OSCR

The genetic architecture of cortical similarity networks.

Code ↔ Paper

5 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 5 matches
  1. [1] § Methods › Magnetic resonance imaging (MRI) data › Structural MRI feature selection ↔ get_vertex_df.py, lines 11–57 · score 0.67 · fractional anisotropy, FA, SA, volume, MC, thickness
  2. [2] § Methods › Magnetic resonance imaging (MRI) data › Structural MRI feature selection ↔ MIND.py, lines 8–49 · score 0.65 · surface area, cortical thickness, Vol, MIND network, biological, SA
  3. [3] § Methods › Magnetic resonance imaging (MRI) data › Structural MRI feature selection ↔ MIND.py, lines 8–49 · score 0.56 · surface area, Cortical thickness, summing, volume, vertex
  4. [4] § Methods › Magnetic resonance imaging (MRI) data › Structural MRI feature selection ↔ MIND.py, lines 8–49 · score 0.56 · surface area, Cortical thickness, summing, volume, vertex
  5. [5] § Methods › Magnetic resonance imaging (MRI) data › Structural MRI feature selection ↔ get_vertex_df.py, lines 11–57 · score 0.53 · FreeSurfer, vertex, MC, CT, thickness, surface

Paper

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The authors' code

Python · 203 lines · 9.4 KB · no license · 2 matches

  1. import sys
  2. import numpy as np
  3. import os
  4. import pandas as pd
  5. from os.path import exists
  6. from nibabel.freesurfer.io import read_morph_data, read_annot
  7. from nibabel.freesurfer.mghformat import load
  8. from collections import defaultdict
  9. from MIND_helpers import calculate_mind_network, is_outlier
  11. def get_vertex_df(surf_dir, features, parcellation):
  13. '''
  14. INPUT SPECIFICATIONS:
  15. • surf_dir (str) : This is a string the location containing all relevant directories output by FreeSurfer (i.e. label, mri, surf).
  17. • features (list):
  18. This function accepts the "features" argument as a list containing items in the following forms:
  19. str:
  21. • One of ['CT','Vol','SA','MC','SD']. In this form, the function will automatically assume that the requested features are found in the surf_dir/surf directory and correspond to the following files:
  22. CT: ?h.thickness
  23. Vol: ?h.volume
  24. SA: ?h.area
  25. MC: ?h.curv
  26. SD: ?h.sulc
  28. • You may also pass a string in the form 'thickness', 'volume', 'sulc', etc, which refer directly to default files already found in the surf_dir/surf directory for both rh and lh.
  29. For example, the function will interpret the entry 'thickness' to refer to the files surf_dir/surf/lh.thickness and surf_dir/surf/rh.thickness
  31. • Finally, you may pass a string in the form of a FULL path as follows: with a question mark '?' indicating the position specifying the hemisphere such as "full/path/to/?h.feature".
  32. Using this formulation will cause the command to look for files that exactly match both "full/path/to/lh.feature" and "full/path/to/rh.feature". If the left and right version of the files aren't exactly the same otherwise, this won't work.
  35. tuple: (path/to/lh_surface_feature, path/to/rh_surface_feature)
  36. • If you would rather pass other features directly into the function, you must specify the locations (using paths) of both the left and right versions of each desired feature as a tuple.
  37. **The files must be readable by nibabel's read_morph_data function, i.e. in FreeSurfer's surface format!***
  38. So for example, if you have used the provided register_and_vol2surf function to generate surface maps of fractional anisotropy in a separate folder, you could pass them as an element in the list like:
  39. (path/to/lh.FA.mgh, path/to/rh.FA.mgh)
  41. A valid list of feature values combining these different input types would therefore be: ['CT','SD',(path/to/lh_feature1, path/to/rh_feature1), (path/to/lh_feature2, path/to/rh_feature2)]
  43. • parcellation (str): This is a string the location containing parcellation scheme to be used. The files 'lh.' + parcellation + '.annot' and 'rh.' + parcellation + '.annot' must exist inside the surf_dir/label directory.
  44. '''
  46. #specify data locations
  47. surfer_location = surf_dir + '/'
  49. #Check inputs!
  50. if (exists(surfer_location + '/label/lh.' + parcellation + '.annot') == False) or (exists(surfer_location + '/label/rh.' + parcellation + '.annot') == False):
  51. raise Exception('Parcellation files not found.')
  53. all_shorthand_features = ['CT','Vol','SA','MC','SD']
  54. all_shorthand_features_dict = dict(zip(all_shorthand_features, ['thickness','volume','area','curv','sulc']))
  56. lh_feature_locs = []
  57. rh_feature_locs = []
  59. #Check feature inputs, store location of files
  60. for feature in features:
  61. if feature in all_shorthand_features:
  62. lh_loc = surfer_location + 'surf/lh.' + all_shorthand_features_dict[feature]
  63. rh_loc = surfer_location + 'surf/rh.' + all_shorthand_features_dict[feature]
  65. if (exists(lh_loc) == False) or (exists(rh_loc) == False):
  66. raise Exception('Feature for input "' + feature +'" not found.')
  67. else:
  68. lh_feature_locs.append(lh_loc)
  69. rh_feature_locs.append(rh_loc)
  71. elif type(feature) is str:
  73. if len(feature.split('/')) == 1:
  74. lh_loc = surfer_location + 'surf/lh.' + feature
  75. rh_loc = surfer_location + 'surf/rh.' + feature
  77. if (exists(lh_loc) == False) or (exists(rh_loc) == False):
  78. raise Exception('Feature for input "' + feature +'" not found.')
  80. else:
  81. lh_feature_locs.append(lh_loc)
  82. rh_feature_locs.append(rh_loc)
  84. else:
  85. lh_loc = feature.split('/')
  86. lh_loc[-1] = 'l' + lh_loc[-1][1:]
  87. lh_loc = '/'.join(lh_loc)
  89. rh_loc = feature.split('/')
  90. rh_loc[-1] = 'r' + rh_loc[-1][1:]
  91. rh_loc = '/'.join(rh_loc)
  93. if (exists(lh_loc) == False) or (exists(rh_loc) == False):
  94. raise Exception('Feature for input "' + feature +'" not found.')
  96. else:
  97. lh_feature_locs.append(lh_loc)
  98. rh_feature_locs.append(rh_loc)
  100. elif type(feature) is tuple:
  102. lh_loc = feature[0]
  103. rh_loc = feature[1]
  105. if (exists(lh_loc) == False) or (exists(rh_loc) == False):
  106. raise Exception('Feature for input "' + feature[0] +'" or "' + feature[1] +'" not found.')
  108. else:
  109. lh_feature_locs.append(lh_loc)
  110. rh_feature_locs.append(rh_loc)
  112. else:
  113. raise Exception('Unrecognized format for feature input: ', feature)
  115. #Get annotation files
  116. lh_annot = read_annot(surfer_location + '/label/lh.' + parcellation + '.annot', orig_ids = True)
  117. rh_annot = read_annot(surfer_location + '/label/rh.' + parcellation + '.annot', orig_ids = True)
  119. annot_dict = {'lh':lh_annot, 'rh':rh_annot}
  121. '''
  122. The regions in the lh and rh need to be renamed and distinct.
  124. So, here we append lh_ or rh_ to the front of each region name and make a conversion dict.
  125. This will likely need to change for different processing pipelines (FS versions) and datasets etc.
  126. so make sure this dict is correct and looks good.
  127. '''
  129. lh_region_names = ['lh_' + str(x).split("'")[1] for x in lh_annot[2]]
  130. rh_region_names = ['rh_' + str(x).split("'")[1] for x in rh_annot[2]]
  132. lh_convert_dict = dict(zip(lh_annot[1][:,-1], lh_region_names))
  133. rh_convert_dict = dict(zip(rh_annot[1][:,-1], rh_region_names))
  135. convert_dicts = {'lh': lh_convert_dict,\
  136. 'rh': rh_convert_dict}
  138. used_labels_l = np.intersect1d(np.unique(lh_annot[0]), list(lh_convert_dict.keys()))
  139. used_labels_r = np.intersect1d(np.unique(rh_annot[0]), list(rh_convert_dict.keys()))
  141. used_labels = {'lh': used_labels_l,\
  142. 'rh': used_labels_r}
  145. used_regions_l = np.array([value for key, value in lh_convert_dict.items() if key in used_labels_l])
  146. used_regions_r = np.array([value for key, value in rh_convert_dict.items() if key in used_labels_r])
  148. combined_regions = np.hstack((used_regions_l, used_regions_r))
  149. unknown_regions = [x for x in combined_regions if (('?' in x) | ('unknown' in x) | ('Unknown' in x) | ('Medial_Wall' in x) | (len(x) == 3))]
  150. combined_regions = np.array([x for x in combined_regions if x not in unknown_regions])
  152. vertex_data_dict = defaultdict()
  154. #Now load up all the vertex-level data!
  155. for hemi in ['lh','rh']:
  156. print(hemi)
  157. hemi_data_dict = defaultdict()
  159. if hemi == 'lh':
  160. print('Loading left hemisphere data:')
  162. for i, lh_feature_loc in enumerate(lh_feature_locs):
  163. print(lh_feature_loc)
  164. #check for mgh/mgz format vs regular curv files
  165. if lh_feature_loc.endswith('mgh') or lh_feature_loc.endswith('mgz'):
  166. hemi_data_dict['Feature_' + str(i)] = load(lh_feature_loc).get_fdata().flatten()
  167. else:
  168. hemi_data_dict['Feature_' + str(i)] = read_morph_data(lh_feature_loc)
  170. elif hemi == 'rh':
  171. print('Loading right hemisphere data:')
  173. for i, rh_feature_loc in enumerate(rh_feature_locs):
  175. if rh_feature_loc.endswith('mgh') or rh_feature_loc.endswith('mgz'):
  176. hemi_data_dict['Feature_' + str(i)] = load(rh_feature_loc).get_fdata().flatten()
  177. else:
  178. hemi_data_dict['Feature_' + str(i)] = read_morph_data(rh_feature_loc)
  180. used_features = list(hemi_data_dict.keys())
  181. print(used_features)
  182. hemi_data = np.zeros((len(used_features) + 1, len(annot_dict[hemi][0])))
  184. hemi_data[0] = annot_dict[hemi][0]
  185. for i, feature in enumerate(used_features):
  186. print(i, feature)
  187. hemi_data[i + 1] = hemi_data_dict[feature]
  189. col_names = ['Label'] + used_features
  190. hemi_data = pd.DataFrame(hemi_data.T, columns = col_names)
  192. #Select only the vertices that map to regions.
  193. hemi_data = hemi_data.loc[hemi_data['Label'].isin(used_labels[hemi])]
  195. hemi_data["Label"] = hemi_data["Label"].map(convert_dicts[hemi])
  196. vertex_data_dict[hemi] = hemi_data
  198. vertex_data = pd.concat([vertex_data_dict['lh'], vertex_data_dict['rh']], ignore_index = True)
  200. #Output data
  201. print("features used: ")
  202. print(used_features)
  203. return vertex_data, combined_regions, used_features

get_vertex_df.py at commit 0d33445, no license · at the source

Overview

Authors: Isaac Sebenius1,2,3, Varun Warrier1,4, Richard A. I. Bethlehem4, Richard Dear1, Eva-Maria Stauffer1, Yuanjun Gu1,5, Rafael Romero-Garcia1,6, Jakob Seidlitz7,8,9, Edward Bullmore1,10, Sarah Morgan2,11
ORCID iDs: Isaac Sebenius, Varun Warrier, Richard A. I. Bethlehem, Yuanjun Gu, Rafael Romero-Garcia, Jakob Seidlitz, Edward Bullmore, Sarah Morgan
  1. Department of Psychiatry, University of Cambridge,Cambridge, United Kingdom
  2. Department of Computer Science and Technology, University of Cambridge,Cambridge, United Kingdom
  3. Harvard Medical School,Boston, MA US
  4. Department of Psychology, University of Cambridge,Cambridge, United Kingdom
  5. Autism Research Centre, University of Cambridge,Cambridge, United Kingdom
  6. Instituto de Biomedicina de Sevilla (IBiS) HUVR/CSIC/Universidad de Sevilla/CIBERSAM, ISCIII, Departamento de Fisiología Médica y Biofísica,Seville, Spain
  7. Department of Psychiatry, University of Pennsylvania,Philadelphia, PA USA
  8. Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia,Philadelphia, PA USA
  9. Lifespan Brain Institute, The Children’s Hospital of Philadelphia,Philadelphia, PA USA
  10. School of Academic Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King’s College London,London, United Kingdom
  11. School of Biomedical Engineering & Imaging Sciences, King’s College London,London, United Kingdom
Institutions: University of Cambridge (United Kingdom); Harvard University (United States); Universidad de Sevilla (Spain); University of Pennsylvania (United States); Children's Hospital of Philadelphia (United States); King's College London (United Kingdom)
Journal: Nature communications, volume 17, issue 1, article 7801
Dates: received 3 March 2025; accepted 29 April 2026; published online 20 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-73714-9 · PMID 42323287 · PMCID PMC13438111 · OpenAlex W4405711577
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2024.12.18.629155
Status: code verified
Categories: genetics / omics (modality), structural MRI / diffusion (modality), human (organism)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Machine learning, fMRI & imaging, Preprocessing
Keywords: Genetics of the nervous system, Neuroscience
MeSH: Cerebral Cortex*, Nerve Net*, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male (* major topic)
Topic: Bioinformatics and Genomic Networks (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Wellcome Trust (214322, 226392/Z/22/Z); National Institute for Health Research (NIHR) (NIHR203312)
Citations: cited by 1 paper (Europe PMC); 111 references in the paper

Abstract

The genetic architecture of human brain networks is central to understanding cortical organisation and evolution, the causal links between brain structure and function, and the pathogenesis of neuropsychiatric disorders. Using N > 48,000 subjects, we investigated common genetic effects on Morphometric INverse Divergence (MIND), a heritable, multi-modal structural MRI metric of inter-areal similarity and connectivity. Genetic correlations between MIND network edges were largely reducible to two gradients, each aligned with distance from one of the two phylogenetically primitive areas (paleocortex and archicortex) predicted by the dual origin theory of cortical evolution. MIND was more heritable than comparable measures of functional (f)MRI connectivity, and the paleocortically-aligned MIND gradient was genetically correlated with, and causally predictive of, fMRI connectivity. Finally, we identified genetic overlaps between MIND gradients and neuropsychiatric and biomedical traits. These results provide fresh insight into the dual origins of the cortex and their implications for brain function and health.

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 5 matches between paragraphs and lines of code.

jrs95/geni.plots

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: aa25803629235b681ada96741a1b0c07dc059870, 14 October 2024
Languages: R (7)
Size: 42 files, 7 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, environment (DESCRIPTION), continuous integration, documentation, 1 notebook
Not found: CITATION.cff, tests
Tools: tidyverse (5 files), ggplot2 (4 files), patchwork (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
9 files
At the source: github.com/jrs95/geni.plots

Zenodo 7974716

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State: the link answers, verified on 27 September 2026
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Size: 1 file
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (5 files), pandas (5 files), NiBabel (3 files), SciPy (3 files), Matplotlib (2 files), AFNI (1 file), FreeSurfer (1 file), Nipype (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
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7 files
At the source:

isebenius/mind

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 0d334454eaac62e49a197801446ce7750256c882, 12 March 2024
Languages: Python (4), Jupyter (2)
Size: 15 files, 6 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, environment (requirements.txt)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (5 files), pandas (5 files), NiBabel (3 files), SciPy (3 files), Matplotlib (2 files), AFNI (1 file), FreeSurfer (1 file), Nipype (1 file)
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/isebenius/MIND/tree/v1.0.0-alpha

Code availability

Code to calculate MIND networks can be accessed at 10.5281/zenodo.7974716. FastGWA was conducted with GCTA software v.1.94.1107. Colocalisation was performed using coloc v5.2.3 and R v4.3.1. Genal was used for for MR sensitivity analysis108. Both plink v1.9 and 2.0 were used in this study109. Gradient decomposition was performed using the BrainSpace Python package v0.1.1048. Data analysis and visualisation was conducted using Python 3.6, 3.9, and 3.11. Locus plots were created using https://github.com/jrs95/geni.plots. Brain network plots were created using NetPlotBrain v0.3.0110. UpSet plots were created using the code from ref. 111. Phenogram chromosome maps were created using the software at https://visualization.ritchielab.org/phenograms/plot.

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:

  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 19 scripts, each with its path and the digest of its content;
  • 5 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

All summary statistics generated in this study are publicly available at 10.17863/CAM.128272. These include summary statistics for the 276 network edges and 2 genetic gradients for both MIND and FC phenotypes. Summary statistics for schizophrenia (SCZ), major depressive disorder (MDD), attention-deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BPD), and Alzheimer’s disease (ALZ) can be downloaded from https://pgc.unc.edu/for-researchers/download-results/95–100. Summary statistics for C-reactive protein concentration (CRP), Body Mass Index (BMI) and gestational duration (GEST) can be downloaded, respectively, from https://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90029001-GCST90030000/GCST90029070/, https://portals.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium_data_files#2018_GIANT_and_UK_BioBank_Meta-analysis, and https://egg-consortium.org/gestational-duration-2019.html101–104. Further information for each of the summary statistics is provided in Supplementary Table 2. Paleocortical and archicortical distance maps were downloaded from ref. 56. The maps of the first genetic principal components of surface area, cortical thickness, mean diffusivity, mean curvature, and intracellular volume fraction were accessed from ref. 20. The first principal component of the T1w/T2w microstructural profile covariance matrix was downloaded from ref. 105. Brain maps of allometric scaling, evolutionary expansion, and externopyramidisation were downloaded from ref. 106. Source data are provided in this paper.

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, 10 authors, 2 keywords, 7 MeSH terms, 2 funders, 108 references.

Cite

This paper

Sebenius, I., Warrier, V., Bethlehem, R. A. I., Dear, R., Stauffer, E.-M., Gu, Y., Romero-Garcia, R., Seidlitz, J., Bullmore, E., & Morgan, S. (2026). The genetic architecture of cortical similarity networks. Nature communications, 17(1), 7801. https://doi.org/10.1038/s41467-026-73714-9

BibTeX

@article{sebenius2026genetic,
author = {Sebenius, Isaac and Warrier, Varun and Bethlehem, Richard A. I. and Dear, Richard and Stauffer, Eva-Maria and Gu, Yuanjun and Romero-Garcia, Rafael and Seidlitz, Jakob and Bullmore, Edward and Morgan, Sarah},
title = {{The genetic architecture of cortical similarity networks}},
journal = {Nature communications},
year = {2026},
month = jun,
volume = {17},
number = {1},
pages = {7801},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-73714-9},
url = {https://doi.org/10.1038/s41467-026-73714-9},
pmid = {42323287},
pmcid = {PMC13438111}
}

RIS

TY - JOUR
AU - Sebenius, Isaac
AU - Warrier, Varun
AU - Bethlehem, Richard A. I.
AU - Dear, Richard
AU - Stauffer, Eva-Maria
AU - Gu, Yuanjun
AU - Romero-Garcia, Rafael
AU - Seidlitz, Jakob
AU - Bullmore, Edward
AU - Morgan, Sarah
TI - The genetic architecture of cortical similarity networks
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/06/20
VL - 17
IS - 1
SP - 7801
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-73714-9
UR - https://doi.org/10.1038/s41467-026-73714-9
LA - en
ER -

CSL-JSON

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In common: NiBabel, ggplot2, tidyverse, 3 other tools, structural MRI / diffusion, 5 references, author Richard Bethlehem
[9] doi:10.1038/s42003-026-10131-0 [code]
Shared genetic architecture between the topology of brain white matter structural connectome and fluid intelligence.
Journal: Communications biology
In common: pandas, SciPy, NumPy, structural MRI / diffusion, genetics / omics, 10 references
[10] doi:10.1073/pnas.2609814123 [code]
Genetic architectures of brain-related traits are shaped by strong selective constraints.
Journal: Proceedings of the National Academy of Sciences of the United States of America
In common: patchwork, ggplot2, tidyverse, 4 other tools, genetics / omics, 7 references

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