OSCR

Convergent and selective representations of pain, appetitive processes, aversive processes, and cognitive control in the insula.

Code ↔ Paper

14 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 14 matches · 5 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Results › Coactivation between insular zones and other brain systems ↔ site/scripts/prepare_labels.py, lines 18–60 · score 0.79 · basal ganglia, subthalamic nucleus, dorsal posterior, ventral posterior, putamen, medial
  2. [2] § Results › Coactivation between insular zones and other brain systems ↔ Atlases_and_parcellations/2023_CANLab_atlas/src/create_thalamus2023_atlas.m, lines 4–94 · score 0.70 · lateral dorsal, ventral lateral, ventral anterior, VM, mediodorsal, inferior
  3. [3] § Methods › Validation with independent datasets ↔ site/scripts/seed_catalog.py, lines 1–50 · score 0.70 · monetary reward, thermal pain, cognitive control, repositories, appetitive, validate
  4. [4] § Methods › Validation with independent datasets ↔ SVM/mtSVM_insula_domain.m, the whole file · a weak match · score 0.68 · ANiC, appetitive processes, aversive processes, cognitive control, domain, pain
  5. [5] § Methods › Multiclass support vector machine classifier ↔ SVM/mtSVM_insula_domain.m, the whole file · a weak match · score 0.66 · hyperparameter optimization, SVM, linear, trained, classifiers, subdomain
  6. [6] § Methods › Multiclass support vector machine classifier ↔ SVM/mtSVMs_insula_apptVSaver_domain.m, the whole file · a weak match · score 0.66 · hyperparameter optimization, SVM, linear, trained, classifiers, subdomain
  7. [7] § Methods › Study design ↔ site/scripts/seed_catalog.py, lines 1–50 · score 0.65 · sexual images, cognitive control, social, drug, food, thermal
  8. [8] § Results › Coactivation between insular zones and other brain systems ↔ Atlases_and_parcellations/2018_CIT168_Reinf_Learn_v1.1.0/CIT168_MNI152NLin2009cAsym_create_atlas_object.m, lines 1–76 · score 0.65 · globus pallidus, subthalamic nucleus, external, internal, putamen, ventral
  9. [9] § Results › Coactivation between insular zones and other brain systems ↔ Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/visualize_contents.m, the whole file · a weak match · score 0.64 · pACC, pMCC, vmPFC, aMCC
  10. [10] § Methods › Study design ↔ site/src/graph.js, the whole file · a weak match · score 0.59 · sexual images, cognitive control, interactions, social, appetitive, aversive
  11. [11] § Results › Domain-general and domain-selective zones identified using Bayes Factors ↔ Atlases_and_parcellations/2024_CANLab_atlas/create_pain_pathways2024_brainnetwork.m, lines 99–168 · score 0.57 · mid insula, anterior insula, gyrus, opercular, hemispheric, clustered
  12. [12] § Results › Domain-general and domain-selective zones identified using Bayes Factors ↔ Atlases_and_parcellations/2019_Wager_pain_pathways/scripts/create_pain_pathways_brainnetwork.m, lines 71–134 · score 0.55 · mid insula, anterior insula, gyrus, opercular, clustered, ventral
  13. [13] § Results › Coactivation between insular zones and other brain systems ↔ Atlases_and_parcellations/2018_Wager_combined_atlas/plugin_canlab_atlas_2018_relabel_larger_units.m, lines 200–241 · score 0.53 · temporal parietal, ventral attention, cortical, cortex, networks
  14. [14] § Results › Coactivation between insular zones and other brain systems ↔ site/scripts/prepare_labels.py, lines 18–60 · score 0.51 · ventral anterior, VA, lobule, caudate, putamen, thalamus

Paper

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The authors' code

Python · 60 lines · 4.6 KB · GPL-3.0 · 2 matches

  1. """Regenerate the descriptive lookup from repository atlas dictionaries (no network).
  2. The generated JSON/CSV are committed; deployment does not require source atlases.
  3. """
  4. from pathlib import Path
  5. import csv,json,re,hashlib
  6. ROOT=Path(__file__).resolve().parents[2]; OUT=ROOT/'site/data/shared'
  7. BASE='https://github.com/canlab/Neuroimaging_Pattern_Masks/blob/master/'
  8. CAN='Atlases_and_parcellations/2024_CANLab_atlas/src/openCANLab2024_MNI152NLin6Asym_labels.csv'
  9. BIO='Atlases_and_parcellations/2023_Bianciardi_BrainstemNavigatorV0.9/source_files/bianciardi_coarse_labels.csv'
  10. GLASSER='https://www.humanconnectome.org/storage/app/media/documentation/AABC2/AreaNamesAndIndices%20-%20NIHMS68870-supplement-Neuroanatomical_Supplementary_Results.pdf'
  11. rows=list(csv.DictReader((ROOT/CAN).open())); brain={}
  12. for r in csv.DictReader((ROOT/BIO).open()):
  13. for side in ['left','right']:
  14. if r[side]: brain[re.sub(r'_([lr])$',lambda m:'_'+m[1].upper(),r[side].strip())]=r['full_label'].replace('prabigeminal','parabigeminal')
  15. def clean(s):
  16. s=s.replace('_',' ').replace('NucleusAccumbens','Nucleus accumbens').replace('triansition','transition').replace('Anteroir','Anterior').replace('cudal','caudal')
  17. return re.sub(r'\s*\((left|right)\)|^(Left|Right)\s+', '', s, flags=re.I).strip()
  18. labels=json.loads((OUT/'atlas-labels.json').read_text()); result=[]
  19. for code,label in enumerate(labels,1):
  20. side={'L':'Left','R':'Right'}.get(label.split('_')[-1]); suffix=', '+side if side else ''
  21. matching=[r for r in rows if r['labels_2']==label] or [r for r in rows if r['labels']==label]
  22. components=list(dict.fromkeys(clean(r['label_descriptions']) for r in matching))
  23. source=BASE+CAN; note=''; name='; '.join(components)
  24. if label.startswith('Ctx_'):
  25. name='Cortex: '+name; source=GLASSER+' ; '+source
  26. elif label.startswith('Cblm_'):
  27. part=label.removeprefix('Cblm_'); part=re.sub(r'_[LR]$','',part)
  28. name='Cerebellum: '+part.replace('Vermis_','Vermis, ').replace('CrusII','Crus II').replace('CrusI','Crus I').replace('I_IV','lobules I–IV')
  29. if 'Crus' not in name and 'lobules' not in name: name=name.replace('Cerebellum: ','Cerebellum: lobule ')
  30. source='https://www.diedrichsenlab.org/imaging/propatlas.htm ; '+source
  31. elif re.match(r'BG_(CAU|PUT)_',label):
  32. _,structure,part,_=label.split('_'); name='Basal ganglia: '+{'CAU':'Caudate','PUT':'Putamen'}[structure]+', '+{'DA':'dorsal anterior','VA':'ventral anterior','DP':'dorsal posterior','VP':'ventral posterior','body':'body','tail':'tail'}[part]
  33. source='https://github.com/yetianmed/subcortex ; '+source
  34. elif label.startswith('BStem_'):
  35. key=label[6:]; plain=re.sub(r'_[LR]$','',key)
  36. overrides={'LC+':'Locus coeruleus and subcoeruleus','RObPaMg':'Raphe obscurus, pallidus and magnus','OC':'Inferior olivary nucleus and superior olivary complex','PAG':'Periaqueductal gray and merged cuneiform nucleus','STH':'Subthalamic nucleus'}
  37. if plain in overrides:
  38. name=overrides[plain]; source=BASE+'Atlases_and_parcellations/2024_CANLab_atlas/create_CANLab2024_atlas.m'; note='Composite atlas grouping; see construction code.' if plain!='STH' else 'Hemisphere follows the displayed label; source fine/coarse naming is inconsistent.'
  39. elif key in brain: name=brain[key]; source=BASE+BIO
  40. if key.startswith('Shen_') and side: name=re.sub(r'\b'+side+r'\b','',name,flags=re.I).strip()
  41. name='Brainstem: '+name
  42. elif label.startswith('BG_'): name='Basal ganglia: '+name
  43. elif label.startswith('MTL_'): name='Medial temporal lobe: '+name
  44. elif label.startswith('Thal_'): name='Thalamus: '+name
  45. elif label.startswith('hypothalamus_'): name='Hypothalamus: '+re.sub(r'_[LR]$','',label[len('hypothalamus_'):]).replace('_',' ')
  46. if len(matching)>1: note=(note+' Coarse parcel merges the listed source components.').strip()
  47. assert name and not name.endswith(': '),label
  48. result.append(dict(code=code,short_name=label,full_name=name+suffix,components=components,source=source,note=note))
  49. (OUT/'atlas-descriptions.json').write_text(json.dumps(result,ensure_ascii=False,indent=2)+'\n')
  50. with (OUT/'atlas-descriptions.csv').open('w') as f:
  51. writer=csv.DictWriter(f,fieldnames=list(result[0]),lineterminator='\n');writer.writeheader()
  52. for r in result: writer.writerow({**r,'components':'; '.join(r['components'])})
  53. assert len(result)==518 and result[0]['code']==1
  54. print(f'Wrote {len(result)} descriptive labels')
  56. provenance_path=OUT/'provenance.json'
  57. provenance=json.loads(provenance_path.read_text())
  58. for filename in ['atlas-descriptions.json','atlas-descriptions.csv']:
  59. provenance['files'][filename]=hashlib.sha256((OUT/filename).read_bytes()).hexdigest()
  60. provenance_path.write_text(json.dumps(provenance,indent=2)+'\n')

prepare_labels.py at commit 51946e4, under GPL-3.0 · at the source

Overview

Authors: Mijin Kwon1, Ke Bo1, Rotem Botvinik-Nezer1,2, Philip A. Kragel3,4, Lukas Van Oudenhove5, Tor D. Wager1, The Affective Neuroimaging Consortium
ORCID iDs: Ke Bo, Rotem Botvinik-Nezer, Philip A. Kragel, Lukas Van Oudenhove, Tor D. Wager
  1. Department of Psychological and Brain Sciences, Dartmouth College,Hanover, NH USA
  2. Department of Psychology, The Hebrew University of Jerusalem,Jerusalem, Israel
  3. Department of Psychology, Emory University,Atlanta, GA USA
  4. Department of Psychiatry and Behavioral Sciences, Emory University,Atlanta, GA USA
  5. Laboratory for Brain-Gut Axis Studies (LaBGAS), Translational Research in Gastrointestinal Disorders (TARGID), Department of Chronic Diseases and Metabolism (CHROMETA), University of Leuven,Leuven, Belgium
Institutions: Dartmouth College (United States); Hebrew University of Jerusalem (Israel); Emory University (United States); KU Leuven (Belgium)
Journal: Nature communications, volume 17, issue 1, article 5186
Dates: received 17 February 2025; accepted 23 March 2026; published online 14 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-71568-9 · PMID 41980935 · PMCID PMC13254212 · OpenAlex W4407720688
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.02.18.638889
Status: code verified
Categories: human (organism), pain (population), cognitive (subfield)
Methods: Connectivity, Statistics, Machine learning, Preprocessing
Keywords: Insula, Cognitive neuroscience
MeSH: Appetitive Behavior*, Cognition*, Insular Cortex*, Pain*, Bayes Theorem, Brain Mapping, Humans, Magnetic Resonance Imaging, Male (* major topic)
Topic: Building Energy and Comfort Optimization (Building and Construction, Engineering), according to OpenAlex
Funding: NIH (R37MH076136, R01MH116026)
Citations: cited by 2 papers (Europe PMC); 183 references in the paper

Abstract

Brain regions that integrate multiple types of information (“convergence zones”) are crucial for the brain to generate coherent experiences and behaviors. The insula, known for its functional diversity, has been hypothesized as a key convergence hub, yet empirical evidence remains incomplete. To address this gap, we analyzed functional convergence across four domains—pain, non-somatic appetitive processes, non-somatic aversive processes, and cognitive control—in a Bayesian mega-analysis of fMRI data (n = 540, 36 study contrasts). Bayes Factor analyses identified both multi-domain convergent and single-domain selective zones, validated with independent datasets (n = 608). Results revealed a hierarchical architecture, with a multi-domain convergence zone in bilateral dorsal anterior insula surrounded by progressively converging zones. Functional decoding and coactivation analyses further support the insula’s role as a convergence hub, while cytoarchitectonic and neurotransmitter profiling characterize the potential neuroanatomical basis of these zones. Together, the findings demonstrate a structured functional topography in the insula that bridges specialized and convergent processing, providing a potential neural basis for combining diverse information streams into unified experiences.

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 14 matches between paragraphs and lines of code.

canlab/neuroimaging_pattern_masks

License: GPL-3.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 51946e43db613b7f5c88134d9f1ce460cc9070e4, 14 September 2026
Languages: MATLAB (262), Python (45), Shell (26), JavaScript (7)
Size: 5,386 files, 340 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: environment (Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/setup.py), continuous integration, documentation, 4 notebooks
Not found: README, license file, CITATION.cff, tests
Tools: NumPy (22 files), NiBabel (21 files), Statistics and Machine Learning Toolbox (15 files), SciPy (12 files), FSL (9 files), pandas (6 files), ANTs (5 files), scikit-learn (5 files), fMRIPrep (4 files), Parallel Computing Toolbox (4 files), Nilearn (4 files), Connectome Workbench (4 files), FreeSurfer (3 files), SPM (3 files), Image Processing Toolbox (2 files), Nipype (2 files), Pillow (2 files), abagen (1 file), Matplotlib (1 file), MRtrix3 (1 file), statsmodels (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
342 files
At the source: github.com/canlab/Neuroimaging_Pattern_Masks/tree/master/neurosynth

canlab

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: the link answers
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 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
At the source: github.com/canlab/

mijinjkwon/proj_insula_anic

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 6ec0273c6b97b730b0bde206d48f37e10ca65ff5, 25 February 2025
Languages: MATLAB (8)
Size: 10 files, 8 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, 5 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (3 files)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
10 files
At the source: github.com/mijinjkwon/proj_insula_anic

Code availability

Matlab code for implementing all analyses is available at https://github.com/canlab/ and https://github.com/mijinjkwon/proj_insula_anic.

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;
  • 348 scripts, each with its path and the digest of its content;
  • 14 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 fMRI data from the main-analysis studies 1, 2, 4, 5, 7, 8, 19, 20, 22, 23, 25, 26, 28, 29, 31, and 32 are available at 10.6084/m9.figshare.24033402.v2. Data from studies 3 and 6 are available at https://neurovault.org/collections/8707/. Data from the validation dataset for cognitive control (n-back working memory task) are available from the Human Connectome Project database. The remaining datasets are available upon request from the corresponding authors of the individual studies. The Neurosynth dataset is available at https://github.com/canlab/Neuroimaging_Pattern_Masks/tree/master/neurosynth, the cytoarchitecture maps at https://github.com/canlab/Neuroimaging_Pattern_Masks/tree/master/Atlases_and_parcellations/2020_JulichBrain_v3.0.3, and the neurotransmitter receptor/transporter maps at https://github.com/canlab/Neuroimaging_Pattern_Masks/tree/master/Atlases_and_parcellations/2022_Hansen_PET_tracer_maps. Source data are provided with 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, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 2 keywords, 9 MeSH terms, 1 funder, 180 references.

Cite

This paper

Kwon, M., Bo, K., Botvinik-Nezer, R., Kragel, P. A., Van Oudenhove, L., Wager, T. D., & The Affective Neuroimaging Consortium. (2026). Convergent and selective representations of pain, appetitive processes, aversive processes, and cognitive control in the insula. Nature communications, 17(1), 5186. https://doi.org/10.1038/s41467-026-71568-9

BibTeX

@article{kwon2026convergent,
author = {Kwon, Mijin and Bo, Ke and Botvinik-Nezer, Rotem and Kragel, Philip A. and Van Oudenhove, Lukas and Wager, Tor D. and {The Affective Neuroimaging Consortium}},
title = {{Convergent and selective representations of pain, appetitive processes, aversive processes, and cognitive control in the insula}},
journal = {Nature communications},
year = {2026},
month = apr,
volume = {17},
number = {1},
pages = {5186},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-71568-9},
url = {https://doi.org/10.1038/s41467-026-71568-9},
pmid = {41980935},
pmcid = {PMC13254212}
}

RIS

TY - JOUR
AU - Kwon, Mijin
AU - Bo, Ke
AU - Botvinik-Nezer, Rotem
AU - Kragel, Philip A.
AU - Van Oudenhove, Lukas
AU - Wager, Tor D.
AU - The Affective Neuroimaging Consortium
TI - Convergent and selective representations of pain, appetitive processes, aversive processes, and cognitive control in the insula
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/04/14
VL - 17
IS - 1
SP - 5186
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-71568-9
UR - https://doi.org/10.1038/s41467-026-71568-9
LA - en
ER -

CSL-JSON

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