OSCR

Sign language narrative reveals universal and modality-specific features of cortical timescale hierarchy.

Code ↔ Paper

The paper beside its authors' code: matches between them have not been computed for this paper yet.

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 · 342 lines · 10 KB · no license

  1. # %%
  2. import os
  3. import glob
  4. import numpy as np
  5. import pandas as pd
  6. import matplotlib.pyplot as plt
  7. import seaborn as sns
  8. from nltools.stats import regress, zscore
  9. from nltools.data import Brain_Data, Design_Matrix
  10. from nltools.stats import find_spikes
  11. from nltools.mask import expand_mask
  13. # %%
  14. from nilearn import datasets
  15. from nilearn import image
  17. dataset_ho = datasets.fetch_atlas_harvard_oxford("cort-maxprob-thr25-2mm", data_dir=None, symmetric_split=True, resume=True, verbose=1)
  18. dataset_ju = datasets.fetch_atlas_juelich("maxprob-thr0-2mm")
  19. dataset_ju=datasets.fetch_atlas_juelich("maxprob-thr0-2mm", data_dir=None, symmetric_split=True, resume=True, verbose=1)
  20. atlas_ho_filename = dataset_ho.filename
  21. atlas_ju_filename = dataset_ju.filename
  24. labels = dataset_ho["labels"]
  25. print(f"Atlas ROIs are located at: {atlas_ho_filename}")
  26. print(f"Atlas ROIs are located at: {atlas_ju_filename}")
  27. from nilearn import plotting
  28. plotting.plot_roi(
  29. atlas_ho_filename, view_type="contours", title="Juelich atlas in contours"
  30. )
  31. plotting.show()
  34. # %%
  36. atlas_filename =dataset_ho["maps"]
  37. print(atlas_filename.shape)
  38. # Loading atlas dadataset_juta stored in 'labels'
  39. labels = dataset_ho["labels"]
  40. print(len(labels))
  41. print(labels)
  43. # %%
  44. from nilearn.datasets import fetch_atlas_harvard_oxford
  45. from nilearn.maskers import NiftiLabelsMasker
  46. from nilearn.image import load_img, index_img, resample_to_img
  47. ho = dataset_ju
  48. ho_img = ho.maps
  49. ho_img_resampled = resample_to_img(ho_img, nii_img, interpolation='nearest')
  50. ho_data = ho_img_resampled.get_fdata()
  51. ho_labels = ho.labels
  53. data_dictionary = {}
  54. for index,label in enumerate(ho_labels):
  55. print(index,label)
  56. if index == 0: # Skip background
  57. continue
  58. ho_region = (ho_data==index)
  59. nii_data_in_region = nii_data[ho_region,:]
  60. data_dictionary[label] = nii_data_in_region
  62. # %%
  63. # Load Image
  64. from nilearn import image
  65. #bilateral_mask_path = '/path/to/mask.nii.gz'
  66. bilateral_mask = image.load_img(atlas_ju_filename)
  67. mask_data = bilateral_mask.get_fdata()
  68. affine = bilateral_mask.affine
  71. # %%
  72. # Get center X-coord
  73. import os
  74. x_dim = mask_data.shape[0]
  75. x_center = int(x_dim/2)
  76. plotting.plot_img(bilateral_mask, title='Bilateral Mask')
  77. plotting.show()
  78. # Get left mask
  79. mask_data_left = mask_data.copy()
  80. mask_data_left[0:x_center,:,:] = 0
  81. mask_left = image.new_img_like(bilateral_mask, mask_data_left, affine=affine, copy_header=True)
  82. plotting.plot_img(mask_left, title='Left Mask')
  83. plotting.show()
  84. os.chdir('/Users/marysia/dropbox/PJM/masks')
  85. mask_left.to_filename('left_mask.nii.gz')
  86. # Get right mask
  87. mask_data_right = mask_data.copy()
  88. mask_data_right[x_center:,:,:] = 0
  89. mask_right = image.new_img_like(bilateral_mask, mask_data_right, affine=affine, copy_header=True)
  90. plotting.plot_img(mask_right, title='Right Mask')
  91. plotting.show()
  92. bilateral_mask.to_filename('mask_J.nii.gz')
  94. # %%
  95. def get_file_names1(data_dir_, task_name_, verbose = False):
  96. """
  97. Get all the participant file names
  99. Parameters
  100. ----------
  101. data_dir_ [str]: the data root dir
  102. task_name_ [str]: the name of the task
  104. Return
  105. ----------
  106. fnames_ [list]: file names for all subjs
  107. """
  108. upper_limit_n_subjs=22
  109. c_ = 0
  110. fnames_ = []
  111. # Collect all file names
  112. #these are natives
  113. #for subj in [1,2,5,6,7,8,9,10,11,13,14,15,16,23,24,25,26,27,29,30]:
  114. for subj in [1,2,3,4,5,6,8,9,10,11,12,13,14,15,16,17,18,19,20,21]:
  115. #late signers
  116. #
  117. #for subj in [2,3,4,6,5,6,8,9,12,13,14,15,16,17,18,20,21]:
  119. #
  120. #for subj in range(1, n_subjs_total):
  121. #fname = os.path.join(
  122. #data_dir_, 'sub-%.2d/func/sub-%.2d_run-6_denoise_smooth6mm_task-%s_desc-preproc_bold.nii.gz' % (subj, subj, task_name_))
  123. fname = os.path.join(
  124. #data_dir_, 'sub-deaf%.2d/func/sub-deaf%.2d_denoise_smooth6mm_task-%s_MNI152NLin2009cAsym_desc-preproc_bold.nii.gz' % (subj, subj, task_name_))
  125. data_dir_, 'sub-deaf%.2d/func/sub-deaf%.2d_denoise_smooth6mm_task-%s_MNI152NLin2009cAsym_desc-preproc_bold.nii.gz' % (subj, subj, task_name_))
  126. #data_dir_, 'sub-%.3d/sub-%.3d-task-%s.nii.gz' % (subj, subj, task_name_))
  127. print(fname)
  129. # If the file exists
  130. if os.path.exists(fname):
  132. # Add to the list of file names
  133. fnames_.append(fname)
  134. if verbose:
  135. print(fname)
  136. c_+= 1
  137. if c_ >= upper_limit_n_subjs:
  138. break
  139. return fnames_
  142. # %%
  143. my_list=[]
  144. from nilearn import plotting
  145. from brainiak import io
  146. from nilearn.image import resample_to_img
  147. dir_mask='/Users/marysia/dropbox/PJM/masks'
  148. mask_name = os.path.join(dir_mask, 'MNI152_T1_3mm_Brain_mask.nii')
  150. import nibabel.processing
  151. isrsa_nn2=np.load('/Users/marysia/dropbox/PJM/results/whole_brain_isc/isc/Correlations/rs_pseudo.npy')
  152. brain_mask = nib.load(mask_name)
  153. brain_mask = io.load_boolean_mask(mask_name)
  154. # Get the list of nonzero voxel coordinates
  155. coords = np.where(brain_mask)
  157. # Load the brain nii image
  158. brain_nii = nib.load(mask_name)
  159. print(brain_nii.shape)
  160. import os
  161. base_dir='/Users/marysia/dropbox/PJM/fmriprep/'
  163. #datad = image.load_img(atlas_ho_filename)
  164. import nibabel as nib
  165. datad= nib.load(os.path.join('/Users/marysia/dropbox/PJM/masks/','FFA2.nii.gz'))
  166. print(datad.shape)
  167. datad= resample_to_img(datad, brain_nii)
  168. mask=Brain_Data(datad)
  170. file_list = glob.glob(os.path.join(base_dir, '*', 'func', f'*denoise_smooth6mm_task-intact22_MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'))
  171. print(file_list)
  172. for f in file_list:
  173. sub = os.path.basename(f).split('_')[0]
  174. data = Brain_Data(f)
  175. roi = data.extract_roi(mask)
  176. pd.DataFrame(roi.T).to_csv(os.path.join(os.path.dirname(f),'ffa2_intact2.csv'), index=False)
  179. # %%
  180. my_list=[]
  181. import os
  182. base_dir='/Users/marysia/dropbox/PJM/fmriprep/'
  184. datad = image.load_img(atlas_ho_filename)
  186. datad= nib.load(FFA1.nii.gz)
  187. mask=Brain_Data(datad)
  188. print(mask)
  189. mask_x = expand_mask(mask)
  190. file_list = glob.glob(os.path.join(base_dir, '*', 'func', f'*denoise_smooth6mm_task-intact22_MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'))
  191. print(file_list)
  192. for f in file_list:
  193. sub = os.path.basename(f).split('_')[0]
  194. data = Brain_Data(f)
  195. roi = data.extract_roi(mask)
  196. pd.DataFrame(roi.T).to_csv(os.path.join(os.path.dirname(f),'harvard_intact2.csv'), index=False)
  199. # %%
  200. my_list=[]
  201. import os
  202. base_dir='/Users/marysia/dropbox/PJM/fmriprep/'
  204. datad = image.load_img(atlas_ho_filename)
  205. mask=Brain_Data(datad)
  206. mask_x = expand_mask(mask)
  207. file_list = glob.glob(os.path.join(base_dir, '*', 'func', f'*denoise_smooth6mm_task-sentences2_MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'))
  208. print(file_list)
  209. for f in file_list:
  210. sub = os.path.basename(f).split('_')[0]
  211. data = Brain_Data(f)
  212. roi = data.extract_roi(mask)
  213. pd.DataFrame(roi.T).to_csv(os.path.join(os.path.dirname(f),'harvard_sentences2.csv'), index=False)
  216. # %%
  217. my_list=[]
  218. import os
  219. base_dir='/Users/marysia/dropbox/PJM/fmriprep/'
  221. datad = image.load_img(atlas_ho_filename)
  222. mask=Brain_Data(datad)
  223. mask_x = expand_mask(mask)
  224. file_list = glob.glob(os.path.join(base_dir, '*', 'func', f'*denoise_smooth6mm_task-words2_MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'))
  225. print(file_list)
  226. for f in file_list:
  227. sub = os.path.basename(f).split('_')[0]
  228. data = Brain_Data(f)
  229. roi = data.extract_roi(mask)
  230. pd.DataFrame(roi.T).to_csv(os.path.join(os.path.dirname(f),'harvard_words2.csv'), index=False)
  232. # %%
  233. my_list=[]
  234. import os
  235. base_dir='/Users/marysia/dropbox/PJM/fmriprep/'
  237. datad = image.load_img(atlas_ho_filename)
  238. mask=Brain_Data(datad)
  239. mask_x = expand_mask(mask)
  240. file_list = glob.glob(os.path.join(base_dir, '*', 'func', f'*denoise_smooth6mm_task-pseudo2_MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'))
  241. print(file_list)
  242. for f in file_list:
  243. sub = os.path.basename(f).split('_')[0]
  244. data = Brain_Data(f)
  245. roi = data.extract_roi(mask)
  246. pd.DataFrame(roi.T).to_csv(os.path.join(os.path.dirname(f),'harvard_pseudo2.csv'), index=False)
  248. # %%
  249. from brainiak import isc
  250. time_series={}
  251. aa=[]
  252. file_list = glob.glob(os.path.join(base_dir, '*', 'func', f'*denoise_smooth6mm_task-pseudo_MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'))
  253. print(file_list)
  254. for f in file_list:
  255. sub = os.path.basename(f).split('_')[0]
  256. part4 = pd.read_csv(os.path.join(base_dir, sub ,'func', 'ffa1_intact2.csv' ))
  257. part4.reset_index(inplace=True, drop=True)
  258. aa.append(part4)
  259. time_series[sub] = part4
  261. print(len(aa))
  262. np.stack((aa))
  263. print(np.shape(aa))
  264. roi_new=np.transpose(aa, (1, 2, 0))
  265. print(np.shape(roi_new))
  266. isc1 = isc.isc(roi_new, summary_statistic=None, pairwise=False)
  267. os.chdir('/Users/marysia/dropbox/PJM/')
  268. np.save('ffa1_ISC_pseudo2',isc1)
  270. df = pd.DataFrame(isc1)
  271. #labels = dataset_ho["labels"]
  272. #print(labels)
  273. ##df.columns = labels[1:97]
  274. #df.insert(loc=0, column='SUB', value=[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20])
  275. df.to_csv('ISC_ffa1.csv', index=[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20])
  277. # %%
  278. import numpy as np
  279. import pandas as pd
  280. import os
  281. os.chdir('/Users/marysia/dropbox/PJM/')
  282. a=pd.read_csv('ISC_harvard_words2.csv')
  283. b=pd.read_csv('ISC_harvard_sentences2.csv')
  284. c=pd.read_csv('ISC_harvard_pseudo2.csv')
  285. d=pd.read_csv('ISC_harvard_intact2.csv')
  286. cond= ['intact'] * 20
  287. cond1= ['sentences'] * 20
  288. cond2= ['words'] * 20
  289. cond3= ['pseudo'] * 20
  290. cond4=cond+cond1+cond2+cond3
  291. print(cond4)
  294. df.to_csv('ISC_ho_words.csv', index=None)
  295. df= pd.concat([d,b,a,c])
  296. new_labels = list(range(1, 97))
  297. new_labels1=[]
  298. for i in new_labels:
  299. new_labels1.append(str(i))
  301. df.insert(loc=0, column='cond', value=cond4)
  302. #new_labels=['cond','no','SUB']+new_labels
  303. #df.columns = new_labels
  304. print(df)
  305. df.to_csv('ISC_ho_all_rois123.csv', index=None)
  307. # %%
  308. print(labels[1:97])
  309. print(df)
  311. # %%
  312. new_labels = list(range(1, 97))
  313. new_labels1=[]
  314. for i in new_labels:
  315. new_labels1.append(str(i))
  316. print(labels)
  317. print(new_labels1[1:97])
  318. df=pd.melt(df, id_vars=['SUB','cond'], value_vars=labels[1:97])
  320. #df = pd.melt(df, id_vars='SUB')
  321. df.to_csv('ISC_ho_all_long.csv', index=None)
  324. # %%
  325. #view updated DataFrame
  326. df
  327. cond= ['words'] * 1920
  328. df.insert(loc=0, column='cond', value=cond)
  329. df.to_csv('ISC_ho_words.csv', index=None)
  331. # %%
  332. import os
  333. import pandas as pd
  334. os.chdir('/Users/marysia/dropbox/PJM/ROI')
  335. df = pd.read_csv("ISC_lang.csv", sep=';')
  336. print(df)
  338. df1 = df[df["ROI"] == "Mask07"]
  339. print(df1)
  340. df2=df1.pivot(index='Subject', columns='Condition', values='ISC')
  341. print(df2)
  342. df2.to_csv('ISC_lang_06.csv', index=None)

ROI_analysis.ipynb, no license · at the source

Overview

Authors: Maria Zimmermann1,2, Piotr Tomaszewski1, Artur Marchewka3, Marcin Szwed4, Marina Bedny2
ORCID iDs: Artur Marchewka, Marcin Szwed
  1. Department of Psychology, University of Warsaw,Warsaw, Poland
  2. Department of Psychological and Brain Sciences, Johns Hopkins University,Baltimore, US
  3. Laboratory of Brain Imaging, Nencki Institute for Experimental Biology,Warsaw, Poland
  4. Institute of Psychology, Jagiellonian University,Kraków, Poland
Institutions: University of Warsaw (Poland); Johns Hopkins University (United States); Instytut Biologii Doświadczalnej im. Marcelego Nenckiego (Poland); Jagiellonian University (Poland)
Journal: Nature communications, volume 17, issue 1, article 7126
Dates: received 22 May 2025; accepted 18 May 2026; published online 3 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-73895-3 · PMID 42236497 · PMCID PMC13396480 · OpenAlex W7163345805
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), cognitive (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, fMRI & imaging
Keywords: Human behaviour, Cognitive neuroscience
MeSH: Cerebral Cortex*, Narration*, Sign Language*, Adult, Brain Mapping, Comprehension, Female, Humans, Magnetic Resonance Imaging, Male, Occipital Lobe, Parietal Lobe, Speech, Young Adult (* major topic)
Topic: Hearing Impairment and Communication (Developmental and Educational Psychology, Psychology), according to OpenAlex
Funding: Narodowe Centrum Nauki (2018/30/A/HS6/00595, 2024/52/C/HS6/00207)
Citations: not cited yet (Europe PMC); 94 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.

OSF n8sv3

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: Jupyter (6)
Size: 65 files, 6 scripts
Software Heritage: not checked
Found in: “Code availability”
Holds: 6 notebooks
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NiBabel (2 files), Nilearn (2 files), NumPy (2 files), pandas (2 files), BrainIAK (1 file), Matplotlib (1 file), nltools (1 file), SciPy (1 file), seaborn (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)
2 files
At the source:

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: OSF n8sv3

Read it in the paper: doi.org/10.1038/s41467-026-73895-3.

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 2 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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

No dataset and no data link were found in the paper.

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:

  • no repository, dataset or request procedure was recognized in it

Read it in the paper: doi.org/10.1038/s41467-026-73895-3.

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 2 keywords, 14 MeSH terms, 1 funder, 71 references.

Cite

This paper

Zimmermann, M., Tomaszewski, P., Marchewka, A., Szwed, M., & Bedny, M. (2026). Sign language narrative reveals universal and modality-specific features of cortical timescale hierarchy. Nature communications, 17(1), 7126. https://doi.org/10.1038/s41467-026-73895-3

BibTeX

@article{zimmermann2026sign,
author = {Zimmermann, Maria and Tomaszewski, Piotr and Marchewka, Artur and Szwed, Marcin and Bedny, Marina},
title = {{Sign language narrative reveals universal and modality-specific features of cortical timescale hierarchy}},
journal = {Nature communications},
year = {2026},
month = jun,
volume = {17},
number = {1},
pages = {7126},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-73895-3},
url = {https://doi.org/10.1038/s41467-026-73895-3},
pmid = {42236497},
pmcid = {PMC13396480}
}

RIS

TY - JOUR
AU - Zimmermann, Maria
AU - Tomaszewski, Piotr
AU - Marchewka, Artur
AU - Szwed, Marcin
AU - Bedny, Marina
TI - Sign language narrative reveals universal and modality-specific features of cortical timescale hierarchy
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/06/03
VL - 17
IS - 1
SP - 7126
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-73895-3
UR - https://doi.org/10.1038/s41467-026-73895-3
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-73895-3",
"type": "article-journal",
"title": "Sign language narrative reveals universal and modality-specific features of cortical timescale hierarchy",
"container-title": "Nature communications",
"author": [
{
"family": "Zimmermann",
"given": "Maria"
},
{
"family": "Tomaszewski",
"given": "Piotr"
},
{
"family": "Marchewka",
"given": "Artur"
},
{
"family": "Szwed",
"given": "Marcin"
},
{
"family": "Bedny",
"given": "Marina"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "7126",
"DOI": "10.1038/s41467-026-73895-3",
"PMID": "42236497",
"PMCID": "PMC13396480",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-73895-3",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
3
]
]
}
}

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:10.1073/pnas.2512071123 [code]
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
In common: Nilearn, NiBabel, statsmodels, 5 other tools, fMRI, cognitive, 7 references
[2] doi:10.1038/s42003-026-09794-6 [code]
The role of dorsal anterior cingulate cortex in dynamic attitude changes in naturalistic settings.
Journal: Communications biology
In common: nltools, Nilearn, NiBabel, 6 other tools, 5 references
[3] doi:10.1038/s41467-026-71151-2 [code]
Common and distinct neural correlates of social interaction processing and theory of mind in narratives.
Journal: Nature communications
In common: nltools, Nilearn, NiBabel, 6 other tools, cognitive, 5 references
[4] doi:10.1038/s41467-026-73914-3 [code]
Cortical reinstatement of causally related events sparks narrative insights by updating neural representation patterns.
Journal: Nature communications
In common: BrainIAK, Nilearn, statsmodels, 5 other tools, cognitive, 5 references
[5] doi:10.1162/imag.a.1283 [code]
The language network responds robustly to sentences across tasks.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: Nilearn, statsmodels, seaborn, 4 other tools, fMRI, 6 references
[6] doi:10.1038/s41467-026-76452-0 [code]
Music evokes shared neural representations of imagined narratives across sensory modalities.
Journal: Nature communications
In common: Nilearn, NiBabel, statsmodels, 5 other tools, cognitive, 6 references
[7] 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: Nilearn, NiBabel, seaborn, 4 other tools, fMRI, cognitive, 4 references
[8] doi:10.64898/2026.03.09.710558 [code]
Multi-task fMRI outperforms resting-state fMRI for revealing task-invariant organization of the human brain
Journal: bioRxiv (preprint)
In common: Nilearn, NiBabel, seaborn, 4 other tools, fMRI, cognitive, 4 references
[9] doi:10.7554/elife.107933 [code]
Modality-agnostic decoding of vision and language from fMRI.
Journal: eLife
In common: Nilearn, NiBabel, statsmodels, 5 other tools, fMRI, cognitive, 2 references
[10] doi:10.1038/s41467-026-75745-8 [code]
A language network in the individualized functional connectomes of 1199 human brains doing arbitrary tasks.
Journal: Nature communications
In common: Nilearn, NiBabel, seaborn, 4 other tools, cognitive, 4 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.