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Congenital blindness reduces myelination in human visual cortex.

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Paper

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The authors' code

R · 134 lines · 4.4 KB · CC-BY-4.0

  1. create_meas_plot <- function(data, y_value, x_value, y_label) {
  2. ggplot(data, aes(
  3. y = !!sym(y_value),
  4. x = !!sym(x_value),
  5. color = group,
  6. fill = group
  7. )) +
  8. geom_point(
  9. position = position_nudge(x = -.25),
  10. size = 3,
  11. alpha = 0.9,
  12. shape = "-"
  13. ) +
  14. geom_boxplot(
  15. position = position_dodge(width = 0.25),
  16. outlier.shape = NA,
  17. width = .2,
  18. # linewidth = 0.3,
  19. fill = "white"
  20. ) +
  21. geom_flat_violin(
  22. aes(fill = group),
  23. position = position_nudge(x = .2, y = 0),
  24. adjust = 1.5,
  25. trim = FALSE,
  26. alpha = .8,
  27. colour = NA
  28. ) +
  29. scale_fill_manual(values = c(blind = "#E794F2", sighted = "#F25C05")) +
  30. scale_color_manual(values = c(blind = "#E794F2", sighted = "#F25C05")) +
  31. ylab(y_label) +
  32. theme_minimal() +
  33. theme(text = element_text(size = 10),
  34. plot.title = element_text(hjust = 0, size = 10),
  35. panel.grid.minor.y = element_blank(),
  36. legend.title = element_blank(),
  37. plot.title.position = "plot",
  38. axis.title.x = element_blank())
  39. }
  40. create_meas_plot_compartment <- function(data, y_value, y_label) {
  41. ggplot(data,
  42. aes(
  43. y = !!sym(y_value),
  44. x = compartment,
  45. color = group,
  46. fill = group
  47. )) +
  48. geom_point(
  49. position = position_nudge(x = -.25),
  50. size = 3,
  51. alpha = 0.9,
  52. shape = "-"
  53. ) +
  54. geom_boxplot(
  55. position = position_dodge(width = 0.25),
  56. outlier.shape = NA,
  57. width = .2,
  58. fill = "white"
  59. ) +
  60. geom_flat_violin(
  61. aes(fill = group),
  62. position = position_nudge(x = .2, y = 0),
  63. adjust = 1.5,
  64. trim = FALSE,
  65. alpha = .8,
  66. colour = NA
  67. ) +
  68. scale_fill_manual(values = c(blind = "#E794F2", sighted = "#F25C05")) +
  69. scale_color_manual(values = c(blind = "#E794F2", sighted = "#F25C05")) +
  70. facet_wrap(~ roi) +
  71. ylab(y_label) +
  72. theme_minimal() +
  73. theme(text = element_text(size = 10),
  74. plot.title = element_text(hjust = 0, size = 10),
  75. axis.text.y = element_text(size = 10),
  76. axis.text.x = element_text(size = 10),
  77. panel.grid.minor.y = element_blank())
  78. }
  79. create_lolli_plot <- function(data, x_var, y_var){
  80. pc_labels <- c("Comp.1" = "PC 1",
  81. "Comp.2" = "PC 2",
  82. "Comp.3" = "PC 3")
  83. ggplot(data, aes(x = {{x_var}},
  84. y = reorder_within({{y_var}}, {{x_var}}, component))) +
  85. geom_segment(aes(x = 0, xend = {{x_var}},
  86. y = reorder_within({{y_var}}, {{x_var}}, component),
  87. yend = reorder_within({{y_var}}, {{x_var}}, component)),
  88. color = "grey") +
  89. geom_point(aes(color = {{x_var}}), size = 1.5) +
  90. theme_minimal() +
  91. scale_color_gradient2(low = "#00AFBB", mid = "#E7B800", high = "#FC4E07") +
  92. ylab(element_blank()) +
  93. xlab("PC loading") +
  94. facet_wrap(~component, ncol = 1, scales = "free_x", strip.position="right", labeller = as_labeller(pc_labels)) +
  95. scale_y_reordered() +
  96. coord_flip() +
  97. labs(color = 'PC loading') +
  98. theme(
  99. legend.margin = margin(t = -5, r = 0, b = 0, l = 0), # Negative top margin pulls legend up
  100. legend.box.margin = margin(t = -5, r = 0, b = 0, l = 0),
  101. panel.spacing = unit(1.5, "lines"),
  102. panel.border = element_blank(),
  103. axis.ticks.y = element_blank(),
  104. panel.grid.minor = element_blank(),
  105. text = element_text(size = 10),
  106. legend.position = "bottom",
  107. legend.title.position = "top",
  108. legend.key.height = unit(0.25, "cm"),
  109. plot.title.position = "plot",
  110. plot.title = element_text(hjust = 0, size = 10),
  111. legend.title.align = 0.5
  112. )
  113. }
  114. create_brain_plot <- function(data, lower_limit, upper_limit) {
  115. ggplot(data) +
  116. geom_brain(atlas = glasser(),
  117. aes(fill = mean_pca),
  118. hemi = "left",
  119. side = "medial") +
  120. scale_color_gradient2(low = "#00AFBB", mid = "#E7B800", high = "#FC4E07") +
  121. theme_void() +
  122. labs(fill = "PC score") +
  123. scale_fill_gradient2(low = "#00AFBB", mid = "#E7B800", high = "#FC4E07",
  124. limits = c(lower_limit, upper_limit)) +
  125. theme(legend.position = "bottom",
  126. legend.key.height = unit(0.25, "cm"),
  127. legend.title.position = "top",
  128. legend.title = element_text(hjust = 0.5),
  129. text = element_text(size = 10),
  130. plot.title = element_text(size = 10))
  131. }

plotting.R, under CC-BY-4.0 · at the source

Overview

  1. Institute of Psychology, Jagiellonian University, ul. Ingardena 6, 30-060 Kraków, Poland
  2. Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1a, 04103 Leipzig, Germany
  3. Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Oxfordlaan 55, 6229 EV Maastricht, The Netherlands
  4. Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany
  5. Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, 52428, Jülich, Germany
  6. Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, 12 Queen Square, London WC1N 3AR, UK
  7. Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth System Sciences, Leipzig University, Linnéstraße 5, 04103 Leipzig, Germany
Journal: Science advances, volume 12, issue 28, article eaec2348
Dates: received 11 September 2025; accepted 29 May 2026; published online 10 July 2026; in print July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1126/sciadv.aec2348 · PMID 42430469 · PMCID PMC13353373 · OpenAlex W7167917700
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), other condition (population)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, fMRI & imaging
MeSH: Blindness*, Myelin Sheath*, Visual Cortex*, Adult, Female, Humans, Iron, Magnetic Resonance Imaging, Male, White Matter (* major topic)
Journal subjects: Neuroscience, Developmental Neuroscience
Topic: Visual perception and processing mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (WE 5046/4-2); Bundesministerium für Bildung und Forschung (Federal Ministry of Education and Research) (01ED2210); Narodowe Centrum Nauki (2018/30/A/HS6/00595); Strategic Programme Excellence Initiative (Initiative of Excellence – Research University)
Citations: cited by 1 paper (Europe PMC); 134 references in the paper

Abstract

Sensory experience is critical for cortical maturation, but the cellular consequences of its absence remain poorly understood in humans. Using in vivo sub-millimeter 3 T and 7 T MRI and ultra-high-gradient diffusion MRI, we investigated the effects of congenital blindness on the human early visual cortex. Blind individuals showed reduced R2* and MTsat—markers of iron and myelin—along with increased diffusivity, orientation dispersion, and reduced neurite density in the gray and superficial white matter. Cortical thickness was increased in blind individuals and associated with lower myelin and iron, questioning the long-standing assumption that increased thickness primarily reflects disrupted pruning. Our results provide no direct evidence for disrupted pruning. However, they suggest reduced myelination and oligodendrogenesis as key effects of congenital blindness and highlight the critical role of sensory input in shaping and stabilizing cortical circuits.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above.

Zenodo 20395292

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data, code, and materials availability:”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (7 files), afex (6 files), data.table (6 files), emmeans (6 files), cowplot (5 files), ggplot2 (1 file), ggseg (1 file), lavaan (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
10 files

Zenodo 1442584

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (2 files), FreeSurfer (1 file), NiBabel (1 file), pandas (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
4 files
At the source:

lenastroh/blindnessmyelination

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: f3c2b68bcf1b523dfe39122b39049c2abee8c427, 26 May 2026
Languages: Quarto (6), R (4)
Size: 12 files, 10 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, 6 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (7 files), afex (6 files), data.table (6 files), emmeans (6 files), cowplot (5 files), ggplot2 (1 file), ggseg (1 file), lavaan (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
11 files

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 22 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data, code, and materials availability

All data and code needed to evaluate and reproduce the results in the paper are present in the paper and/or the Supplementary Materials. This study did not generate new materials. Due to European Union General Data Protection Regulations governing the sharing of sensitive data, data sharing is possible only on the basis of bilateral data sharing agreements ratified by the Jagiellonian University, the Max Planck Institute for Human Cognitive and Brain Sciences, and the inquirer’s institution. Please direct inquiries to . Quantitative parameter maps were computed using the hMRI toolbox (73) (v0.2.4–335-geec6213, https://hmri-group.github.io/hMRI-toolbox/) implemented in SPM12. DWI analysis was performed using Matlab version R2022b, the NODDI Matlab toolbox (v1.05, http://www.nitrc.org/projects/noddi_toolbox), MRtrix3 3.0 (85), FSL 6.0.3 (83, 86, 87), and ANTs 2.3.5 (88) software packages, as well as the gradunwarp toolbox (80) (v1.2.1, https://github.com/Washington-University/gradunwarp). All custom code to produce figures and graphs are available on Zenodo (https://doi.org/10.5281/zenodo.20395292).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 10 MeSH terms, 4 funders, 125 references.

Cite

This paper

Stroh, A.-L., Edwards, L. J., Haenelt, D., Movahedian Attar, F., Pine, K. J., Trampel, R., Szwed, M., & Weiskopf, N. (2026). Congenital blindness reduces myelination in human visual cortex. Science advances, 12(28), eaec2348. https://doi.org/10.1126/sciadv.aec2348

BibTeX

@article{stroh2026congenital,
author = {Stroh, Anna-Lena and Edwards, Luke J. and Haenelt, Daniel and Movahedian Attar, Fakhereh and Pine, Kerrin J. and Trampel, Robert and Szwed, Marcin and Weiskopf, Nikolaus},
title = {{Congenital blindness reduces myelination in human visual cortex}},
journal = {Science advances},
year = {2026},
month = jul,
volume = {12},
number = {28},
pages = {eaec2348},
publisher = {American Association for the Advancement of Science},
issn = {2375-2548},
doi = {10.1126/sciadv.aec2348},
url = {https://doi.org/10.1126/sciadv.aec2348},
pmid = {42430469},
pmcid = {PMC13353373}
}

RIS

TY - JOUR
AU - Stroh, Anna-Lena
AU - Edwards, Luke J.
AU - Haenelt, Daniel
AU - Movahedian Attar, Fakhereh
AU - Pine, Kerrin J.
AU - Trampel, Robert
AU - Szwed, Marcin
AU - Weiskopf, Nikolaus
TI - Congenital blindness reduces myelination in human visual cortex
T2 - Science advances
J2 - Sci Adv
PY - 2026
DA - 2026/07/10
VL - 12
IS - 28
SP - eaec2348
SN - 2375-2548
PB - American Association for the Advancement of Science
DO - 10.1126/sciadv.aec2348
UR - https://doi.org/10.1126/sciadv.aec2348
LA - en
ER -

CSL-JSON

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"container-title": "Science advances",
"author": [
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"family": "Stroh",
"given": "Anna-Lena"
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{
"family": "Edwards",
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{
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"PMCID": "PMC13353373",
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