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Cortical reinstatement of causally related events sparks narrative insights by updating neural representation patterns.

Code ↔ Paper

4 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 4 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 › FMRI image preprocessing ↔ code/code_extractbold.py, lines 32–82 · score 0.81 · head motion, brain mask, fMRI, BIDS, movie, EPI
  2. [2] § Methods › Neural reinstatement of causally related past events ↔ code/code_ahahmmreinst_stats.R, the whole file · a weak match · score 0.80 · behavioral retrieval neural, lme4, neural pattern shift, neural reinstatement, binomial, glmer
  3. [3] § Methods › Parcellation ↔ code/code_extractbold.py, lines 32–82 · score 0.69 · brain mask, subcortical parcels, fMRI, EPI, voxels
  4. [4] § Results › Neural reinstatement of causally related past events drives neural pattern shifts at insight moments ↔ code/code_ahahmmreinst_stats.R, the whole file · a weak match · score 0.55 · neural pattern shift, behavioral retrieval, Neural reinstatement, mediation

Paper

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The authors' code

Python · 87 lines · 4.5 KB · MIT · 2 matches

  1. # extracting voxel time series after applying a parcel mask to preprocessed EPIs
  2. # Nov 24, 2024, Hayoung Song
  4. # preprocessed fMRI data can be downloaded from: https://openneuro.org/datasets/ds005658
  6. import numpy as np
  7. from nilearn.image import load_img
  8. import matplotlib.pyplot as plt
  9. import scipy.stats
  10. import pandas as pd
  12. def niftimask(nroi_cor, nroi_sub, directory):
  13. cortical = directory+'/template/tpl-MNI152NLin2009cAsym/tpl-MNI152NLin2009cAsym_res-02_atlas-Schaefer2018_desc-'+str(nroi_cor)+'Parcels17Networks_dseg.nii.gz'
  14. if nroi_sub==16: subcortical = directory+'/template/Tian2020MSA_v1.1_3T_Subcortex-Only/Tian_Subcortex_S1_3T_2009cAsym.nii.gz'
  15. elif nroi_sub==32: subcortical = directory+'/template/Tian2020MSA_v1.1_3T_Subcortex-Only/Tian_Subcortex_S2_3T_2009cAsym.nii.gz'
  16. elif nroi_sub == 50: subcortical = directory + '/template/Tian2020MSA_v1.1_3T_Subcortex-Only/Tian_Subcortex_S3_3T_2009cAsym.nii.gz'
  17. elif nroi_sub == 54: subcortical = directory + '/template/Tian2020MSA_v1.1_3T_Subcortex-Only/Tian_Subcortex_S4_3T_2009cAsym.nii.gz'
  18. mask_cor = load_img(cortical).dataobj[:]
  19. mask_sub = load_img(subcortical).dataobj[:]
  21. for i1 in range(mask_sub.shape[0]):
  22. for i2 in range(mask_sub.shape[1]):
  23. for i3 in range(mask_sub.shape[2]):
  24. if mask_sub[i1,i2,i3]>0:
  25. mask_sub[i1,i2,i3] = mask_sub[i1,i2,i3] + nroi_cor
  27. id = np.where(np.multiply(mask_cor, mask_sub)>0)
  28. mask = mask_cor + mask_sub
  29. mask[id[0],id[1],id[2]] = 0
  30. return mask
  32. ''' setting '''
  33. flist = {}
  34. flist[1] = ['sub-1001', 'sub-1005', 'sub-1008', 'sub-1011', 'sub-1014', 'sub-1017', 'sub-1020', 'sub-1023', 'sub-1026', 'sub-1029', 'sub-1033', 'sub-1039']
  35. flist[2] = ['sub-2006', 'sub-2009', 'sub-2012', 'sub-2015', 'sub-2018', 'sub-2021', 'sub-2024', 'sub-2027', 'sub-2034', 'sub-2038', 'sub-2040'] # 'sub-2030'
  36. flist[3] = ['sub-3004', 'sub-3007', 'sub-3013', 'sub-3016', 'sub-3019', 'sub-3022', 'sub-3025', 'sub-3031', 'sub-3037', 'sub-3041'] # 'sub-3010', 'sub-3028'
  37. tasklist = ['01','02','03','04','05','06','07','08','09','10']
  38. # sub-2030, sub-3010, sub-3028: large head motion participants
  39. # sub-1023 task-03: only movie watching portion was recorded
  40. nsubj = len(flist[1])+len(flist[2])+len(flist[3])
  42. directory = '/foldername'
  44. nroi_cor, nroi_sub = 100, 16
  45. hrf = 4 # 4TR = 4.8s
  46. mask = niftimask(nroi_cor, nroi_sub, directory)
  48. for groupid in range(1, 3+1):
  49. run = np.array(pd.read_csv(directory+'/socialaha-fMRI/socialaha_groupscene.csv')['run'])
  50. scene = np.array(pd.read_csv(directory+'/socialaha-fMRI/socialaha_groupscene.csv')['g'+str(groupid)+'.sceneid'])
  52. for si, subname in enumerate(flist[groupid]):
  53. # parcel mask is multiplied by each participant's brain mask applied during preprocessing
  54. submask = load_img(directory+'/masks/'+subname+'/'+subname+'_combined.nii.gz').dataobj[:]
  55. submask = np.multiply(submask, mask)
  57. for ti, task in enumerate(tasklist):
  58. print(subname+' task-'+task)
  60. # time stamps
  61. tst = pd.read_csv(directory+'/bids/'+subname+'/func/'+subname+'_task-'+task+'_events.tsv', sep='\t')
  62. tst['offset'] = tst['onset'] + tst['duration']
  63. tst['onset'] = tst['onset'] + hrf
  64. tst['offset'] = tst['offset'] + hrf
  66. # normalized BOLD time series of all voxels corresponding to each of the cortical & subcortical parcels
  67. epi = load_img(directory + '/derivatives/'+subname+'/'+subname+'_task-'+task+'_smoothed.nii.gz').dataobj[:]
  68. roi_ts = {}
  69. for roi in range(1, nroi_cor+nroi_sub+1):
  70. mask_id = np.where(submask == roi)
  71. ts = np.array([])
  72. ts = epi[mask_id[0], mask_id[1], mask_id[2], :]
  74. # remove time steps that were censored due to motion
  75. for tr in range(ts.shape[1]):
  76. if np.all(ts[:,tr]==0):
  77. ts[:,tr] = np.nan
  79. # normalize each voxel time series
  80. ts = scipy.stats.zscore(ts, axis=1, ddof=1, nan_policy='omit')
  82. roi_ts[roi] = ts
  84. # example
  85. # roi_ts[1][:,int(tst['onset'][0]):int(tst['offset'][0])] # time series of roi=1 during first-event movie watching
  86. # roi_ts[50][:,int(tst['onset'][5]):int(tst['offset'][5])].shape # time series of roi=50 during first-event aha explanation
  87. # roi_ts[50][:,int(tst['onset'][10]):int(tst['offset'][10])].shape # time series of roi=50 during first-character impression

code_extractbold.py at commit b910bab, under MIT · at the source

Overview

Authors: Hayoung Song1,2, Jin Ke1,3, Rhea Madhogarhia1, Yuan Chang Leong1,4,5, Monica D. Rosenberg1,4,5
ORCID iDs: Hayoung Song, Jin Ke, Rhea Madhogarhia, Yuan Chang Leong, Monica D. Rosenberg
  1. Department of Psychology, University of Chicago,Chicago, IL USA
  2. Center for Theoretical and Computational Neuroscience, Washington University in St. Louis,St. Louis, MO USA
  3. Department of Psychology, Yale University,New Haven, CT USA
  4. Neuroscience Institute, University of Chicago,Chicago, IL USA
  5. Institute for Mind and Biology, University of Chicago,Chicago, IL USA
Institutions: University of Chicago (United States); Washington University in St. Louis (United States); Yale University (United States)
Journal: Nature communications, volume 17, issue 1, article 7362
Dates: received 7 April 2025; accepted 22 May 2026; published online 10 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-73914-3 · PMID 42270596 · PMCID PMC13402326 · OpenAlex W4408413088
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.03.12.642853
Status: code verified
Categories: human (organism), cognitive (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Connectivity, fMRI & imaging, Machine learning
Keywords: Problem solving, Long-term memory, Human behaviour
MeSH: Brain*, Cerebral Cortex*, Comprehension*, Adult, Brain Mapping, Cognition, Female, Humans, Magnetic Resonance Imaging, Male, Young Adult (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: American Psychological Association (American Psychological Association, Inc.); University of Chicago; NSF (BCS-2043740)
Citations: cited by 3 papers (Europe PMC); 41 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.

Repositories

Its files are read in the Code ↔ Paper reader above, with 4 matches between paragraphs and lines of code.

Zenodo 19389961

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
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: Matplotlib (5 files), NumPy (5 files), pandas (5 files), SciPy (4 files), seaborn (2 files), BrainIAK (1 file), lme4 (1 file), lmerTest (1 file), Nilearn (1 file), statsmodels (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
7 files
At the source:

hyssong/memoryaha

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: b910bab1d86e24bdeab0af69e0f8f52f2e2689f0, 13 May 2026
Languages: Python (5), R (1)
Size: 21 files, 6 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, license file, environment (environment.yml)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: Matplotlib (5 files), NumPy (5 files), pandas (5 files), SciPy (4 files), seaborn (2 files), BrainIAK (1 file), lme4 (1 file), lmerTest (1 file), Nilearn (1 file), statsmodels (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
8 files
At the source: github.com/hyssong/memoryaha/tree/v.1.0.0

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:

  • it points to the authors' code: Zenodo 19389961

Read it in the paper: doi.org/10.1038/s41467-026-73914-3.

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What the map holds:

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Data

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Code and data availability statement

The paper has a code and 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/s41467-026-73914-3.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 3 keywords, 11 MeSH terms, 3 funders, 38 references.

Cite

This paper

Song, H., Ke, J., Madhogarhia, R., Leong, Y. C., & Rosenberg, M. D. (2026). Cortical reinstatement of causally related events sparks narrative insights by updating neural representation patterns. Nature communications, 17(1), 7362. https://doi.org/10.1038/s41467-026-73914-3

BibTeX

@article{song2026cortical,
author = {Song, Hayoung and Ke, Jin and Madhogarhia, Rhea and Leong, Yuan Chang and Rosenberg, Monica D.},
title = {{Cortical reinstatement of causally related events sparks narrative insights by updating neural representation patterns}},
journal = {Nature communications},
year = {2026},
month = jun,
volume = {17},
number = {1},
pages = {7362},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-73914-3},
url = {https://doi.org/10.1038/s41467-026-73914-3},
pmid = {42270596},
pmcid = {PMC13402326}
}

RIS

TY - JOUR
AU - Song, Hayoung
AU - Ke, Jin
AU - Madhogarhia, Rhea
AU - Leong, Yuan Chang
AU - Rosenberg, Monica D.
TI - Cortical reinstatement of causally related events sparks narrative insights by updating neural representation patterns
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/06/10
VL - 17
IS - 1
SP - 7362
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-73914-3
UR - https://doi.org/10.1038/s41467-026-73914-3
LA - en
ER -

CSL-JSON

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