Can MRI measure myelin? Systematic review, qualitative assessment, and meta-analysis of studies validating microstructural imaging with myelin histology

Lazari, Alberto; Lipp, Ilona

doi:10.1016/j.neuroimage.2021.117744

Cited by 126 publications

(119 citation statements)

References 206 publications

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“…These include, but are not limited to, exchange between water pools ( West et al, 2019 ; Kalantari et al, 2011 ; Myint and Ishima, 2009 ), magnetization transfer between free water protons and macromolecules ( West et al, 2019 ; J Zhang et al, 2015), iron content ( Birkl et al, 2019 ), internal gradients ( Washburn et al, 2008 ; Seland et al, 2004 ), and differential signal attenuation due to water diffusion in underlying compartments ( Ziener et al, 2010 ; Carney et al, 1991 ; Le Bihan et al, 1989 ), which are not considered in either the BMC-mcDESPOT formalism or the other MWF measurement methods available. The importance of these effects in a particular experiment will depend both on the specifics of the sample or subject under investigation and on the details of the pulse sequence, including the selection of parameters such as TE, TR, FA, RF pulse characteristics and gradient durations and amplitudes ( Does, 2018 ; Alonso-Ortiz et al, 2015 ; Dvorak et al, 2021 ; Levesque et al, 2010 ; Lazari and Lipp, 2021 ; Bouhrara and Bonny, 2012 ). However, it must be emphasized that multi-spin echo-based MRI sequences, namely the Carr–Purcell–Meiboom–Gill sequence or its accelerated version, the gradient and spin echo sequence, remain the reference methods for MWF mapping ( MacKay and Laule, 2016 ; Alonso-Ortiz et al, 2015 ; Piredda et al, 2021 ; Dvorak et al, 2021 ; Lazari and Lipp, 2021 ; Lee et al, 2021 ; Heath et al, 2018 ).…”

Section: Discussionmentioning

confidence: 99%

“…The importance of these effects in a particular experiment will depend both on the specifics of the sample or subject under investigation and on the details of the pulse sequence, including the selection of parameters such as TE, TR, FA, RF pulse characteristics and gradient durations and amplitudes ( Does, 2018 ; Alonso-Ortiz et al, 2015 ; Dvorak et al, 2021 ; Levesque et al, 2010 ; Lazari and Lipp, 2021 ; Bouhrara and Bonny, 2012 ). However, it must be emphasized that multi-spin echo-based MRI sequences, namely the Carr–Purcell–Meiboom–Gill sequence or its accelerated version, the gradient and spin echo sequence, remain the reference methods for MWF mapping ( MacKay and Laule, 2016 ; Alonso-Ortiz et al, 2015 ; Piredda et al, 2021 ; Dvorak et al, 2021 ; Lazari and Lipp, 2021 ; Lee et al, 2021 ; Heath et al, 2018 ). This is likely due to the availability of these imaging sequences on most preclinical and clinical MRI systems, the simplicity of the signal model, and the extensive histological validation conducted over the last two decades ( Laule et al, 2006 ; Laule et al, 2008 ).…”

Section: Discussionmentioning

confidence: 99%

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Investigation of the association between cerebral iron content and myelin content in normative aging using quantitative magnetic resonance neuroimaging

et al. 2021

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Myelin loss and iron accumulation are cardinal features of aging and various neurodegenerative diseases. Oligodendrocytes incorporate iron as a metabolic substrate for myelin synthesis and maintenance. An emerging hypothesis in Alzheimer’s disease research suggests that myelin breakdown releases substantial stores of iron that may accumulate, leading to further myelin breakdown and neurodegeneration. We assessed associations between iron content and myelin content in critical brain regions using quantitative magnetic resonance imaging (MRI) on a cohort of cognitively unimpaired adults ranging in age from 21 to 94 years. We measured whole-brain myelin water fraction (MWF), a surrogate of myelin content, using multicomponent relaxometry, and whole-brain iron content using susceptibility weighted imaging in all individuals. MWF was negatively associated with iron content in most brain regions evaluated indicating that lower myelin content corresponds to higher iron content. Moreover, iron content was significantly higher with advanced age in most structures, with men exhibiting a trend towards higher iron content as compared to women. Finally, relationship between MWF and age, in all brain regions investigated, suggests that brain myelination continues until middle age, followed by degeneration at older ages. This work establishes a foundation for further investigations of the etiology and sequelae of myelin breakdown and iron accumulation in neurodegeneration and may lead to new imaging markers for disease progression and treatment.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Investigation of the association between cerebral iron content and myelin content in normative aging using quantitative magnetic resonance neuroimaging

et al. 2021

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“…Despite heterogeneous reports, each of these imaging modalities present a fairly good association with the underlying histology (see Refs. 150–152 for recent review and meta-analyses) and are sensitive to cortical demyelination, remyelination, neuronal density and axonal loss and can each be applied to independently predict future disability progression in the long-term. 153 However, no consensus still exist in terms of their validity and specificity to myelin.…”

Section: Advanced Imaging Techniques Imagingmentioning

confidence: 99%

Linking immune-mediated damage to neurodegeneration in multiple sclerosis: could network-based MRI help?

Groppa

González‐Escamilla

Eshaghi

et al. 2021

Brain Communications

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Inflammatory demyelination characterizes the initial stages of multiple sclerosis, while progressive axonal and neuronal loss are coexisting and significantly contribute to the long-term physical and cognitive impairment. There is an unmet need for a conceptual shift from a dualistic view of multiple sclerosis pathology, involving either inflammatory demyelination or neurodegeneration, to integrative dynamic models of brain reorganization, where, glia-neuron interactions, synaptic alterations, and grey matter pathology are longitudinally envisaged at the whole-brain level. Functional and structural MRI can delineate network hallmarks for relapses, remissions or disease progression, which can be linked to the pathophysiology behind inflammatory attacks, repair, and neurodegeneration. Here, we aim to unify recent findings of grey matter circuits dynamics in multiple sclerosis within the framework of molecular and pathophysiological hallmarks combined with disease-related network reorganization, while highlighting advances from animal models (in vivo and ex vivo) and human clinical data (imaging and histological). We propose that MRI-based brain networks characterization is essential for better delineating ongoing pathology and elaboration of particular mechanisms that may serve for accurate modelling and prediction of disease courses throughout disease stages.

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“…The same principle can be exploited to make qMRI sensitive to tissue microstructure, such as iron concentration or myelination. Recent meta analyses revealed that a majority of qMRI methods are comparably sensitive to the myelin content 30,31 , although certain parameters such as myelin water fraction (MWF, relaxometrybased) and macromolecular pool fraction (MPF, MT-based) appear to be more specific.…”

Section: Introductionmentioning

confidence: 99%

qMRI-BIDS: an extension to the brain imaging data structure for quantitative magnetic resonance imaging data

Karakuzu

Appelhoff

Auer

et al. 2021

Preprint

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The Brain Imaging Data Structure (BIDS) established community consensus on the organization of data and metadata for several neuroimaging modalities. Traditionally, BIDS had a strong focus on functional magnetic resonance imaging (MRI) datasets and lacked guidance on how to store multimodal structural MRI datasets. Here, we present and describe the BIDS Extension Proposal 001 (BEP001), which adds a range of quantitative MRI (qMRI) applications to the BIDS. In general, the aim of qMRI is to characterize brain microstructure by quantifying the physical MR parameters of the tissue via computational, biophysical models. By proposing this new standard, we envision standardization of qMRI which makes multicenter dissemination of interoperable data possible. As a result, BIDS can act as a catalyst of convergence between qMRI methods development and application-driven neuroimaging studies that can help develop quantitative biomarkers for neural tissue characterization. Finally, our BIDS extension offers a common ground for developers to exchange novel imaging data and tools, reducing the practical barriers to standardization that is currently lacking in the field of neuroimaging.

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Can MRI measure myelin? Systematic review, qualitative assessment, and meta-analysis of studies validating microstructural imaging with myelin histology

Cited by 126 publications

References 206 publications

Investigation of the association between cerebral iron content and myelin content in normative aging using quantitative magnetic resonance neuroimaging

Investigation of the association between cerebral iron content and myelin content in normative aging using quantitative magnetic resonance neuroimaging

Linking immune-mediated damage to neurodegeneration in multiple sclerosis: could network-based MRI help?

qMRI-BIDS: an extension to the brain imaging data structure for quantitative magnetic resonance imaging data

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