron content was 35.6 ± 8.9 mg/kg tissue in patients, 43.1 ± 8.3 mg/kg in controls, and 47.8 ± 8.2 mg/kg in siblings. All differences in iron content between groups were significant, while the difference in myelin content between MS patients and the siblings of MS patients was significant. In conclusion, we demonstrate that a model that combines myelin‐induced orientation‐dependent and iron‐induced orientation‐independent components is able to fit in vivo R2* data.

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“…from the assumptions of their models, that might not hold true in tissue with crossing fibers 163 and in tissue with compartments with different diffusion properties. The MVF also saw 164 several implementations including mcDESPOT (26,65), multi-exponential T2 (66-68), T2* 165 (69,70), quantitative magnetization transfer (qMT) (25,29,63,68,(71)(72)(73)(74), and non-water 166 fraction (27,28,75). Most of the MVF estimates, while sensitive to myelin, are not specific.…”

Section: Diffusion and Axon Properties 137mentioning

confidence: 99%

“…Therefore, some of these methods require calibration, and/or may provide a poor estimate 168 of MVF in tissue that is not healthy white matter tissue (63,70,76). The myelin water 169 fraction is arguably the only specific MVF MRI measurement (67), yet it is currently hard 170 to measure in clinical scanners which often comes with a reduced signal to noise ratio 171 (SNR).…”

Section: Diffusion and Axon Properties 137mentioning

confidence: 99%

Modelling conduction delays in the corpus callosum using MRI-measured g-ratio

Berman

Filo

Mezer

2018

Preprint

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Conduction of action potentials along myelinated axons is affected by their structural features, such as the axonal g-ratio, the ratio between the inner and outer diameters of the myelin sheath surrounding the axon. The effect of g-ratio variance on conduction properties has been quantitatively evaluated using single-axon models. It has recently become possible to estimate a g-ratio weighted measurement in vivo using quantitative MRI. Nevertheless, it is still unclear whether the variance in the g-ratio in the healthy human brain leads to significant differences in conduction velocity. In this work we tested whether the g-ratio MRI measurement can be used to predict conduction delays in the corpus callosum.We present a novel framework in which the structural properties of fibers (i.e. length and g-ratio, measured using MRI), are incorporated in a biophysical model of axon conduction, to predict conduction delays of long-range white matter fibers. We applied this framework to the corpus callosum, and found conduction delay estimates that are compatible with previously estimated values of conduction delays. We account for the variance in the velocity given the axon diameter distribution in the splenium, mid-body and genu, to further compare the fibers within the corpus callosum.Conduction delays have been suggested to increase with age. Therefore, we investigated whether there are differences in the g-ratio and the fiber length between young and old adults, and whether this leads to a difference in conduction speed and delays. We found small but significant differences between the predicted delays of the two groups in the motor fibers of the corpus callosum. We also found that the motor fibers of the corpus callosum have the fastest conduction estimates. Using the axon diameter distributions, we found that the occipital fibers have the slowest estimations, while the frontal and motor fiber tracts have similar estimates.Our study provides a framework for predicting conduction latencies in vivo. The framework could have major implications for future studies of white matter diseases and large range network computations. Our results highlight the need for improving additional in vivo measurements of white matter microstructure.

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Whole brain g-ratio mapping using myelin water imaging (MWI) and neurite orientation dispersion and density imaging (NODDI)

Cited by 47 publications

References 69 publications

Promise and pitfalls of g-ratio estimation with MRI

Promise and pitfalls of g-ratio estimation with MRI

The role of iron and myelin in orientation dependent R₂^* of white matter

Modelling conduction delays in the corpus callosum using MRI-measured g-ratio

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Whole brain g-ratio mapping using myelin water imaging (MWI) and neurite orientation dispersion and density imaging (NODDI)

Cited by 47 publications

References 69 publications

Promise and pitfalls of g-ratio estimation with MRI

Promise and pitfalls of g-ratio estimation with MRI

The role of iron and myelin in orientation dependent R2* of white matter

Modelling conduction delays in the corpus callosum using MRI-measured g-ratio

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The role of iron and myelin in orientation dependent R₂^* of white matter