CoChat
Sign Up
Cited by 156Advanced Neuroimaging Techniques and ApplicationsNeurogenesis and neuroplasticity mechanismsMultiple Sclerosis Research Studies

The DOI matches its Crossref record; 79 of the 79 references it cites were found in a registry or an index.

Unraveling the secrets of white matter--bridging the gap between cellular, animal and human imaging studies

Open access
Walhovd, K.B., Johansen-Berg, H., Káradóttir, R.T.·2014·Neuroscience
DOI 10.1016/j.neuroscience.2014.06.058PMC PMC4155933
Open this paper in CoChat — free accountOpen PDF

Abstract

The CNS white matter makes up about half of the human brain, and with advances in human imaging it is increasingly becoming clear that changes in the white matter play a major role in shaping human behavior and learning. However, the mechanisms underlying these white matter changes remain poorly understood. Within this special issue of Neuroscience on white matter, recent advances in our knowledge of the function of white matter, from the molecular level to human imaging, are reviewed. Collaboration between fields is essential to understand the function of the white matter, but due to differences in methods and field-specific 'language', communication is often hindered. In this review, we try to address this hindrance by introducing the methods and providing a basic background to myelin biology and human imaging as a prelude to the other reviews within this special issue.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.neuroscience.2014.06.058

Cites

79 of 79 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1 White matter voxel. (A) 100 μm × 100 μm × 50-μm 3D projection of corpus callosum white matter area in adult rodent. Note in this particular area not all the axons are myelinated (yellow: astrocytes; red: myelin; blue: axons). (B) A Lucifer yellow dye filled oligodendrocyte, via patch pipette, in the corpus callosum revealing its morphology and the number of internodes it makes. (C–F) Confocal projections of (C) oligodendrocyte cell nuclei labeled with Olig2 (an oligodendrocyte lineage-specific transcription factor) (D) astrocytes labeled with GFAP, (E) oligodendrocyte precursor cells labeled with NG2 and (F) microglia labeled with Iba1, the length and width of each image is 100 μm × 100 μm and the projection is ∼50 μm deep. White arrows show the direction of the axons. (F) Quantification of the glial cell number (oligodendrocyte identified as Olig2postive cells, astrocytes as GFAP positive, OPCs as NG2-positive cells and microglia as Iba1) in a voxel of 100 μm × 100 μm × 100 μm, from adult rodent white matter tracts (corpus callosum and cerebellum).
Fig. 1 White matter voxel. (A) 100 μm × 100 μm × 50-μm 3D projection of corpus callosum white matter area in adult rodent. Note in this particular area not all the axons are myelinated (yellow: astrocytes; red: myelin; blue: axons). (B) A Lucifer yellow dye filled oligodendrocyte, via patch pipette, in the corpus callosum revealing its morphology and the number of internodes it makes. (C–F) Confocal projections of (C) oligodendrocyte cell nuclei labeled with Olig2 (an oligodendrocyte lineage-specific transcription factor) (D) astrocytes labeled with GFAP, (E) oligodendrocyte precursor cells labeled with NG2 and (F) microglia labeled with Iba1, the length and width of each image is 100 μm × 100 μm and the projection is ∼50 μm deep. White arrows show the direction of the axons. (F) Quantification of the glial cell number (oligodendrocyte identified as Olig2postive cells, astrocytes as GFAP positive, OPCs as NG2-positive cells and microglia as Iba1) in a voxel of 100 μm × 100 μm × 100 μm, from adult rodent white matter tracts (corpus callosum and cerebellum).CC BY · PubMed Central
Fig. 2 The structure of myelin. (A) A schematic diagram of myelinated axon illustration how g-ratio is measured, a parameter used to quantify the thickness of myelin on electronmicrographs as shown in C. (B) Semi-thin section of a cerebellar penduncle, stained with toluidine blue (image is provided by Dr. Helene Gautier). (C) Electro micrograph of corpus callosum (courtesy of Mr. Ginez Gonzalez and Dr. Mark Kotter) showing the variety of axonal diameter and myelin thickness. (D) A schematic diagram of myelinated fibers, illustrating an internode (a myelinated segment along an axon) and the node of Ranvier (the gap between internodes).
Fig. 2 The structure of myelin. (A) A schematic diagram of myelinated axon illustration how g-ratio is measured, a parameter used to quantify the thickness of myelin on electronmicrographs as shown in C. (B) Semi-thin section of a cerebellar penduncle, stained with toluidine blue (image is provided by Dr. Helene Gautier). (C) Electro micrograph of corpus callosum (courtesy of Mr. Ginez Gonzalez and Dr. Mark Kotter) showing the variety of axonal diameter and myelin thickness. (D) A schematic diagram of myelinated fibers, illustrating an internode (a myelinated segment along an axon) and the node of Ranvier (the gap between internodes).CC BY · PubMed Central
Fig. 3 T1- and T2-weighted brain scans. Here, two coronal images from the same young female (age 20 years) are shown in the coronal view. As can be seen, they show inverse intensities. In the T1-weighted image (left panel), compartments with greater water contents appear darker, so that cerebrospinal fluid (CSF) has the darkest intensity, gray matter (for instance in the cortical ribbon, basal ganglia and hippocampi) intermediate intensities, while white matter shows brighter intensities. Such images are often used for reconstruction and quantitative analyses of subcortical and cortical structures. The T2-weighted image (right panel) show inverse intensities with white matter appearing darker, and may be useful for visualizing features such as white matter lesions. When using both types of scans in combination, and T1/T2 ratios, contrast can be improved, and this can be used for myelin mapping (see also Fig. 7).
Fig. 3 T1- and T2-weighted brain scans. Here, two coronal images from the same young female (age 20 years) are shown in the coronal view. As can be seen, they show inverse intensities. In the T1-weighted image (left panel), compartments with greater water contents appear darker, so that cerebrospinal fluid (CSF) has the darkest intensity, gray matter (for instance in the cortical ribbon, basal ganglia and hippocampi) intermediate intensities, while white matter shows brighter intensities. Such images are often used for reconstruction and quantitative analyses of subcortical and cortical structures. The T2-weighted image (right panel) show inverse intensities with white matter appearing darker, and may be useful for visualizing features such as white matter lesions. When using both types of scans in combination, and T1/T2 ratios, contrast can be improved, and this can be used for myelin mapping (see also Fig. 7).CC BY · PubMed Central
Fig. 4 Multiple examples of ways of measuring and reconstructing white matter properties based on MRI scans.
Fig. 4 Multiple examples of ways of measuring and reconstructing white matter properties based on MRI scans.CC BY · PubMed Central
Fig. 5 Contributions to tract anisotropy. (A) Water diffuses more easily along the axis of a fiber bundle than it does across the axis of the bundle, due to the presence of barriers such as membranes and myelin. (B) Typically, multiple different diffusion-weighted images are acquired, with each one sensitized to diffusion along a different direction in space. (C) One can fit a mathematical model to the measurements in order to estimate certain model parameters that describe diffusion behavior within each voxel. The most commonly used model, the diffusion tensor model, fits the measurements to a tensor, or ellipsoid, which is fully characterized by its three orthogonal eigenvectors and their associated lengths, or eigenvalues (λ1, λ2, λ3). (D) In cerebral spinal fluid (CSF), water diffuses freely in all directions and so FA is close to zero; in white matter, diffusion is directionally dependent and so FA is closer to one. (E) The long axis of the diffusion tensor corresponds to the principal diffusion direction. Within a coherent fiber bundle this aligns with the fiber direction. (F) By following these voxel-wise estimates of principal diffusion directions it is possible to perform diffusion tractography, and reconstruct estimates of fiber pathways. (G) Variations in diffusion parameters along tracts during normative development are likely a combination of tract-specific (e.g. myelin content, axonal characteristics) and local environment contributions. Voxel 1 contains a tract of interest (yellow) as well as a crossing tract (gray), resulting in low anisotropy measurements at this point. Voxel 2 contains only the tract of interest and exhibits high anisotropy. Within voxel 3 axons from nearby gray matter join the tract and some axons break off heading toward gray matter targets. The result would be a drop in anisotropy measurements at this point in the tract. The figure is from (Johnson et al., 2013).
Fig. 5 Contributions to tract anisotropy. (A) Water diffuses more easily along the axis of a fiber bundle than it does across the axis of the bundle, due to the presence of barriers such as membranes and myelin. (B) Typically, multiple different diffusion-weighted images are acquired, with each one sensitized to diffusion along a different direction in space. (C) One can fit a mathematical model to the measurements in order to estimate certain model parameters that describe diffusion behavior within each voxel. The most commonly used model, the diffusion tensor model, fits the measurements to a tensor, or ellipsoid, which is fully characterized by its three orthogonal eigenvectors and their associated lengths, or eigenvalues (λ1, λ2, λ3). (D) In cerebral spinal fluid (CSF), water diffuses freely in all directions and so FA is close to zero; in white matter, diffusion is directionally dependent and so FA is closer to one. (E) The long axis of the diffusion tensor corresponds to the principal diffusion direction. Within a coherent fiber bundle this aligns with the fiber direction. (F) By following these voxel-wise estimates of principal diffusion directions it is possible to perform diffusion tractography, and reconstruct estimates of fiber pathways. (G) Variations in diffusion parameters along tracts during normative development are likely a combination of tract-specific (e.g. myelin content, axonal characteristics) and local environment contributions. Voxel 1 contains a tract of interest (yellow) as well as a crossing tract (gray), resulting in low anisotropy measurements at this point. Voxel 2 contains only the tract of interest and exhibits high anisotropy. Within voxel 3 axons from nearby gray matter join the tract and some axons break off heading toward gray matter targets. The result would be a drop in anisotropy measurements at this point in the tract. The figure is from (Johnson et al., 2013).CC BY · PubMed Central
Fig. 6 Multimodal imaging of white matter through the lifespan. Results are based on 430 well-screened healthy participants between 8 and 85 years (mean 41.6 years). Values in the scatterplots are expressed in z-scores (standard deviations) to ease comparison between metrics. Values represent for FA, axial, radial and mean diffusion the mean of all voxels that were included in the left superior longitudinal fasciculus. The tract-based spatial statistics skeleton represents the middle of the tract for all participants (red and green voxels in the lower left brain image). White matter volume represents the total volume of all cerebral white matter, and cortical volume represents the volume of all cortical gray matter, in both cases corrected for total intracranial volume. Cortical myelin content is based on the ratio between T1- and T2-weighted MR images in an overlapping sample (n = 339, age 8–83 years), sampled 0.2 mm from the white matter/gray matter boundary into the gray matter in the superior frontal cortex.
Fig. 6 Multimodal imaging of white matter through the lifespan. Results are based on 430 well-screened healthy participants between 8 and 85 years (mean 41.6 years). Values in the scatterplots are expressed in z-scores (standard deviations) to ease comparison between metrics. Values represent for FA, axial, radial and mean diffusion the mean of all voxels that were included in the left superior longitudinal fasciculus. The tract-based spatial statistics skeleton represents the middle of the tract for all participants (red and green voxels in the lower left brain image). White matter volume represents the total volume of all cerebral white matter, and cortical volume represents the volume of all cortical gray matter, in both cases corrected for total intracranial volume. Cortical myelin content is based on the ratio between T1- and T2-weighted MR images in an overlapping sample (n = 339, age 8–83 years), sampled 0.2 mm from the white matter/gray matter boundary into the gray matter in the superior frontal cortex.CC BY · PubMed Central
Fig. 7 MRI myelin mapping. T1w/T2w ratio myelin maps from a group of young subjects (n = 85, 43 females (50.6%), mean age (SD) = 14.7 (3.3), min–max age = 8.4–19.7). Data are shown for the left hemisphere (left panel: lateral view, right panel: medial view). Values were sampled across cortex (20 times, at a 5% spacing) along the normal from the pial to the white surface, and then averaged. The T1w/T2w ratios are dimensionless quantities, and values are displayed between third and 96th percentiles with saturation below (black) and above (white) these values to allow for comparisons of data from different imaging acquisition parameters; for instance, the myelin maps show striking similarities with the original maps presented in Glasser and Van Essen (2011). Figure by Håkon Grydeland.
Fig. 7 MRI myelin mapping. T1w/T2w ratio myelin maps from a group of young subjects (n = 85, 43 females (50.6%), mean age (SD) = 14.7 (3.3), min–max age = 8.4–19.7). Data are shown for the left hemisphere (left panel: lateral view, right panel: medial view). Values were sampled across cortex (20 times, at a 5% spacing) along the normal from the pial to the white surface, and then averaged. The T1w/T2w ratios are dimensionless quantities, and values are displayed between third and 96th percentiles with saturation below (black) and above (white) these values to allow for comparisons of data from different imaging acquisition parameters; for instance, the myelin maps show striking similarities with the original maps presented in Glasser and Van Essen (2011). Figure by Håkon Grydeland.CC BY · PubMed Central
Fig. 8 Areas of group differences in white matter microstructural properties between children prenatally exposed to opioids and other drugs versus controls. Clusters of voxels (⩾100) with significant (P < .05) group differences in fractional anisotropy (FA) are shown. For all clusters, FA was lower in the prenatally substance-exposed children. The depicted clusters are based on Walhovd et al. (2010).
Fig. 8 Areas of group differences in white matter microstructural properties between children prenatally exposed to opioids and other drugs versus controls. Clusters of voxels (⩾100) with significant (P < .05) group differences in fractional anisotropy (FA) are shown. For all clusters, FA was lower in the prenatally substance-exposed children. The depicted clusters are based on Walhovd et al. (2010).CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Cited by

Sorted by citations

See all 156

Related

See all

Metadata

Authors
Walhovd, K. B., Johansen-Berg, H., & Káradóttir, R. T.
Affiliations
  • Oslo University Hospital (NO)
  • Wellcome/MRC Cambridge Stem Cell Institute (GB)
Corresponding author
  • Walhovd, K.B.
  • Káradóttir, R.T.
ORCID
Venue
Neuroscience
Publisher
Elsevier BV
Published
2014-09
Volume
276
Pages
2-13
ISSN
0306-4522 (ISSN-L)
DOI
10.1016/j.neuroscience.2014.06.058
PMC
PMC4155933
OpenAlex
W1975478767
Cited by
156
References
79
Citations by year
2026: 10 · 2025: 9 · 2024: 9 · 2023: 8 · 2022: 17 · 2021: 19 · 2020: 13 · 2019: 10 · 2018: 15 · 2017: 14 · 2016: 19 · 2015: 12
FWCI
2.31
Type
journal article
Language
en
Open access
Licence
Locations
  • Neuroscience (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
Wellcome Trust (WT090955AIA, 090955, 091543/Z/10/Z, 091543)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
  • Life Sciences › Neuroscience › Developmental Neuroscience › Neurogenesis and neuroplasticity mechanisms
  • Health Sciences › Medicine › Clinical Neurology › Multiple Sclerosis Research Studies
Keywords
white matter, human brain imaging, translational neuroscience, interdisciplinary collaboration, myelination
MeSH terms
Animals, Brain Mapping, Humans, Immunohistochemistry, Magnetic Resonance Imaging, Microscopy, Electron, Myelin Sheath, Neuroglia, Neuroimaging, White Matter

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review