OSCR

A rostral prefrontal mediolateral gradient predicts creativity in frontotemporal dementia.

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] § Materials and methods › Participants ↔ Codes/RSFC_First_Level_Voxel_to_Voxel_connectivity_inside_ROI.ipynb, lines 29–135 · score 0.54 · rs fMRI, row, inside, Intrinsic, voxel, mapping
  2. [2] § Materials and methods › Participants ↔ Codes/RSFC_First_Level_ROI_to_voxel_connectivity.ipynb, lines 50–180 · score 0.52 · rs fMRI, row, Intrinsic, voxel, mapping, VBM

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 · 135 lines · 7.8 KB · no license · 1 match

  1. # %%
  2. import os
  3. import nilearn
  4. from nilearn import image as nimg
  5. import nibabel as nib
  6. import os
  7. import bids
  8. from bids import BIDSLayout
  9. import numpy as np
  10. import pandas as pd
  11. from nilearn.maskers import NiftiMasker
  12. from nilearn.connectome import ConnectivityMeasure
  14. %matplotlib inline
  16. # %%
  17. # Choose BIDS data folder
  18. fmriprep_dir ='/.../All_Subjects'
  20. layout=BIDSLayout(fmriprep_dir,
  21. validate=False,
  22. config=['bids','derivatives'])
  24. subjects = layout.get_subjects()
  26. # %%
  27. listBA = ['10', ... ]
  29. # %%
  30. for BA in listBA:
  31. ROI_map = nimg.load_img('.../Mask_BrodmannArea' + BA + '.nii')
  33. # CHECK MASK ORIENTATION AND REVERSE TO SAME ORIENTATION AS RSFC DATA IF NEEDED
  34. print(nib.orientations.aff2axcodes(ROI_map.affine))
  35. if nib.orientations.aff2axcodes(ROI_map.affine)[0] == 'L':
  36. ornt = np.array([[0, -1],
  37. [1, 1],
  38. [2, 1]])
  39. ROI_map = ROI_map.as_reoriented(ornt)
  40. print(nib.orientations.aff2axcodes(ROI_map.affine))
  41. # Transform it into a mask (binary 0-1)
  42. ROI_mask = nilearn.masking.compute_background_mask(ROI_map)
  45. for sub in subjects:
  46. func_files = layout.get(subject=sub,
  47. datatype='func', task='rest', desc='preproc',
  48. space='MNI152NLin2009cAsym',
  49. extension="nii.gz",
  50. return_type='file')
  52. func_mask_files = layout.get(subject=sub,
  53. datatype='func', task='rest', suffix='mask',
  54. desc='brain',
  55. space='MNI152NLin2009cAsym',
  56. extension="nii.gz",
  57. return_type='file')
  59. confound_files = layout.get(subject=sub,
  60. datatype='func', task='rest',
  61. desc='confounds',
  62. extension="tsv",
  63. return_type='file')
  65. func_mni = func_files[0]
  66. # = func_mask_files[0]
  67. confound_file = confound_files[0]
  68. confounds = pd.read_csv(confound_file, delimiter='\t')
  70. # Create list of confounds of interest
  71. confound_vars = ['trans_x','trans_y','trans_z',
  72. 'rot_x','rot_y','rot_z',
  73. 'global_signal',
  74. 'csf', 'white_matter']
  76. # Get also temporal derivatives columns names
  77. derivative_columns = ['{}_derivative1'.format(c) for c
  78. in confound_vars]
  80. temporal_derivative_columns = ['{}_derivative1'.format(c) for c
  81. in confound_vars]
  83. # ± Optional
  84. quadratic_derivative_columns = ['{}_power2'.format(c) for c
  85. in confound_vars]
  86. quadratic_derivative_from_temporal_derivatives_columns = ['{}_power2'.format(c) for c
  87. in temporal_derivative_columns]
  89. # Join lists together
  90. final_confounds = confound_vars + derivative_columns + quadratic_derivative_columns + quadratic_derivative_from_temporal_derivatives_columns
  91. confound_df = confounds[final_confounds] # Use this list to select the confounds of interest from the confound_df dataframe
  92. drop_confound_df = confound_df.loc[:289] # Remove confounds for time-series after 290 (to keep same number of time series for each Ecocapture participant)
  93. drop_confound_df = drop_confound_df.loc[4:]
  94. confounds_matrix = drop_confound_df.values # Create matrix with values of dataframe
  96. raw_func_img = nimg.load_img(func_mni) # Look at raw image shape
  97. func_img = raw_func_img.slicer[:,:,:,:290] # Remove time-series after 290 (to keep same number of time series for each Ecocapture participant)
  98. func_img = func_img.slicer[:,:,:,4:] # Remove first 4 timepoints
  100. # ± optional : Create Scrubbing mask to remove volumes for which Framewise displacement > 0.5 or std_dvars > 1.5 (= motion)
  101. # ± Removal of all series inbetween 2 removed volumes if separated by x or below volumes, as they are considered impacted by the motion ("scrub = x", sinon scrub = 0)
  102. # = Procedure from Power 2014 10.1016/j.neuroimage.2013.08.048
  103. confounds_scrub, sample_mask = nilearn.interfaces.fmriprep.load_confounds(func_mni, strategy=["scrub"], scrub=5, fd_threshold=0.5, std_dvars_threshold=1.5)
  104. NoneType = type(None)
  105. if type(sample_mask)==NoneType: sample_mask = np.arange(start=0, stop=len(confounds_scrub))
  106. print("After scrubbing, {} out of {} volumes remains".format(sample_mask.shape[0], confounds_scrub.shape[0]))
  107. print(sub + ": volumes scrubed = {} %".format(100*(confounds_scrub.shape[0] - sample_mask.shape[0])/confounds_scrub.shape[0]))
  108. average_fd = np.nanmean(confounds['framewise_displacement'])
  109. print(sub + " : Average Framewise Displacement:", average_fd)
  110. # IF YOU WANT TO KEEP ONLY THE SAME NUMBER OF TIME SERIES FOR EACH PARTICIPANT (Ecocapture, n = 290), ADD THESE 2 LINES :
  111. IDX = np.max(np.argwhere(sample_mask < 290))
  112. sample_mask = sample_mask[:IDX+1]
  114. sample_mask = np.delete(sample_mask, [0, 1, 2, 3], 0) # delete first 4 rows (as in the fMRI data)
  115. sample_mask = sample_mask - 4 # readapt the index
  117. masker = NiftiMasker(standardize=True,
  118. memory='nilearn_cache',
  119. smoothing_fwhm= 8.0,
  120. detrend = True, # DETREND : SHOULD it be activated or not ??? (doesn't seem to make that much of a difference)
  121. low_pass=0.1, # SET LOW-PASS FILTER : Low pass filters out high frequency signals from our data. fMRI signals are slow evolving processes, any high frequency signals are likely due to noise
  122. high_pass=0.01, # SET HIGH-PASS FILTER : High pass filters out any very low frequency signals (below 0.009Hz), which may be due to intrinsic scanner instabilities
  123. t_r=2.05, # SET REPETITION TIME OF ACQUISITION (imaging metadata), required if band-pass filtering
  124. mask_img=ROI_mask) # ATTENTION !!! : QUEL MASK CHOISIR ? LE MEME QUE LA VBM ? OU UN PLUS GENERAL ? OU UN AVERAGE DE LA POPULATION en rsFMRI ?
  126. time_series = masker.fit_transform(func_img, confounds=confounds_matrix, sample_mask=sample_mask) # sample_mask option to scrubb image, cf above
  128. correlation_measure = ConnectivityMeasure(kind='correlation') # Correlation OK
  129. correlation_matrix = correlation_measure.fit_transform([time_series])[0]
  130. correlation_matrix_fisher_z = np.arctanh(correlation_matrix)
  132. np.savetxt('/.../' + BA + '_VoxelToVoxel_FisherZ_CorrelationMatrices_CSVs/' + sub + 'csv', correlation_matrix_fisher_z, delimiter=';')
  134. print(sub + ' = Done')
  135. print(BA + ' = Done')

RSFC_First_Level_Voxel_to_Voxel_connectivity_inside_ROI.ipynb, no license · at the source

Overview

Authors: Victor Altmayer1,2, Marcela Ovando-Tellez1, Théophile Bieth1,2, Bénédicte Batrancourt1, Armelle Rametti-Lacroux1, Sarah Moreno-Rodriguez1, Arabella Bouzigues1, Vincent Ledu1, Béatrice Garcin1,3, Alizée Lopez-Persem1, Daniel Margulies4, Richard Levy1,2, Emmanuelle Volle1, ECOCAPTURE study group
ORCID iDs: Alizée Lopez-Persem, Emmanuelle Volle
  1. FrontLab, Inserm, CNRS, AP-HP, Hôpital de la Pitié Salpêtrière, Sorbonne University, Institut du Cerveau – Paris Brain Institute – ICM, Paris 75013, France
  2. AP-HP, Department of Neurology, IM2A, Groupe Hospitalier Pitié-Salpêtrière, Paris 75013, France
  3. AP-HP, Department of Neurology, Hôpitaux Universitaires de Paris - Seine Saint Denis, Hôpital Avicenne, Bobigny 93000, France
  4. Integrative Neuroscience and Cognition Center, CNRS, University of Paris, Paris 75006, France
Institutions: Centre National de la Recherche Scientifique (France); Inserm (France); Sorbonne Université (France); Assistance Publique – Hôpitaux de Paris (France); Pitié-Salpêtrière Hospital (France); Institut du Cerveau (France); Hôpital Avicenne (France); Université Paris Cité (France); Centre Neurosciences intégratives et Cognition (France)
Journal: Brain : a journal of neurology, volume 149, issue 9, pages 3015-3030
Dates: received 19 May 2025; accepted 21 December 2025; published online 30 January 2026; in print September 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1093/brain/awag032 · PMID 41614679 · PMCID PMC13548864 · OpenAlex W7126253609
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), fMRI (modality), human (organism), Alzheimer's / dementia (population)
Methods: Statistics, Connectivity, fMRI & imaging, Preprocessing
Keywords: frontotemporal dementia, creativity, associative thinking, voxel-based morphometry, resting state functional connectivity, connectivity gradients
MeSH: Creativity*, Frontotemporal Dementia*, Prefrontal Cortex*, Aged, Brain Mapping, Executive Function, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Neural Pathways, Neuropsychological Tests (* major topic)
Topic: Creativity in Education and Neuroscience (Experimental and Cognitive Psychology, Psychology), according to OpenAlex
Funding: Agence Nationale de la Recherche (French National Research Agency) (ANR-19-CE37-0001-01); Investissements d'avenir (ANR-11-INBS-0006, ANR-10-IAIHU-06); Fondation pour la Recherche Médicale (Foundation for Medical Research in France) (M2R202003011383, FDM202206015358, DEQ20150331725); Agencia Nacional de Investigación y Desarrollo; Comisión Nacional de Investigación Científica y Tecnológica; Horizon 2020 (101026191, ANR-10-IAIHU-06); ENEDIS; Malakoff Humanis; Fédération pour la Recherche sur le Cerveau (FRC)
Citations: not cited yet (Europe PMC); 171 references in the paper

Abstract

Creative thinking is a fundamental aspect of human cognition, enabling the production of novel and useful ideas. It is hypothesized to emerge from the binding and reconfiguration of existing knowledge, through the generation of remote semantic associations and their combination in original and meaningful ways, respectively supported by the default mode (DMN) and executive control (ECN) networks. At the crossroads of these two networks, the rostral prefrontal cortex (PFC) is proposed as a key hub for DMN-ECN interactions, possibly supporting the interplay between generative and combinatory creative processes. However, the specific contributions of its medial and lateral subdivisions to creativity remain unclear.

In this study, we aimed to characterize the involvement of the rostral PFC in creative cognition through the lens of behavioural variant frontotemporal dementia (bvFTD), a relevant pathological model as it primarily affects the rostral PFC and alters intrinsic connectivity within the DMN and ECN. Using whole-brain voxel-based morphometry, we explored brain regions critical for the generation and combination of remote semantic associates, as well as for creative abilities, thought to involve both types of processes. Using resting-state functional connectivity and gradient mapping techniques, we also explored functional connectivity profiles within the rostral PFC and how variations in connectivity within this region predict creative performance.

As a result, we found a critical role for the rostromedial PFC in generating remote semantic associations and for the rostrolateral PFC in combining semantic associates, while both regions were critical for creative abilities. Moreover, we showed that the intrinsic connectivity of the rostral PFC is organised along a mediolateral functional gradient, segregating the rostromedial PFC, connected to the DMN, and the rostrolateral PFC, connected to the ECN. Finally, we showed that the range of this functional gradient, representing the functional differentiation between the ECN and DMN, predicts creative abilities.

Overall, this study advances our understanding of creative cognition, its relationships to the anatomical and functional organization of the PFC, and its impairment in bvFTD.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

OSF rtnzw

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Languages: MATLAB (4), Jupyter (3)
Size: 9 files, 7 scripts
Software Heritage: not checked
Found in: “Data availability”
Holds: 3 notebooks
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: SPM (4 files), NiBabel (3 files), Nilearn (3 files), NumPy (3 files), pandas (3 files), Matplotlib (2 files), PyBIDS (2 files), BrainSpace (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)
  • 26 September 2026: the link answers (HTTP 200)
7 files
At the source: osf.io/rtnzw/overview?view_only=f6c54494c28349b083f4eec0bded9562

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

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;
  • 7 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);
  • 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

We report all methods in the main manuscript or Supplementary material. Data and analysis codes are openly available at https://osf.io/rtnzw/overview?view_only=f6c54494c28349b083f4eec0bded9562, except for structural and RSFC brain imaging scans, which are not provided due to ethical protocol constraints related to the bvFTD clinical population. Task materials are available upon reasonable request.

Reproduced under the paper's license (CC BY-NC), 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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 14 authors, 6 keywords, 13 MeSH terms, 9 funders, 137 references.

Cite

This paper

Altmayer, V., Ovando-Tellez, M., Bieth, T., Batrancourt, B., Rametti-Lacroux, A., Moreno-Rodriguez, S., Bouzigues, A., Ledu, V., Garcin, B., Lopez-Persem, A., Margulies, D., Levy, R., Volle, E., & ECOCAPTURE study group. (2026). A rostral prefrontal mediolateral gradient predicts creativity in frontotemporal dementia. Brain : a journal of neurology, 149(9), 3015-3030. https://doi.org/10.1093/brain/awag032

BibTeX

@article{altmayer2026rostral,
author = {Altmayer, Victor and Ovando-Tellez, Marcela and Bieth, Théophile and Batrancourt, Bénédicte and Rametti-Lacroux, Armelle and Moreno-Rodriguez, Sarah and Bouzigues, Arabella and Ledu, Vincent and Garcin, Béatrice and Lopez-Persem, Alizée and Margulies, Daniel and Levy, Richard and Volle, Emmanuelle and {ECOCAPTURE study group}},
title = {{A rostral prefrontal mediolateral gradient predicts creativity in frontotemporal dementia}},
journal = {Brain : a journal of neurology},
year = {2026},
month = sep,
volume = {149},
number = {9},
pages = {3015--3030},
publisher = {Oxford University Press},
issn = {0006-8950},
doi = {10.1093/brain/awag032},
url = {https://doi.org/10.1093/brain/awag032},
pmid = {41614679},
pmcid = {PMC13548864}
}

RIS

TY - JOUR
AU - Altmayer, Victor
AU - Ovando-Tellez, Marcela
AU - Bieth, Théophile
AU - Batrancourt, Bénédicte
AU - Rametti-Lacroux, Armelle
AU - Moreno-Rodriguez, Sarah
AU - Bouzigues, Arabella
AU - Ledu, Vincent
AU - Garcin, Béatrice
AU - Lopez-Persem, Alizée
AU - Margulies, Daniel
AU - Levy, Richard
AU - Volle, Emmanuelle
AU - ECOCAPTURE study group
TI - A rostral prefrontal mediolateral gradient predicts creativity in frontotemporal dementia
T2 - Brain : a journal of neurology
J2 - Brain
PY - 2026
DA - 2026/09/01
VL - 149
IS - 9
SP - 3015
EP - 3030
SN - 0006-8950
PB - Oxford University Press
DO - 10.1093/brain/awag032
UR - https://doi.org/10.1093/brain/awag032
LA - en
ER -

CSL-JSON

{
"id": "10.1093/brain/awag032",
"type": "article-journal",
"title": "A rostral prefrontal mediolateral gradient predicts creativity in frontotemporal dementia",
"container-title": "Brain : a journal of neurology",
"author": [
{
"family": "Altmayer",
"given": "Victor"
},
{
"family": "Ovando-Tellez",
"given": "Marcela"
},
{
"family": "Bieth",
"given": "Théophile"
},
{
"family": "Batrancourt",
"given": "Bénédicte"
},
{
"family": "Rametti-Lacroux",
"given": "Armelle"
},
{
"family": "Moreno-Rodriguez",
"given": "Sarah"
},
{
"family": "Bouzigues",
"given": "Arabella"
},
{
"family": "Ledu",
"given": "Vincent"
},
{
"family": "Garcin",
"given": "Béatrice"
},
{
"family": "Lopez-Persem",
"given": "Alizée"
},
{
"family": "Margulies",
"given": "Daniel"
},
{
"family": "Levy",
"given": "Richard"
},
{
"family": "Volle",
"given": "Emmanuelle"
},
{
"literal": "ECOCAPTURE study group"
}
],
"container-title-short": "Brain",
"volume": "149",
"issue": "9",
"page": "3015-3030",
"DOI": "10.1093/brain/awag032",
"PMID": "41614679",
"PMCID": "PMC13548864",
"ISSN": "0006-8950",
"publisher": "Oxford University Press",
"URL": "https://doi.org/10.1093/brain/awag032",
"language": "en",
"issued": {
"date-parts": [
[
2026,
9,
1
]
]
}
}

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:10.1038/s42003-026-10354-1 [code]
REM sleep favors the restructuring of problem-related semantic associations.
Journal: Communications biology
In common: 7 references, 2 authors
[2] doi:10.1371/journal.pbio.3003684 [code]
The retrieval of previously learned motor memories is facilitated by the reinstatement of default mode network manifold structures.
Journal: PLoS biology
In common: BrainSpace, Nilearn, NiBabel, 3 other tools, fMRI, 6 references
[3] doi:10.1038/s41467-026-71568-9 [code]
Convergent and selective representations of pain, appetitive processes, aversive processes, and cognitive control in the insula.
Journal: Nature communications
In common: Nilearn, SPM, NiBabel, 3 other tools, 5 references
[4] doi:10.1038/s41467-026-71270-w [code]
Spatiotemporal dynamics of the human cortical functional hierarchy across the lifespan.
Journal: Nature communications
In common: PyBIDS, BrainSpace, Nilearn, 4 other tools, fMRI, 2 references
[5] doi:10.1002/hbm.70483 [code]
Untamed: Unconstrained Tensor Decomposition and Graph Node Embedding for Cortical Parcellation.
Journal: Human brain mapping
In common: Nilearn, SPM, NiBabel, 2 other tools, fMRI, 4 references
[6] doi:10.1002/hbm.70485 [code]
Exploring the Role of the Rich Club in Network Control of Neurocognitive States.
Journal: Human brain mapping
In common: BrainSpace, Nilearn, NiBabel, 2 other tools, 4 references
[7] doi:10.1038/s41467-026-71428-6 [code]
Binding items to contexts through conjunctive neural representations with the method of loci.
Journal: Nature communications
In common: Nilearn, NiBabel, pandas, 2 other tools, 4 references
[8] doi:10.1038/s41467-026-72931-6 [code]
Three parsimonious spatiotemporal patterns in cerebellum reveal individual traits in function and behavior.
Journal: Nature communications
In common: BrainSpace, Nilearn, NiBabel, 3 other tools, fMRI, 3 references
[9] 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: BrainSpace, Nilearn, NiBabel, 3 other tools, fMRI, 2 references
[10] doi:10.1038/s41398-026-04025-2 [code]
Brain energetic landscapes shape state dysregulation in major depressive disorder: a morphological network controllability perspective.
Journal: Translational psychiatry
In common: BrainSpace, Nilearn, SPM, 4 other tools, 2 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.