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Narrative "twist" shifts within-individual neural representations of dissociable story features.

A correction to this paper has been published: the notice, 42150088, from Europe PMC.

Code ↔ Paper

2 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 2 matches
  1. [1] § Results › Representations of the Narrative Model. ↔ code/Fig2._narrativemodel/1.4._compute_samples_matched_length_set_up_df.ipynb, lines 30–88 · score 0.52 · intra SC, matched length, post twist, pretwist, timepoint, median
  2. [2] § Results › Representations of the Narrative Model. › Greater neural shifts in the pre-twist segment. ↔ code/Fig2._narrativemodel/1.1._prepost_lmer_set_up_LMER_pre_post_twist.ipynb, lines 10–47 · score 0.51 · intra SC, post twist, pre twist, timepoint, hippocampus, correlation

Paper

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

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The authors' code

Jupyter notebook · 96 lines · 2.8 KB · no license · 1 match

  1. # %% [markdown]
  2. # ### The goal of this code is to generate the matched length iterations of the pre and post-twist segments
  4. # %%
  5. import pandas as pd
  6. import numpy as np
  7. import os
  9. # %%
  10. timepoints_cut_off = 20
  11. timepoints_cut_off_end = 10
  12. stim_end = 1107 #because it is 1105 seconds + 2 seconds!
  13. pre_twist = 552
  14. post_twist = 752
  16. node = 1
  17. sub = 0
  18. sub_corr_reint = np.load(f'../../../darkend/data/_multivariate_intraSC/intraSC_pattern_all_node{node}_pearsonr.npy',allow_pickle=True).item()
  20. pre_length = len(sub_corr_reint[sub][timepoints_cut_off:pre_twist])
  21. post_length = len(sub_corr_reint[sub][post_twist:])
  24. # %%
  26. nodes = list(range(1,101)) + ['hippocampus_L','hippocampus_R']
  27. node = nodes[0]
  30. # %%
  32. sub_list = []
  33. r_val = [] #actually adding in the Fisher z-transformed valued
  34. node_list = []
  35. iteration_list = []
  36. label = []
  38. node = nodes[0]
  39. sub_corr_reint = np.load(f'../../../darkend/data/_multivariate_intraSC/intraSC_pattern_all_node{node}_pearsonr.npy', allow_pickle=True).item()
  40. sample_pretwist = sub_corr_reint[0][timepoints_cut_off:pre_twist]
  41. sample_posttwist = sub_corr_reint[0][post_twist:stim_end]
  43. pre_length = len(sample_pretwist)
  44. post_length = len(sample_posttwist)
  46. # Calculate the number of possible iterations
  47. num_iterations = pre_length - post_length + 1
  48. print(f"Number of possible iterations: {num_iterations}")
  50. # Iterate through nodes
  51. for node in nodes:
  52. sub_corr_reint = np.load(f'../../../darkend/data/_multivariate_intraSC/intraSC_pattern_all_node{node}_pearsonr.npy', allow_pickle=True).item()
  54. for sub in range(len(sub_corr_reint.keys())):
  55. pretwist_this_sub = sub_corr_reint[sub][timepoints_cut_off:pre_twist]
  56. posttwist_this_sub = sub_corr_reint[sub][post_twist:stim_end]
  58. for i in range(num_iterations):
  59. pre_segment = pretwist_this_sub[i:i + post_length]
  60. post_segment = posttwist_this_sub
  61. pre_segment = [np.arctanh(r) for r in pre_segment]
  62. post_segment = [np.arctanh(r) for r in post_segment]
  65. # Append results for pre-twist
  66. sub_list.append(sub)
  67. r_val.append(np.median(pre_segment))
  68. node_list.append(node)
  69. iteration_list.append(i + 1) # Iteration number
  70. label.append('pre')
  72. # Append results for post-twist (same iteration)
  73. sub_list.append(sub)
  74. r_val.append(np.median(post_segment))
  75. node_list.append(node)
  76. iteration_list.append(i + 1) # Iteration number
  77. label.append('post')
  79. # Create DataFrame
  80. results_df = pd.DataFrame({
  81. 'Node': node_list,
  82. 'Subject': sub_list,
  83. 'Iteration': iteration_list,
  84. 'Median_R_Value': r_val,
  85. 'Label': label
  86. })
  88. results_df
  91. # %%
  92. results_df.to_csv('1.6._pretwist_posttwist_matched_length_median_segments_subcortical_new.csv', index=False)
  95. # %%

1.4._compute_samples_matched_length_set_up_df.ipynb at commit 7c37a04, no license · at the source

Overview

Authors: Clara Sava-Segal1, Clare Grall1, Emily S. Finn1
ORCID iDs: Clara Sava-Segal, Emily S. Finn
  1. Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755
Institutions: Dartmouth College (United States)
Journal: Proceedings of the National Academy of Sciences of the United States of America, volume 123, issue 11, article e2512071123
Dates: received 17 May 2025; accepted 30 January 2026; published online 13 March 2026; in print 17 March 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1073/pnas.2512071123 · PMID 41824498 · PMCID PMC12994196 · OpenAlex W7135181464
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), cognitive (subfield)
Methods: Machine learning, Statistics, fMRI & imaging
Keywords: naturalistic neuroimaging, functional MRI, cognitive neuroscience, narratives
MeSH: Auditory Perception*, Brain*, Comprehension*, Narration*, Acoustic Stimulation, Adult, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male, Young Adult (* major topic)
Journal subjects: Biological Sciences, Psychological and Cognitive Sciences, Social Sciences
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NIH (R00MH120257, 1F31MH138084-01); NSF | National Science Foundation Graduate Research Fellowship Program (GRFP) (2236868)
Citations: cited by 3 papers (Europe PMC); 67 references in the paper
Notices: A correction to this paper has been published (42150088, from Europe PMC)

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 2 matches between paragraphs and lines of code.

thefinnlab/darkend_narrative_rep

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 7c37a048bc3e80abdca0a85ae1e26db31b2ed45e, 16 May 2026
Languages: Jupyter (19), R (3), Python (2)
Size: 239 files, 24 scripts
Software Heritage: not archived
Found in: “Data, Materials, and Software Availability”
Holds: README, 9 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (21 files), pandas (19 files), Matplotlib (15 files), BrainSpace (12 files), NiBabel (12 files), Nilearn (12 files), neuromaps (11 files), statsmodels (11 files), seaborn (6 files), SciPy (5 files), lme4 (3 files), lmerTest (3 files), tidyverse (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
25 files
At the source: github.com/thefinnlab/darkend_narrative_rep

The paper's code and data availability statement is in the Data section.

Tracing map

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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;
  • 24 scripts, each with its path and the digest of its content;
  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

Datasets cited

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/10.1073/pnas.2512071123.

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 4 keywords, 12 MeSH terms, 2 funders, 59 references, 1 integrity notice.

Cite

This paper

Sava-Segal, C., Grall, C., & Finn, E. S. (2026). Narrative "twist" shifts within-individual neural representations of dissociable story features. Proceedings of the National Academy of Sciences of the United States of America, 123(11), e2512071123. https://doi.org/10.1073/pnas.2512071123

BibTeX

@article{savasegal2026narrative,
author = {Sava-Segal, Clara and Grall, Clare and Finn, Emily S.},
title = {{Narrative "twist" shifts within-individual neural representations of dissociable story features}},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
year = {2026},
month = mar,
volume = {123},
number = {11},
pages = {e2512071123},
publisher = {National Academy of Sciences},
issn = {0027-8424},
doi = {10.1073/pnas.2512071123},
url = {https://doi.org/10.1073/pnas.2512071123},
pmid = {41824498},
pmcid = {PMC12994196}
}

RIS

TY - JOUR
AU - Sava-Segal, Clara
AU - Grall, Clare
AU - Finn, Emily S.
TI - Narrative "twist" shifts within-individual neural representations of dissociable story features
T2 - Proceedings of the National Academy of Sciences of the United States of America
J2 - Proc Natl Acad Sci U S A
PY - 2026
DA - 2026/03/13
VL - 123
IS - 11
SP - e2512071123
SN - 0027-8424
PB - National Academy of Sciences
DO - 10.1073/pnas.2512071123
UR - https://doi.org/10.1073/pnas.2512071123
LA - en
ER -

CSL-JSON

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"container-title": "Proceedings of the National Academy of Sciences of the United States of America",
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"family": "Sava-Segal",
"given": "Clara"
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"container-title-short": "Proc Natl Acad Sci U S A",
"volume": "123",
"issue": "11",
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"PMID": "41824498",
"PMCID": "PMC12994196",
"ISSN": "0027-8424",
"publisher": "National Academy of Sciences",
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13
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