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Characteristics of brain structural damage related to cerebral small vessel disease in general population with intracranial artery stenosis.

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] § Patients and methods › Assessment of WMH ↔ orientation_alignment.sh, lines 44–85 · score 0.64 · probability mapping, WMH masks, applywarp, alignment, MNI, FLAIR
  2. [2] § Patients and methods › Statistical image analysis ↔ ICAS_glmfit.sh, lines 187–242 · score 0.59 · mri_glmfit, FreeSurfer, cortex, Cluster, volume, threshold

Paper

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The authors' code

Shell · 93 lines · 3.3 KB · MIT · 1 match

  1. #!/bin/bash
  3. # ---------------
  4. # Instructions
  5. # ---------------
  6. # 1. Align orientation and match to MNI152 space with FSL and Freesurfer
  7. # 2. Pre-require:
  8. # 1. WMH series（nii.gz）
  9. # 2. WMH mask series（nii.gz）
  10. # 3. Fundation brain MRI in MNI space: T2_FLAIR_brain_to_MNI.nii.gz
  11. # 4. Fundation brain MRI to MNI space transform matrix: T2_FLAIR_orig_to_MNI_warp.nii.gz
  12. # 3. Results:
  13. # 1. WMH series in MNI space
  14. # 2. WMH mask series in MNI space
  15. # 3. WMH probability map in MNI space
  17. # ---------------
  18. # 参数设置
  19. # ---------------
  20. INITDIR="" # 初始工作文件夹
  21. BASE_DIR="${INITDIR}"data # 数据文件夹
  22. WMH_MASK_NAME="WMH_FLAIR_orig_ud_mask.nii.gz" # 工作mask序列名称
  23. WMH_NAME="WMH_FLAIR_orig_ud.nii.gz" #工作序列名称
  24. WM_MASK_NAME="T2_FLAIR_orig_ud_bianca_mask.nii.gz"
  25. WMH_CUT_MASK_NAME="CUTTED_WMH_FLAIR_orig_ud_mask.nii.gz"
  26. WMH_CUT_NAME="CUTTED_WMH_FLAIR_orig_ud.nii.gz"
  27. PROBMAP_WMH_NAME="PROBMAP_WMH_FLAIR_orig_ud.nii.gz"
  28. WMH_MERGED_PATH="$INITDIR/result" # 合并后的序列输出路径
  29. LOG_FILE="$INITDIR/ICAS_analysis.log" # 工作输出记录
  30. RESULT_DIR="$INITDIR/ICAS_res/" # 结果输出目录
  32. # ---------------
  33. # 初始化配置
  34. # ---------------
  35. set_SYSPATH(){
  36. export FREESURFER_HOME="/PATH/TO/FREESURFER"
  37. export SUBJECTS_DIR=$1
  38. export FSLDIR="/PATH/TO/FSL"
  39. export FS_LICENSE="FREESURFER/LICENSE.txt"
  40. source $FREESURFER_HOME/SetUpFreeSurfer.sh
  41. source $FSLDIR/etc/fslconf/fsl.sh
  42. }
  44. orientation_align(){
  45. echo "---------------------Align WMH mask...---------------------"
  46. for subpath in ${SUBJECTS_DIR}/*/ ; do
  47. if [ ! -d "$subpath" ]; then
  48. continue
  49. fi
  50. subfolder_name=$(basename "$subpath")
  51. # Skip hidden directories
  52. if [[ "$subfolder_name" == .* ]]; then
  53. continue
  54. fi
  55. # Skip directories that don't contain the substring "sub-"
  56. if [[ ! "$subfolder_name" == *"sub-"* ]]; then
  57. continue
  58. fi
  59. echo "Aligning $subfolder_name"
  61. # generate WMH_mask remove area outside white matter
  62. fslmaths ${subpath}/${WMH_MASK_NAME} -mul ${subpath}/${WM_MASK_NAME} -thr 0.5 -bin ${subpath}/${WMH_CUT_MASK_NAME}
  63. # generate WMH image remove area outside white matter
  64. fslmaths ${subpath}/${WMH_NAME} -mul ${subpath}/${WM_MASK_NAME} ${subpath}/${WMH_CUT_NAME}
  65. # generate WMH probability map by mask WMH mask on WMH image
  66. fslmaths ${subpath}/${WMH_CUT_NAME} -mas ${subpath}/${WMH_CUT_MASK_NAME} ${subpath}/${PROBMAP_WMH_NAME}
  68. # mapping to MNI152
  69. applywarp -i ${subpath}/${WMH_CUT_MASK_NAME}\
  70. -r ${subpath}/T2_FLAIR_brain_to_MNI.nii.gz \
  71. -w ${subpath}/T2_FLAIR_orig_to_MNI_warp.nii.gz \
  72. -o ${subpath}aligned_${WMH_MASK_NAME}
  74. applywarp -i ${subpath}/${WMH_CUT_NAME}\
  75. -r ${subpath}/T2_FLAIR_brain_to_MNI.nii.gz \
  76. -w ${subpath}/T2_FLAIR_orig_to_MNI_warp.nii.gz \
  77. -o ${subpath}aligned_${WMH_NAME}
  79. applywarp -i ${subpath}/${PROBMAP_WMH_NAME}\
  80. -r ${subpath}/T2_FLAIR_brain_to_MNI.nii.gz \
  81. -w ${subpath}/T2_FLAIR_orig_to_MNI_warp.nii.gz \
  82. -o ${subpath}aligned_${PROBMAP_WMH_NAME}
  84. done
  85. }
  88. main() {
  89. set_SYSPATH "$BASE_DIR" #设定环境变量
  90. orientation_align
  91. }
  93. main

orientation_alignment.sh at commit 1a70330, under MIT · at the source

Overview

Authors: Zi-Ang Pan1,2, Zi-Yue Liu1,2, Xing-Qi Pan1,2, Fei-Fei Zhai1,2, Ding-Ding Zhang2,3, Ming Yao1,2, Li-Xin Zhou1,2, Jun Ni1,2, Zheng-Yu Jin2,4, Shu-Yang Zhang2,5, Li-Ying Cui1,2, Fei Han1,2, Yi-Cheng Zhu1,2
ORCID iDs: Zi-Ang Pan, Yi-Cheng Zhu
  1. Department of Neurology, Peking Union Medical College Hospital, Beijing, China
  2. Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
  3. Medical Research Center, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Beijing, China
  4. Department of Radiology, Peking Union Medical College Hospital, Beijing, China
  5. Department of Cardiology, Peking Union Medical College Hospital, Beijing, China
Institutions: Chinese Academy of Medical Sciences & Peking Union Medical College (China); Peking Union Medical College Hospital (China)
Journal: Stroke and vascular neurology, volume 11, issue 3, article e004471
Dates: received 11 June 2025; accepted 3 September 2025; published online 16 September 2025
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1136/svn-2025-004471 · PMID 40957669 · PMCID PMC13347908 · OpenAlex W4414266468
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), other (modality), human (organism), stroke (population), clinical / translational (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, fMRI & imaging
Keywords: Atherosclerosis, Magnetic Resonance Imaging, Stroke, Magnetic Resonance Angiography, Arteries
MeSH: Brain*, Cerebral Hemorrhage*, Cerebral Small Vessel Diseases*, Leukoencephalopathies*, Magnetic Resonance Angiography*, Magnetic Resonance Imaging*, Stroke, Lacunar*, Adult, Aged, Atrophy, Cerebral Angiography, China, Constriction, Pathologic, Cross-Sectional Studies, Female, Humans, Male, Middle Aged, Predictive Value of Tests, Risk Assessment, Risk Factors (* major topic)
Topic: Intracerebral and Subarachnoid Hemorrhage Research (Neurology, Medicine), according to OpenAlex
Funding: CAMS Innovation Fund for Medical Sciences (CIFMS #2021-I2M-1-025); National High Level Hospital Clinical Research Funding (2022-PUMCH-D-007); National Natural Science Foundation of China (82271368)
Citations: not cited yet (Europe PMC); 31 references in the paper

Abstract

Background and aims: Covert MRI markers of cerebral small vessel disease (CSVD) can coexist with large artery atherosclerosis. We aimed to explore whether the spatial distributions of these markers were diverse in people with or without intracranial artery stenosis (ICAS).

Methods: This cross-sectional analysis included 1206 stroke-free participants (aged 55.69±9.27, 62.94% female) with brain MRI and MR angiography from community-based Shunyi cohort. We analysed the relationships between ICAS and CSVD markers. We also compared the probability maps of lacunes, cerebral microbleeds (CMB), white matter hyperintensities (WMH) and cortex morphology at a voxel/vertex-wise level in groups with and without ICAS.

Results: ICAS increased the risk of lacunes by 2.99-fold (95% CI 1.99 to 4.50, p<0.001), lacunes ≥3 by 5.32 times (95% CI 2.76 to 10.28, p<0.001), correlated with WMH volume (β=0.332, SE=0.059, p<0.001), WMH Fazekas scores ≥5 (OR 4.50, 95% CI 2.44 to 8.29, p<0.001) and brain parenchymal fraction (β=−0.012, SE=0.002, p<0.001), but not with CMB. ICAS is associated with lacunes in the corresponding blood supply area. Lacunes that coexist with ICAS were prone in basal ganglia, while the lacunes without ICAS appeared in centrum semiovale more often. WMH with ICAS was prone to present in deep white matter involving the bilateral pyramidal tracts and superior thalamic radiation. People with ICAS were susceptible to worse cortical atrophy of right superior frontal and left rostral anterior cingulate. No obvious distributional differences were found for CMB between the two groups.

Conclusions: Since ICAS may be involved in the upstream pathogenesis of lacunes, white matter lesions and cortical atrophy, the impact of ICAS should not be ignored when evaluating MRI markers of CSVD.

Reproduced under the paper's license (CC BY-NC), 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.

anniepan-pumc/TBSS-ICAS

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 1a7033092c68736230fe17007d332626915dfa70, 23 July 2025
Languages: Shell (5)
Size: 11 files, 5 scripts
Software Heritage: not archived
Found in: the text, “Statistical image analysis”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FreeSurfer (5 files), FSL (5 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
7 files
At the source: github.com/anniepan-pumc/TBSS-ICAS

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;
  • 5 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

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

Data availability statement

Data are available on reasonable request.

Reproduced under the paper's license (CC BY-NC), 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, 13 authors, 5 keywords, 21 MeSH terms, 3 funders, 31 references.

Cite

This paper

Pan, Z.-A., Liu, Z.-Y., Pan, X.-Q., Zhai, F.-F., Zhang, D.-D., Yao, M., Zhou, L.-X., Ni, J., Jin, Z.-Y., Zhang, S.-Y., Cui, L.-Y., Han, F., & Zhu, Y.-C. (2026). Characteristics of brain structural damage related to cerebral small vessel disease in general population with intracranial artery stenosis. Stroke and vascular neurology, 11(3), e004471. https://doi.org/10.1136/svn-2025-004471

BibTeX

@article{pan2026characteristics,
author = {Pan, Zi-Ang and Liu, Zi-Yue and Pan, Xing-Qi and Zhai, Fei-Fei and Zhang, Ding-Ding and Yao, Ming and Zhou, Li-Xin and Ni, Jun and Jin, Zheng-Yu and Zhang, Shu-Yang and Cui, Li-Ying and Han, Fei and Zhu, Yi-Cheng},
title = {{Characteristics of brain structural damage related to cerebral small vessel disease in general population with intracranial artery stenosis}},
journal = {Stroke and vascular neurology},
year = {2026},
month = jun,
volume = {11},
number = {3},
pages = {e004471},
publisher = {BMJ Publishing Group},
issn = {2059-8688},
doi = {10.1136/svn-2025-004471},
url = {https://doi.org/10.1136/svn-2025-004471},
pmid = {40957669},
pmcid = {PMC13347908}
}

RIS

TY - JOUR
AU - Pan, Zi-Ang
AU - Liu, Zi-Yue
AU - Pan, Xing-Qi
AU - Zhai, Fei-Fei
AU - Zhang, Ding-Ding
AU - Yao, Ming
AU - Zhou, Li-Xin
AU - Ni, Jun
AU - Jin, Zheng-Yu
AU - Zhang, Shu-Yang
AU - Cui, Li-Ying
AU - Han, Fei
AU - Zhu, Yi-Cheng
TI - Characteristics of brain structural damage related to cerebral small vessel disease in general population with intracranial artery stenosis
T2 - Stroke and vascular neurology
J2 - Stroke Vasc Neurol
PY - 2026
DA - 2026/06/29
VL - 11
IS - 3
SP - e004471
SN - 2059-8688
PB - BMJ Publishing Group
DO - 10.1136/svn-2025-004471
UR - https://doi.org/10.1136/svn-2025-004471
LA - en
ER -

CSL-JSON

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