Assessing molecular, cellular and transcriptomic bases of laminar perfusion and cytoarchitecture coupling in the human cortex.

Code ↔ Paper

15 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 15 matches

[1] § Results › Gene ontology signatures of CCSI ↔ code/step04_GO_02_metabolicCate.R, lines 1–40 · score 0.89 · amino acid, transmembrane transport, glycogen metabolism, catabolic process, lipid metabolism, biosynthesis
[2] § Methods › Histological data ↔ code/step00_preprocessing_07_Bigbrain_Disposal_layerprofile.m, lines 57–75 · score 0.74 · cell body staining, HCP MMP1, laminar profile, CSF, WM, intensity
[3] § Methods › Mitochondria-related maps ↔ code/step00_preprocessing_10_GetMaps_Mito.m, lines 6–14 · score 0.72 · oxidative phosphorylation, MitoD, MNI, TRC, CII, CIV
[4] § Methods › MRI data preprocessing, parcellation and cortical layers definition ↔ code/step00_preprocessing_06_Bigbrain_depthCompute_Parcellation.sh, lines 41–120 · score 0.70 · HCP MMP1, AFNI, laminar profile, dresample, NN, SUMA
[5] § Methods › Gene set selection of GO items ↔ code/step00_preprocessing_12_align_CellType_GenesName.R, lines 45–104 · score 0.67 · biomaRt, getBM, symbols, Ensembl, GO, preprocessing
[6] § Methods › MRI data preprocessing, parcellation and cortical layers definition ↔ code/step00_preprocessing_04_preprocessing_ASLdata_individual.sh, lines 173–262 · score 0.66 · HCP MMP1, AFNI, dresample, T1w, NN, SUMA
[7] § Methods › Histological data ↔ code/step00_preprocessing_06_Bigbrain_depthCompute_Parcellation.sh, lines 1–37 · score 0.60 · AFNI, laminar profile, meshes, mripy, depth, BigBrain
[8] § Methods › Allen Human Brain Atlas (AHBA) gene expression data ↔ code/step00_preprocessing_09_Get_Maps_inNeuromaps.ipynb, lines 38–83 · score 0.60 · left hemisphere, HCP MMP1, Microarray, Allen, parcellation, Atlas
[9] § Results › CCSI is selectively associated with oligodendrocyte and capillary organization ↔ reference/2022Nature_VascularAtlas/code/2integration.R, lines 1–86 · score 0.59 · smooth muscle cells, vascular cells, Pericytes, Endothelial, subtype, selectively
[10] § Methods › Allen Human Brain Atlas (AHBA) gene expression data ↔ code/step01_CBFandCCSImaps_02_generate_spinNullmaps_by_neuromaps.ipynb, lines 41–86 · score 0.58 · left hemisphere, HCP MMP1, Microarray, Allen, parcellation, Atlas
[11] § Methods › Gene set selection of cell class ↔ reference/2022Nature_VascularAtlas/code/2integration.R, lines 1–86 · score 0.56 · smooth muscle, pericyte, endothelial, subtypes, Vascular, human
[12] § Results › Laminar-specific coupling between perfusion and cytoarchitectonic architecture ↔ code/step00_preprocessing_07_Bigbrain_Disposal_layerprofile.m, lines 57–75 · score 0.54 · cell body staining, laminar profiles, V1, template, intensity, BigBrain
[13] § Methods › MRI data preprocessing, parcellation and cortical layers definition ↔ code/step00_preprocessing_04_preprocessing_ASLdata_individual.sh, lines 125–167 · score 0.53 · HCP MMP1, AFNI, ANTs, SUMA, preprocessing, ASL
[14] § Methods › Gene set selection of GO items ↔ code/step00_preprocessing_11_getGOlabel.R, lines 1–57 · score 0.53 · getBM, namespaces, Ensembl, GO, AHBA, preprocessing
[15] § Methods › Statistical analysis ↔ code/swtest.m, lines 167–231 · score 0.51 · Shapiro Wilk, tailed, coefficient

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

MATLAB · 79 lines · 2.1 KB · MIT · 2 matches

clc
clear all
close all
set(0,'defaultfigurecolor',[1 1 1]);% set figure background color
% compare laminar profile of perfusion signals and cell body density
%% set parameters
% basic parameters
NumROIs = 360;
Numlayers = 15;
AnalysisDir = ['/Users/guofanhua/Desktop/gfh/work/StandardBrainTemplateAndAtlas/BigBrain_LaminarProfile/whole_SUMA/LaminarProfile_HCP_MMP1_layer' num2str(Numlayers) '/'];
SavePath = '/Users/guofanhua/Desktop/gfh/work/StandardBrainTemplateAndAtlas/BigBrain_LaminarProfile/Results/';
% SaveName = ['wholeBB_' num2str(Numlayers) 'layers_LaminarProfile.mat'];
ifsave = 1;
%% read and disposal
LP = [];
for i = 1:NumROIs
tmpLP = [];
for j = 1:Numlayers
tmp = load([AnalysisDir 'ROI' num2str(i) '_depth' num2str(j) '.1D']);
if isempty(tmp) tmp = 0; end
tmpLP = [tmpLP tmp];
end
LP = [LP; tmpLP];
end
LP = 65535-LP;
BB_LaminarProfile = LP;
%% correct
Label_NoSignal = [];
for i = 1:size(LP,2)
if mean(LP(:,i)) > 60000
Label_NoSignal = [Label_NoSignal; 0];
else
Label_NoSignal = [Label_NoSignal; 1];
end
end
outNumlayers = sum(Label_NoSignal);
BB_LaminarProfile = BB_LaminarProfile(:,Label_NoSignal>0);
%% draw
figure; hold on;
for i = 1:NumROIs
plot(BB_LaminarProfile(i,:));
end
%% save
if ifsave
SaveName = ['wholeBB_glasser360_' num2str(outNumlayers) 'layers_LaminarProfile.mat'];
save([SavePath SaveName],'BB_LaminarProfile');
end
%% draw V1
drawV1 = 1;
if drawV1
Numlayers = 50;
AnalysisDir = '/Users/guofanhua/Desktop/gfh/work/StandardBrainTemplateAndAtlas/BigBrain_LaminarProfile/whole_SUMA/LaminarProfile_HCP_MMP1p0_V1/';
V1LP = [];
for j = 3:Numlayers-2
tmp = load([AnalysisDir 'V1_depth' num2str(j) '.1D']);
V1LP = [V1LP; tmp];
end
V1LP = 65535-V1LP;
figure; hold on;
plot(3:Numlayers-2,V1LP,'k-','linewidth',4);
set(gca,'xlim',[0 Numlayers+1],'xTick',[1 Numlayers],'XTickLabels',{'WM','CSF'},'FontSize',16,'linewidth',2);
set(gca,'yTick',[],'FontSize',16,'linewidth',2);
ylabel('cell-body staining intensity','FontSize',16,'linewidth',10);
box off
end

step00_preprocessing_07_Bigbrain_Disposal_layerprofile.m at commit 6e9b4be, under MIT · at the source

OverviewAuthors: Fanhua Guo1, Chenyang Zhao1, Ravi R Bhatt1, Zixuan Liu1,2, Andy Jeesu Kim3, Zidong Yang1,2, Siyi Xu4, Kay Jann1, Xingfeng Shao1, Mara Mather2,3,5, Neda Jahanshad1, Danny JJ Wang1,2
ORCID iDs: Fanhua Guo, Ravi R Bhatt, Andy Jeesu Kim, Kay Jann, Mara Mather, Danny JJ Wang
Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA USA
Department of Biomedical Engineering, University of Southern California, Los Angeles, CA USA
Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA USA
University of Washington, Seattle, WA USA
Department of Psychology, University of Southern California, Los Angeles, CA USA
Institutions: University of Southern California (United States); University of Washington (United States)
Journal: Nature communications, volume 17, issue 1, article 9748
Dates: received 22 May 2026; accepted 7 August 2026; published online 13 August 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-76812-w · PMID 42733079 · PMCID PMC13572400 · OpenAlex W4415930977
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.11.03.686411
Status: code verified
Categories: genetics / omics (modality), structural MRI / diffusion (modality), human (organism), stroke (population), cellular / molecular (subfield)
Methods: Connectivity, Statistics, Machine learning, Preprocessing, Spectral & time-frequency, fMRI & imaging
Keywords: Neuro-vascular interactions, Magnetic resonance imaging
MeSH: Cerebral Cortex*, Cerebrovascular Circulation*, Transcriptome*, Adult, Female, Humans, Magnetic Resonance Imaging, Male, Mitochondria, Oligodendroglia, Perfusion Magnetic Resonance Imaging, Young Adult (* major topic)
Topic: Advanced MRI Techniques and Applications (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: NINDS NIH HHS (R01 NS134712, UF1 NS100614); NIBIB NIH HHS (R01 EB032169); U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS) (UF1-NS100614); NIA NIH HHS (RF1 AG084072); U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB) (R01-EB032169, R01-NS134712); U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) (RF1-AG084072)
Citations: not cited yet (Europe PMC); 105 references in the paper
AbstractThe abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 15 matches between paragraphs and lines of code.

FanhuaGuo/ASL-Bigbrain-CCSI

License: MIT

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: 6e9b4beefb86540af4331caec2acc1ee9360543c, 25 August 2026

Languages: MATLAB (53), R (7), Shell (5), Jupyter (3)

Size: 493 files, 68 scripts

Software Heritage: not archived

Found in: “Code availability”

Holds: README, license file, 3 notebooks

Not found: CITATION.cff, environment file, tests, continuous integration, documentation

Tools: Statistics and Machine Learning Toolbox (23 files), tidyverse (6 files), AFNI (3 files), FreeSurfer (3 files), ANTs (2 files), BrainSpace (2 files), ggplot2 (2 files), Harmony (2 files), Matplotlib (2 files), neuromaps (2 files), NiBabel (2 files), Nilearn (2 files), NumPy (2 files), pandas (2 files), patchwork (2 files), pheatmap (2 files), Seurat (2 files), Brain Connectivity Toolbox (1 file), cowplot (1 file), ggseg (1 file), limma (1 file), scikit-learn (1 file), SciPy (1 file), SPM (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

70 files

code/fdr_BH.m, MATLAB, 105 lines
code/step00_preprocessing_01_T1toMNIspace_individual.sh, Shell, 51 lines
code/step00_preprocessing_02_SUMA_recon_Surface_Prepare_individual.sh, Shell, 90 lines
code/step00_preprocessing_03_Surface_Prepare_edit_brain_individual.sh, Shell, 18 lines
code/step00_preprocessing_04_preprocessing_ASLdata_individual.sh, Shell, 265 lines, 2 matches
code/step00_preprocessing_05_parcel_laminarCBF_glasser360_group.m, MATLAB, 123 lines
code/step00_preprocessing_05_parcel_laminarCBF_glasser360_individual.m, MATLAB, 131 lines
code/step00_preprocessing_05_parcel_laminarT1_glasser360_group.m, MATLAB, 75 lines
code/step00_preprocessing_06_Bigbrain_depthCompute_Parcellation.sh, Shell, 122 lines, 2 matches
code/step00_preprocessing_07_Bigbrain_Disposal_layerprofile.m, MATLAB, 79 lines, 2 matches
code/step00_preprocessing_08_Disposal_AHBA_GeneExpressionData.ipynb, Jupyter, 39 lines
code/step00_preprocessing_09_Get_Maps_inNeuromaps.ipynb, Jupyter, 126 lines, 1 match
code/step00_preprocessing_10_GetMaps_Mito.m, MATLAB, 52 lines, 1 match
code/step00_preprocessing_11_getGOlabel.R, R, 64 lines, 1 match
code/step00_preprocessing_12_align_CellType_GenesName.R, R, 169 lines, 1 match
code/step00_preprocessing_13_CellType_GeneSet.m, MATLAB, 507 lines
code/step00_preprocessing_14_PrepareSCmatrixInfo.m, MATLAB, 253 lines
code/step01_CBFandCCSImaps_00_ComputeBasicResults.m, MATLAB, 220 lines
code/step01_CBFandCCSImaps_00_init.m, MATLAB, 177 lines
code/step01_CBFandCCSImaps_00_retest_init.m, MATLAB, 110 lines
code/step01_CBFandCCSImaps_01_Disposal_all_maps_to_1D_csv.m, MATLAB, 226 lines
code/step01_CBFandCCSImaps_02_generate_spinNullmaps_by_neuromaps.ipynb, Jupyter, 636 lines, 1 match
code/step01_CBFandCCSImaps_03_plot_maps.R, R, 358 lines
code/step01_CBFandCCSImaps_04_CBFMapsAnalysis.m, MATLAB, 297 lines
code/step01_CBFandCCSImaps_05_regionalCCSI.m, MATLAB, 519 lines
code/step01_CBFandCCSImaps_06_retest.m, MATLAB, 317 lines
code/step02_withMitomaps_01_correlation.m, MATLAB, 320 lines
code/step03_CellType_00_GeneInit.m, MATLAB, 85 lines
code/step03_CellType_01_GCEAforWholeCT.m, MATLAB, 691 lines
code/step03_CellType_02_GCEAforVascularCT.m, MATLAB, 615 lines
code/step03_CellType_03_GCEAforOligoCT.m, MATLAB, 591 lines
code/step03_CellType_04_GCEAforOligoCT2.m, MATLAB, 504 lines
code/step03_CellType_05_GCEAforOligoCT3.m, MATLAB, 509 lines
code/step04_GO_01_GCEA.m, MATLAB, 494 lines
code/step04_GO_02_metabolicCate.R, R, 304 lines, 1 match
code/step05_retest_01_Disposal_all_maps_to_1D_csv_draft.m, MATLAB, 348 lines
code/step06_IntegrateStruFunc_01_CCSI.m, MATLAB, 727 lines
code/step06_IntegrateStruFunc_02_CBF.m, MATLAB, 522 lines
code/step06_IntegrateStruFunc_03_CCSIcontrol.m, MATLAB, 601 lines
code/step07_verification_01_GenerateRandomData.m, MATLAB, 101 lines
code/step07_verification_02_retestAnalysis.m, MATLAB, 218 lines
code/step07_verification_03_GenRandDataAnalysis.m, MATLAB, 635 lines
code/step07_verification_04_GenerateSplitHalfData.m, MATLAB, 63 lines
code/step07_verification_05_SplitHalfDataAnalysis.m, MATLAB, 381 lines
code/subfun_GenerateNetworkCalcMatrix_PermMatrix.m, MATLAB, 38 lines
code/subfun_GlobalMeanOffsetCorrection.m, MATLAB, 16 lines
code/subfun_PCAtoPC.m, MATLAB, 10 lines
code/subfun_ProfileSimilarity.m, MATLAB, 31 lines
code/subfun_ProfileSimilarity_mri2mri.m, MATLAB, 31 lines
code/subfun_computeMPC.m, MATLAB, 18 lines
code/subfun_compute_icc3_1.m, MATLAB, 31 lines
code/subfun_disposalEKcData.m, MATLAB, 18 lines
code/subfun_disposalYeoFNData.m, MATLAB, 18 lines
code/subfun_fisher_z.m, MATLAB, 6 lines
code/subfun_fisher_z_inverse.m, MATLAB, 6 lines
code/subfun_laminar_partialcorr.m, MATLAB, 50 lines
code/subfun_merge_CNRm2CNRcm.m, MATLAB, 26 lines
code/subfun_merge_bil2uni.m, MATLAB, 10 lines
code/subfun_plot_scatter_density_isopotential.m, MATLAB, 54 lines
code/subfun_plot_scatter_regression.m, MATLAB, 24 lines
code/subfun_plotting_corr_Pcorr_heatmaps.m, MATLAB, 100 lines
code/subfun_replace_dateGenes.m, MATLAB, 19 lines
code/subfun_write_to_MapPlot_byR.m, MATLAB, 34 lines
code/swtest.m, MATLAB, 231 lines, 1 match
code/violin.m, MATLAB, 266 lines
reference/2022Nature_VascularAtlas/code/1seurat.processing.R, R, 223 lines
reference/2022Nature_VascularAtlas/code/2integration.R, R, 177 lines, 2 matches
reference/2022Nature_VascularAtlas/code/3multiresolutionDEGs.R, R, 144 lines
LICENSE, License, 21 lines
README.md, Text, 46 lines

At the source: github.com/FanhuaGuo/ASL-Bigbrain-CCSI

Zenodo 21363179

License: MIT

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Size: 1 file

Software Heritage: not checked

Found in: “Code availability”

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)

27 September 2026: the link answers (HTTP 200)

At the source:

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

it points to the authors' code: FanhuaGuo/ASL-Bigbrain-CCSI, Zenodo 21363179

Read it in the paper: doi.org/10.1038/s41467-026-76812-w.

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:

2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
68 scripts, each with its path and the digest of its content;
15 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

doi:10.18112/openneuro.ds006871.v1.0.0, at OpenNeuro; found in “Data availability”
netneurolab.github.io/neuromaps, at netneurolab.github.io; found in “Data availability”

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

it points to 2 datasets: OpenNeuro 10.18112/openneuro.ds006871.v1.0.0, netneurolab.github.io/neuromaps
it points to the authors' code: FanhuaGuo/ASL-Bigbrain-CCSI, Zenodo 21363179

Read it in the paper: doi.org/10.1038/s41467-026-76812-w.

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, 12 authors, 2 keywords, 12 MeSH terms, 6 funders, 99 references.

Cite

This paper

Guo, F., Zhao, C., Bhatt, R. R., Liu, Z., Kim, A. J., Yang, Z., Xu, S., Jann, K., Shao, X., Mather, M., Jahanshad, N., & Wang, D. J. (2026). Assessing molecular, cellular and transcriptomic bases of laminar perfusion and cytoarchitecture coupling in the human cortex. Nature communications, 17(1), 9748. https://doi.org/10.1038/s41467-026-76812-w

BibTeX

@article{guo2026assessing, author = {Guo, Fanhua and Zhao, Chenyang and Bhatt, Ravi R and Liu, Zixuan and Kim, Andy Jeesu and Yang, Zidong and Xu, Siyi and Jann, Kay and Shao, Xingfeng and Mather, Mara and Jahanshad, Neda and Wang, Danny JJ}, title = {{Assessing molecular, cellular and transcriptomic bases of laminar perfusion and cytoarchitecture coupling in the human cortex}}, journal = {Nature communications}, year = {2026}, month = aug, volume = {17}, number = {1}, pages = {9748}, publisher = {Nature Publishing Group}, issn = {2041-1723}, doi = {10.1038/s41467-026-76812-w}, url = {https://doi.org/10.1038/s41467-026-76812-w}, pmid = {42733079}, pmcid = {PMC13572400} }

RIS

TY - JOUR AU - Guo, Fanhua AU - Zhao, Chenyang AU - Bhatt, Ravi R AU - Liu, Zixuan AU - Kim, Andy Jeesu AU - Yang, Zidong AU - Xu, Siyi AU - Jann, Kay AU - Shao, Xingfeng AU - Mather, Mara AU - Jahanshad, Neda AU - Wang, Danny JJ TI - Assessing molecular, cellular and transcriptomic bases of laminar perfusion and cytoarchitecture coupling in the human cortex T2 - Nature communications J2 - Nat Commun PY - 2026 DA - 2026/08/13 VL - 17 IS - 1 SP - 9748 SN - 2041-1723 PB - Nature Publishing Group DO - 10.1038/s41467-026-76812-w UR - https://doi.org/10.1038/s41467-026-76812-w LA - en ER -

CSL-JSON

{ "id": "10.1038/s41467-026-76812-w", "type": "article-journal", "title": "Assessing molecular, cellular and transcriptomic bases of laminar perfusion and cytoarchitecture coupling in the human cortex", "container-title": "Nature communications", "author": [ { "family": "Guo", "given": "Fanhua" }, { "family": "Zhao", "given": "Chenyang" }, { "family": "Bhatt", "given": "Ravi R" }, { "family": "Liu", "given": "Zixuan" }, { "family": "Kim", "given": "Andy Jeesu" }, { "family": "Yang", "given": "Zidong" }, { "family": "Xu", "given": "Siyi" }, { "family": "Jann", "given": "Kay" }, { "family": "Shao", "given": "Xingfeng" }, { "family": "Mather", "given": "Mara" }, { "family": "Jahanshad", "given": "Neda" }, { "family": "Wang", "given": "Danny JJ" } ], "container-title-short": "Nat Commun", "volume": "17", "issue": "1", "page": "9748", "DOI": "10.1038/s41467-026-76812-w", "PMID": "42733079", "PMCID": "PMC13572400", "ISSN": "2041-1723", "publisher": "Nature Publishing Group", "URL": "https://doi.org/10.1038/s41467-026-76812-w", "language": "en", "issued": { "date-parts": [ [ 2026, 8, 13 ] ] } }

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