OSCR

Brain functional-structural gradient coupling reflects development, behavior and genetic influences.

Code ↔ Paper

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  1. [1] § Methods › Functional and structural gradient construction ↔ brainspace/gradient/embedding.py, lines 57–198 · score 0.89 · anisotropic diffusion parameter, random walk, affinity matrices, diffusion map, operator, power
  2. [2] § Methods › Data harmonization ↔ Harmonization/main_BatchEffect.R, lines 210–267 · score 0.84 · ComBat, ABCD HCP, site harmonization, HCP YA, batch, fd
  3. [3] § Methods › Imaging processing and connectivity construction ↔ ADNI_example/adni_fmri_correction.sh, lines 50–115 · score 0.78 · phase encode, fMRI, distortion correction, Philips, echo, head
  4. [4] § Methods › Imaging processing and connectivity construction ↔ ADNI_example/preproc_step5_fmri.sh, lines 1–43 · score 0.77 · distortion correction, motion correction, MRI preprocessing, fMRI, slices, Siemens
  5. [5] § Methods › Imaging processing and connectivity construction ↔ ACT_example/dti_rpe_minimal_process.sh, lines 84–122 · score 0.73 · bias field correction, multi shell, EPI, Minimal, intensities, eddy
  6. [6] § Results › SFGC reveals regional and developmental genetic topographies ↔ Heritability-Analysis/imging_transcriptomics.py, lines 1–68 · score 0.69 · LH inferior, LH lateral, lingual, precuneus, frontal, superior
  7. [7] § Methods › Imaging processing and connectivity construction ↔ ADNI_example/adni_dwi_eddy_correction.sh, lines 1–41 · score 0.68 · head motion, fMRI, undistorted, eddy, T1w, distortions
  8. [8] § Methods › Imaging processing and connectivity construction ↔ ACT_example/dti_rpe_minimal_process.sh, lines 1–42 · score 0.66 · phase encoding, Diffusion imaging, platform, Minimal, PA, AP
  9. [9] § Methods › Heritability estimation using the ACE model ↔ Heritability-Analysis/ACE_heritability.py, the whole file · a weak match · score 0.65 · shared environment matrices, ACE model, covariates, phenotype, Yeo, genetic
  10. [10] § Methods › Imaging processing and connectivity construction ↔ pipeline_scripts/sbci_step5_atlas_connectivity.sh, the whole file · a weak match · score 0.62 · continuous SC, continuous FC, SC matrix, connections, smoothed, mesh
  11. [11] § Methods › Imaging transcriptomics of gradient coupling heritability ↔ Heritability-Analysis/imging_transcriptomics.py, lines 1–68 · score 0.62 · imaging transcriptomic, Gene expression, abagen, AHBA, heritability, atlas
  12. [12] § Methods › Heritability estimation using the ACE model ↔ Heritability-Analysis/ACE_heritability.py, the whole file · a weak match · score 0.61 · covariate matrix, ACE model, h2, Phenotypes, heritability
  13. [13] § Methods › Associations between SFGC and behavioral outcomes ↔ Association-Study/main_MLP.py, lines 226–309 · score 0.61 · Sigmoid, dropout, leaky, epochs, layers, optimize
  14. [14] § Methods › Imaging processing and connectivity construction ↔ ADNI_example/adni_fmri_correction.sh, lines 50–115 · score 0.56 · phase encoding, echo, head, TR, Siemens, space
  15. [15] § Methods › Imaging transcriptomics of gradient coupling heritability ↔ brainspace/null_models/variogram.py, lines 1620–1674 · score 0.54 · spin permutations, spatial autocorrelation, empirical, map
  16. [16] § Results › SFGC and mental health associations by age and sex ↔ Association-Study/main_KRR.py, lines 172–218 · score 0.53 · kernel ridge regression, KRR, train, predictive, fitting, modeling
  17. [17] § Methods › Model performance and feature importance ↔ Association-Study/main_KRR.py, lines 172–218 · score 0.53 · primal coefficients, KRR, kernel, predictive, fitting, model
  18. [18] § Methods › Model performance and feature importance ↔ brainspace/gradient/kernels.py, lines 44–98 · score 0.52 · correlation coefficients, kernel functions, vector, gradient
  19. [19] § Methods › Imaging transcriptomics of gradient coupling heritability ↔ Heritability-Analysis/imging_transcriptomics.py, lines 127–167 · score 0.52 · ranked gene, Scanpy, heritability, transcriptomics
  20. [20] § Methods › Imaging processing and connectivity construction ↔ ACT_example/dti_rpe_minimal_process.sh, lines 84–122 · score 0.51 · spherical deconvolution, tissue, field, map

Paper

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The authors' code

Shell · 228 lines · 12 KB · no license · 3 matches

  1. #!/bin/bash
  2. #SBATCH -t 3-3:00:00
  3. #SBATCH --mem-per-cpu=32gb
  5. # Written by Andrew Jahn, University of Michigan, 02.25.2019
  6. # Updated 07.10.2020 to incorporate changes from MRtrix version 3.0.1
  7. # Based on Marlene Tahedl's BATMAN tutorial (http://www.miccai.org/edu/finalists/BATMAN_trimmed_tutorial.pdf)
  8. # The main difference between this script and the other one in this repository, is that this script assumes that your diffusion images were acquired with AP phase encoding
  9. # Thanks to John Plass and Bennet Fauber for useful comments
  10. # Adapted by Mia Anthony, March 2023
  12. # USAGE
  13. # $(basename $0) [Raw Diffusion] [RevPhaseImage] [AP bvec] [AP bval] [PA bvec] [PA bval] [Anatomical]
  15. # Arguments:
  16. # 1) The raw diffusion image;
  17. # 2) The image acquired with the reverse phase-encoding direction;
  18. # 3) The bvec file for the data acquired in the AP direction;
  19. # 4) The bval file for the data acquired in the AP direction;
  20. # 5) The bvec file for the data acquired in the PA direction;
  21. # 6) The bval file for the data acquired in the PA direction;
  22. # 7) The anatomical image"
  24. RAW_DWI=$1 # raw diffusion image /dwi/sub-0105_ses-01_dwi.nii.gz
  25. REV_PHASE=$2 # image acquired with the reverse phase-encoding direction
  26. # fmap/sub-0105_ses-01_dir-PA_dwi.nii.gz
  27. AP_BVEC=$3 # bvec file for AP dwi/bvec
  28. AP_BVAL=$4 # bval file for AP dwi/bval
  29. PA_BVEC=$5 # bvec file for PA not use
  30. PA_BVAL=$6 # bval file for PA not use
  31. ANAT=$7 # T1 anatomical
  32. BIDS_PATH=$8 # BIDS path
  33. ########################### STEP 0 ###################################
  34. # TO-DO before preprocessing (only once) #
  35. ######################################################################
  36. cd $BIDS_PATH
  37. # Create QC folder and assign the path to a variable named 'QC_DIR'
  38. QC_DIR=${BIDS_PATH}/QC/
  40. export QT_QPA_PLATFORM=offscreen
  41. export XDG_RUNTIME_DIR=${BIDS_PATH}/run
  42. export RUNLEVEL=3
  44. # export TMP_DIR=${BIDS_PATH}/tmp
  46. # Create the following folders in the QC dir to save QC images
  47. # /residual
  48. # /corrupt
  49. # /mask
  50. # /fod_voxels
  51. # /fod_overlay
  52. # /tissue_align
  53. # /gm_wm
  54. # mkdir -p $QC_DIR/residual $QC_DIR/corrupt $QC_DIR/mask $QC_DIR/fod_voxels $QC_DIR/fod_overlay $QC_DIR/tissue_align $QC_DIR/gm_wm
  56. ########################### STEP 1 ###################################
  57. # Convert data to .mif format and denoise #
  58. ######################################################################
  60. # Convert .nii to .mif format
  61. mrconvert $RAW_DWI raw_dwi.mif -fslgrad $AP_BVEC $AP_BVAL
  63. # Denoise dwi
  64. dwidenoise raw_dwi.mif dwi_den.mif -noise noise.mif
  66. # Calculate residual to check whether any region is disproportionately affected by noise.
  67. mrcalc raw_dwi.mif dwi_den.mif -subtract noise_residual.mif
  69. # ---------------------- QC steps ---------------------- #
  70. # Everything within the grey and white matter should be relatively uniform and blurry. If any clear anatomical landmarks are visible in the residual image, those parts of the brain have been corrupted by noise - increase the extent of the denoising filter from 5 (default) to a larger number, e.g. 7.
  71. # mkdir $QC_DIR/residual
  72. # mrview noise_residual.mif -capture.folder $QC_DIR/residual -capture.prefix $SBJ -capture.grab
  74. # dwidenoise raw_dwi.mif dwi_den.mif -extent 7 -noise noise_7extent.nii.gz
  76. # Determine whether Gibbs denoising is needed. Check diffusion data for ringing artifacts before and after to determine whether Gibbs denoising improved the data. If the data looks worse or the same, then skip Gibbs denoising.
  77. # mrdegibbs raw_dwi.mif dwi_den.mif
  78. mrdegibbs raw_dwi.mif dwi_den.mif -force
  80. # mrview raw_dwi.mif dwi_den.mif -capture.folder $QC_DIR/gibbs -capture.prefix $SBJ -capture.grab
  82. ##### Check for gibbs ringing before continuing to process
  84. # -------------------------------------------------------- #
  86. # Extract the b0 images in the AP direction. For the PA_BVEC and PA_BVAL files, they should be in the follwing format (assuming you extract only one volume):
  87. # PA_BVEC: 0 0 0
  88. # PA_BVAL: 0
  89. dwiextract dwi_den.mif - -bzero | mrmath - mean mean_b0_AP.mif -axis 3
  91. # Average b0s for AP direction to calculate mean intensity
  92. mrconvert $REV_PHASE PA.mif # If the PA map contains only 1 image, add the option "-coord 3 0"
  94. # Average b0s for PA direction
  95. # mrconvert PA.mif -fslgrad $PA_BVEC $PA_BVAL - | mrmath - mean mean_b0_PA.mif -axis 3
  96. mrconvert PA.mif - | mrmath - mean mean_b0_PA.mif -axis 3
  98. # Concatenate the b0 images from AP and PA directions to create a paired b0 image
  99. mrcat mean_b0_AP.mif mean_b0_PA.mif -axis 3 b0_pair.mif
  101. # Run dwipreproc (wrapper for eddy and topup). *CHANGE READOUT TIME based on TotalReadoutTime in the AP/PA JSON file*
  102. # CogTE = 0.0476
  103. readout_time=0.0476
  104. dwifslpreproc dwi_den.mif dwi_den_preproc.mif -nocleanup -pe_dir AP -rpe_pair -se_epi b0_pair.mif -readout_time $readout_time -align_seepi # -align_seepi ensures that the first volume in the series provided to topup is also the first volume in the series provided to eddy to make sure the volumes are aligned.
  107. # Bias field correction. Needs ANTs to be installed in order to use the "ants" option
  108. dwibiascorrect ants dwi_den_preproc.mif dwi_den_preproc_unbiased.mif -bias bias.mif
  110. # Create whole brain mask from bias corrected data - mask should be tight
  111. dwi2mask dwi_den_preproc_unbiased.mif mask.mif
  113. # create new bvecs and bvals files for the preprocessed data
  114. mrinfo dwi_den_preproc_unbiased.mif -export_grad_fsl dwi/bvecs dwi/bvals
  116. mrconvert dwi_den_preproc_unbiased.mif dwi/dwi.nii.gz
  118. # ########################### STEP 2 ###################################
  119. # # Basis function for each tissue type #
  120. # ######################################################################
  122. # # Create a basis function from the subject's DWI data to estimate response function for spherical deconvolution. The "dhollander" function is best used for multi-shell acquisitions; it will estimate different basis functions for each tissue type. For single-shell acquisition, use the "tournier" function instead
  124. # # -------------- Type of shell-acquisition -------------- #
  125. # ### SINGLE-SHELL (single b-value)
  126. # # dwi2response tournier dwi_den_preproc_unbiased.mif response_wm.txt response_gm.txt response_csf.txt -voxels voxels.mif
  127. # dwi2response tournier dwi_den_preproc_unbiased.mif response_wm.txt -voxels voxels.mif
  129. # # move files into QC folder
  130. # # mv response_wm.txt response_gm.txt response_csf.txt $QC_DIR/response/$SBJ
  132. # # Estimate fibre orientation distributions (FOD) from diffusion data using spherical deconvolution, using the basis functions estimated above
  133. # # dwi2fod csd dwi.mif response_wm.txt wmfod.mif
  134. # dwi2fod csd dwi_den_preproc_unbiased.mif response_wm.txt wmfod.mif
  135. # # -------------------------------------------------------- #
  137. # ### MULTI-SHELL (more than 1 b-value)
  138. # # dwi2response dhollander dwi_den_preproc_unbiased.mif response_wm.txt response_gm.txt response_csf.txt-voxels voxels.mif
  140. # # Estimate fibre orientation distributions from diffusion data using spherical deconvolution, using the basis functions estimated above
  141. # # dwi2fod msmt_csd dwi_den_preproc_unbiased.mif -mask mask.mif response_wm.txt wmfod.mif response_gm.txt gmfod.mif response_csf.txt csffod.mif
  142. # # -------------------------------------------------------- #
  144. # # Create an image of the FODs overlaid onto the estimated tissues (Blue=WM; Green=GM; Red=CSF)
  145. # # mrconvert -coord 3 0 wmfod.mif - | mrcat csffod.mif gmfod.mif - vf.mif
  147. # # Normalize the FODs to enable comparison between subjects
  148. # # mtnormalise wmfod.mif wmfod_norm.mif gmfod.mif gmfod_norm.mif csffod.mif csffod_norm.mif -mask mask.mif
  149. # mtnormalise wmfod.mif wmfod_norm.mif -mask mask.mif
  151. # ########################### STEP 3 ###################################
  152. # # Create a GM/WM boundary for seed analysis #
  153. # ######################################################################
  155. # # Convert the anatomical image to .mif format, and then extract all five tissue catagories (1=GM; 2=Subcortical GM; 3=WM; 4=CSF; 5=Pathological tissue)
  156. # mrconvert $ANAT anat.mif
  158. # # Segment the anatomical image into the tissue types
  159. # 5ttgen fsl anat.mif 5tt_nocoreg.mif
  161. # # Average the b0 images
  162. # dwiextract dwi_den_preproc_unbiased.mif - -bzero | mrmath - mean mean_b0_processed.mif -axis 3
  164. # # Convert b0 average and 5tt image to NIFTI for co-registration
  165. # mrconvert mean_b0_processed.mif mean_b0_processed.nii.gz
  166. # mrconvert 5tt_nocoreg.mif 5tt_nocoreg.nii.gz
  168. # # Extract the first volume (gray matter) of the 5tt dataset
  169. # fslroi 5tt_nocoreg.nii.gz 5tt_vol0.nii.gz 0 1
  171. # # Use fsl to create a transformation matrix for co-registration between the tissue map and the b0 images
  172. # flirt -in mean_b0_processed.nii.gz -ref 5tt_vol0.nii.gz -interp nearestneighbour -dof 6 -omat diff2struct_fsl.mat
  174. # # Convert back to format that mrtrix can read
  175. # transformconvert diff2struct_fsl.mat mean_b0_processed.nii.gz 5tt_nocoreg.nii.gz flirt_import diff2struct_mrtrix.txt
  177. # # Co-register the anatomical image to the diffusion image
  178. # mrtransform 5tt_nocoreg.mif -linear diff2struct_mrtrix.txt -inverse 5tt_coreg.mif
  180. # # Create a seed region along the GM/WM boundary
  181. # 5tt2gmwmi 5tt_coreg.mif gmwmSeed_coreg.mif
  184. # ########################### STEP 4 ###################################
  185. # # QC #
  186. # ######################################################################
  188. # # --------------------------- QC for Step 1 --------------------------
  189. # # View original diffusion data overlaid on top of the eddy-corrected data and colored in red
  190. # # mrview dwi_den_preproc.mif -overlay.load raw_dwi.mif
  192. # ### Corrupt slices
  193. # # dwi_post_eddy.eddy_outlier_map indicates whether a slice is an outlier (1) or not (0), because of too much motion, eddy currents, or something else.
  194. # cd dwifslpreproc-tmp-*
  196. # # Calculate the total number of slices by multiplying the number of slices for a single volume by the total number of volumes in the dataset.
  197. # totalSlices=`mrinfo dwi.mif | grep Dimensions | awk '{print $6 * $8}'`
  199. # # The total number of 1?s in the outlier map is then calculated, and the percentage of outlier slices is generated by dividing the number of outlier slices by the total number of slices. If this number is greater than 10 - i.e., if more than 10 percent of the slices are flagged as outliers - you should consider removing the subject from further analyses.
  200. # totalOutliers=`awk '{ for(i=1;i<=NF;i++)sum+=$i } END { print sum }' dwi_post_eddy.eddy_outlier_map`
  202. # echo "scale=5; ($totalOutliers / $totalSlices * 100)/1" | bc | tee ${SBJ}_percentageOutliers.txt
  204. # mv ${SBJ}_percentageOutliers.txt $QC_DIR/corrupt
  206. # cd ..
  208. # # --------------------------- QC for Step 2 --------------------------
  209. # ### Brain mask
  210. # # mrview mask.mif -capture.folder $QC_DIR/mask -capture.prefix $SBJ -capture.grab
  212. # ### Basis function
  213. # # View the response functions for each tissue type (response_*.txt files in the QC folder). The WM function should flatten out at higher b-values, while the other tissues should remain spherical
  215. # ### FOD estimation
  216. # # View the voxels used for FOD estimation (Blue=WM; Green=GM; Red=CSF)"
  217. # # mrview dwi_den_preproc_unbiased.mif -overlay.load voxels.mif -capture.folder $QC_DIR/fod_voxels -capture.prefix $SBJ -capture.grab
  219. # # Views the FODs overlaid on the tissue types (Blue=WM; Green=GM; Red=CSF)
  220. # # mrview vf.mif -odf.load_sh wmfod.mif -capture.folder $QC_DIR/fod_overlay -capture.prefix $SBJ -capture.grab
  223. # # --------------------------- QC for Step 3 --------------------------
  224. # # Check alignment of the 5 tissue types before and after alignment (new alignment in red, old alignment in blue)
  225. # # mrview dwi_den_preproc_unbiased.mif -overlay.load 5tt_nocoreg.mif -overlay.colourmap 2 -overlay.load 5tt_coreg.mif -overlay.colourmap 1 -capture.folder $QC_DIR/tissue_align -capture.prefix $SBJ -capture.grab
  227. # # Check the seed region (should match up along the GM/WM boundary)
  228. # # mrview dwi_den_preproc_unbiased.mif -overlay.load gmwmSeed_coreg.mif -capture.folder $QC_DIR/gm_wm -capture.prefix $SBJ -capture.grab

dti_rpe_minimal_process.sh at commit 722e68c, no license · at the source

Overview

Authors: Simiao Gao1, Zhiling Gu1, Shengxian Ding1, Gefei Wang1, Zhengwu Zhang2, Hongyu Zhao1, Yize Zhao1
ORCID iDs: Simiao Gao, Zhiling Gu, Shengxian Ding, Gefei Wang, Hongyu Zhao, Yize Zhao
  1. Department of Biostatistics, Yale University,New Haven, CT USA
  2. Department of Statistics and Operations Research, University of North Carolina at Chapel Hill,Chapel Hill, NC USA
Institutions: Yale University (United States); University of North Carolina at Chapel Hill (United States)
Journal: Nature communications, volume 17, issue 1, article 4850
Dates: received 8 July 2025; accepted 23 March 2026; published online 9 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-71719-y · PMID 41957369 · PMCID PMC13222887 · OpenAlex W4414321762
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.09.16.25335918
Status: code verified
Categories: human (organism), developmental (subfield)
Methods: Connectivity, Spectral & time-frequency, Statistics, Machine learning, Preprocessing, fMRI & imaging
Keywords: Neural patterning, Predictive markers
MeSH: Behavior*, Brain*, Adolescent, Adult, Child, Cognition, Connectome, Female, Humans, Magnetic Resonance Imaging, Male, Young Adult (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NIH (R01EB034720, RF1AG081413, R01AG068191)
Citations: cited by 1 paper (Europe PMC); 69 references in the paper

Abstract

Gradients provide low-dimensional representations of macroscale brain organization, yet how structural-functional gradient coupling develops and relates to behavioral and molecular features remains unclear. Here, we studied structural-functional gradient coupling across multiple metrics and spatial scales using high-resolution structural and functional connectivity from 5343 children in the Adolescent Brain Cognitive Development study and 875 adults from the Human Connectome Project. We find that gradient coupling shows developmental refinement from childhood to adulthood and distinct sex-specific patterns. Gradient coupling metrics are significantly associated with cognitive and mental health measures and enable robust out-of-sample prediction. Heritability analyses reveal that gradient coupling is strongly influenced by genetic factors. Transcriptomic analyses further demonstrate that highly heritable coupling patterns are enriched for genes expressed in deep-layer excitatory neurons. Together, our findings establish structural-functional gradient coupling as a biologically meaningful feature of brain organization that bridges macroscale connectivity, cognition, behavior, and molecular architecture.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 20 matches between paragraphs and lines of code.

sbci-brain/SBCI_Pipeline

License: none: the authors keep all their rights
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Commit: 722e68c41001af38a02d6a867e8153cc9720b69f, 22 May 2026
Languages: Shell (133), Python (51), C/C++ (4), MATLAB (2)
Size: 583 files, 190 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (48 files), MRtrix3 (35 files), FreeSurfer (30 files), FSL (27 files), SciPy (25 files), ANTs (15 files), NiBabel (14 files), DIPY (2 files), Dcm2Bids (1 file), dcm2niix (1 file), scikit-learn (1 file)
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At the source: github.com/sbci-brain/SBCI_Pipeline

mica-mni/brainspace

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Languages: Python (77), MATLAB (40), Jupyter (4)
Size: 373 files, 121 scripts
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Holds: README, license file, CITATION.cff, environment (Dockerfile, requirements.txt, setup.cfg, setup.py, docs/requirements.txt), tests, continuous integration, documentation, 4 notebooks
Tools: NumPy (52 files), BrainSpace (32 files), SciPy (18 files), scikit-learn (13 files), Matplotlib (9 files), NiBabel (3 files), Nilearn (3 files), GIfTI library for MATLAB (1 file), Parallel Computing Toolbox (1 file), Statistics and Machine Learning Toolbox (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
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At the source: github.com/MICA-MNI/BrainSpace/tree/master

Zhao-team/SF-Gradient-Coupling

License: MIT
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Commit: ff163a9e9aa01dc7600ac3cc3f0c4a0d0676574f, 15 December 2025
Languages: Python (6), R (1)
Size: 15 files, 7 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (6 files), pandas (6 files), SciPy (5 files), scikit-learn (4 files), Matplotlib (3 files), BrainSpace (2 files), h5py (2 files), PyTorch (2 files), abagen (1 file), anndata (1 file), ggplot2 (1 file), limma (1 file), patchwork (1 file), Scanpy (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
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9 files
At the source: github.com/Zhao-team/SF-Gradient-Coupling.git

Zenodo 18912522

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At the source:

Code availability

The data preprocessing software FreeSurfer v6.0 is available at https://surfer.nmr.mgh.harvard.edu/. The Surface-Based Connectivity Integration pipeline can be accessed at https://github.com/sbci-brain/SBCI_Pipeline, and the BrainSpace toolbox is available at https://github.com/MICA-MNI/BrainSpace/tree/master. Python (2.7 and 3.12) and R v4.4 were used for data processing and analysis. Code used in this study is publicly available at https://github.com/Zhao-team/SF-Gradient-Coupling.git and stored at 10.5281/zenodo.1891252268.

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

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:

  • 4 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 318 scripts, each with its path and the digest of its content;
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Data

Datasets cited

Data availability

Neuroimaging and behavioral data from the ABCD Study can be obtained via the NIH Data Archive (https://nda.nih.gov/abcd) with approval from the ABCD consortium. Neuroimaging data and most behavioral measures from the HCP-YA are publicly available at https://db.humanconnectome.org; access to restricted data is subject to approval. Data from the HCP-D study are available through the NIH Data Archive (https://nda.nih.gov) and require approval for access. The raw data are protected and are not available due to data privacy laws and the terms of the original ethical approvals. Source data are provided with this paper. The data are supplied in multiple formats, including Python pickle (.pkl) files, which can be read using standard Python packages such as pickle or pandas. Source data are provided with this paper.

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

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 2 keywords, 12 MeSH terms, 1 funder, 64 references.

Cite

This paper

Gao, S., Gu, Z., Ding, S., Wang, G., Zhang, Z., Zhao, H., & Zhao, Y. (2026). Brain functional-structural gradient coupling reflects development, behavior and genetic influences. Nature communications, 17(1), 4850. https://doi.org/10.1038/s41467-026-71719-y

BibTeX

@article{gao2026brain,
author = {Gao, Simiao and Gu, Zhiling and Ding, Shengxian and Wang, Gefei and Zhang, Zhengwu and Zhao, Hongyu and Zhao, Yize},
title = {{Brain functional-structural gradient coupling reflects development, behavior and genetic influences}},
journal = {Nature communications},
year = {2026},
month = apr,
volume = {17},
number = {1},
pages = {4850},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-71719-y},
url = {https://doi.org/10.1038/s41467-026-71719-y},
pmid = {41957369},
pmcid = {PMC13222887}
}

RIS

TY - JOUR
AU - Gao, Simiao
AU - Gu, Zhiling
AU - Ding, Shengxian
AU - Wang, Gefei
AU - Zhang, Zhengwu
AU - Zhao, Hongyu
AU - Zhao, Yize
TI - Brain functional-structural gradient coupling reflects development, behavior and genetic influences
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/04/09
VL - 17
IS - 1
SP - 4850
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-71719-y
UR - https://doi.org/10.1038/s41467-026-71719-y
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-71719-y",
"type": "article-journal",
"title": "Brain functional-structural gradient coupling reflects development, behavior and genetic influences",
"container-title": "Nature communications",
"author": [
{
"family": "Gao",
"given": "Simiao"
},
{
"family": "Gu",
"given": "Zhiling"
},
{
"family": "Ding",
"given": "Shengxian"
},
{
"family": "Wang",
"given": "Gefei"
},
{
"family": "Zhang",
"given": "Zhengwu"
},
{
"family": "Zhao",
"given": "Hongyu"
},
{
"family": "Zhao",
"given": "Yize"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "4850",
"DOI": "10.1038/s41467-026-71719-y",
"PMID": "41957369",
"PMCID": "PMC13222887",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-71719-y",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
9
]
]
}
}

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