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The neuroimaging correlates of depression established across six large-scale population datasets.

Code ↔ Paper

6 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 6 matches
  1. [1] § Results › Visual and somatomotor involvement in depression ↔ code_for_figures/meta_violin.py, lines 14–90 · score 0.80 · dorsal attention, spatial network, ventral attention, absolute meta, cortical surface area, box
  2. [2] § Results › Structural neuroimaging correlates of depression ↔ meta.py, lines 82–91 · score 0.68 · HCP Aging, HCP YA, HCP Development, Sadness, RDS, ANXPE
  3. [3] § Results › Visual and somatomotor involvement in depression ↔ ANOVA_notebook.ipynb, lines 1–75 · score 0.61 · dorsal attention, ventral attention, cortical surface area, gray matter volume, limbic, frontoparietal
  4. [4] § Methods › Statistical comparisons ↔ code_for_figures/meta_violin.py, lines 14–90 · score 0.58 · cortical surface area, cortical thickness, imaging metric, gray matter volume, functional connectivity, depression phenotype
  5. [5] § Methods › Statistical comparisons ↔ ANOVA_notebook.ipynb, lines 1–75 · score 0.53 · cortical surface area, cortical thickness, imaging metric, gray matter volume, ANOVAs, Yeo
  6. [6] § Methods › Nonlinearity ↔ Source_Code/regression.py, lines 116–194 · score 0.51 · sex, GAM, edof, freedom, covariates, variables

Paper

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The authors' code

Python · 91 lines · 3.5 KB · MIT · 2 matches

  1. import os
  2. import pandas as pd
  3. import seaborn as sns
  4. import matplotlib.pyplot as plt
  6. df_volume = pd.read_csv("../meta_results_volume/meta_analysis_without_covars.csv")
  7. df_area = pd.read_csv("../meta_results_area/meta_analysis_without_covars.csv")
  8. df_thickness = pd.read_csv("../meta_results_thickness/meta_analysis_without_covars.csv")
  10. structural_dfs = [df_volume, df_area, df_thickness]
  12. # load functional data
  14. schaefer_partial_path = "../meta_analysis_without_covars_with_yeo.csv"
  15. schaefer_partial_df = pd.read_csv(schaefer_partial_path)
  16. yeo_mapping = {
  17. 1: "Visual",
  18. 2: "Somatomotor",
  19. 3: "Dorsal Attention",
  20. 4: "Ventral Attention",
  21. 5: "Limbic",
  22. 6: "Frontoparietal",
  23. 7: "Default",
  24. 8: "Subcortical"
  25. }
  26. schaefer_partial_df['Yeo'] = schaefer_partial_df['Yeo1']
  27. schaefer_partial_df['Yeo_name'] = schaefer_partial_df['Yeo1'].map(yeo_mapping)
  29. functional_dfs = [schaefer_partial_df]
  31. df_volume["estimate"] = df_volume["estimate"].abs()
  32. df_volume["imaging_metric"] = "Gray Matter Volume"
  33. df_area["estimate"] = df_area["estimate"].abs()
  34. df_area["imaging_metric"] = "Cortical Surface Area"
  35. df_thickness["estimate"] = df_thickness["estimate"].abs()
  36. df_thickness["imaging_metric"] = "Cortical Thickness"
  37. schaefer_partial_df["estimate"] = schaefer_partial_df["estimate"].abs()
  38. schaefer_partial_df["imaging_metric"] = "Functional Connectivity"
  40. df_total = pd.concat([df_volume, df_area, df_thickness, schaefer_partial_df], ignore_index=True)
  42. print(df_total.head())
  44. print(schaefer_partial_df.columns)
  45. print(schaefer_partial_df["phen_group"].unique())
  46. print(max(schaefer_partial_df["estimate"]))
  48. all_dfs = structural_dfs + functional_dfs
  51. df_total.loc[df_total['phen_group'] == 'Neuroticism', 'phen_group'] = 'Predisposition'
  52. df_total.loc[df_total['phen_group'] == 'Depression', 'phen_group'] = 'Severity'
  53. df_total['Depression phenotype']=df_total['phen_group']
  54. plt.figure(figsize=(10, 6))
  55. sns.violinplot(data=df_total, x='imaging_metric', y='estimate', hue='Depression phenotype', dodge=True,inner='box', palette=['lightgray','skyblue'])
  56. plt.xlabel('Imaging Metric Type')
  57. plt.ylabel('Absolute Meta-analysis Estimate')
  58. plt.ylim(-0.02,0.09)
  59. plt.tight_layout()
  60. #plt.show()
  61. plt.savefig("../FIGURE3.png", dpi=600)
  63. df_volume['estimate_abs'] = df_volume['estimate'].abs()
  64. df_area['estimate_abs'] = df_area['estimate'].abs()
  65. df_volume.loc[df_volume['phen_group'] == 'Neuroticism', 'phen_group'] = 'Predisposition'
  66. df_volume.loc[df_volume['phen_group'] == 'Depression', 'phen_group'] = 'Severity'
  67. df_volume['Depression phenotype']=df_volume['phen_group']
  68. df_area['Depression phenotype']=df_area['phen_group']
  69. plt.figure(figsize=(12, 6))
  70. sns.violinplot(data=df_volume, x='Yeo_name', y='estimate_abs', hue='Depression phenotype', dodge=True,palette=['lightgray','skyblue'])
  71. # plt.title('ARI Score Distribution by k')
  72. plt.xlabel('Spatial Network')
  73. plt.ylabel('Absolute Meta-analysis Estimate')
  74. plt.ylim(-0.02,0.1)
  75. plt.tight_layout()
  76. #plt.show()
  77. plt.savefig("../FIGURE4_A.png", dpi=600)
  79. df_area.loc[df_area['phen_group'] == 'Neuroticism', 'phen_group'] = 'Predisposition'
  80. df_area.loc[df_area['phen_group'] == 'Depression', 'phen_group'] = 'Severity'
  81. plt.figure(figsize=(12, 6))
  82. sns.violinplot(data=df_area, x='Yeo_name', y='estimate_abs', hue='Depression phenotype', dodge=True,palette=['lightgray','skyblue'])
  83. # plt.title('ARI Score Distribution by k')
  84. plt.xlabel('Spatial Network')
  85. plt.ylim(-0.02,0.1)
  86. plt.ylabel('Absolute Meta-analysis Estimate')
  87. plt.legend().remove()
  88. plt.tight_layout()
  89. #plt.show()
  90. plt.savefig("../FIGURE4-B.png", dpi=600)

meta_violin.py at commit 47955c3, under MIT · at the source

Overview

Authors: Kassandra Miyoko Hamilton1, Xiaoke Luo1, Ty Easley1,2, Fyzeen Ahmad1, Thomas Guo1, Setthanan Jarukasemkit1,3,4, Hailey Modi1, Samuel Naranjo Rincón1, Cabria Shelton1, Lyn Stahl1, Zijian Wang1, Yuling Zhu1, Petra Lenzini1, Deanna M. Barch1,5,6, Yvette I. Sheline7, Kayla Hannon1,8, Janine D. Bijsterbosch1
ORCID iDs: Kassandra Miyoko Hamilton, Deanna M. Barch, Yvette I. Sheline, Kayla Hannon, Janine D. Bijsterbosch
  1. Department of Radiology, Washington University School of Medicine,Saint Louis, MO USA
  2. Department of Mathematics, Washington University,Saint Louis, MO USA
  3. Division of Neurology, Department of Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University,Bangkok, Thailand
  4. Cognitive Clinical and Computational Neuroscience (CCCN) Center of Excellence, Chulalongkorn University,Bangkok, Thailand
  5. Department of Psychiatry, Washington University School of Medicine,Saint Louis, MO USA
  6. Department of Psychological and Brain Sciences, Washington University,Saint Louis, MO USA
  7. Departments of Psychiatry, Neurology and Radiology Perelman School of Medicine, University of Pennsylvania,Philadelphia, PA USA
  8. McLean Imaging Center, Harvard Medical School,Boston, MA USA
Institutions: Washington University in St. Louis (United States); Mahidol University (Thailand); Chulalongkorn University (Thailand); University of Pennsylvania (United States); McLean Imaging Center, Harvard Medical School,Boston, MA USA
Journal: Nature. Mental health, volume 4, issue 8, pages 1329-1341
Dates: received 7 August 2025; accepted 11 June 2026; published online 13 July 2026; in print 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s44220-026-00680-y · PMID 42564120 · PMCID PMC13442040 · OpenAlex W7168143105
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: depression (population), methods / tools (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Connectivity, fMRI & imaging, Preprocessing
Keywords: Emotion, Depression
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Institute of Mental Health (MH128286-03S2, R01 MH128286, R01 MH132962); NIH (R25 NS130965-02)
Citations: not cited yet (Europe PMC); 92 references in the paper

Abstract

The 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.

Repository

Its files are read in the Code ↔ Paper reader above, with 6 matches between paragraphs and lines of code.

kassiehamilton/WAPIAW2024

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 47955c36c9da966b8dbb792a66d702f1ec9ee5a6, 20 April 2026
Languages: Python (6), Jupyter (1), R (1)
Size: 16 files, 8 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, 2 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: pandas (6 files), NumPy (5 files), SciPy (4 files), statsmodels (3 files), Matplotlib (2 files), scikit-learn (2 files), seaborn (2 files), ggplot2 (1 file), ggseg (1 file), Pillow (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
10 files
At the source: github.com/kassiehamilton/WAPIAW2024.git

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:

Read it in the paper: doi.org/10.1038/s44220-026-00680-y.

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

Datasets cited

Data availability statement

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

Read it in the paper: doi.org/10.1038/s44220-026-00680-y.

Versions

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Version 2, 28 September 2026

  • Publisher: n/a → Springer Science+Business Media

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 17 authors, 2 keywords, 2 funders, 90 references.

Cite

This paper

Hamilton, K. M., Luo, X., Easley, T., Ahmad, F., Guo, T., Jarukasemkit, S., Modi, H., Naranjo Rincón, S., Shelton, C., Stahl, L., Wang, Z., Zhu, Y., Lenzini, P., Barch, D. M., Sheline, Y. I., Hannon, K., & Bijsterbosch, J. D. (2026). The neuroimaging correlates of depression established across six large-scale population datasets. Nature. Mental health, 4(8), 1329-1341. https://doi.org/10.1038/s44220-026-00680-y

BibTeX

@article{hamilton2026neuroimaging,
author = {Hamilton, Kassandra Miyoko and Luo, Xiaoke and Easley, Ty and Ahmad, Fyzeen and Guo, Thomas and Jarukasemkit, Setthanan and Modi, Hailey and Naranjo Rincón, Samuel and Shelton, Cabria and Stahl, Lyn and Wang, Zijian and Zhu, Yuling and Lenzini, Petra and Barch, Deanna M. and Sheline, Yvette I. and Hannon, Kayla and Bijsterbosch, Janine D.},
title = {{The neuroimaging correlates of depression established across six large-scale population datasets}},
journal = {Nature. Mental health},
year = {2026},
month = jul,
volume = {4},
number = {8},
pages = {1329--1341},
publisher = {Springer Science+Business Media},
issn = {2731-6076},
doi = {10.1038/s44220-026-00680-y},
url = {https://doi.org/10.1038/s44220-026-00680-y},
pmid = {42564120},
pmcid = {PMC13442040}
}

RIS

TY - JOUR
AU - Hamilton, Kassandra Miyoko
AU - Luo, Xiaoke
AU - Easley, Ty
AU - Ahmad, Fyzeen
AU - Guo, Thomas
AU - Jarukasemkit, Setthanan
AU - Modi, Hailey
AU - Naranjo Rincón, Samuel
AU - Shelton, Cabria
AU - Stahl, Lyn
AU - Wang, Zijian
AU - Zhu, Yuling
AU - Lenzini, Petra
AU - Barch, Deanna M.
AU - Sheline, Yvette I.
AU - Hannon, Kayla
AU - Bijsterbosch, Janine D.
TI - The neuroimaging correlates of depression established across six large-scale population datasets
T2 - Nature. Mental health
J2 - Nat Ment Health
PY - 2026
DA - 2026/07/13
VL - 4
IS - 8
SP - 1329
EP - 1341
SN - 2731-6076
PB - Springer Science+Business Media
DO - 10.1038/s44220-026-00680-y
UR - https://doi.org/10.1038/s44220-026-00680-y
LA - en
ER -

CSL-JSON

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