OSCR

Multi-task fMRI outperforms resting-state fMRI for revealing task-invariant organization of the human brain

Code ↔ Paper

10 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 10 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Functional Data preprocessing ↔ Functional_Fusion/atlas_map.py, lines 1041–1090 · score 0.76 · pial surface, white matter surfaces, functional fusion, FreeSurfer, vertex, atlas
  2. [2] § Methods › Cross-tentorial correlation ratio ↔ scripts/internal_validity.py, lines 80–118 · score 0.68 · dilated voxels, MNI152NLin6Asym, cerebellar mask, wholebrain, neocortical, space
  3. [3] § Methods › Cross-tentorial correlation ratio ↔ scripts/internal_validity_masks.sh, the whole file · a weak match · score 0.67 · MNI152NLin6Asym, MNI space, cerebellar mask, dilating, resampling, correlations
  4. [4] § Methods › Data types › Cleaned time series ↔ preprocessing/fix_language.py, lines 70–132 · score 0.64 · automated classifier, hand classification, component, subset, training, noise
  5. [5] § Methods › Functional Data preprocessing ↔ preprocessing/somatotopic_imana.m, the whole file · a weak match · score 0.60 · FreeSurfer, reconstructed, preprocessed, diedrichsenlab, pipeline, resampled
  6. [6] § Methods › Parcellations › Evaluation ↔ parcellation_evaluation.py, lines 58–106 · score 0.56 · voxel pairs, individual parcellation, metric, DCBC, Distance, correlations
  7. [7] § Methods › Parcellations ↔ HierarchBayesParcel/emissions.py, lines 449–484 · score 0.56 · von Mises Fisher, hierarchical, parcels, voxel, model
  8. [8] § Methods › Dependence on task set › Generalization to novel task set ↔ notebooks/evaluate_similarity_hcp.ipynb, lines 28–64 · score 0.54 · working memory, gambling, emotion, social, language, HCP
  9. [9] § Methods › Covariance matrices ↔ scripts/export_matrix.py, lines 122–196 · score 0.53 · voxel covariance matrix, MNISymC3, icosahedron, neocortex, space, cerebellum
  10. [10] § Methods › Data acquisition › HCP Dataset ↔ notebooks/evaluate_similarity_hcp.ipynb, lines 28–64 · score 0.53 · working memory, gambling, emotion, HCP, social, language

Paper

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

The paper is loaded when this pane is shown.

The authors' code

Jupyter notebook · 147 lines · 5.6 KB · MIT · 2 matches

  1. # %% [markdown]
  2. # # Evaluate the similarity between covariances
  4. # %%
  5. import numpy as np
  6. import TaskRest.paths as trest_paths
  7. import numpy as np
  8. import covariance as cov
  9. import pandas as pd
  10. import matplotlib.pyplot as plt
  11. import seaborn as sb
  12. import TaskRest.plotting as plotting
  13. from scipy.stats import ttest_rel
  16. # Set trest_paths
  17. base_dir = trest_paths.set_base_dir()
  18. atlas_dir = trest_paths.set_atlas_dir(base_dir)
  19. figure_dir = trest_paths.set_figure_dir()
  20. model_dir = trest_paths.set_model_dir(base_dir)
  21. fusion_dir = trest_paths.set_fusion_dir(base_dir)
  22. # results_dir = trest_paths.set_results_dir(base_dir)
  23. results_dir = '/Users/callithrix/Documents/Projects/TaskRest/'
  25. # %%
  26. results_dir
  28. # %%
  29. # duration per run in seconds
  30. wm = 301 # working memory hcp task
  31. gamb = 192 # gambling hcp task
  32. mot = 180+34 # motor hcp task
  33. lang = 180 + 57 # language hcp task
  34. soc = 180 + 27 # social hcp task
  35. rel = 120 + 56 # relational hcp task
  36. emot = 120 + 16 # emotion hcp task
  38. # Multiply all by number of runs (2) to get total task time per subject
  39. wm_total = wm * 2
  40. gamb_total = gamb * 2
  41. mot_total = mot * 2
  42. lang_total = lang * 2
  43. soc_total = soc * 2
  44. rel_total = rel * 2
  45. emot_total = emot * 2
  46. print(f"Working Memory Task Time (minutes): {wm_total / 60.:.2f} minutes")
  47. print(f"Gambling Task Time (minutes): {gamb_total / 60.:.2f} minutes")
  48. print(f"Motor Task Time (minutes): {mot_total / 60.:.2f} minutes")
  49. print(f"Language Task Time (minutes): {lang_total / 60.:.2f} minutes")
  50. print(f"Social Task Time (minutes): {soc_total / 60.:.2f} minutes")
  51. print(f"Relational Task Time (minutes): {rel_total / 60.:.2f} minutes")
  52. print(f"Emotion Task Time (minutes): {emot_total / 60.:.2f} minutes")
  53. print(f"Total Task Times (minutes): {(wm_total + gamb_total + mot_total + lang_total + soc_total + rel_total + emot_total) / 60.:.2f} minutes")
  55. rest_trs = 2400 * 2 # two resting state runs of 2400 TRs each
  56. tr_duration = 0.72 # duration of each TR in seconds
  57. rest_time = rest_trs * tr_duration # total resting state time in seconds
  58. print(f"Total Resting State Time (minutes): {rest_time / 60.:.2f} minutes")
  59. # show leave-one-task-out total time
  60. total_task_time = (wm_total + gamb_total + mot_total + lang_total + soc_total + rel_total + emot_total)
  61. for task_time, task_name in zip([wm_total, gamb_total, mot_total, lang_total, soc_total, rel_total, emot_total],
  62. ['Working Memory', 'Gambling', 'Motor', 'Language', 'Social', 'Relational', 'Emotion']):
  63. loo_time = (total_task_time - task_time)
  64. print(f"Leave-One-Task-Out Time without {task_name} (minutes): {loo_time / 60.:.2f} minutes")
  66. # %%
  67. results_cov = pd.read_csv(results_dir + 'results/hcp/similarity_hcp_loo_method-cov_rerun.tsv', sep='\t')
  69. # Calculate for each subject and each structure the similarity_cov of all tasks to the left out task and the similarity_cov of the rest to the left out task
  70. similarity_cov = []
  71. task_cols = [col for col in results_cov.columns if ('hcptask' in col) and ('_other' not in col)]
  72. for s,subject in enumerate(results_cov['subject'].unique()):
  73. for structure in results_cov['structure'].unique():
  74. mask = (results_cov['subject'] == subject) & (results_cov['structure'] == structure)
  75. task_similarity = results_cov.loc[mask, task_cols].mean(axis=1).values[0]
  76. rest_similarity = results_cov.loc[mask, 'hcprest_tseriesfix_complete'].mean()
  77. similarity_cov.append({'subject': subject, 'structure': structure, 'task_similarity': task_similarity, 'rest_similarity': rest_similarity})
  79. similarity_cov = pd.DataFrame(similarity_cov)
  80. similarity_cov = similarity_cov.melt(id_vars=['subject', 'structure'], var_name='covariance', value_name='similarity')
  81. noise_ceiling_cov = results_cov[['noise_ceiling', 'subject', 'structure']]
  83. # Print the first few rows of the data
  84. similarity_cov.head()
  87. # %%
  88. covariance_order = ['task_similarity', 'rest_similarity']
  89. covariance_palette = {
  91. 'task_similarity': (0.3882, 0.4745, 0.2235),
  92. 'rest_similarity': (0.5176, 0.2353, 0.2235),
  94. }
  96. # %% [markdown]
  97. # ## Plot similarity for task and rest
  99. # %%
  100. def plot_similarity(similarity, outname='similarity_hcp_cov', ylim=(0, 0.4)):
  101. """Plot similarity for each structure and covariance method."""
  102. plt.figure(figsize=(8, 4), facecolor='white')
  103. # Use seaborn's set_theme instead of deprecated matplotlib style
  104. sb.set_theme(style="white")
  106. structure = ['cortex', 'cereb', 'cc']
  108. for i, s in enumerate(structure):
  109. plt.subplot(1, 3, i + 1)
  110. data_s = similarity[similarity['structure'] == s].sort_values(by='similarity', ascending=False)
  111. if i == 2:
  112. leg = True
  113. ax = sb.barplot(
  114. data=data_s,
  115. x='covariance', y='similarity',
  116. order=covariance_order,
  117. palette=covariance_palette,
  118. hue='covariance',
  119. legend=False,
  120. )
  121. sb.stripplot(
  122. data=data_s,
  123. x='covariance', y='similarity',
  124. order=covariance_order,
  125. color='black', size=4, alpha=0.2, jitter=True
  126. )
  127. sb.despine(ax=ax)
  128. ax.set_xticklabels(['task', 'rest'])
  129. plt.xlabel(f'{s}', fontsize=14)
  130. plt.ylabel(f'Similarity ({outname.split("_")[-1]})', fontsize=14)
  131. plt.ylim(ylim)
  133. plt.tight_layout()
  134. plt.savefig(figure_dir + outname + '.pdf', bbox_inches='tight')
  135. plt.show()
  137. # %%
  138. plot_similarity(similarity_cov, outname='similarity_hcp_cov', ylim=(0, 0.8))
  141. # %% [markdown]
  142. # ## Statistics
  144. # %%
  145. # Perform paired t-tests for each structure for covariance
  146. for structure in ['cortex', 'cereb', 'cc']:
  147. plotting.test_paired('task_similarity', 'rest_similarity', similarity_cov, structure)

evaluate_similarity_hcp.ipynb at commit 2492fc3, under MIT · at the source

Overview

Authors: Caroline Nettekoven1,2,3, Ali Shahbazi1, Bassel Arafat1, Matea Skenderija1, Jinkang Derrick Xiang1, Ana Luísa Pinho1,2,4, Jörn Diedrichsen1,2,5
ORCID iDs: Caroline Nettekoven, Bassel Arafat, Ana Luísa Pinho, Jörn Diedrichsen
  1. Western Centre for Brain and Mind, Western University, London, Ontario, Canada
  2. Department of Computer Science, Western University, London, Ontario, Canada
  3. Department of Experimental Psychology, University of Oxford, Oxford, UK
  4. Department of Psychology, Western University, London, Ontario, Canada
  5. Department of Statistical and Actuarial Sciences, Western University, London, Ontario, Canada
Institutions: Western University (Canada); University of Oxford (United Kingdom)
Journal: bioRxiv (preprint)
Dates: published online 10 March 2026
Type: Preprint
License: CC BY
Identifiers: DOI 10.64898/2026.03.09.710558 · OpenAlex W7134979997
Open access: green, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), cognitive (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, fMRI & imaging, Preprocessing
Keywords: task-based fMRI, resting-state fMRI, functional connectivity, brain parcellation, multi-task fMRI, individual differences, functional precision mapping
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Wellcome Trust (306553/Z/23/Z)
Citations: cited by 2 papers (Europe PMC); 112 references in the paper

Abstract

Resting-state functional Magnetic Resonance Imaging (fMRI) is widely used to infer the intrinsic functional organization of the brain, yet it remains unclear how well this approach can predict the structure of brain activity observed across a diverse set of mental states. Here we compare resting-state to task-based fMRI using diverse task batteries within the same individuals. We find that multi-task fMRI data consistently outperform resting-state estimates in predicting functional organization during novel tasks. This advantage persists across preprocessing strategies, brain regions, and independent datasets. While task activation estimates do show task-dependency when using only few tasks, increasing task diversity reduced task-specific bias, with convergence achieved using modest task sets. These improvements translate into superior individual parcellations and connectivity models. Together, our results dissociate reliability from validity in neuroimaging and challenge the prevailing assumption that rest provides a privileged window into intrinsic brain organization. Instead, functional architecture appears most faithfully revealed when the brain is actively driven through diverse task states.

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 10 matches between paragraphs and lines of code.

DiedrichsenLab/Functional_Fusion

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: f9a47ef263e63f1cfc148043521ffc5031b3a0e0, 22 September 2026
Languages: Python (93), Jupyter (13), MATLAB (7), Shell (6)
Size: 310 files, 119 scripts
Software Heritage: not archived
Found in: “Code Availability”
Holds: README, CITATION.cff, environment (requirements.txt, docs/requirements-build-docs.txt), tests, documentation, 13 notebooks
Not found: license file, continuous integration
Tools: NumPy (76 files), pandas (62 files), NiBabel (58 files), Matplotlib (31 files), SciPy (15 files), FSL (7 files), SPM (7 files), Nilearn (6 files), FreeSurfer (5 files), PyTorch (3 files), seaborn (3 files), Image Processing Toolbox (2 files), ANTs (1 file), GIfTI library for MATLAB (1 file), neuromaps (1 file), Plotly (1 file), Connectome Workbench (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
120 files
At the source: github.com/DiedrichsenLab/Functional_Fusion

carobellum/RestMultitask

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 2492fc3f78c7b3659e3eac9f475c22f90e16d1b7, 26 April 2026
Languages: Python (24), Jupyter (11), MATLAB (1), Shell (1)
Size: 50 files, 37 scripts
Software Heritage: not archived
Found in: “Code Availability”
Holds: README, license file, environment (requirements.txt), 11 notebooks
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (33 files), pandas (31 files), Matplotlib (21 files), SciPy (16 files), seaborn (12 files), NiBabel (9 files), PyTorch (4 files), FSL (1 file), Nilearn (1 file), statannotations (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
39 files
At the source: github.com/carobellum/RestMultitask.git

DiedrichsenLab/HierarchBayesParcel

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: d6060db0c9821ac9097cbfeb6949ec1716d8bf30, 20 May 2026
Languages: Python (49), Jupyter (21)
Size: 162 files, 70 scripts
Software Heritage: not archived
Found in: “Code Availability”
Holds: README, license file, environment (docs/requirements-build-docs.txt), tests, documentation, 21 notebooks
Not found: CITATION.cff, continuous integration
Tools: NumPy (23 files), Matplotlib (14 files), PyTorch (13 files), pandas (11 files), NiBabel (7 files), SciPy (7 files), Nilearn (4 files), scikit-learn (4 files), h5py (3 files), seaborn (3 files), Plotly (2 files)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
30 files
At the source: github.com/DiedrichsenLab/HierarchBayesParcel

DiedrichsenLab/cortico_cereb_connectivity

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: a0d38b52ba64aea5ed09d17e065ff05550f84124, 24 May 2025
Languages: Jupyter (18), Python (16)
Size: 53 files, 34 scripts
Software Heritage: not archived
Found in: “Code Availability”
Holds: README, license file, tests, 17 notebooks
Not found: CITATION.cff, environment file, continuous integration, documentation
Tools: NumPy (31 files), pandas (25 files), seaborn (16 files), Matplotlib (15 files), NiBabel (13 files), SciPy (12 files), scikit-learn (5 files), h5py (1 file), Nilearn (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
36 files
At the source: github.com/DiedrichsenLab/cortico_cereb_connectivity

diedrichsenlab/MultiTaskBattery

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: c980ff65152e15d490ed6820577e7abaedfa7550, 26 September 2026
Languages: Python (20)
Size: 848 files, 20 scripts
Software Heritage: not archived
Found in: “Code Availability”
Holds: README, license file, environment (requirements.txt, docs/requirements-build-docs.txt), documentation
Not found: CITATION.cff, tests, continuous integration
Tools: pandas (8 files), NumPy (7 files), PsychoPy (5 files), NiBabel (3 files), Matplotlib (1 file), Nilearn (1 file), Numba (1 file), Pillow (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
22 files
At the source: github.com/diedrichsenlab/MultiTaskBattery.git

Code Availability

The code for generating the results and figures in this paper is publicly available as the GitHub repository https://github.com/carobellum/RestMultitask.git. The code for the hierarchical Bayesian parcellation framework is available at https://github.com/DiedrichsenLab/HierarchBayesParcel. The organization, file system, and code for managing the diverse set of datasets is available at https://github.com/DiedrichsenLab/Functional_Fusion. The code for connectivity modelling is available at https://github.com/DiedrichsenLab/cortico_cereb_connectivity. The Python toolbox for running multi-domain task batteries, along with an implementation of most of the tasks used in the current batteries is openly available at https://github.com/diedrichsenlab/MultiTaskBattery.git.

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:

  • 5 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 238 scripts, each with its path and the digest of its content;
  • 10 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

Data Availability

The raw task and rest fMRI data of the MDTB has been deposited in the openneuro database under accession code ds002105 [doi: 10.18112/openneuro.ds002105.v1.1.0 (https://doi.org/10.18112/openneuro.ds002105.v1.1.0)]. For the HCP dataset, raw and preprocessed data is available at https://www.humanconnectome.org/study/hcp-young-adult/data-releases. The replication dataset has not yet been openly released.

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, 30 September 2026: the first record

Recorded: type, journal, dates, 7 authors, 7 keywords, 1 funder, 106 references.

Cite

This paper

Nettekoven, C., Shahbazi, A., Arafat, B., Skenderija, M., Xiang, J. D., Luísa Pinho, A., & Diedrichsen, J. (2026). Multi-task fMRI outperforms resting-state fMRI for revealing task-invariant organization of the human brain. bioRxiv (preprint). https://doi.org/10.64898/2026.03.09.710558

BibTeX

@article{nettekoven2026multi,
author = {Nettekoven, Caroline and Shahbazi, Ali and Arafat, Bassel and Skenderija, Matea and Xiang, Jinkang Derrick and Luísa Pinho, Ana and Diedrichsen, Jörn},
title = {{Multi-task fMRI outperforms resting-state fMRI for revealing task-invariant organization of the human brain}},
journal = {bioRxiv (preprint)},
year = {2026},
month = mar,
publisher = {bioRxiv},
issn = {2692-8205},
doi = {10.64898/2026.03.09.710558},
url = {https://doi.org/10.64898/2026.03.09.710558}
}

RIS

TY - JOUR
AU - Nettekoven, Caroline
AU - Shahbazi, Ali
AU - Arafat, Bassel
AU - Skenderija, Matea
AU - Xiang, Jinkang Derrick
AU - Luísa Pinho, Ana
AU - Diedrichsen, Jörn
TI - Multi-task fMRI outperforms resting-state fMRI for revealing task-invariant organization of the human brain
T2 - bioRxiv (preprint)
J2 - bioRxiv
PY - 2026
DA - 2026/03/10
SN - 2692-8205
PB - bioRxiv
DO - 10.64898/2026.03.09.710558
UR - https://doi.org/10.64898/2026.03.09.710558
ER -

CSL-JSON

{
"id": "10.64898/2026.03.09.710558",
"type": "article",
"title": "Multi-task fMRI outperforms resting-state fMRI for revealing task-invariant organization of the human brain",
"container-title": "bioRxiv (preprint)",
"author": [
{
"family": "Nettekoven",
"given": "Caroline"
},
{
"family": "Shahbazi",
"given": "Ali"
},
{
"family": "Arafat",
"given": "Bassel"
},
{
"family": "Skenderija",
"given": "Matea"
},
{
"family": "Xiang",
"given": "Jinkang Derrick"
},
{
"family": "Luísa Pinho",
"given": "Ana"
},
{
"family": "Diedrichsen",
"given": "Jörn"
}
],
"container-title-short": "bioRxiv",
"DOI": "10.64898/2026.03.09.710558",
"ISSN": "2692-8205",
"publisher": "bioRxiv",
"URL": "https://doi.org/10.64898/2026.03.09.710558",
"issued": {
"date-parts": [
[
2026,
3,
10
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.21203/rs.3.rs-9326213/v1 [code]
Multi-task fMRI outperforms resting-state fMRI for revealing task-invariant organization of the human brain
Journal: Research Square (preprint)
In common: neuromaps, statannotations, PsychoPy, 20 other tools, OpenNeuro 10.18112/openneuro.ds002105.v1.1.0, humanconnectome.org/study/hcp-young-adult, fMRI, cognitive, 87 references, 3 authors
[2] doi:10.1016/j.isci.2026.116903 [code]
Neurobiological and behavioral relevance of intrinsic functional connectome constraints on task-evoked neural activation.
Journal: iScience
In common: Connectome Workbench, GIfTI library for MATLAB, ANTs, 11 other tools, humanconnectome.org/study/hcp-young-adult, fMRI, cognitive, 11 references
[3] doi:10.1002/hbm.70483 [code]
Untamed: Unconstrained Tensor Decomposition and Graph Node Embedding for Cortical Parcellation.
Journal: Human brain mapping
In common: Connectome Workbench, GIfTI library for MATLAB, ANTs, 12 other tools, humanconnectome.org/study/hcp-young-adult, fMRI, 10 references
[4] doi:10.1162/imag.a.1222 [code]
Network-based near-scalp personalized brain stimulation targets.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: Connectome Workbench, GIfTI library for MATLAB, ANTs, 11 other tools, 13 references
[5] doi:10.1016/j.neuron.2026.04.011 [code]
Precision fMRI reveals densely interdigitated network patches with conserved motifs in the lateral prefrontal cortex.
Journal: Neuron
In common: Connectome Workbench, GIfTI library for MATLAB, ANTs, 11 other tools, fMRI, 13 references
[6] doi:10.1162/imag.a.1323 [code]
SUITPy: A Python-based toolbox for the analysis of cerebellar functional and anatomical imaging data across the human lifespan.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: ANTs, Nilearn, Plotly, 5 other tools, 3 references, 4 authors
[7] doi:10.1038/s41467-026-73668-y [code]
Convergent and divergent brain-cognition development in early adolescence.
Journal: Nature communications
In common: Connectome Workbench, GIfTI library for MATLAB, ANTs, 13 other tools, fMRI, 8 references
[8] doi:10.1038/s41467-026-72931-6 [code]
Three parsimonious spatiotemporal patterns in cerebellum reveal individual traits in function and behavior.
Journal: Nature communications
In common: Connectome Workbench, Nilearn, NiBabel, 7 other tools, humanconnectome.org/study/hcp-young-adult, fMRI, cognitive, 11 references
[9] doi:10.1038/s41467-026-71568-9 [code]
Convergent and selective representations of pain, appetitive processes, aversive processes, and cognitive control in the insula.
Journal: Nature communications
In common: Connectome Workbench, ANTs, FreeSurfer, 11 other tools, cognitive, 8 references
[10] doi:10.1038/s41398-026-04025-2 [code]
Brain energetic landscapes shape state dysregulation in major depressive disorder: a morphological network controllability perspective.
Journal: Translational psychiatry
In common: neuromaps, Connectome Workbench, GIfTI library for MATLAB, 15 other tools, 3 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.