Cited by 57

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Virtual histology of multi-modal magnetic resonance imaging of cerebral cortex in young men

Patel, Yash, Shin, Jean, Drakesmith, Mark, Evans, John, Pausova, Zdenka, Paus, Tomas·2020·NeuroImage

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.neuroimage.2020.116968

Cites

9 of 9 references verified0 citation errors · 0 not checkable

Statistics

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

Flagged first, then by citations

Show all 9 references

Research works printed but not linked: 65

  1. Avants, 2009, Advanced normalization tools (ANTS), Insight j, 2, 1
  2. Barbas, 2015, General cortical and special prefrontal connections: principles from structure to function, Annu. Rev. Neurosci., 38, 269, 10.1146/annurev-neuro-071714-033936 · DOI
  3. Benjamini, 1995, Controlling the false discovery rate: a practical and powerful approach to multiple testing, J. Roy. Stat. Soc. B, 57, 289, 10.1111/j.2517-6161.1995.tb02031.x · DOI · Paper page
  4. Bernhardt, 2018, Preferential susceptibility of limbic cortices to microstructural damage in temporal lobe epilepsy: a quantitative T1 mapping study, Neuroimage, 182, 294, 10.1016/j.neuroimage.2017.06.002 · DOI
  5. Björnholm, 2017, Structural properties of the human corpus callosum: multimodal assessment and sex differences, Neuroimage, 152, 108, 10.1016/j.neuroimage.2017.02.056 · DOI
  6. Boyd, 2013, Cohort profile: the ’children of the 90s’--the index offspring of the Avon longitudinal study of Parents and children, Int. J. Epidemiol., 42, 111, 10.1093/ije/dys064 · DOI · Paper page
  7. Breschi, 2017, Comparative transcriptomics in human and mouse, Nat. Rev. Genet., 18, 425, 10.1038/nrg.2017.19 · DOI
  8. Caceres, 1984, MAP2 is localized to the dendrites of hippocampal neurons which develop in culture, Dev. Brain Res., 13, 314, 10.1016/0165-3806(84)90167-6 · DOI
  9. Cercignani, 2001, Magnetisation transfer ratio and mean diffusivity of normal appearing white and grey matter from patients with multiple sclerosis, J. Neurol. Neurosurg. Psychiatr., 70, 311, 10.1136/jnnp.70.3.311 · DOI
  10. Charvet, 2015, Systematic, cross-cortex variation in neuron numbers in rodents and primates, Cerebr. Cortex, 25, 147, 10.1093/cercor/bht214 · DOI
  11. Chen, 2013, Clinically feasible MTR is sensitive to cortical demyelination in MS, Neurology, 80, 246, 10.1212/WNL.0b013e31827deb99 · DOI
  12. De Boer, 1995, Magnetization transfer contrast. Part 1: MR Physics, Philips Med. Syst., 64
  13. Deoni, 2008, Gleaning multicomponent T1 and T2 information from steady-state imaging data, Magn. Reson. Med.: Off. J. Int. Soc. Magn. Resonan. Med., 60, 1372, 10.1002/mrm.21704 · DOI
  14. Desikan, 2006, An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest, Neuroimage, 31, 968, 10.1016/j.neuroimage.2006.01.021 · DOI · Paper page
  15. Duhamel, 2019, Validating the sensitivity of inhomogeneous magnetization transfer (ihMT) MRI to myelin with fluorescence microscopy, Neuroimage, 199, 289, 10.1016/j.neuroimage.2019.05.061 · DOI
  16. Edwards, 2018, Microstructural imaging of human neocortex in vivo, Neuroimage, 182, 184, 10.1016/j.neuroimage.2018.02.055 · DOI
  17. Eickhoff, 2005, High-resolution MRI reflects myeloarchitecture and cytoarchitecture of human cerebral cortex, Hum. Brain Mapp., 24, 206, 10.1002/hbm.20082 · DOI
  18. Fields, 2015, A new mechanism of nervous system plasticity: activity-dependent myelination, Nat. Rev. Neurosci., 16, 756, 10.1038/nrn4023 · DOI · Paper page
  19. Fjær, 2013, Deep gray matter demyelination detected by magnetization transfer ratio in the cuprizone model, PLoS One, 8, 10.1371/journal.pone.0084162 · DOI
  20. Fjær, 2015, Magnetization transfer ratio does not correlate to myelin content in the brain in the MOG-EAE mouse model, Neurochem. Int., 83, 28, 10.1016/j.neuint.2015.02.006 · DOI
  21. Fraser, 2013, Cohort profile: the Avon longitudinal study of Parents and children: ALSPAC mothers cohort, Int. J. Epidemiol., 42, 97, 10.1093/ije/dys066 · DOI · Paper page
  22. French, 2015, A FreeSurfer view of the cortical transcriptome generated from the allen human brain atlas, Front. Neurosci., 9, 10.3389/fnins.2015.00323 · DOI
  23. Fulcher, 2019, Multimodal gradients across mouse cortex, Proc. Natl. Acad. Sci. Unit. States Am., 116, 4689, 10.1073/pnas.1814144116 · DOI
  24. Gelman, 2001, Interregional variation of longitudinal relaxation rates in human brain at 3.0 T: relation to estimated iron and water contents, Magn. Reson. Med.: Off. J. Int. Soc. Magn. Resonan. Med., 45, 71, 10.1002/1522-2594(200101)45:1<71::AID-MRM1011>3.0.CO;2-2 · DOI
  25. Giedd, 1999, Brain development during childhood and adolescence: a longitudinal MRI study, Nat. Neurosci., 2, 861, 10.1038/13158 · DOI · Paper page
  26. Glasser, 2014, Trends and properties of human cerebral cortex: correlations with cortical myelin content, Neuroimage, 93, 165, 10.1016/j.neuroimage.2013.03.060 · DOI
  27. Glasser, 2011, Mapping human cortical areas in vivo based on myelin content as revealed by T1-and T2-weighted MRI, J. Neurosci., 31, 11597, 10.1523/JNEUROSCI.2180-11.2011 · DOI · Paper page
  28. Goriounova, 2018, Large and fast human pyramidal neurons associate with intelligence, Elife, 7, 10.7554/eLife.41714 · DOI
  29. Gracien, 2016, Changes and variability of proton density and T1 relaxation times in early multiple sclerosis: MRI markers of neuronal damage in the cerebral cortex, Eur. Radiol., 26, 2578, 10.1007/s00330-015-4072-x · DOI
  30. Grömping, 2006, Relative importance for linear regression in R: the package relaimpo, J. Stat. Software, 17, 1
  31. Huang, 2008, Quantitative cortical mapping of fractional anisotropy in developing rat brains, J. Neurosci., 28, 1427, 10.1523/JNEUROSCI.3194-07.2008 · DOI
  32. Jacobs, 2001, Regional dendritic and spine variation in human cerebral cortex: a quantitative golgi study, Cerebr. Cortex, 11, 558, 10.1093/cercor/11.6.558 · DOI · Paper page
  33. Jenkinson, 2012, Fsl. Neuroimage, 62, 782, 10.1016/j.neuroimage.2011.09.015 · DOI · Paper page
  34. Kamman, 1988, Nuclear magnetic resonance relaxation in experimental brain edema: effects of water concentration, protein concentration, and temperature, Magn. Reson. Med., 6, 265, 10.1002/mrm.1910060304 · DOI
  35. Kuleshov, 2016, Enrichr: a comprehensive gene set enrichment analysis web server 2016 update, Nucleic Acids Res., 44, W90, 10.1093/nar/gkw377 · DOI · Paper page
  36. Larsson, 1986, Regional differences in the proton magnetic resonance relaxation times T1 and T2 within the normal human brain, Acta Radiol. Diagn., 27, 231, 10.1177/028418518602700218 · DOI
  37. Laule, 2006, Myelin water imaging in multiple sclerosis: quantitative correlations with histopathology, Multiple Sclerosis J., 12, 747, 10.1177/1352458506070928 · DOI · Paper page
  38. Mangeat, 2015, Multivariate combination of magnetization transfer, T 2∗ and B0 orientation to study the myelo-architecture of the in vivo human cortex, Neuroimage, 119, 89, 10.1016/j.neuroimage.2015.06.033 · DOI
  39. Miller, 2012, Prolonged myelination in human neocortical evolution, Proc. Natl. Acad. Sci. Unit. States Am., 109, 16480, 10.1073/pnas.1117943109 · DOI · Paper page
  40. Mori, 1999, vol. 257, 102
  41. Murray, 2015, Simple, scalable proteomic imaging for high-dimensional profiling of intact systems, Cell, 163, 1500, 10.1016/j.cell.2015.11.025 · DOI
  42. Ouyang, 2019, Differential cortical microstructural maturation in the preterm human brain with diffusion kurtosis and tensor imaging, Proc. Natl. Acad. Sci. Unit. States Am., 116, 4681, 10.1073/pnas.1812156116 · DOI
  43. Parry, 2003, MRI brain T1 relaxation time changes in MS patients increase over time in both the white matter and the cortex, J. Neuroimaging, 13, 234, 10.1111/j.1552-6569.2003.tb00184.x · DOI
  44. Patel, 2018, Maturation of the human cerebral cortex during adolescence: myelin or dendritic arbor?, Cerebr. Cortex, 29, 3351, 10.1093/cercor/bhy204 · DOI
  45. Pérez-Cerdá, 2015, Pío del Río Hortega and the discovery of the oligodendrocytes, Front. Neuroanat., 9, 92, 10.3389/fnana.2015.00092 · DOI
  46. Pohmann, 2011, Contrast at high field: relaxation times, magnetization transfer and phase in the rat brain at 16.4 T, Magn. Reson. Med., 66, 1572, 10.1002/mrm.22949 · DOI
  47. Preziosa, 2019, Axonal degeneration as substrate of fractional anisotropy abnormalities in multiple sclerosis cortex, Brain, 142, 1921, 10.1093/brain/awz143 · DOI
  48. Reeves, 2016, Combined ex vivo 9.4 T MRI and quantitative histopathological study in normal and pathological neocortical resections in focal epilepsy, Brain Pathol., 26, 319, 10.1111/bpa.12298 · DOI
  49. Righart, 2017, Cortical pathology in multiple sclerosis detected by the T 1/T 2-weighted ratio from routine magnetic resonance imaging, Ann. Neurol., 82, 519, 10.1002/ana.25020 · DOI
  50. Ritchie, 2018, Transcriptomic characterization of MRI contrast with focus on the T1-w/T2-w ratio in the cerebral cortex, Neuroimage, 174, 504, 10.1016/j.neuroimage.2018.03.027 · DOI
  51. Rowley, 2017, Age-related mapping of intracortical myelin from late adolescence to middle adulthood using T1-weighted MRI, Hum. Brain Mapp., 38, 3691, 10.1002/hbm.23624 · DOI
  52. Schmierer, 2004, Magnetization transfer ratio and myelin in postmortem multiple sclerosis brain, Ann. Neurol., 56, 407, 10.1002/ana.20202 · DOI
  53. Seifert, 2019, Formalin tissue fixation biases myelin-sensitive MRI, Magn. Reson. Med., 82, 1504, 10.1002/mrm.27821 · DOI · Paper page
  54. Shafee, 2015, Gray matter myelination of 1555 human brains using partial volume corrected MRI images, Neuroimage, 105, 473, 10.1016/j.neuroimage.2014.10.054 · DOI
  55. Shatil, 2018, Quantitative ex vivo MRI changes due to progressive formalin fixation in whole human brain specimens: longitudinal characterization of diffusion, relaxometry, and myelin water fraction measurements at 3T, Front. Med., 5, 31, 10.3389/fmed.2018.00031 · DOI
  56. Shin, 2017, Cell-specific gene-expression profiles and cortical thickness in the human brain, Cerebr. Cortex, 1
  57. Tardif, 2012, Quantitative magnetic resonance imaging of cortical multiple sclerosis pathology, Multiple sclerosis Int., 2012, 10.1155/2012/742018 · DOI
  58. Tuor, 2014, Cellular correlates of longitudinal diffusion tensor imaging of axonal degeneration following hypoxic–ischemic cerebral infarction in neonatal rats, Neuroimage: Clinica, 6, 32, 10.1016/j.nicl.2014.08.003 · DOI
  59. Uematsu, 2004, Magnetization transfer micro-MR imaging of live excised lamprey spinal cord: characterization and immunohistochemical correlation, Am. J. Neuroradiol., 25, 1816
  60. Vavasour, 1998, A comparison between magnetization transfer ratios and myelin water percentages in normals and multiple sclerosis patients, Magn. Reson. Med., 40, 763, 10.1002/mrm.1910400518 · DOI
  61. Walhovd, 2016, Neurodevelopmental origins of lifespan changes in brain and cognition, Proc. Natl. Acad. Sci. Unit. States Am., 113, 9357, 10.1073/pnas.1524259113 · DOI
  62. Warntjes, 2017, Myelin detection using rapid quantitative MR imaging correlated to macroscopically registered luxol fast blue–stained brain specimens, Am. J. Neuroradiol., 38, 1096, 10.3174/ajnr.A5168 · DOI
  63. West, 2019, Inherent and unpredictable bias in multi-component DESPOT myelin water fraction estimation, Neuroimage, 195, 78, 10.1016/j.neuroimage.2019.03.049 · DOI
  64. Whitaker, 2016, Adolescence is associated with genomically patterned consolidation of the hubs of the human brain connectome, Proc. Natl. Acad. Sci. Unit. States Am., 113, 9105, 10.1073/pnas.1601745113 · DOI
  65. Zeisel, 2015, Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq, Science, 347, 1138, 10.1126/science.aaa1934 · DOI · Paper page

Cited by

Sorted by citations

See all 57

Metadata

Authors
Patel, Y., Shin, J., Drakesmith, M., Evans, J., Pausova, Z., & Paus, T.
Venue
NeuroImage
Publisher
Elsevier BV
Published
2020-09
Volume
218
Pages
116968
ISSN
1053-8119
Cited by
57
References
9
Language
en
Funders
  • National Institutes of Health (R01MH085772)
  • Wellcome Trust (102215/2/13/2)
  • Medical Research Council
  • Natural Sciences and Engineering Research Council of Canada
  • University of Bristol

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