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The role of dorsal anterior cingulate cortex in dynamic attitude changes in naturalistic settings.

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] § Methods › fMRI data preprocessing ↔ scripts/2 prepare neural data/2.a task-debate denoise&parcel date after fmriprep.ipynb, lines 10–75 · score 0.67 · global signal, FWHM, fMRIPrep, smoothing, motion, denoising
  2. [2] § Methods › fMRI data preprocessing ↔ scripts/2 prepare neural data/2.b task-debate denoise&parcel date without spatial smoothimg.ipynb, lines 10–75 · score 0.67 · global signal, FWHM, smoothing, motion, denoising, fMRIPrep
  3. [3] § Methods › fMRI data preprocessing ↔ scripts/3 attitude change, FD, ISC test/FD test.ipynb, lines 74–99 · score 0.56 · framewise displacement, fMRIPrep, FD
  4. [4] § Methods › Decoding dynamic attitude changes from dACC-related neural indices ↔ scripts/6 predict attitude change/1.a MVP prediction 2min.ipynb, lines 122–168 · score 0.56 · class weights, folds, shuffled, SVM, cross, Classification
  5. [5] § Methods › Decoding dynamic attitude changes from dACC-related neural indices ↔ scripts/6 predict attitude change/2.a MVP prediction each shift.ipynb, lines 127–205 · score 0.55 · class weights, folds, shuffled, SVM, cross, Classification
  6. [6] § Results › Intolerance of uncertainty (IU) amplifies the association between attitude change similarity and neural similarity ↔ scripts/7 IU modulate analysis with LME/LME moderation models and figures.ipynb, lines 68–125 · score 0.52 · IU modulate, joint IU, interaction, LMEs, models

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 · 128 lines · 5.6 KB · no license · 1 match

  1. # %%
  2. import os
  3. import glob
  4. import numpy as np
  5. import pandas as pd
  6. import nibabel as nib
  7. from nilearn import image, masking, input_data
  10. # %%
  12. denoised = 'denoised 5'
  14. fwhm=6
  15. tr = 2
  17. tmp = 'Schaefer'
  18. Schaefer_mask_file = '/Users/li/Desktop/template/Schaefer/tpl-MNI152NLin2009cAsym_res-02_atlas-Schaefer2018_desc-200Parcels7Networks_dseg.nii.gz'
  19. roi_label_file = '/Users/li/Desktop/template/Schaefer/Schaefer2018_200Parcels_7Networks_order_FSLMNI152_2mm.Centroid_RAS.csv'
  20. Schaefer_mask = nib.load(Schaefer_mask_file)
  21. roi_label = pd.read_csv(roi_label_file)['ROI Name'].values.tolist()
  23. brain_mask_path = '/Users/li/Desktop/template/brain mask/tpl-MNI152NLin2009cAsym_res-02_desc-brain_mask.nii.gz'
  25. sub_list = [f'sub-{x:0>3d}' for x in range(1,51)]
  26. run_list = [1,2,3,4,5,6]
  28. for sub in sub_list:
  29. new_folder_path = os.path.join('/Volumes/Li/task-debate/braindata', denoised, 'denoised', sub)
  30. if not os.path.exists(new_folder_path):
  31. os.makedirs(new_folder_path)
  33. for run in run_list:
  34. run_file = f'/Volumes/Li/数据备份_task-debate/fmriprep/{sub}/func/{sub}_task-debate_run-{run}_space-MNI152NLin2009cAsym_res-2_desc-preproc_bold.nii.gz'
  35. run_img = nib.load(run_file)
  37. confound_file = f'/Volumes/Li/数据备份_task-debate/fmriprep/{sub}/func/{sub}_task-debate_run-{run}_desc-confounds_timeseries.tsv'
  38. confound_df = pd.read_csv(confound_file, delimiter ='\t')
  40. # motion_outlier
  41. # motion_outlier_columns = [col for col in confound_df.columns if 'motion_outlier' in col]
  43. selected_confound_cols = [ # csf_wm tcompcor std_dvars dvars framewise_displacement rmsd
  45. # 'global_signal', 'global_signal_derivative1', 'global_signal_power2', 'global_signal_derivative1_power2',
  46. # 'csf', 'csf_derivative1', 'csf_derivative1_power2', 'csf_power2',
  47. # 'white_matter', 'white_matter_derivative1', 'white_matter_derivative1_power2', 'white_matter_power2',
  49. 'global_signal', 'csf', 'white_matter',
  50. 'trans_x','trans_y','trans_z','rot_x','rot_y','rot_z',
  51. 'trans_x_derivative1','trans_y_derivative1','trans_z_derivative1',
  52. 'rot_x_derivative1','rot_y_derivative1','rot_z_derivative1'
  54. # 'trans_x_power2','trans_y_power2','trans_z_power2',
  55. # 'rot_x_power2','rot_y_power2','rot_z_power2',
  56. # 'trans_x_derivative1_power2','trans_y_derivative1_power2','trans_z_derivative1_power2',
  57. # 'rot_x_derivative1_power2','rot_y_derivative1_power2','rot_z_derivative1_power2'
  58. ]
  59. # selected_confound_cols = selected_confound_cols + motion_outlier_columns
  61. confound_selected = confound_df[selected_confound_cols]
  62. confound_selected = confound_selected.fillna(value=0)
  63. confound_matrix = confound_selected.values
  65. denoised_img = image.clean_img(run_img, confounds=confound_matrix,
  66. detrend=True, standardize=True, high_pass=0.008, t_r=tr, mask_img=brain_mask_path)
  68. smoothed_img = image.smooth_img(denoised_img, fwhm=fwhm)
  70. save_path = os.path.join('/Volumes/Li/task-debate/braindata', denoised, 'denoised', sub, f'{sub}_task-debate_run-{run}_denoised_smooth{fwhm}mm_space-MNI152NLin2009cAsym_res-2_bold.nii.gz')
  71. nib.save(smoothed_img,save_path)
  72. # print(f'{sub} {run} denoised_smoothed data saved successfully!')
  74. print(f'{sub} Done!')
  75. print('-----------------------------------------------')
  79. # %%
  81. for sub in sub_list:
  83. new_folder_path = os.path.join(f'/Volumes/Li/task-debate/braindata/{denoised}', 'parcel data', tmp, sub)
  84. if not os.path.exists(new_folder_path):
  85. os.makedirs(new_folder_path)
  87. for run in run_list:
  89. run_file = f'/Volumes/Li/task-debate/braindata/{denoised}/denoised/{sub}/{sub}_task-debate_run-{run}_denoised_smooth6mm_space-MNI152NLin2009cAsym_res-2_bold.nii.gz'
  90. run_image = nib.load(run_file)
  92. masker = input_data.NiftiLabelsMasker(labels_img = Schaefer_mask_file, standardize=True)
  94. parcellations_time_series = masker.fit_transform(run_image)
  96. parcellations_time_series_df = pd.DataFrame(parcellations_time_series) # , columns=roi_label
  99. csv_file_path = f'/Volumes/Li/task-debate/braindata/{denoised}/parcel data/{tmp}/{sub}/{sub}_run-{run}_task-health_{tmp}_time-series.csv'
  100. parcellations_time_series_df.to_csv(csv_file_path, index=False)
  102. print(f'{sub} Done!')
  106. # %% [markdown]
  107. # # combine 6 runs
  109. # %%
  110. new_folder_path = os.path.join('/Volumes/Li/task-debate/braindata', denoised, 'parcel data','Schaefer 200 combine 6 runs')
  111. if not os.path.exists(new_folder_path):
  112. os.makedirs(new_folder_path)
  115. for sub in sub_list:
  116. all_data = []
  117. for run in run_list:
  119. csv_files = f'/Volumes/Li/task-debate/braindata/{denoised}/parcel data/Schaefer/{sub}/{sub}_run-{run}_task-health_Schaefer_time-series.csv'
  120. run_data = pd.read_csv(csv_files)
  121. all_data.append(run_data)
  123. combined_data = pd.concat(all_data, ignore_index=True)
  124. combined_csv_path = os.path.join(f'/Volumes/Li/task-debate/braindata/{denoised}', 'parcel data','Schaefer 200 combine 6 runs', f'{sub}_combined_time-series_Schaefer2018_200Parcels_7Networks.csv')
  125. combined_data.to_csv(combined_csv_path, index=False)
  127. print(f"{sub} Combined CSV saved Done!!!")

2.a task-debate denoise&parcel date after fmriprep.ipynb at commit f330526, no license · at the source

Overview

Authors: Haiming Li1,2, Senmu Yao3, Yu Zhang1,2, Bing Wu3, Yi Liu1,2
ORCID iDs: Haiming Li, Senmu Yao, Bing Wu, Yi Liu
  1. School of Psychology, Northeast Normal University, Changchun, China
  2. Jilin Provincial Key Laboratory of Cognitive Neuroscience and Brain Development, Changchun, China
  3. Department of Radiology, The Seventh Medical Center of Chinese PLA General Hospital/Medical School, Beijing, China
Institutions: Northeast Normal University (China); Chinese PLA General Hospital (China)
Journal: Communications biology, volume 9, issue 1, article 505
Dates: received 26 September 2025; accepted 20 February 2026; published online 2 March 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s42003-026-09794-6 · PMID 41772025 · PMCID PMC13066430 · OpenAlex W7133190320
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), systems (subfield)
Methods: Connectivity, Statistics, Machine learning, Preprocessing, Evoked potentials, fMRI & imaging
Keywords: Motivation, Agency
MeSH: Attitude*, Gyrus Cinguli*, Adult, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male, Young Adult (* major topic)
Topic: Hemispheric Asymmetry in Neuroscience (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 70 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.

Llihaiming/dynamic-attitude-change

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: f330526f6cf141ccae8e12195071d99f30d8c936, 12 May 2025
Languages: Jupyter (41)
Size: 43 files, 41 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, 41 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (38 files), pandas (38 files), Matplotlib (32 files), seaborn (30 files), SciPy (27 files), NiBabel (25 files), Nilearn (21 files), scikit-learn (20 files), nltools (17 files), statsmodels (17 files), NetworkX (8 files), imbalanced-learn (5 files), scikit-image (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
39 files
At the source: github.com/Llihaiming/dynamic-attitude-change.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/s42003-026-09794-6.

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;
  • 38 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);
  • 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 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/s42003-026-09794-6.

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, language, journal, volume, issue, pages, dates, 5 authors, 2 keywords, 9 MeSH terms, 67 references.

Cite

This paper

Li, H., Yao, S., Zhang, Y., Wu, B., & Liu, Y. (2026). The role of dorsal anterior cingulate cortex in dynamic attitude changes in naturalistic settings. Communications biology, 9(1), 505. https://doi.org/10.1038/s42003-026-09794-6

BibTeX

@article{li2026role,
author = {Li, Haiming and Yao, Senmu and Zhang, Yu and Wu, Bing and Liu, Yi},
title = {{The role of dorsal anterior cingulate cortex in dynamic attitude changes in naturalistic settings}},
journal = {Communications biology},
year = {2026},
month = mar,
volume = {9},
number = {1},
pages = {505},
publisher = {Nature Publishing Group},
issn = {2399-3642},
doi = {10.1038/s42003-026-09794-6},
url = {https://doi.org/10.1038/s42003-026-09794-6},
pmid = {41772025},
pmcid = {PMC13066430}
}

RIS

TY - JOUR
AU - Li, Haiming
AU - Yao, Senmu
AU - Zhang, Yu
AU - Wu, Bing
AU - Liu, Yi
TI - The role of dorsal anterior cingulate cortex in dynamic attitude changes in naturalistic settings
T2 - Communications biology
J2 - Commun Biol
PY - 2026
DA - 2026/03/02
VL - 9
IS - 1
SP - 505
SN - 2399-3642
PB - Nature Publishing Group
DO - 10.1038/s42003-026-09794-6
UR - https://doi.org/10.1038/s42003-026-09794-6
LA - en
ER -

CSL-JSON

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"id": "10.1038/s42003-026-09794-6",
"type": "article-journal",
"title": "The role of dorsal anterior cingulate cortex in dynamic attitude changes in naturalistic settings",
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"author": [
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"family": "Zhang",
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"container-title-short": "Commun Biol",
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"PMCID": "PMC13066430",
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