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The potential of myelin-sensitive imaging: Redefining spatiotemporal patterns of myeloarchitecture

  • Casey Paquola
    Correspondence
    Correspondence:
    Affiliations
    Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Jülich, Germany
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  • Seok-Jun Hong
    Affiliations
    Center for Neuroscience Imaging Research, Institute for Basic Science, Sungkyunkwan University, Suwon, South Korea

    Center for the Developing Brain, Child Mind Institute, New York, New York, United States of America

    Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
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Open AccessPublished:September 09, 2022DOI:https://doi.org/10.1016/j.biopsych.2022.08.031

      Abstract

      Recent advances in magnetic resonance imaging (MRI) pave the way for approximation of myelin content in-vivo. In this review, our main goal was to determine how to best capitalise on myelin-sensitive imaging. First, we briefly overview the theoretical and empirical basis for the myelin sensitivity of different MRI markers, and in doing so highlight how multi-modal imaging approaches are important for enhancing specificity to myelin. Then, we discuss recent studies that probe the non-uniform distribution of myelin across cortical layers and along white matter tracts. These approaches, collectively known as “myelin profiling”, have provided detailed depictions of myeloarchitecture in both the post-mortem and living human brain. Notably, MRI-based profiling studies have recently focused on investigating whether it can capture inter-individual variability in myelin characteristics as well as trajectories across the lifespan. Finally, another line of recent evidence emphasises the contribution of region-specific myelination to large-scale organisation, demonstrating the impact of myelination on global brain networks. In conclusion, we suggest that combining well-validated MRI markers with profiling techniques holds strong potential to elucidate individual differences in myeloarchitecture, which has important implications for understanding brain function and disease.

      Keywords

      Introduction

      Myelin ensheathes axons of the central and peripheral nervous systems, providing the structural basis for fast and stable impulse propagation (
      • Nave K.-A.
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      ). The process of myelination is highly dynamic, involving rapid changes after even a few hours of a task and continuous reorganisation across the lifespan (
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      Live imaging of remyelination in the adult mouse corpus callosum.
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      Gibson EM, Purger D, Mount CW, Goldstein AK, Lin GL, Wood LS, et al. (2014): Neuronal Activity Promotes Oligodendrogenesis and Adaptive Myelination in the Mammalian Brain. Science 344: 1252304–1252304.

      ,
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      ). Indeed, the dynamic nature of myelin is thought to be central to its role in enabling flexible responses to rapidly changing environments and to maximising efficiency of neural communication (
      • Nave K.-A.
      • Werner H.B.
      Myelination of the Nervous System: Mechanisms and Functions.
      ). Elucidating the temporal dynamics and spatial patterns of myelination has been a topic of interest for over a century (
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      ,

      Kaes T (1907): Die Grosshirnrinde Des Menschen in Ihren Massen Und in Ihrem Fasergehalt. Ein Gehirnanatomischer Atlas. Jena: Gustav Fischer.

      ). As myelination throughout life is activity-dependent (
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      ). Defining region-specific trajectories of myelination is thought to inform upon the maturational sequence of functional specialisation in the brain (
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      Yakovlev P, Lecours A (1967): The Myelogenetic Cycles of Regional Maturation of the Brain. In: Minkowski A, editor. Regional Development of the Brain in Early Life. Oxford: Blackwell, pp 3–70.

      ), while region-specific breakdown of myelin in older age may reveal the structural underpinnings of cognitive decline (
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      ). Classically, the spatial distribution of myelin has been described from post-mortem study (

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      ,

      Meynert T, Sachs B (1885): Psychiatry: A Clinical Treatise on Diseases of the Fore-Brain. New York: G.P. Putnam’s Sons. Retrieved from https://collections.nlm.nih.gov/bookviewer?PID=nlm:nlmuid-66530050R-bk#page/158/mode/2up

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      ,
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      ), but such approaches are inherently limited in characterising intra-subject changes across time. Uncovering the spatiotemporal patterns of myelin necessitates in-vivo imaging of myelin.
      Magnetic resonance imaging (MRI) holds promise for enabling in-vivo histology, whereby the cellular composition of living tissue may be discerned non-invasively. Crucially, MRI allows large, longitudinal cohort studies to track individual trajectories of tissue changes (
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      ). Nevertheless, challenges remained for adoption of MRI for myelin mapping, as many previous approaches were limited to indirectly measuring myelin via magnetic fields and were confronted by the apparent discrepancy between imaging resolution and the size of myelin. New imaging sequences, contrasts and biophysical models are helping to address these limitations, though not all MRI-derived myelin markers are equally valid. As interest in myelin mapping increases across foundational and clinical neuroscience, it is high time to discuss the validity of emerging techniques and identify promising avenues for future work.
      The present review aims to demonstrate the significant contribution that myelin-sensitive imaging can make to understanding the spatial patterns and dynamic changes of myeloarchitecture in the human brain. First, we lay the groundwork for how myelin markers are derived from MRI. Considering theoretical and histological validations, several multi-modal approaches are highlighted that benefit disambiguation of myelin from other neurobiological factors. Next, we discuss the emergence of “myelin profiling” techniques, which provide nuanced characterisation of the myeloarchitecture of cortical regions and white matter bundles. Finally, we highlight how several recent studies capitalise on methodological improvements, and in doing so advance understanding of how myeloarchitecture evolves across the lifespan.

      Developing and validating in-vivo myelin markers

      Myelin, the lipid-rich material that insulates nerve fibres, is liable to MRI measurement because it contributes to key determinants of MR relaxation times: water mobility and the interaction between water and macromolecules (
      • Kucharczyk W.
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      Relaxivity and magnetization transfer of white matter lipids at MR imaging: importance of cerebrosides and pH.
      ,
      • Weiskopf N.
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      ) (Figure 1A). Relative to free water (e.g. in cerebrospinal fluid), bound water produces shorter longitudinal (T1) and transverse (T2) relaxation times (Figure 1Aiii). As the primary location of bound water in the brain, myelin water has been shown to have distinctively short T1 and T2 (
      • MacKay A.
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      • Bjarnason T.
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      Insights into brain microstructure from the T2 distribution.
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      • Aubert-Frécon M.
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      ). Yet, looking across brain tissue types, the dominant source of T1 contrast are lipids (
      • Leuze C.
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      • Ferenczi E.
      • Liu C.W.
      • Hsueh B.
      • Goubran M.
      • et al.
      The Separate Effects of Lipids and Proteins on Brain MRI Contrast Revealed Through Tissue Clearing.
      ). Cholesterol and cerebroside, in particular, both rich in myelin, are related to T1 shortening (
      • Kucharczyk W.
      • Macdonald P.M.
      • Stanisz G.J.
      • Henkelman R.M.
      Relaxivity and magnetization transfer of white matter lipids at MR imaging: importance of cerebrosides and pH.
      ,
      • Koenig S.H.
      Cholesterol of myelin is the determinant of gray-white contrast in MRI of brain.
      ), which produces the distinctive appearance of grey and white matter in T1 images. In contrast, diffusion weighted imaging targets anisotropy of hindered water (e.g. in axons or extracellular spaces), using multiple diffusion echo gradients to sensitise the MR signal to the random motion of water molecules. While myelin modulates anisotropy, for example decreasing the permeability of axons, its effects on water diffusion is minimal (
      • Beaulieu C.
      The basis of anisotropic water diffusion in the nervous system - a technical review.
      ). Therefore, classic diffusion-based measures are not posed as specific proxies of myelin. Turning to the macromolecular make-up of myelin, its protein and lipid composition render it diamagnetic. This distinctive property may be detected by combining the magnitude and phase maps from a gradient echo sequence, known as susceptibility mapping (
      • Deistung A.
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      Overview of quantitative susceptibility mapping.
      ,
      • Liu C.
      • Li W.
      • Tong K.A.
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      Susceptibility-weighted imaging and quantitative susceptibility mapping in the brain.
      ). This technique can identify susceptibility anisotropy created by ordered molecular structures such as myelin sheath, offering a new opportunity for clinical phenotyping of neurological disorders such as multiple sclerosis, Parkinson's disease, Alzheimer’s disease and Huntington’s disease (
      • Deistung A.
      • Schweser F.
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      Overview of quantitative susceptibility mapping.
      ,
      • Liu C.
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      Susceptibility-weighted imaging and quantitative susceptibility mapping in the brain.
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      ,
      • Bulk M.
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      Postmortem MRI and histology demonstrate differential iron accumulation and cortical myelin organization in early- and late-onset Alzheimer’s disease.
      ).
      Figure thumbnail gr1
      Figure 1Sensitivity of MRI contrasts to myelin. A) i. Electron micrograph of a myelinated axon in the central nervous system (

      Pierre Morell, Richard H Quarles (1999): Myelin Formation, Structure and Biochemistry. Basic Neurochemistry, 6th ed. Lippincott-Raven.

      ). ii. The schematic depicts distinctive features of myelinated axons that make them susceptible to MRI [adapted from (
      • Min Y.
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      • Husted C.
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      Interaction forces and adhesion of supported myelin lipid bilayers modulated by myelin basic protein.
      )]. Myelin is composed of lipids (70 to 85%) and proteins (15 to 30%) (

      Pierre Morell, Richard H Quarles (1999): Myelin Formation, Structure and Biochemistry. Basic Neurochemistry, 6th ed. Lippincott-Raven.

      ). Water molecules are “bound” in the myelin sheath, which contrasts with the more motile water molecules in intra- and extracellular spaces (described as “restricted” and “hindered” water, respectively (
      • Zhang H.
      • Schneider T.
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      )). iii. T1 and T2 vary as a function of molecular tumbling rate (defined by correlation time) (
      • Besghini D.
      • Mauri M.
      • Simonutti R.
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      ). Thereby, water in different compartments, as well as macromolecules may be distinguished by T1 and T2. Positions of molecules along the x-axis are approximate (

      McRobbie DW, Moore EA, Graves MJ, Prince MR (Eds.) (2017): Getting in Tune: Resonance and Relaxation. MRI from Picture to Proton, 3rd ed. Cambridge: Cambridge University Press, pp 124–143.

      ). While T2 decay of macromolecules is too quick to be captured by human MRI scanners (

      McRobbie DW, Moore EA, Graves MJ, Prince MR (Eds.) (2017): Getting in Tune: Resonance and Relaxation. MRI from Picture to Proton, 3rd ed. Cambridge: Cambridge University Press, pp 124–143.

      ), the difference in T2 decay of water in different compartments can enable identification of myelin water [see for example (
      • Borich M.R.
      • MacKay A.L.
      • Vavasour I.M.
      • Rauscher A.
      • Boyd L.A.
      Evaluation of white matter myelin water fraction in chronic stroke.
      )]. B) i. An exemplar validation study, Stüber et al., (2014) show the similar pattern of myelin basic protein staining and quantitative T1 in the same tissue. ii. Cross-correlation of multiple stains and multiple contrasts can help to disambiguate the contribution of different neurobiological features to an MRI marker. Here, Hametner et al., (2018) show iron is linearly related to R2*, but iron and myelin interact to determine quantitative susceptibility mapping (QSM). For instance, in the frontal cortex, high myelin and high iron produce moderate QSM (top arrow) but low myelin and low iron also produce moderate QSM (lower arrow). In other words, the myelin and iron appear to cancel each other out, related to diamagnetism of myelin (lowers QSM) and paramagneticism of iron (increases QSM). Notably, the study showed that removing the contribution of iron from the QSM, resulted in a strong correlation of QSM with myelin.
      While the spatial resolution of myelin-sensitive MRI techniques is increasingly higher with advanced hardware and sequence development [∼500um, (
      • Dinse J.
      • Härtwich N.
      • Waehnert M.D.
      • Tardif C.L.
      • Schäfer A.
      • Geyer S.
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      A cytoarchitecture-driven myelin model reveals area-specific signatures in human primary and secondary areas using ultra-high resolution in-vivo brain MRI.
      )], it is still a relatively coarse level, such that each voxel likely includes multiple tissue types, contaminating the purity of its signal. Biophysical models can theoretically disentangle such heterogeneous signal sources and potentially enhance specificity to myelin. For example, “myelin water fraction” (MWF) is based on the fact that in the central nervous system, the T2 signal decay follows a multi-exponential curve (
      • MacKay A.
      • Laule C.
      • Vavasour I.
      • Bjarnason T.
      • Kolind S.
      • Mädler B.
      Insights into brain microstructure from the T2 distribution.
      ). By fitting the decay curve using least square methods, these models can distinguish the myelin water pool, with ultra-short T2 decay, from the non-myelin water pool (Figure 1aiii). In contrast, magnetisation transfer (MT) indirectly measures myelin based on the exchange and cross-relaxation between macromolecules (found predominantly in myelin) and water (
      • Sled J.G.
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      ,
      • Sled J.G.
      • Pike G.B.
      Quantitative imaging of magnetization transfer exchange and relaxation properties in vivo using MRI.
      ). Longitudinal relaxation rate (R1, also reported as “1/T1”) is similarly driven by the cross-relaxation of lipids and water (
      • Koenig S.H.
      Cholesterol of myelin is the determinant of gray-white contrast in MRI of brain.
      ). Overall, this non-exhaustive summary of imaging contrasts and biophysical models serves to demonstrate that the influence of myelin on water mobility, as well as the lipid composition of myelin, allow sensitisation of the MR signal to myelin in various ways with different assumptions, complexity and presumed specificity.
      Empirical validation has been essential to support the myelin-sensitivity of MR markers described above. The principal approach for validation is comparison with myelin-specific staining in the same tissue. Early studies described the correspondence of T1w imaging contrasts with myelin distribution, such as distinctive myeloarchitectural features (e.g. stria of Gennari) or highly myelinated areas (e.g. primary sensory areas and middle temporal visual area) (
      • Clark V.P.
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      Visualizing the entire cortical myelination pattern in marmosets with magnetic resonance imaging.
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      Mapping human cortical areas in vivo based on myelin content as revealed by T1- and T2-weighted MRI.
      ). The potential of myelin-sensitive imaging was further established by showing reduced values in individuals with multiple sclerosis (
      • Jeong I.H.
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      Magnetization transfer ratio and myelin in postmortem multiple sclerosis brain.
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      ), non-human animals without myelin basic protein (“shiverer mouse”) (
      • Ou X.
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      The MT pool size ratio and the DTI radial diffusivity may reflect the myelination in shiverer and control mice.
      ,
      • Ou X.
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      ) or myelin proteolipid protein (“shaking pup”) (
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      ) and rodents with cuprizole induced-demyelination (
      • Thiessen J.D.
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      ). More recently, statistical approaches have been employed to estimate the correlation between MRI markers and the degree of myelin staining, comparing many matched samples across regions and/or across individuals (Figure 1Bi). Collating such studies, several recent meta-analyses found that many MRI markers are positively correlated with myelin content (

      Lazari A, Lipp I (2020): Can MRI measure myelin? Systematic review, qualitative assessment, and meta-analysis of studies validating microstructural imaging with myelin histology. bioRxiv 2020.09.08.286518-2020.09.08.286518.

      ,

      Mancini M, Karakuzu A, Cohen-Adad J, Cercignani M, Nichols TE, Stikov N (2020): An interactive meta-analysis of MRI biomarkers of myelin ((S. Jbabdi, C. I. Baker, S. Jbabdi, & M. Does, editors)). eLife 9: e61523.

      ,
      • van der Weijden C.W.J.
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      • Borra R.J.H.
      • Thurner P.
      • Meilof J.F.
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      • et al.
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      ). MWF and MT approaches exhibit the highest sensitivity to myelin, but effect sizes are heterogenous. The heterogeneity in estimates, which produce wide confidence intervals and overlapping prediction estimates across MRI markers, suggests that currently no single MRI marker may be considered the ideal myelin measure (

      Lazari A, Lipp I (2020): Can MRI measure myelin? Systematic review, qualitative assessment, and meta-analysis of studies validating microstructural imaging with myelin histology. bioRxiv 2020.09.08.286518-2020.09.08.286518.

      ,

      Mancini M, Karakuzu A, Cohen-Adad J, Cercignani M, Nichols TE, Stikov N (2020): An interactive meta-analysis of MRI biomarkers of myelin ((S. Jbabdi, C. I. Baker, S. Jbabdi, & M. Does, editors)). eLife 9: e61523.

      ,
      • van der Weijden C.W.J.
      • García D.V.
      • Borra R.J.H.
      • Thurner P.
      • Meilof J.F.
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      • et al.
      Myelin quantification with MRI: A systematic review of accuracy and reproducibility.
      ). Further studies are necessary to determine the interaction of MRI markers with influential methodological parameters, such as histological processing, sampling in grey or white matter and statistical design. Alternative approaches include comparing MRI markers to oligodendrocyte- or myelin-related genes derived from brain transcriptomics. Such comparisons performed using T1w/T2w, suggest enrichment for myelin-related genes, but the association to myelin is not specific nor is it the strongest enrichment present for T1w/T2w (
      • Fulcher B.D.
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      ,
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      ). This approach has also been extended to other MRI markers, such as MT and MWF by comparing transcriptomic maps in one set of subjects [e.g. Allen Human Brain Atlas; (
      • Hawrylycz M.J.
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      )] to imaging maps in another set of subjects. Associations of these MRI markers to myelin-related genes and oligodendrocytes have been reported, though effect sizes tend to be small (r<0.4) (
      • Whitaker K.J.
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      ,
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      ), as may be expected due to the confounding influence of inter-individual differences, especially when the samples are not age matched.
      The utility of myelin-sensitive imaging is also dependent on reliability. Test-retest reliability is high (intraclass correlation coefficient>0.8) for many proposed myelin markers, including T2-derived MWF, quantitative MT, R1 and calibrated T1w/T2w ratio, but more moderate for other markers (intraclass correlation coefficient 0.5-0.8; e.g. MTR and raw T1w/T2w) (
      • Arshad M.
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      • Raz N.
      Test–retest reliability and concurrent validity of in vivo myelin content indices: Myelin water fraction and calibrated T1w/T2w image ratio.
      ,
      • Nerland S.
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      • Bugge R.A.B.
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      Reproducibility of inhomogeneous magnetization transfer (ihMT): A test-retest, multi-site study.
      ,
      • Shams Z.
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      A comparison of in vivo MRI based cortical myelin mapping using T1w/T2w and R1 mapping at 3T.
      ,

      Haast RAM, Ivanov D, Formisano E, Uludaǧ K (2016): Reproducibility and Reliability of Quantitative and Weighted T1 and T2∗ Mapping for Myelin-Based Cortical Parcellation at 7 Tesla. Frontiers in Neuroanatomy 10: 112–112.

      ,
      • Lévy S.
      • Guertin M.-C.
      • Khatibi A.
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      • Martinu K.
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      • et al.
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      ). While supporting a certain level of consistency of these markers, the estimates are mostly derived from moderate sample sizes (average n=24). More systematic efforts to optimise these metrics based on a larger population and different sites are therefore required to confirm the high reliability of their use in an actual clinical setting. Notably, recent work demonstrates that T2-derived MWF, quantitative MT and R1 have good inter-site reliability (
      • Zhang L.
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      • et al.
      Reproducibility of inhomogeneous magnetization transfer (ihMT): A test-retest, multi-site study.
      ,

      Haast RAM, Ivanov D, Formisano E, Uludaǧ K (2016): Reproducibility and Reliability of Quantitative and Weighted T1 and T2∗ Mapping for Myelin-Based Cortical Parcellation at 7 Tesla. Frontiers in Neuroanatomy 10: 112–112.

      ,
      • Meyers S.M.
      • Vavasour I.M.
      • Mädler B.
      • Harris T.
      • Fu E.
      • Li D.K.B.
      • et al.
      Multicenter measurements of myelin water fraction and geometric mean T2 : intra- and intersite reproducibility.
      ). This evidence further bolsters their potential to become reproducible and clinically translatable biomarkers, especially relevant to neurological disorders with a well-defined myelin pathology, such as multiple sclerosis, and psychiatric disorders with likely myelin alterations, such as schizophrenia (
      • Dietz A.G.
      • Goldman S.A.
      • Nedergaard M.
      Glial cells in schizophrenia: a unified hypothesis.
      ).
      Future work may benefit from multimodal imaging, whereby the combination of several MRI markers can enhance in-vivo approximation of myelin distribution. Indeed, discrimination of normal myelin, demyelination and remyelinated lesions in mice is improved by using the combination of three contrasts (T1w, T2w and MTR; 95% accuracy) (
      • Merkler D.
      • Boretius S.
      • Stadelmann C.
      • Ernsting T.
      • Michaelis T.
      • Frahm J.
      • Brück W.
      Multicontrast MRI of remyelination in the central nervous system.
      ). The combination of susceptibility mapping with T2* (or R2*) can help to disambiguate myelin from iron (
      • Deistung A.
      • Schäfer A.
      • Schweser F.
      • Biedermann U.
      • Turner R.
      • Reichenbach J.R.
      Toward in vivo histology: A comparison of quantitative susceptibility mapping (QSM) with magnitude-, phase-, and R2*-imaging at ultra-high magnetic field strength.
      ). (Figure 1Bii). Myelin and iron often co-localise in the cortex and additively contribute to certain MR contrasts (e.g increase in R2*) (
      • Fukunaga M.
      • Li T.-Q.
      • van Gelderen P.
      • de Zwart J.A.
      • Shmueli K.
      • Yao B.
      • et al.
      Layer-specific variation of iron content in cerebral cortex as a source of MRI contrast.
      ). However, they have opposing effects on resonance frequency (diamagnetic and paramagnetic, respectively), which may be detected by susceptibility mapping, and differ across tissue types, such as deep grey matter vs. white matter (
      • Deistung A.
      • Schäfer A.
      • Schweser F.
      • Biedermann U.
      • Turner R.
      • Reichenbach J.R.
      Toward in vivo histology: A comparison of quantitative susceptibility mapping (QSM) with magnitude-, phase-, and R2*-imaging at ultra-high magnetic field strength.
      ). Consequently, contrasting R2* with susceptibility maps may provide a more specific MRI marker of myelin (
      • Hametner S.
      • Endmayr V.
      • Deistung A.
      • Palmrich P.
      • Prihoda M.
      • Haimburger E.
      • et al.
      The influence of brain iron and myelin on magnetic susceptibility and effective transverse relaxation - A biochemical and histological validation study.
      ,
      • Lambrecht V.
      • Hanspach J.
      • Hoffmann A.
      • Seyler L.
      • Mennecke A.
      • Straub S.
      • et al.
      Quantitative susceptibility mapping depicts severe myelin deficit and iron deposition in a transgenic model of multiple system atrophy.
      ,
      • Marques J.P.
      • Khabipova D.
      • Gruetter R.
      Studying cyto and myeloarchitecture of the human cortex at ultra-high field with quantitative imaging: R1, R2* and magnetic susceptibility.
      ). Notably, other multimodal approaches, such as T1w/ T2w (
      • Glasser M.F.
      • Van Essen D.C.
      Mapping human cortical areas in vivo based on myelin content as revealed by T1- and T2-weighted MRI.
      ) and T2*/B0 (
      • Cohen-Adad J.
      • Polimeni J.R.
      • Helmer K.G.
      • Benner T.
      • McNab J.A.
      • Wald L.L.
      • et al.
      T2* mapping and B0 orientation-dependence at 7T reveal cyto- and myeloarchitecture organization of the human cortex.
      ), use the commonalities of myelin and iron to characterise cortical microstructure with indiscriminate applicability to either source.
      Multimodal protocols can also enable quantification of myelin sheath thickness, relative to the thickness of myelinated axon, namely the “g-ratio” (
      • Campbell J.S.W.
      • Leppert I.R.
      • Narayanan S.
      • Boudreau M.
      • Duval T.
      • Cohen-Adad J.
      • et al.
      Promise and pitfalls of g-ratio estimation with MRI.
      ). Known to be influential on conduction speed (
      • Drakesmith M.
      • Harms R.
      • Rudrapatna S.U.
      • Parker G.D.
      • Evans C.J.
      • Jones D.K.
      Estimating axon conduction velocity in vivo from microstructural MRI.
      ,
      • Rushton W.a.H.
      A theory of the effects of fibre size in medullated nerve.
      ,
      • Waxman S.G.
      • Bennett M.V.L.
      Relative Conduction Velocities of Small Myelinated and Non-myelinated Fibres in the Central Nervous System [no. 85].
      ), the g-ratio is evaluated from axonal volume and myelin volume fractions, which may be proxied in-vivo using diffusion models, such as NODDI (
      • Zhang H.
      • Schneider T.
      • Wheeler-Kingshott C.A.
      • Alexander D.C.
      NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain.
      ), with myelin-sensitive imaging, such as MT. Notably, g-ratio values derived from MRI closely approximate g-ratio values calculated with electron microscopy, outperforming estimates of axonal volume and myelin volume fractions that come from each imaging modality separately (
      • Stikov N.
      • Campbell J.S.W.
      • Stroh T.
      • Lavelée M.
      • Frey S.
      • Novek J.
      • et al.
      In vivo histology of the myelin g-ratio with magnetic resonance imaging.
      ). Therefore, while multi-modal imaging approaches require extra consideration of theoretical assumptions and modality-specific distortions (
      • Mohammadi S.
      • Carey D.
      • Dick F.
      • Diedrichsen J.
      • Sereno M.I.
      • Reisert M.
      • et al.
      Whole-Brain In-vivo Measurements of the Axonal G-Ratio in a Group of 37 Healthy Volunteers.
      ), this work demonstrates that combining modalities can benefit quantitative evaluation of myelin in-vivo.
      Overall, sensitivity, specificity and reliability are important considerations in selecting a myelin-sensitive imaging sequence. These must, however, be balanced against more practical requirements that vary across studies, such as acquisition time, available field strength and the desired spatial resolution. R1 mapping emerges as a simple and efficient method for myelin-sensitive imaging, with whole-brain coverage in only 8minutes (MP2RAGE: 1mm on 3T, 0.7mm on 7T) (

      Haast RAM, Ivanov D, Formisano E, Uludaǧ K (2016): Reproducibility and Reliability of Quantitative and Weighted T1 and T2∗ Mapping for Myelin-Based Cortical Parcellation at 7 Tesla. Frontiers in Neuroanatomy 10: 112–112.

      ,
      • Marques J.P.
      • Kober T.
      • Krueger G.
      • van der Zwaag W.
      • Van de Moortele P.-F.F.
      • Gruetter R.
      MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field.
      ). Higher signal to noise ratio, spatial resolution and image sharpness of R1 can be achieved using a recently developed MS-IR-EPI sequence, though the acquisition time is longer (20minutes at 0.5mm on 7T) (
      • Sanchez Panchuelo R.M.
      • Mougin O.
      • Turner R.
      • Francis S.T.
      Quantitative T1 mapping using multi-slice multi-shot inversion recovery EPI.
      ). Towards multi-modal imaging, the multi-parameter mapping (MPM) sequence provides four whole-brain quantitative maps (proton density, MTsat, R1 and R2*) in approximately 20minutes (1mm on 3T, 0.5mm on 7T) (

      Weiskopf N, Suckling J, Williams G, Correia MM, Inkster B, Tait R, et al. (2013): Quantitative multi-parameter mapping of R1, PD(*), MT, and R2(*) at 3T: a multi-center validation. Frontiers in neuroscience 7: 95–95.

      ,
      • McColgan P.
      • Helbling S.
      • Vaculčiaková L.
      • Pine K.
      • Wagstyl K.
      • Attar F.M.
      • et al.
      Relating quantitative 7T MRI across cortical depths to cytoarchitectonics, gene expression and connectomics.
      ). By reducing the resolution of the MPM sequence to 1.6mm on 3T, the acquisition time may be reduced to less than 10 minutes (

      Cooper G, Hirsch S, Scheel M, Brandt AU, Paul F, Finke C, et al. (2020): Quantitative Multi-Parameter Mapping Optimized for the Clinical Routine. Frontiers in Neuroscience 14. Retrieved July 29, 2022, from https://www.frontiersin.org/articles/10.3389/fnins.2020.611194

      ). Finally, recent developments with FAST-T2 allows for whole-brain MWF, which exhibits high variability across white matter (
      • Liu H.
      • Rubino C.
      • Dvorak A.V.
      • Jarrett M.
      • Ljungberg E.
      • Vavasour I.M.
      • et al.
      Myelin Water Atlas: A Template for Myelin Distribution in the Brain.
      ), to be captured in 10minutes (1mm on 3T) (
      • Nguyen T.D.
      • Deh K.
      • Monohan E.
      • Pandya S.
      • Spincemaille P.
      • Raj A.
      • et al.
      Feasibility and Reproducibility of Whole Brain Myelin Water Mapping in 4 Minutes Using Fast Acquisition with Spiral Trajectory and Adiabatic T2prep (FAST-T2) at 3T.
      ).

      Myelin profiling

      A major advantage of myelin-sensitive MRI is the ability to explore myeloarchitecture in 3D.
      Myelin profiling, the measurement of myelin along biologically-meaningful anatomical axes, is inspired by classic histology, but significantly benefits from the 3D nature of MRI. While profiling in histological sections must follow the cutting plane, myelin profiling with MRI enables characterisation of myelin along the natural courses of the cortex (from pial to white matter boundary) and along white matter tracts.
      The origins of myelin profiling can be traced to the start of the 20th century. Around that time, several researchers developed myeloarchitectonic atlases of the cortex (

      Kaes T (1907): Die Grosshirnrinde Des Menschen in Ihren Massen Und in Ihrem Fasergehalt. Ein Gehirnanatomischer Atlas. Jena: Gustav Fischer.

      ,
      • Smith G.E.
      A New Topographical Survey of the Human Cerebral Cortex, being an Account of the Distribution of the Anatomically Distinct Cortical Areas and their Relationship to the Cerebral Sulci.
      ,
      • Vogt O.
      Die myeloarchitektonische Felderung des Menschlichen Stirnhirns.
      ,

      Campbell AW (1905): Histological Studies on the Localisation of Cerebral Function,. Cambridge: University Press.

      ). In each case, cortical areas were delineated with respect to the vertical arrangement of myelinated fibres (Figure 2Ai). Myelinated fibres produce distinctive striations in the cortex, allowing categorisation of types and the approximation of areal borders (see (
      • Nieuwenhuys R.
      • Broere C.A.J.
      • Cerliani L.
      A new myeloarchitectonic map of the human neocortex based on data from the Vogt–Vogt school.
      ) for an excellent review). Beyond qualitative types, histological studies also employed a photometric slice-capturing technique, by which myeloarchitecture may be more quantitatively compared across the cortex based on myelin density across cortical depths (
      • Braitenberg V.
      A note on myeloarchitectonics.
      ). Hopf (1968b, 1968a, 1969) showed that this quantitative approach allows comparison of distributed areas, elucidating large-scale patterns of myeloarchitectural change (Figure 2Aii). In particular, he demonstrated that myelin content decreases with distance from primary sensory and motor areas, with the lowest levels in paralimbic cortex, such as the medial orbitofrontal cortex. Legacy data from these classic studies has the potential to serve as a histological gold standard to validate contemporary in-vivo myelin-sensitive imaging. Yet the accessibility of such datasets has been severely limited, in part due to their qualitative reporting and the 2D illustrations of the brain that are incompatible with modern neuroimaging. Recently, leveraging seminal meta-analyses of the Vogt-Vogt school (
      • Nieuwenhuys R.
      • Broere C.A.J.
      A map of the human neocortex showing the estimated overall myelin content of the individual architectonic areas based on the studies of Adolf Hopf.
      ), a myelin-based cortical parcellation was generated for neuroimaging analysis, whereby the parcel boundaries represent histology-derived estimates of myelin (Figure 2Aiii). Notably, the atlas also incorporates intracortical myelin profiles of multiple cortical areas based on Hopf’s photometric studies, providing a unique resource to bridge ex- and in-vivo imaging studies (

      Foit NA, Yung S, Lee HM, Bernasconi A, Bernasconi N, Hong S-J (2022, January 20): A Whole-Brain 3D Myeloarchitectonic Atlas: Mapping the Vogt-Vogt Legacy to the Cortical Surface. bioRxiv, p 2022.01.17.476369.

      ) (https://bic.mni.mcgill.ca/∼noel/noel-myelin).
      Depth-wise profiling of cortical myelin, pioneered by histological studies, has been increasingly adopted by in-vivo imaging (
      • Trampel R.
      • Ott D.V.M.
      • Turner R.
      Do the congenitally blind have a stria of Gennari? First intracortical insights in vivo.
      ,
      • Tardif C.L.
      • Schäfer A.
      • Waehnert M.
      • Dinse J.
      • Turner R.
      • Bazin P.L.
      Multi-contrast multi-scale surface registration for improved alignment of cortical areas.
      ,
      • Sprooten E.
      • O’Halloran R.
      • Dinse J.
      • Lee W.H.
      • Moser D.A.
      • Doucet G.E.
      • et al.
      Depth-dependent intracortical myelin organization in the living human brain determined by in vivo ultra-high field magnetic resonance imaging.
      ). In a typical workflow, cortical surfaces are segmented using a standard T1w image, multiple intracortical surfaces are generated between the pial and white matter boundaries, then the intensities of a co-registered myelin-sensitive image are sampled along the intracortical surfaces at matched vertices [code for protocol may be found with (

      Paquola C, Vos De Wael R, Wagstyl K, Bethlehem RAI, Hong S-J, Seidlitz J, et al. (2019): Microstructural and functional gradients are increasingly dissociated in transmodal cortices ((H. Kennedy, editor)). PLoS Biology 17: e3000284–e3000284.

      )] (Figure 2Bi). While depth-wise profiling is agnostic to cortical layers, it is important to note that the vertical arrangement of cells and myelinated fibres vary with cortical curvature (
      • Bok S.T.
      Der Einfluß der in den Furchen und Windungen auftretenden Krümmungen der Großhirnrinde auf die Rindenarchitektur.
      ). As such, depth-wise profiling approaches can utilise equivolumetric surface generation to account for these effects and minimise the influence of curvature on profiles (
      • Waehnert M.D.
      • Dinse J.
      • Weiss M.
      • Streicher M.N.
      • Waehnert P.
      • Geyer S.
      • et al.
      Anatomically motivated modeling of cortical laminae.
      ). Another key concern is resolution. Dinse et al., (2015) showed that distinctive features within intracortical profiles, such as turning points, disappear at lower resolutions (towards 1mm) (Figure 2Bii), but areal differences remain evident. Areal differences are more pronounced at 0.4-0.5mm resolution, which is increasingly feasible for whole-brain neuroimaging studies [e.g. 7T MP2RAGE (
      • Marques J.P.
      • Kober T.
      • Krueger G.
      • van der Zwaag W.
      • Van de Moortele P.-F.F.
      • Gruetter R.
      MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field.
      )]. Furthermore, Dinse et al., (2015) showed that areas are best discriminated by combining mean intensity and profile shape differences, rather than using either feature alone, reinforcing the benefit of using profiles to describe myeloarchitecture. These features (mean and shape) represent unique axes of cortical differentiation (Figure 2Biii) (
      • Paquola C.
      • Bethlehem R.A.
      • Seidlitz J.
      • Wagstyl K.
      • Romero-Garcia R.
      • Whitaker K.J.
      • et al.
      Shifts in myeloarchitecture characterise adolescent development of cortical gradients.
      ). Mean decreases with distance from primary sensory and motor areas and is lowest in frontal and temporal poles, in line with histological evidence (
      • Hopf A.
      Registration of the myeloarchitecture of the human frontal lobe with an extinction method.
      ,
      • Hopf A.
      Photometric studies on the myeloarchitecture of the human temporal lobe.
      ). Shape is often summarised by profile skewness, a parameter adopted from cytoarchitectural histology (
      • Schleicher A.
      • Amunts K.
      • Geyer S.
      • Morosan P.
      • Zilles K.
      Observer-Independent Method for Microstructural Parcellation of Cerebral Cortex: A Quantitative Approach to Cytoarchitectonics.
      ), which pertains to the balance of intensities in upper v.s. lower layers. For MT-derived intracortical profiles, primary sensory-motor areas exhibit negative skewness, related to the gradual increase in MT across cortical depths, whereas cingulate and inferior temporal areas exhibit high skewness, related to relatively flat profiles with a sudden uptick in MT in the deepest layers (Figure 2Biii). Together, the variations in mean and skewness illustrate the existence of distinct, overlapping organisational axes of myeloarchitecture in the human cortex (Figure 2Biii). This work emphasises the importance of incorporating multiple features of intracortical profiles in in-vivo imaging studies to better understand myeloarchitectural differences and map large-scale patterns of cortical differentiation.
      Extending the profiling approach past the grey/white matter boundary, recent work has evaluated the density of superficial white matter (SWM) (
      • Kirilina E.
      • Helbling S.
      • Morawski M.
      • Pine K.
      • Reimann K.
      • Jankuhn S.
      • et al.
      Superficial white matter imaging: Contrast mechanisms and whole-brain in vivo mapping.
      ). Historically, SWM (also known as the U-fibre system) has been difficult to study in-vivo. The typical approach for fibre tracking, namely diffusion-weighted imaging, must be employed at ultra-high-resolution for SWM (
      • Movahedian Attar F.
      • Kirilina E.
      • Haenelt D.
      • Pine K.J.
      • Trampel R.
      • Edwards L.J.
      • Weiskopf N.
      Mapping Short Association Fibers in the Early Cortical Visual Processing Stream Using In Vivo Diffusion Tractography.
      ) to account for its thinness and preponderance of crossing fibres (
      • Reveley C.
      • Seth A.K.
      • Pierpaoli C.
      • Silva A.C.
      • Yu D.
      • Saunders R.C.
      • et al.
      Superficial white matter fiber systems impede detection of long-range cortical connections in diffusion MR tractography.
      ). Alternatively, elevated iron levels in SWM (
      • Stüber C.
      • Morawski M.
      • Schäfer A.
      • Labadie C.
      • Wähnert M.
      • Leuze C.
      • et al.
      Myelin and iron concentration in the human brain: A quantitative study of MRI contrast.
      ) may be leveraged to target it with R2* (
      • Kirilina E.
      • Helbling S.
      • Morawski M.
      • Pine K.
      • Reimann K.
      • Jankuhn S.
      • et al.
      Superficial white matter imaging: Contrast mechanisms and whole-brain in vivo mapping.
      ). Immunohistochemical analysis of SWM showed its iron content co-localises with oligodendrocytes, reinforcing the relevance of this approach to understanding myelin processes. Using an extended profiling approach, Kirilina et al., (2020) identified higher density of SWM in frontal-temporal-parietal association areas, compared to primary sensorimotor areas. This pattern notably differs from the regional distribution of intracortical myelin (Figure 2B), suggesting that together these profiling approaches can reveal the unique combinations of short- and long-range fibres in different cortical areas.
      Figure thumbnail gr2
      Figure 2Myelin Profiling. A) i. Classic drawings of myelin stained cortical sections highlight how cortical areas differ with regards to myelin striation, signifying the potential to characterise areas with respect to depth-wise variations in myelin density [reproduced from (
      • Hopf A.
      Registration of the myeloarchitecture of the human frontal lobe with an extinction method.
      )]. ii. Histology-derived myelin profiles from different areas (left). Comparing profiles to areal positions (right) reveals a large-scale gradient in the myeloarchitecture. In this case “confirming steplike decrease in myelin content with increasing distance from the auditory region” (
      • Hopf A.
      Photometric studies on the myeloarchitecture of the human temporal lobe.
      ). Based on the area naming convention of the original text: ttr=regio temporalis transversa (primary auditory area); tpartr=regio temporalis paratransversa; tmag d=subregio temporalis magna dorsalis; tmag m= subregio temporalis magna ventralis. Additional anatomical landmarks are STG=superior temporal gyrus; MTG=middle temporal gyrus; ITG=inferior temporal gyrus [reproduced from (
      • Hopf A.
      Photometric studies on the myeloarchitecture of the human temporal lobe.
      ), colours added to aid comparison between line and surface plots]. iii. Histology-derived atlas of myeloarchitecture (
      • Nieuwenhuys R.
      • Broere C.A.J.
      A map of the human neocortex showing the estimated overall myelin content of the individual architectonic areas based on the studies of Adolf Hopf.
      ), generated on an MRI-compatible cortical surface (

      Foit NA, Yung S, Lee HM, Bernasconi A, Bernasconi N, Hong S-J (2022, January 20): A Whole-Brain 3D Myeloarchitectonic Atlas: Mapping the Vogt-Vogt Legacy to the Cortical Surface. bioRxiv, p 2022.01.17.476369.

      ), including intracortical myelin profiles for many areas, based on Hopf’s photomicrograph studies. B) i. MRI-derived myelin profiling involves generating pial and white matter surfaces, generating equivolumetric surfaces between these two boundaries, then sampling imaging intensities along a vertex that crosses the surfaces. ii. Imaging resolution influences the smoothness of the profiles, yet distinctive elements of the profile shape remain evident even at 1mm resolution [reproduced with permission from (
      • Dinse J.
      • Härtwich N.
      • Waehnert M.D.
      • Tardif C.L.
      • Schäfer A.
      • Geyer S.
      • et al.
      A cytoarchitecture-driven myelin model reveals area-specific signatures in human primary and secondary areas using ultra-high resolution in-vivo brain MRI.
      )]. iii. The mean and skewness of MRI-derived myelin profiles capture unique information about myeloarchitecture, as shown by the different patterns of each feature across the cortical surface. Cortical maps were generated using quantitative magnetisation transfer (MT) imaging of healthy adolescents (
      • Paquola C.
      • Bethlehem R.A.
      • Seidlitz J.
      • Wagstyl K.
      • Romero-Garcia R.
      • Whitaker K.J.
      • et al.
      Shifts in myeloarchitecture characterise adolescent development of cortical gradients.
      ). Profiles on the left show extreme cases of high and low features, exemplifying how the features capture different aspects of the profiles. C) Average myelin water fraction (MWF) across segments of white matter bundles. x-axes represent the dominant spatial dimension of the specific tract. Error bars show standard deviation across subjects, highlighting the robustness of tract profiles. IFOF=inferior fronto-occipital fasciculus [reproduced with permission from (
      • Baumeister T.R.
      • Kolind S.H.
      • MacKay A.L.
      • McKeown M.J.
      Inherent spatial structure in myelin water fraction maps.
      )].
      Myelin profiling can also shed light on ensheathment along white matter tracts. Electron microscopy studies of non-human animal brains show that myelin thickness can vary along axons (
      • Giorgi P.P.
      • DuBois H.
      Regional differences in thickness and metabolism of the myelin sheath along the optic nerve and tract of rabbit.
      ,
      • Tomassy G.S.
      • Berger D.R.
      • Chen H.-H.
      • Kasthuri N.
      • Hayworth K.J.
      • Vercelli A.
      • et al.
      Distinct profiles of myelin distribution along single axons of pyramidal neurons in the neocortex.
      ). Relaxometry, diffusion-based and g-ratio measures are known to vary along white matter tracts (
      • Stikov N.
      • Campbell J.S.W.
      • Stroh T.
      • Lavelée M.
      • Frey S.
      • Novek J.
      • et al.
      In vivo histology of the myelin g-ratio with magnetic resonance imaging.
      ,
      • Jones D.K.
      • Deoni S.C.
      Visualization of Absolute T1 and T2 Along Specific White Matter Tracts.
      ,
      • Yeatman J.D.
      • Dougherty R.F.
      • Myall N.J.
      • Wandell B.A.
      • Feldman H.M.
      Tract Profiles of White Matter Properties: Automating Fiber-Tract Quantification.
      ). Explicitly profiling MWF along white matter bundles, Baumeister et al., (2020) recently identified characteristic patterns for each tract, that could be replicated in all subjects (Figure 2C). The approach is more in its infancy that intracortical myelin profiling and potential caveats, such as crossing fibres, need to be addressed (
      • Schiavi S.
      • Lu P.-J.
      • Weigel M.
      • Lutti A.
      • Jones D.K.
      • Kappos L.
      • et al.
      Bundle myelin fraction (BMF) mapping of different white matter connections using microstructure informed tractography.
      ). Even so, profiling approaches show promise for disambiguating sub-bundles within fasculi (
      • Schurr R.
      • Zelman A.
      • Mezer A.A.
      Subdividing the superior longitudinal fasciculus using local quantitative MRI.
      ). All in all, the profiling approach benefits from eschewing the assumption of uniform myelin distribution along white matter tracts and holds promise for offering greater sensitivity and specificity to inter- and intra-individual differences.

      Linking local myelin markers to connectome organisation

      Next, we ask how local myeloarchitecture, revealed by myelin profiling, contributes to the large-scale function of the human connectome. In particular, we highlight two avenues of recent work that probe the inter-relation between local myelin markers with structural connectome topology and network efficiency.
      Contemporary perspectives emphasise that cortical gradients capture multi-factorial changes in neurobiological features (
      • Fulcher B.D.
      • Murray J.D.
      • Zerbi V.
      • Wang X.-J.
      Multimodal gradients across mouse cortex.
      ,
      • Mesulam M.-M.
      From sensation to cognition.
      ,
      • Huntenburg J.M.
      • Bazin P.-L.
      • Margulies D.S.
      Large-Scale Gradients in Human Cortical Organization.
      ,
      • Hilgetag C.C.
      • Goulas A.
      • Changeux J.-P.
      A natural cortical axis connecting the outside and inside of the human brain.
      ,
      • Paquola C.
      • Amunts K.
      • Evans A.
      • Smallwood J.
      • Bernhardt B.
      Closing the mechanistic gap: the value of microarchitecture in understanding cognitive networks.
      ). The most prominent gradient, the “sensory-fugal axis”, runs from primary sensory areas towards limbic areas (

      Paquola C, Vos De Wael R, Wagstyl K, Bethlehem RAI, Hong S-J, Seidlitz J, et al. (2019): Microstructural and functional gradients are increasingly dissociated in transmodal cortices ((H. Kennedy, editor)). PLoS Biology 17: e3000284–e3000284.

      ,
      • Mesulam M.-M.
      From sensation to cognition.
      ) and involves concomitant changes in myeloarchitecture, cytoarchitecture, connectivity and function (
      • Hilgetag C.C.
      • Goulas A.
      • Changeux J.-P.
      A natural cortical axis connecting the outside and inside of the human brain.
      ,
      • Zikopoulos B.
      • García-Cabezas M.Á.
      • Barbas H.
      Parallel trends in cortical gray and white matter architecture and connections in primates allow fine study of pathways in humans and reveal network disruptions in autism.
      ,
      • García-Cabezas M.Á.
      • Hacker J.L.
      • Zikopoulos B.
      A Protocol for Cortical Type Analysis of the Human Neocortex Applied on Histological Samples, the Atlas of Von Economo and Koskinas, and Magnetic Resonance Imaging.
      ) (Figure 3Ai-ii). Many projections pass step-wise along the sensory-fugal axis, producing a set of parallel processing hierarchies, emanating from each primary sensory area (
      • Felleman D.J.
      • Van Essen D.C.
      Distributed hierarchical processing in the primate cerebral cortex.
      ) (Figure 3Aiii). Sequential processing through these hierarchies, with graded changes in underlying myelo- and cyto-architecture, is thought to allow integration of information from several sources and gradual abstraction of neural code (
      • Mesulam M.-M.
      From sensation to cognition.
      ,
      • Hilgetag C.C.
      • Goulas A.
      • Changeux J.-P.
      A natural cortical axis connecting the outside and inside of the human brain.
      ). We recently found that individual-level sensory-fugal axes may be defined by applying non-linear dimensionality reduction to myelin profiles [(

      Paquola C, Vos De Wael R, Wagstyl K, Bethlehem RAI, Hong S-J, Seidlitz J, et al. (2019): Microstructural and functional gradients are increasingly dissociated in transmodal cortices ((H. Kennedy, editor)). PLoS Biology 17: e3000284–e3000284.

      ); Figure 3B]. Thus, this approach links local properties of intracortical myeloarchitecture to global axes of cortical differentiation and provides a new foundation to map the dominant streams of information processing in individual human brains.
      Figure thumbnail gr3
      Figure 3Relationship of local myelin to large-scale cortical organisation. A) i. While myelin profiles (left) and cytoarchitectural profiles (right) differ in shape, they often capture a common spatial axis of changes across areas. For example, in the prefrontal cortex shown here, total myelin content and density of neurons in layer III increase along an axis that runs in a medial-lateral loop via orbitofrontal cortex. dACC=dorsal anterior cingulate cortex. sgACC=subgenual anterior cingulate cortex. OFC=orbitofrontal cortex. LPFC=lateral prefrontal cortex. [Adapted from (
      • Zikopoulos B.
      • García-Cabezas M.Á.
      • Barbas H.
      Parallel trends in cortical gray and white matter architecture and connections in primates allow fine study of pathways in humans and reveal network disruptions in autism.
      ). Line plot colours match the Brodmann areas delineated on the cortical surface (
      • Pijnenburg R.
      • Scholtens L.H.
      • Ardesch D.J.
      • de Lange S.C.
      • Wei Y.
      • van den Heuvel M.P.
      Myelo- and cytoarchitectonic microstructural and functional human cortical atlases reconstructed in common MRI space.
      )]. ii. Intracortical neuronal density is strongly correlated with the density of axons projecting from the cortical area, mirroring the spatial axis identified in Figure 3Ai, suggesting that intracortical myeloarchitecture is also associated with projection density [Reproduced from (
      • Zikopoulos B.
      • García-Cabezas M.Á.
      • Barbas H.
      Parallel trends in cortical gray and white matter architecture and connections in primates allow fine study of pathways in humans and reveal network disruptions in autism.
      ). High and low myelin labels were added to aid interpretation]. iii. Cortical types capture multi-factorial changes in cyto- and myelo-architecture that systematically vary along spatial axes (
      • García-Cabezas M.Á.
      • Hacker J.L.
      • Zikopoulos B.
      A Protocol for Cortical Type Analysis of the Human Neocortex Applied on Histological Samples, the Atlas of Von Economo and Koskinas, and Magnetic Resonance Imaging.
      ). The relative type of two cortical areas informs upon whether projections are in the feedforward (left) or feedback (right) direction, as well as the laminar origin of projections [adapted from (
      • Barbas H.
      • Rempel-Clower N.
      Cortical structure predicts the pattern of corticocortical connections.
      )]. Thus, the relative myeloarchitecture of two cortical areas can index functional and structural characteristics of a neural network. B) The principal axes of myeloarchitectural differentiation are resolved by first calculating myeloarchitectural similarity (MS) between regions, based on the correlation of myelin profiles. Then, dimensionality reduction, typically diffusion map embedding (
      • Coifman R.R.
      • Lafon S.
      Diffusion maps.
      ), is applied to a matrix that contains MS between many cortical regions. The resultant dimensions reflect different spatial axes and are ranked according to the variance they explain in the MS matrix. In healthy adults, the first dimension runs from primary sensory and motor areas to limbic areas, closely approximating the sensory-fugal axis defined by post mortem histology (

      Paquola C, Vos De Wael R, Wagstyl K, Bethlehem RAI, Hong S-J, Seidlitz J, et al. (2019): Microstructural and functional gradients are increasingly dissociated in transmodal cortices ((H. Kennedy, editor)). PLoS Biology 17: e3000284–e3000284.

      ). Colouring myelin profiles by their position on dimension 1 illustrates how the axis reflects a decrease in myelin content, as well as shift from a concave to a convex curve. Notably, each dimension is sensitive to a different aspect of the myelin profile shape, related to myelin content at certain cortical depths [produced with data from (

      Paquola C, Vos De Wael R, Wagstyl K, Bethlehem RAI, Hong S-J, Seidlitz J, et al. (2019): Microstructural and functional gradients are increasingly dissociated in transmodal cortices ((H. Kennedy, editor)). PLoS Biology 17: e3000284–e3000284.

      )].
      Figure thumbnail gr4
      Figure 4Myelin profiling approaches link local developmental changes to large-scale patterns of brain organisation. A) i. Scatterplots represent sampling points along white matter bundles, coloured by (left) bundle, (centre) R1 in newborns and (right) development rate of R1 from 0-6months. Across all points, development rate could be largely explained (67% of variance) by R1 in newborns and spatial position (anterior-posterior and inferior-superior axes) [reproduced from (
      • Grotheer M.
      • Rosenke M.
      • Wu H.
      • Kular H.
      • Querdasi F.R.
      • Natu V.S.
      • et al.
      White matter myelination during early infancy is linked to spatial gradients and myelin content at birth [no. 1].
      )]. ii. Examining bundle-specific myelin, the developmental rate (“slope”, dotted line) is sometimes inversely correlated to R1 in newborns (“0m”, full line) and other times correlated with a spatial axis (posterior-anterior depicted as x-axis) [reproduced from (
      • Grotheer M.
      • Rosenke M.
      • Wu H.
      • Kular H.
      • Querdasi F.R.
      • Natu V.S.
      • et al.
      White matter myelination during early infancy is linked to spatial gradients and myelin content at birth [no. 1].
      )]. CS=cortico-spinal. ATR=anterior thalamic radiation. ILF=inferior longitudinal fasciculus. MLF=middle longitudinal fasciculus. B) i. Regional variation in intracortical myelin profiles, derived from MT, can be surmised by the myeloarchitectural axis. The more distant two regions are on the myeloarchitectural axis, the more dissimilar their intracortical myelin profiles are. In contrast, the more distant two regions are on the developmental axis, the more dissimilar their age-related changes in intracortical myelin profiles are. Notably, the baseline myeloarchitectural axis (derived from the earliest timepoints) and the developmental axis (calculated across the full age range) were strongly correlated (r=0.89). Comparing myeloarchitectural axes at <16years and >24years indicated that the most prominent age-related changes (Cohen’s d effect size) were evident in the regions in the centre of the developmental axis, suggesting differentiation of association cortex (orange) during this age range towards either the sensory (purple) or paralimbic (yellow) extremes [reproduced from (
      • Paquola C.
      • Bethlehem R.A.
      • Seidlitz J.
      • Wagstyl K.
      • Romero-Garcia R.
      • Whitaker K.J.
      • et al.
      Shifts in myeloarchitecture characterise adolescent development of cortical gradients.
      )]. ii. Age-related changes in mean and skewness of myelin profiles shown on the cortical surfaces (threshold: qFDR <0.00625). Scatterplots show t-statistic (mean ± SD) of age-related changes in MT intensity at each sampled cortical depth, within significant regions. Mean increases were balanced across surfaces, whereas decreases in skewness were driven by preferential MT increases at mid-to-deeper surfaces. Line plots exemplify how myelin profiles change from the lowest to oldest age groups in significant regions [reproduced from (
      • Paquola C.
      • Bethlehem R.A.
      • Seidlitz J.
      • Wagstyl K.
      • Romero-Garcia R.
      • Whitaker K.J.
      • et al.
      Shifts in myeloarchitecture characterise adolescent development of cortical gradients.
      )].
      Turning towards white matter, the distribution of myelin influences the relationship between structural connectome topology and functional efficiency. The g-ratio, in particular, is a key contributor to conduction velocity (
      • Drakesmith M.
      • Harms R.
      • Rudrapatna S.U.
      • Parker G.D.
      • Evans C.J.
      • Jones D.K.
      Estimating axon conduction velocity in vivo from microstructural MRI.
      ). Thus, myelin-sensitive imaging can be combined with diffusion imaging to approximate conduction velocity across the connectome (
      • Mancini M.
      • Tian Q.
      • Fan Q.
      • Cercignani M.
      • Huang S.Y.
      Dissecting whole-brain conduction delays through MRI microstructural measures.
      ). In doing so, recent work has shown that the rich-club has lower g-ratio compared to local edges, which may enable faster and more efficient propagation within a set of densely-connected but widely-distributed regions (
      • Mancini M.
      • Giulietti G.
      • Dowell N.
      • Spanò B.
      • Harrison N.
      • Bozzali M.
      • Cercignani M.
      Introducing axonal myelination in connectomics: A preliminary analysis of g-ratio distribution in healthy subjects.
      ). Initial work in Parkinson’s disease also suggests that mapping myelin across the connectome can help to identify aberrant tracts that are associated with scores on motor performance tasks, signifying the potential importance of local myelin measures on more distributed brain function (
      • Boshkovski T.
      • Cohen-Adad J.
      • Misic B.
      • Arnulf I.
      • Corvol J.-C.
      • Vidailhet M.
      • et al.
      The Myelin-Weighted Connectome in Parkinson’s Disease.
      ).

      Advancing lifespan research with myelin-sensitive imaging

      The human lifespan is an ideal target for myelin research. Post-mortem studies have long evidenced correlations between age and myelin, yet uncertainty remains regarding the principles of myeloarchitectural maturation. In-vivo myelin-sensitive imaging offers the opportunity to track spatiotemporal patterns of myelin across the entire lifespan in large cohorts, helping to show how local changes can shape trajectories of larger-scale brain organisation and their inter-relation with other neurobiological features.
      A principal challenge for lifespan research is determining the maturational sequence of myelination. As early as the 1870’s, Flechsig sought to prove that certain laws dictate the developmental sequence of pathways in the brain and spinal cord (

      Flechsig P (1876): Die Leitungsbahnen im Gehirn und Rückenmark des Menschen auf Grund entwicklungsgeschichtlicher Untersuchungen dargestellt. Engelmann.

      ). Through detailed examination of white matter tracts and intracortical myelin in post mortem tissue, he showed that developmental myelination is protracted and asynchronous (
      • Flechsig P.
      Developmental (Myelogenetic) Localisation of the Cerebral Cortex in the Human Subject.
      ). The onset and duration of myelination (the “myelogenetic cycle”) varies across fibre systems and regions, with cycles spanning from in utero to adulthood. Kinney et al., (1988) set forth general rules that explain the temporal patterns of myelination; (i) proximal pathways myelinate earlier and have shorter duration of myelination than distal pathways, (ii) sensory pathways myelinate before motor pathways, (iii) projection pathways myelinate earlier and have shorter myelination intervals than associative pathways, (iv) myelination progresses from the central sulcus towards the poles, (v) occipital then frontal then temporal poles myelinate, and (iv) posterior fronto-parietal-occipital areas have faster myelination than anterior fronto-temporal regions. These large-scale rules provide a benchmark for imaging studies, which can in turn extend upon the post-mortem research by providing non-binary, quantitative assessment of myelin through investigation of healthy development and their individual variability in large cohorts. For example, Kulikova et al., (2015) demonstrated the potential of using multi-modal parameters (R1, R2 and diffusion-based) to infer the maturational sequence of white matter bundles. Notably, the multi-variate approach conformed to histological benchmarks with higher accuracy than uni-variate approaches, supporting the utility of multi-modal imaging for tracking myelin changes across the lifespan.
      Recent studies have used MRI-derived myelin profiles, evaluated at multiple timepoints, to show how the patterns of myelin changes relate to brain organisation at different life stages. Several theories have been proposed regarding the determinants of age-related myelin changes, such as the last-in/first-out hypothesis (

      Raz N (2000): Aging of the brain and its impact on cognitive performance: Integration of structural and functional findings. The Handbook of Aging and Cognition, 2nd Ed. Mahwah, NJ, US: Lawrence Erlbaum Associates Publishers, pp 1–90.

      ) or the spatial gradient hypothesis (
      • Kinney H.C.
      • Ann brody B.
      • Kloman A.S.
      • Gilles F.H.
      Sequence of Central Nervous System Myelination in Human Infancy. II. Patterns of Myelination in Autopsied Infants.
      ). Myelin-sensitive imaging is well posed to test these hypotheses, given the possibilities to examine myelin across the entire brain and to track individual developmental trajectories. In infants, Grotheer et al., (2022) showed that myelin levels at birth as well as spatial position contribute to age-related R1 changes in white matter bundles (Figure 4Ai). In certain tracts speed of myelination is inversely correlated to the pre-existing degree of myelin, whereas in other tracts it is associated with spatial axes (Figure 4Aii). In adolescents, age-related changes in myelin also appear to reflect with pre-existing differences in intracortical myelin profiles (Figure 4Bi) (
      • Paquola C.
      • Bethlehem R.A.
      • Seidlitz J.
      • Wagstyl K.
      • Romero-Garcia R.
      • Whitaker K.J.
      • et al.
      Shifts in myeloarchitecture characterise adolescent development of cortical gradients.
      ). Specifically, areas that were less myeloarchitecturally distinct (relative to sensory and limbic areas) at early adolescence exhibit the strongest age-related changes throughout adolescence and young adulthood (Figure 4Bi). Notably, intracortical myelin profiles linked local changes (i.e. myelination at a specific cortical depth) to large-scale patterns of myeloarchitectural maturation (Figure 4Bii). Beyond mere correlative effects, myelin profiles may hold predictive power for later life. Imaging studies suggest that earlier patterns of myeloarchitecture can predict myelin decline in order age. Specifically, the speed of myelin accumulation in adolescence can index the speed of myelin decline in older age (i.e. “fastest in, fastest out” hypothesis) (
      • Yeatman J.D.
      • Wandell B.A.
      • Mezer A.A.
      Lifespan maturation and degeneration of human brain white matter [no. 1].
      ). Fine-grained spatial variations in later life are yet to be explored with profiling approaches, which could help to discern bundle- and region-specific differences. Together, these studies demonstrate how myelin-sensitive imaging is helping to test hypotheses of how development and degeneration progress across the brain. This research suggests that a basic set of organisational axes may govern bundle- and region-specific myelination across the lifespan.
      In parallel, multi-modal approaches are helping to elucidate the inter-relation of age-dependent changes in myelin with other neurobiological features. Particular attention has been paid to disentangling the contributions of myelin and axonal properties from diffusion-based parameters (
      • Nossin-Manor R.
      • Card D.
      • Morris D.
      • Noormohamed S.
      • Shroff M.M.
      • Whyte H.E.
      • et al.
      Quantitative MRI in the very preterm brain: Assessing tissue organization and myelination using magnetization transfer, diffusion tensor and T1 imaging.
      ). Furthermore, the balance between myelin and axonal measures can inform upon clinically-relevant aspects of development and degeneration. For instance, throughout infancy, the g-ratios of white matter bundles decrease logarithmically towards adult levels (
      • Dean D.C.
      • O’Muircheartaigh J.
      • Dirks H.
      • Travers B.G.
      • Adluru N.
      • Alexander A.L.
      • Deoni S.C.L.
      Mapping an index of the myelin g-ratio in infants using magnetic resonance imaging.
      ), likely related to increasing thickness of myelin sheaths (
      • Schröder J.M.
      • Bohl J.
      • von Bardeleben U.
      Changes of the ratio between myelin thickness and axon diameter in human developing sural, femoral, ulnar, facial, and trochlear nerves.
      ) as well as efficient conduction speed in brain networks (
      • Chomiak T.
      • Hu B.
      What Is the Optimal Value of the g-Ratio for Myelinated Fibers in the Rat CNS? A Theoretical Approach.
      ). At the other end of the lifespan, the equilibrium between remyelination, myelin degradation and axonal loss can be approximated by multi-parameter decomposition of T2 and may help to indicate healthy vs. pathological aging (
      • Bartzokis G.
      Age-related myelin breakdown: a developmental model of cognitive decline and Alzheimer’s disease.
      ,
      • Lynn J.D.
      • Anand C.
      • Arshad M.
      • Homayouni R.
      • Rosenberg D.R.
      • Ofen N.
      • et al.
      Microstructure of Human Corpus Callosum across the Lifespan: Regional Variations in Axon Caliber, Density, and Myelin Content.
      ).

      Concluding Remarks and Future Perspectives

      The neuroimaging field provides strong evidence that myelin may be evaluated with in-vivo MRI. Various MRI markers of myelin have been validated by comparing their values to well-established myelin markers acquired in the same tissue (i.e. histology, immunochemistry or electron microscopy). Theoretically and empirically, T2-derived MWF, qMT and R1 to provide good specificity to myelin. More caution is warranted in interpreting contrasts without such validation as specific myelin markers (e.g. T1w/T2w). Identifying the optimal multimodal combinations that can disambiguate myelin from artefactual and naturally occurring confounds, such as iron, is an important line of future research.
      Moving forward, complementary “wide” and “deep” studies can progress understanding of myeloarchitectural changes across the lifespan. On the one hand, large cohort studies that span a wide age range and demographic spectrum can leverage efficient whole-brain quantitative myelin-sensitive sequences to confirm (or deny) whether laws of myelination (
      • Kinney H.C.
      • Ann brody B.
      • Kloman A.S.
      • Gilles F.H.
      Sequence of Central Nervous System Myelination in Human Infancy. II. Patterns of Myelination in Autopsied Infants.
      ) generalise across the population, and to extend these laws to intracortical myelin and aging populations. On the other hand, ultra-high-field MRI studies can investigate attributes of myeloarchitecture previously only accessible with post mortem microscopy, such as laminar detail in the cortex (
      • Dinse J.
      • Härtwich N.
      • Waehnert M.D.
      • Tardif C.L.
      • Schäfer A.
      • Geyer S.
      • et al.
      A cytoarchitecture-driven myelin model reveals area-specific signatures in human primary and secondary areas using ultra-high resolution in-vivo brain MRI.
      ) as well as myelin distribution along U-fibres (
      • Kirilina E.
      • Helbling S.
      • Morawski M.
      • Pine K.
      • Reimann K.
      • Jankuhn S.
      • et al.
      Superficial white matter imaging: Contrast mechanisms and whole-brain in vivo mapping.
      ) and deep white matter tracts (
      • Giorgi P.P.
      • DuBois H.
      Regional differences in thickness and metabolism of the myelin sheath along the optic nerve and tract of rabbit.
      ). Ideally, such studies would focus on deeply phenotyping a few individuals with repeated scans, helping to reveal the dynamic intra-individual changes in myelin across short- and long-time frames.
      Overall, a key advantage of progress in investigating myeloarchitecture in-vivo is the ability to directly assess the relationship of myelin with function (
      • Turner R.
      Myelin and modeling: Bootstrapping cortical microcircuits.
      ), cognition (
      • Raz N.
      • Daugherty A.M.
      Pathways to Brain Aging and Their Modifiers: Free-Radical-Induced Energetic and Neural Decline in Senescence (FRIENDS) Model - A Mini-Review.
      ), behaviour (
      • Kaller M.S.
      • Lazari A.
      • Blanco-Duque C.
      • Sampaio-Baptista C.
      • Johansen-Berg H.
      Myelin plasticity and behaviour-connecting the dots.
      ) and disease (
      • Bartzokis G.
      Age-related myelin breakdown: a developmental model of cognitive decline and Alzheimer’s disease.
      ,

      Stassart RM, Möbius W, Nave K-A, Edgar JM (2018): The Axon-Myelin Unit in Development and Degenerative Disease. Frontiers in Neuroscience 12. Retrieved August 5, 2022, from https://www.frontiersin.org/articles/10.3389/fnins.2018.00467

      ). Previous MRI studies have shown increases in myelin markers on specific tracts are associated with more mature activity patterns in certain brain regions, supporting the notion of concomitant maturation of myelin and function in the brain (
      • Fornari E.
      • Knyazeva M.G.
      • Meuli R.
      • Maeder P.
      Myelination shapes functional activity in the developing brain.
      ,
      • Meissner T.W.
      • Genç E.
      • Mädler B.
      • Weigelt S.
      Myelin development in visual scene-network tracts beyond late childhood: A multimethod neuroimaging study.
      ). Conversely, MRI markers of myelin degradation have been linked to cognitive decline in healthy elderly individuals and individuals with Alzheimer’s disease (
      • Brickman A.M.
      • Meier I.B.
      • Korgaonkar M.S.
      • Provenzano F.A.
      • Grieve S.M.
      • Siedlecki K.L.
      • et al.
      Testing the white matter retrogenesis hypothesis of cognitive aging.
      ). Further work is necessary to characterise the likely bidirectional relationship between myelin and brain function. “Wide” studies of myeloarchitecture across large cohorts will help to establish the associations between regional myelin and cognitive skills, but “deep” approaches offer greater promise in disentangling their causal relationships. In particular, behavioural training and neurofeedback protocols may be able to reveal the dynamic intra-individual changes in myelin and brain activity that support cognitive development or decline (
      • Sampaio-Baptista C.
      • Neyedli H.F.
      • Sanders Z.-B.
      • Diosi K.
      • Havard D.
      • Huang Y.
      • et al.
      fMRI neurofeedback in the motor system elicits bidirectional changes in activity and in white matter structure in the adult human brain.
      ).
      Myelin-sensitive imaging has the potential to complement and advance upon current approaches for understanding psychiatric and neurological diseases, especially those with neurodevelopmental or neurodegenerative origins. Schizophrenia, for example, is associated with reduced oligodendrocytes and myelin-related gene expression [for review see (
      • Takahashi N.
      • Sakurai T.
      • Davis K.L.
      • Buxbaum J.D.
      Linking oligodendrocyte and myelin dysfunction to neurocircuitry abnormalities in schizophrenia.
      )]. Yet, the nature of dysmyelination in schizophrenia, especially its progression, remains a contentious issue. Hypotheses variably focus on abnormal neurodevelopment (
      • Weinberger D.R.
      On the plausibility of “the neurodevelopmental hypothesis” of schizophrenia.
      ) or accelerated neurodegeneration (
      • Stone W.S.
      • Phillips M.R.
      • Yang L.H.
      • Kegeles L.S.
      • Susser E.S.
      • Lieberman J.A.
      Neurodegenerative model of schizophrenia: Growing evidence to support a revisit.
      ). The above-discussed advances in myelin imaging, analytics and lifespan research pave the way to accurately track and characterise aberrant age-related changes in myelin in individuals with schizophrenia, thereby disentangling competing theories, improving understanding of the aetiology of the disease and benefitting models of clinical course. Similar outcomes are now possible for a wide range of psychiatric and neurological diseases thanks to recent advances in myelin-sensitive imaging.

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      Acknowledgements

      This work was funded by Helmholtz Association’s Initiative and Networking Fund through the Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL) under the Helmholtz International Lab grant agreement InterLabs-0015 and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation; 491111487).

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