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Multiscale characterization of the human claustrum from histology to MRI.

Code ↔ Paper

2 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 2 matches
  1. [1] § Materials and Methods › MRI Alignment and Registration. ↔ MRI/code/run_4_nonlinearRegistration.sh, lines 56–121 · score 0.64 · GenericLabel, nonlinear registration, ANTs, affine, Warped, transformations
  2. [2] § Materials and Methods › MRI Alignment and Registration. ↔ MRI/code/run_3_rigidRegistration.sh, lines 55–119 · score 0.58 · GenericLabel, ANTs, affine, Warped, template, rigidly

Paper

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The authors' code

Shell · 121 lines · 3.9 KB · CC-BY-4.0 · 1 match

  1. #!/bin/bash
  2. ####################################################################################################################################
  3. # run_4_nonlinearRegistration.sh
  4. #
  5. # Performs nonlinear registration of preprocessed brain images to a resolution-matched MNI152 template
  6. # using ANTs. Registration proceeds in three stages: rigid initialisation, full SyN registration,
  7. # and transform application to manual segmentations. Supports three datasets selected at runtime.
  8. #
  9. # Usage: ./run_4_nonlinearRegistration.sh <dataset> <subjectsFile> <preprocessedDir> <segmentationDir> <outputDir>
  10. # Available datasets: 0p5, 0p7, 1p0
  11. ####################################################################################################################################
  13. if [ $# -lt 5 ]; then
  14. echo "Usage: $0 <dataset> <subjectsFile> <preprocessedDir> <segmentationDir> <outputDir>"
  15. echo "Available datasets: 0p5, 0p7, 1p0"
  16. exit 1
  17. fi
  19. #assign positional arguments
  20. dataset=$1
  21. subjects_file=$2
  22. preprocessed_dir=$3
  23. segmentation_dir=$4
  24. output_dir=$5
  26. #select resolution-matched MNI template and brainmask based on dataset
  27. case "$dataset" in
  28. "0p5")
  29. mni_brain=<MNI152_0.5mm_skullstripped>
  30. mni_brainmask=<MNI152_0.5mm_brainmask>
  31. ;;
  32. "0p7")
  33. mni_brain=<MNI152_0.7mm_skullstripped>
  34. mni_brainmask=<MNI152_0.7mm_brainmask>
  35. ;;
  36. "1p0")
  37. mni_brain=<MNI152_1.0mm_skullstripped>
  38. mni_brainmask=<MNI152_1.0mm_brainmask>
  39. ;;
  40. *)
  41. echo "Error: Unknown dataset '$dataset'"
  42. echo "Available datasets: 0p5, 0p7, 1p0"
  43. exit 1
  44. ;;
  45. esac
  47. mkdir -p "$output_dir"
  49. if [ ! -f "$subjects_file" ]; then
  50. echo "Error: Subject list not found: $subjects_file"
  51. exit 1
  52. fi
  54. echo "Starting nonlinear registration for dataset: $dataset"
  56. #iterate over subjects
  57. while read -r subject_id_raw; do
  58. [[ -z "$subject_id_raw" || "$subject_id_raw" =~ ^# ]] && continue
  60. subject_id="sub-${subject_id_raw}"
  61. echo "Processing subject: $subject_id"
  63. subject_output_dir="$output_dir/$subject_id"
  64. mkdir -p "$subject_output_dir"
  66. #set input paths
  67. subject_brain="${preprocessed_dir}/$dataset/$subject_id/${subject_id}_brain.nii.gz"
  68. subject_brainmask="${preprocessed_dir}/$dataset/$subject_id/${subject_id}_brainmask.nii.gz"
  69. subject_segmentation="${segmentation_dir}/$dataset/$subject_id/${subject_id}_segmentation_manual.nii.gz"
  71. if [ ! -f "$subject_brain" ] || [ ! -f "$subject_brainmask" ] || [ ! -f "$subject_segmentation" ]; then
  72. echo "Warning: One or more input files not found for $subject_id. Skipping."
  73. continue
  74. fi
  76. init_prefix="${subject_output_dir}/${subject_id}_initialisation_"
  77. stage_prefix="${subject_output_dir}/${subject_id}_3stage_"
  78. seg_output="${subject_output_dir}/${subject_id}_segmentation_manual_3stage.nii.gz"
  80. #step 1: rigid initialisation
  81. antsRegistrationSyNQuick.sh \
  82. -d 3 \
  83. -n 20 \
  84. -p f \
  85. -j 1 \
  86. -e 13 \
  87. -f "$mni_brain" \
  88. -m "$subject_brain" \
  89. -o "$init_prefix" \
  90. -t r
  92. #step 2: full SyN registration using initialisation and brain masks
  93. antsRegistrationSyN.sh \
  94. -d 3 \
  95. -n 20 \
  96. -t s \
  97. -p f \
  98. -j 1 \
  99. -e 13 \
  100. -x "$mni_brainmask","$subject_brainmask" \
  101. -i "${init_prefix}0GenericAffine.mat" \
  102. -f "$mni_brain" \
  103. -m "$subject_brain" \
  104. -o "$stage_prefix"
  106. #step 3: apply transforms to manual segmentation
  107. antsApplyTransforms \
  108. -d 3 \
  109. -n GenericLabel \
  110. -t "${stage_prefix}1Warp.nii.gz" \
  111. -t "${stage_prefix}0GenericAffine.mat" \
  112. -r "$mni_brain" \
  113. -i "$subject_segmentation" \
  114. -o "$seg_output"
  116. echo "Completed: $subject_id"
  117. echo "--------------------------------------------"
  119. done < "$subjects_file"
  121. echo "Nonlinear registration complete for dataset: $dataset"

run_4_nonlinearRegistration.sh at commit ff1e624, under CC-BY-4.0 · at the source

Overview

Authors: Navona Calarco1,2, Skerdi Progri1, Sriranga Kashyap1,3, Shuting Xie1, Claude Lepage4, Donna Gift Cabalo4, Boris C Bernhardt4, Alan C Evans4, Kâmil Uludağ1,2,3,5,6,7
ORCID iDs: Navona Calarco, Sriranga Kashyap, Shuting Xie, Donna Gift Cabalo, Boris C Bernhardt, Alan C Evans
  1. Krembil Brain Institute, University Health Network, Toronto, ON M5T 2S8, Canada
  2. Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 2C4, Canada
  3. Physical Sciences Platform, Sunnybrook Research Institute,Toronto, ON M4N 3M5, Canada
  4. McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 0G4, Canada
  5. Harquail Centre for Neuromodulation, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada
  6. Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon 16419, Republic of Korea
  7. Department of Biomedical Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
Institutions: University Health Network (Canada); University of Toronto (Canada); Krembil Brain Institute; Sunnybrook Research Institute; Montreal Neurological Institute and Hospital (Canada); McGill University (Canada); Institute for Basic Science (South Korea); Harquail Centre for Neuromodulation (Canada); Sungkyunkwan University (South Korea)
Journal: Proceedings of the National Academy of Sciences of the United States of America, volume 123, issue 27, article e2604111123
Dates: received 3 February 2026; accepted 8 May 2026; published online 29 June 2026; in print 7 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1073/pnas.2604111123 · PMID 42372138 · PMCID PMC13342998 · OpenAlex W7166512432
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), histology / microscopy (modality), human (organism)
Methods: Connectivity, Machine learning
Keywords: human claustrum, 7-Tesla MRI, brain anatomy, histology, brain parcellation
MeSH: Basal Ganglia*, Claustrum*, Magnetic Resonance Imaging*, Brain, Humans, Imaging, Three-Dimensional (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: cited by 1 paper (Europe PMC); 111 references in the paper

Abstract

The claustrum is a thin, bilateral structure embedded deep within the human brain. Its widespread cortical connectivity has motivated perhaps the broadest range of functional hypotheses of any subcortical structure. Yet its complex, sheet-like morphology has hindered investigation in living humans, leaving a small in vivo MRI literature marked by large and often implausible discrepancies. Here, we construct a three-dimensional histological “gold standard” model of the human claustrum and systematically evaluate three ultra-high field 7-Tesla MRI datasets against this reference and its downsampled derivatives. We show that apparent discrepancies in MRI-based claustrum morphology arise primarily from resolution-dependent effects rather than contrast limitations, which transform the claustrum’s intricate sheet into an artifactually thickened ribbon. Despite this, submillimeter MRI reliably captures a dorsal “core” containing most claustral volume and cell density and encompassing major corticoclaustral connectivity, and at the highest acquired resolution (0.5 mm isotropic), the ventral claustrum’s extension into the temporal lobe is partially recovered, with uncertainty reflecting boundary imprecision rather than anatomical absence. Together, these findings overturn the view that the human claustrum is inaccessible to MRI and establish a foundation for future functional and clinical investigation in the living human brain.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

navonacalarco/Claustrum

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: ff1e62461c09b5208e9a8aa9764a51d0dd41e359, 15 July 2026
Languages: Shell (4)
Size: 70 files, 4 scripts
Software Heritage: not archived
Found in: “Data, Materials, and Software Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ANTs (3 files), FreeSurfer (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
5 files
At the source: github.com/navonacalarco/Claustrum

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 4 scripts, each with its path and the digest of its content;
  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • 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

Datasets cited

Data, Materials, and Software Availability

All newly generated data, including the manually segmented BigBrain-derived claustrum “gold standard,” individual MRI claustrum segmentations in native space (n=30), and the cross-modal probabilistic claustrum atlas, are available via Zenodo at https://doi.org/10.5281/zenodo.19656275 (108). MRI preprocessing code is available at https://github.com/navonacalarco/Claustrum (109). The BigBrain histological dataset (100 μm isotropic resolution) is publicly available at https://ftp.bigbrainproject.org/bigbrain-ftp/ (110). The 0.5 mm isotropic MICA-PNI MRI dataset is publicly available at https://osf.io/mhq3f/overview (111). The 0.7 mm and 1.0 mm isotropic MRI datasets acquired at Maastricht University for prior publications (60, 65) are now made available at https://doi.org/10.5281/zenodo.19656275 (108).

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, 9 authors, 5 keywords, 6 MeSH terms, 93 references.

Cite

This paper

Calarco, N., Progri, S., Kashyap, S., Xie, S., Lepage, C., Gift Cabalo, D., Bernhardt, B. C., Evans, A. C., & Uludağ, K. (2026). Multiscale characterization of the human claustrum from histology to MRI. Proceedings of the National Academy of Sciences of the United States of America, 123(27), e2604111123. https://doi.org/10.1073/pnas.2604111123

BibTeX

@article{calarco2026multiscale,
author = {Calarco, Navona and Progri, Skerdi and Kashyap, Sriranga and Xie, Shuting and Lepage, Claude and Gift Cabalo, Donna and Bernhardt, Boris C and Evans, Alan C and Uludağ, Kâmil},
title = {{Multiscale characterization of the human claustrum from histology to MRI}},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
year = {2026},
month = jun,
volume = {123},
number = {27},
pages = {e2604111123},
publisher = {National Academy of Sciences},
issn = {0027-8424},
doi = {10.1073/pnas.2604111123},
url = {https://doi.org/10.1073/pnas.2604111123},
pmid = {42372138},
pmcid = {PMC13342998}
}

RIS

TY - JOUR
AU - Calarco, Navona
AU - Progri, Skerdi
AU - Kashyap, Sriranga
AU - Xie, Shuting
AU - Lepage, Claude
AU - Gift Cabalo, Donna
AU - Bernhardt, Boris C
AU - Evans, Alan C
AU - Uludağ, Kâmil
TI - Multiscale characterization of the human claustrum from histology to MRI
T2 - Proceedings of the National Academy of Sciences of the United States of America
J2 - Proc Natl Acad Sci U S A
PY - 2026
DA - 2026/06/29
VL - 123
IS - 27
SP - e2604111123
SN - 0027-8424
PB - National Academy of Sciences
DO - 10.1073/pnas.2604111123
UR - https://doi.org/10.1073/pnas.2604111123
LA - en
ER -

CSL-JSON

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