Towards in vivo g-ratio mapping using MRI: Unifying myelin and diffusion imaging

Mohammadi, Siawoosh; Callaghan, Martina F.

doi:10.1016/j.jneumeth.2020.108990

Cited by 53 publications

(77 citation statements)

References 151 publications

(380 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A common metric to quantify the degree of myelination is the g -ratio, which is defined as the inner over the outer radii of the myelin sheath 2 . Using multiple MRI modalities one can obtain an estimate of the average voxel-wise g -ratio in a voxel in- vivo by combining measurements of myelin and axonal volume fractions 10-13 . The axonal volume fraction can be estimated from diffusion MRI, using a multi-compartment fit to the diffusion-weighted signal 14-18 .…”

Section: Introductionmentioning

confidence: 99%

“…It is also an average across fibre populations in voxels where multiple fibres cross each other, which is a common configuration in the human brain 29,30 . Furthermore, this approach relies on the accuracy of the volume fraction estimates 31 , which has been questioned for both the axonal volume fractions 32 and the myelin volume fractions 13,19,20 . Here we aim to overcome these limitations by proposing a novel sequence, which is directly sensitive to the g -ratio (rather than the volume fractions) and allows to distinguish between crossing fibres.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

DIffusion-Prepared Phase Imaging (DIPPI): quantifying myelin in crossing fibres

Cottaar

Tendler

et al. 2020

Preprint

View full text Add to dashboard Cite

PurposeMyelin has long been the target of neuroimaging research due to its importance in brain development, plasticity, and disease. However, most available techniques can only provide a voxel-averaged estimate of myelin content. In the human brain, white matter fibre pathways connecting different brain areas and carrying different functions often cross each other in the same voxel. A measure that can differentiate the degree of myelination of crossing fibres would provide a more specific marker of myelination.Theory & MethodsOne MRI signal property sensitive to myelin is the phase accumulation, which to date has also been limited to voxel-averaged myelin estimates. We use this sensitivity by measuring the phase accumulation of the signal remaining after diffusion weighting, which we call DIffusion-Prepared Phase Imaging (DIPPI). Including diffusion weighting before estimating the phase accumulation has two distinct advantages for estimating the degree of myelination: (1) it increases the relative contribution of intra-axonal water, whose phase is related linearly to the amount of myelin surrounding the axon (in particular the log g-ratio) and (2) it gives directional information, which can be used to distinguish between crossing fibres.ResultsUsing simulations and phantom data we argue that other sources of phase accumulation (i.e., movement-induced phase shift during the diffusion gradients, eddy currents, and other sources of susceptibility) can be either corrected for or are sufficiently small to still allow the g-ratio to be reliably estimated.ConclusionsThis new sequence is capable of providing a g-ratio estimate per fibre population crossing within a voxel.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

DIffusion-Prepared Phase Imaging (DIPPI): quantifying myelin in crossing fibres

Cottaar

Tendler

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…axon) and outer (i.e. axon and myelin) diameter of the WM fibre and reflects myelin thickness relative to the axon radius [52–54]. Preliminary studies in small subject groups have observed g-ratio abnormalities in MS, suggestive of a thinner myelin sheath, in accordance with known pathological changes in this disease [53,55,56].…”

Section: Introductionmentioning

confidence: 95%

Rationale and design of the brain magnetic resonance imaging protocol for FutureMS: a longitudinal multi-centre study of newly diagnosed patients with relapsing-remitting multiple sclerosis in Scotland

Meijboom

Wiseman

York

et al. 2021

Preprint

View full text Add to dashboard Cite

Multiple sclerosis (MS) is a chronic neuroinflammatory and neurodegenerative disease. MS prevalence varies geographically, both between and within countries and is notably high in Scotland. Disease trajectory varies significantly between individuals and the causes for this are largely unclear. Biomarkers predictive of disease course are urgently needed to allow improved stratification for current disease modifying therapies and future targeted treatments aimed at neuroprotection and remyelination, and as endpoints for clinical trials. Magnetic resonance imaging (MRI) can detect disease activity and underlying neurological damage non-invasively in vivo at the micro and macro structural level. FutureMS is a prospective Scottish longitudinal multi-centre cohort study which focuses on deeply phenotyping and genotyping patients with recently diagnosed relapsing-remitting MS (RRMS) to identify predictors of disease activity and severity. Neuroimaging is a central component of the study and provides two main primary endpoints for disease activity and neurodegeneration. The aim of the current paper is to provide an overview of MRI data acquisition, management and processing in FutureMS. MRI is acquired at baseline (N=431) and 1-year follow-up, in Dundee, Glasgow and Edinburgh (3T Siemens) and in Aberdeen (3T Philips), and managed and processed in Edinburgh. The core structural MRI protocol comprises T1-weighted, T2-weighted, 2D/3D FLAIR and proton density images. The original study primary imaging outcome measures are new/enlarging white matter lesions (WML) assessed by neuroradiological visual read and reduction in brain volume over one year. Secondary imaging outcomes comprise WML volume as an additional quantitative structural MRI outcome measure, rim lesions on susceptibility-weighted imaging (SWI), and microstructural MRI measures, including diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) metrics, relaxometry, magnetisation transfer (MT) ratio, MT saturation and derived g-ratio measures. FutureMS aims to reduce uncertainty around disease course and allow for targeted treatment in RRMS by exploring the role of conventional and advanced MRI measures as biomarkers of disease severity and progression in a large population of RRMS patients in Scotland.

show abstract

“…The MT saturation map was obtained from the hMRI toolbox as described above. For calibration of the MT saturation map to a myelin-volume fraction map (MVF MR = α MT sat ), we used the g-ratio based calibration method as reported in Ellerbrock and Mohammadi (2018) and Mohammadi and Callaghan (2020), with a reference g-ratio value of 0.7 in the splenium (Mohammadi et al, 2015a), yielding α = 0.217.…”

Section: Application To G-ratio Mappingmentioning

confidence: 99%

“…The beneficial effect of using the consecutive HySCO WA combined diffusion data was illustrated for g-ratio mapping, where blurring effects can reduce the overlap between the diffusion-MRI-based axonal biomarker and the myelin marker maps. The blurring artefacts in diffusion MRI (which are decreased when using a WA combination) lead to a worse delimitation between grey and white matter, which in turn can propagate towards the resulting g-ratio map (Mohammadi and Callaghan, 2020). As the MR g-ratio is defined only in white matter (Stikov et al, 2015), it is typically created by combining an axonal water fraction estimated in the white matter probability maps from diffusion MRI (here, the AWF) and a white matter myelin biomarker map (here, the MT saturation).…”

Section: Application To G-ratio Mappingmentioning

confidence: 99%

Reducing susceptibility distortion related image blurring in diffusion MRI EPI data

Clark

Callaghan

Weiskopf

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Diffusion magnetic resonance imaging (MRI) is an increasingly popular technique in basic and clinical neuroscience. One promising application is to combine diffusion MRI with myelin maps from complementary MRI techniques such as multi-parameter mapping (MPM) to produce g-ratio maps that represent the relative myelination of axons and predict their conduction velocity. Statistical Parametric Mapping (SPM) can process both diffusion data and MPMs, making SPM the only widely accessible software that contains all the processing steps required to perform group analyses of g-ratio data in a common space. However, limitations have been identified in its method for reducing susceptibility-related distortion in diffusion data. More generally, susceptibility-related image distortion is often corrected by combining oppositely phase-encoded images (blip-up and blip-down) using the arithmetic mean (AM), however, this can lead to blurred images. In this study we sought to (1) improve the susceptibility-related distortion correction for diffusion MRI data in SPM; (2) deploy an alternative approach to the AM to reduce image blurring in diffusion MRI data when combining blip-up and blip-down EPI data after susceptibility-related distortion correction; and (3) assess the benefits of these changes for g-ratio mapping. We found that the new processing pipeline, called consecutive HySCO, improved distortion correction when compared to the standard approach in the ACID toolbox for SPM. Moreover, using a weighted average (WA) method to combine the distortion corrected data from each phase-encoding polarity achieved greater overlap of diffusion and more anatomically faithful structural white matter probability maps derived from minimally distorted multi-parameter maps as compared to the AM. Third, we showed that the consecutive HySCO WA performed better than the AM method when combined with multi-parameter maps to perform g-ratio mapping. These improvements mean that researchers can conveniently access a wide range of diffusion-related analysis methods within one framework because they are now available within the open-source ACID toolbox as part of SPM, which can be easily combined with other SPM toolboxes, such as the hMRI toolbox, to facilitate computation of myelin biomarkers that are necessary for g-ratio mapping.

show abstract

Towards in vivo g-ratio mapping using MRI: Unifying myelin and diffusion imaging

Abstract: Highlights Second review on the topic of g-ratio mapping using MRI. A summary of the most recent developments in the fieldproviding methodological background. Discussion of pitfalls associated with g-ratio mapping using MRI.

Cited by 53 publications

References 151 publications

DIffusion-Prepared Phase Imaging (DIPPI): quantifying myelin in crossing fibres

DIffusion-Prepared Phase Imaging (DIPPI): quantifying myelin in crossing fibres

Rationale and design of the brain magnetic resonance imaging protocol for FutureMS: a longitudinal multi-centre study of newly diagnosed patients with relapsing-remitting multiple sclerosis in Scotland

Reducing susceptibility distortion related image blurring in diffusion MRI EPI data

Contact Info

Product

Resources

About