OSCR

Longitudinal MRI template of the baboon brain from birth to adolescence.

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 › Multimodal template creation ↔ postprocessing/MM_template_construction.py, lines 13–132 · score 0.71 · antsMultivariateTemplateConstruction2.sh, pairwise registration, resampled, resolution, sharpening, iterative
  2. [2] § Methods › Multimodal template creation ↔ preprocessing/realign_subjects_2_Haiko89.py, lines 128–190 · score 0.61 · antsRegistration, shrink factor, Haiko89, smoothing, rigid, space
  3. [3] § Methods › Tissue probability maps and binary brain mask generation ↔ postprocessing/correct_TPM.py, lines 44–168 · score 0.58 · corrected TPM, WM TPM, filling, BM, thresholding, CSF
  4. [4] § Methods › Multimodal template creation ↔ postprocessing/antsMultivariateTemplateConstruction2.sh, lines 102–158 · score 0.57 · downsampled, correlation, pairwise, cross, shrink, voxel
  5. [5] § Methods › Multimodal template creation ↔ postprocessing/register_long_templates.py, lines 106–155 · score 0.57 · antsRegistration, shrink factor, smoothing, rigid, transformation, T1w
  6. [6] § Methods › Alignment across timepoints ↔ postprocessing/sym_template.py, lines 226–295 · score 0.51 · registered template, rigid registration, propagate, symmetrical, flipped, temporary

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

Python · 135 lines · 6 KB · no license · 1 match

  1. #!/usr/bin/env python3
  3. import argparse
  4. import os
  5. import subprocess
  6. import pandas as pd
  8. def run_command(cmd, dry_run=False, env=None, workdir=None, shell=False):
  9. print("Running:", " ".join(map(str, cmd)))
  10. if not dry_run:
  11. subprocess.run(cmd, check=True, env=env, shell=shell, cwd=workdir)
  13. def main():
  14. parser = argparse.ArgumentParser(description="Two-stage multivariate template construction with ANTs, BIDS-style outputs")
  15. parser.add_argument("-s", "--subject", required=True,help="Subject label (e.g. BaBa21)")
  16. parser.add_argument("-S", "--session", required=True,help="Session label (e.g. ses-0)")
  17. parser.add_argument("-b", "--bids-root", required=True,help="BIDS root folder")
  18. parser.add_argument("-j", "--jobs", type=int, default=12,help="Number of CPU cores to use (default: 12)")
  19. parser.add_argument("--modalities", nargs="+", required=True,help="List of modalities to look for (e.g., T1w T2w label-WM_mask)")
  20. parser.add_argument("--input-list-LR", required=True,help="CSV file with list of input NIfTI images for first stage")
  21. parser.add_argument("--LR-reg-metrics", default="MI",help="Type of similarity metric used for pairwise registration (MI by default)")
  22. parser.add_argument("--ite1", type=int, default=1,help="Number of iterations for Stage 1 (default: 1)")
  23. parser.add_argument("--q1", default="50x30x15",help="Steps for Stage 1 -q option (default: 50x30x15)")
  24. parser.add_argument("--w1", default="0.5x0.5x1",help="Weights for Stage 1 modalities (default: 0.5x0.5x1)" )
  25. parser.add_argument("--input-list-HR", required=True,help="CSV file with list of input NIfTI images for second stage")
  26. parser.add_argument("--HR-reg-metrics", default="CC",help="Type of similarity metric used for pairwise registration (CC by default)")
  27. parser.add_argument("--ite2", type=int, default=1,help="Number of iterations for Stage 2 (default: 1)")
  28. parser.add_argument("--q2", default="70x50x30",help="Steps for Stage 2 -q option (default: 70x50x30)")
  29. parser.add_argument("--w2", default="1x1x1",help="Weights for Stage 2 modalities (default: 1x1x1)")
  30. parser.add_argument('--res-HR', type=float, default=0.4, help="pixel resolution in mm (default: 0.4)")
  31. parser.add_argument('--dry-run', action="store_true", help="Print commands without executing them")
  34. args = parser.parse_args()
  36. # Load CSV and determine modalities count for stage 1
  37. df = pd.read_csv(args.input_list_LR)
  38. modalities = list(df.columns)
  39. modalities_count = len(modalities)
  41. res_HR = args.res_HR
  42. dry_run = args.dry_run
  44. LR_reg_metrics = args.LR_reg_metrics
  45. HR_reg_metrics = args.HR_reg_metrics
  47. print(f"[INFO] Detected {modalities_count} modalities: {modalities}")
  49. # Define output directories under BIDS derivatives/template
  50. derivatives_dir = os.path.join(args.bids_root, "derivatives", "template")
  51. subject_dir = f"sub-{args.subject}"
  52. session_dir = args.session
  53. output_base = os.path.join(derivatives_dir, subject_dir, session_dir)
  54. tmp_LR = os.path.join(output_base, "tmp_LR")
  55. tmp_HR = os.path.join(output_base, "tmp_HR")
  56. final_dir = os.path.join(output_base, "final")
  58. for d in [tmp_LR, tmp_HR, final_dir]:
  59. os.makedirs(d, exist_ok=True)
  61. print(f"[INFO] All outputs will go to: {output_base}")
  63. # Stage 1: low-resolution template building
  64. print(f"[INFO] Starting Stage 1: Low-resolution template construction")
  66. ants_script_path = os.path.join("$ANTSPATH", "antsMultivariateTemplateConstruction2.sh")
  68. run_command([
  69. f"{ants_script_path} "
  70. f"-d 3 -i {args.ite1} -k {modalities_count} -c 2 -j {args.jobs} "
  71. f"-f 4x2x1 -s 2x1x0vox -q {args.q1} "
  72. f"-w {args.w1} -t SyN -A 1 -n 0 -m {LR_reg_metrics} "
  73. f"-o {tmp_LR}/MY {args.input_list_LR}"
  74. ], dry_run, workdir='./', shell=True)
  76. print(f"[INFO] Resampling Stage 1 outputs to higher resolution at {res_HR} ")
  77. for i in range(modalities_count):
  78. in_file = os.path.join(tmp_LR, "intermediateTemplates", f"SyN_iteration{args.ite1 - 1}_MYtemplate{i}.nii.gz")
  79. out_file = os.path.join(tmp_HR, f"{args.subject}_{args.session}_SyN_iteration{args.ite1 - 1}_MYtemplate{i}.nii.gz")
  81. run_command([
  82. "mri_convert", "-i",
  83. f"{in_file}",
  84. "-o",
  85. f"{out_file}",
  86. "-vs", f"{res_HR}", f"{res_HR}", f"{res_HR}"
  87. ], dry_run)
  89. # Stage 2: high-resolution template building using resampled priors
  91. # Load CSV and determine modalities count for stage 2
  92. df = pd.read_csv(args.input_list_HR)
  93. modalities = list(df.columns)
  94. modalities_count = len(modalities)
  96. print(f"[INFO] Detected {modalities_count} modalities: {modalities}")
  98. print("[INFO] Starting Stage 2: High-resolution template construction")
  99. paths = [
  100. os.path.join(
  101. tmp_HR,
  102. f"{args.subject}_{args.session}_SyN_iteration{args.ite1 - 1}_MYtemplate{i}.nii.gz"
  103. )
  104. for i in range(modalities_count)
  105. ]
  107. z_opts = " ".join([f"-z {p}" for p in paths])
  108. run_command([
  109. f"{ants_script_path} "
  110. f"-d 3 -i {args.ite2} -k {modalities_count} -c 2 -j {args.jobs} "
  111. f"-f 4x2x1 -s 2x1x0vox -q {args.q2} "
  112. f"-t SyN -w {args.w2} {z_opts} -A 1 -n 0 -m {HR_reg_metrics} "
  113. f"-o {tmp_HR}/MY {args.input_list_HR}"
  114. ], dry_run , workdir='./', shell=True)
  116. # Copy final outputs with BIDS-style names
  117. print("[INFO] Copying final templates to BIDS-style outputs")
  119. i=0
  120. for modality in args.modalities:
  122. print(f"[INFO] modality {modality}")
  123. desc = f"desc-sharpen_{modality}"
  125. dst_name = f"sub-{args.subject}_{args.session}_{desc}.nii.gz"
  126. src = os.path.join(tmp_HR, "intermediateTemplates", f"SyN_iteration{args.ite2 - 1}_MYtemplate{i}.nii.gz")
  127. dst = os.path.join(final_dir, dst_name)
  128. run_command([f"cp -f {src} {dst}"], dry_run , workdir='./', shell=True)
  129. print(f"{dst}")
  130. i += 1
  132. print(f"[INFO] Template construction complete! Results in {final_dir}")
  134. if __name__ == "__main__":
  135. main()

MM_template_construction.py at commit c8f1d6f, no license · at the source

Overview

Authors: Katherine L Bryant1,2, Arnaud Le Troter3, David Meunier3, Yannick Becker2,4, Scott A Love5,6, Siham Bouziane1,2, Kep Kee Loh1,3,7, Julien Sein1,3, Luc Renaud3, Olivier Coulon1,3, Adrien Meguerditchian1,2,8
ORCID iDs: Katherine L Bryant, Arnaud Le Troter, David Meunier, Yannick Becker, Scott A Love, Siham Bouziane, Kep Kee Loh, Julien Sein, Luc Renaud, Olivier Coulon, Adrien Meguerditchian
  1. Institute for Language, Communication, and the Brain, Aix-Marseille Univ, CNRS, Marseille, France
  2. Centre de Recherche en Psychologie et Neurosciences, Aix-Marseille Univ, CNRS, UMR 7077, Marseille, France
  3. Institut de Neurosciences de la Timone, Aix-Marseille Univ, CNRS, UMR 7289, Marseille, France
  4. Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
  5. Lise Meitner Research Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
  6. INRAE, CNRS, Université de Tours, PRC, 37380, Nouzilly, France
  7. Department of Psychology, National University of Singapore, Singapore
  8. Station de Primatologie, CNRS-CELPHEDIA UAR846, Rousset, France
Institutions: Centre National de la Recherche Scientifique (France); Aix-Marseille Université (France); Centre de Recherche en Psychologie et Neurosciences (France); Institut de Neurosciences de la Timone (France); Max Planck Institute for Human Cognitive and Brain Sciences (Germany); Université de Tours (France); Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (France); Physiologie de la Reproduction et des Comportements (France); National University of Singapore (Singapore); CELPHEDIA (France); Station de primatologie (France)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1316
Dates: received 21 March 2025; accepted 2 July 2026; published online 30 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1316 · PMID 42569348 · PMCID PMC13449925 · OpenAlex W7167623147
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), non-human primate (organism), developmental (subfield)
Methods: Connectivity, Machine learning
Keywords: structural imaging, comparative, primate, evolution, resource
Topic: Primate Behavior and Ecology (Social Psychology, Psychology), according to OpenAlex
Funding: European Commission (ERC-2016-STG, ANR-11-INBS-0006); Agence Nationale de la Recherche (ANR-11-656 IDEX-0001-02, ANR-16-CONV-0002, ANR-16, ANR-11-INBS, ANR-11-INBS-0006, ANR-23, IDEX-0001-02, ANR-23-CE28-0029); Fondation Fyssen; Centre National de la Recherche Scientifique (UMR 7289, ANR-11-INBS-0006); Horizon 2020 (716931 - GESTIMAGE - ERC-2016-STG)
Citations: not cited yet (Europe PMC); 52 references in the paper

Abstract

The baboon (Papio) is an invaluable resource within nonhuman primate research, having the advantage of being a cercopithecoid (Old World monkey) with one of the largest brains among non-hominid primates. In order to facilitate comparative developmental neuroscience research, we present the BABACOOL (BAby Brain Atlas COnstruction for Optimized Labeled segmentation) approach for creating multi-modal developmental atlases, which we used to produce BaBa21, a population-based longitudinal developmental baboon template. BaBa21 is a spatio-temporal template that consists of structural (T1- and T2-weighted) images and tissue probability maps from a population of 21 baboons (Papio anubis) scanned at 4 timepoints beginning from about 2 weeks after birth and continuing to sexual maturity (5 years). Further, this study offers a fully automatic method for generating a template at any intermediate age for future age-specific group studies. This resource is made available to provide a normalization target for baboon data across the lifespan, including intermediate timepoints, and moreover facilitate neuroimaging research in baboons, comparative research with humans and nonhuman primate species for which developmental templates are available (e.g., macaques).

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

Repository

Its files are read in the Code ↔ Paper reader above, with 6 matches between paragraphs and lines of code.

arnaudletroter/BABACOOL

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: c8f1d6fb9215236d6a1c563b62d312da57017978, 1 September 2026
Languages: Python (15), Shell (6), Jupyter (1)
Size: 237 files, 22 scripts
Software Heritage: not archived
Found in: “Data and Code Availability”
Holds: README, environment (Dockerfile, requirements.txt), 1 notebook
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: pandas (9 files), FSL (7 files), ANTs (4 files), Matplotlib (2 files), NiBabel (2 files), NumPy (2 files), Pillow (1 file), SciPy (1 file), seaborn (1 file), statannotations (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
23 files
At the source: github.com/arnaudletroter/BABACOOL

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;
  • 22 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 and Code Availability

The full multimodal BABA21 dataset (BIDS formatted) containing all contrasts and TPMs is publicly available on the OpenNeuro platform (Dataset ds005424; Le Troter et al., 2025) at https://openneuro.org/datasets/ds005424. Source code for multimodal (4D+t) BaBa21 template generation and scripts for contrast normalization can be found at https://github.com/arnaudletroter/BABACOOL.

Reproduced under the paper's license (CC BY), 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 2, 28 September 2026

  • Funding: added European Commission: ERC-2016-STG, ANR-11-INBS-0006; Agence Nationale de la Recherche: ANR-11-656 IDEX-0001-02, ANR-16-CONV-0002, ANR-16, ANR-11-INBS, ANR-11-INBS-0006, ANR-23, IDEX-0001-02, ANR-23-CE28-0029; Fondation Fyssen; Centre National de la Recherche Scientifique: UMR 7289, ANR-11-INBS-0006; Horizon 2020: 716931 - GESTIMAGE - ERC-2016-STG

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 11 authors, 5 keywords, 52 references.

Cite

This paper

Bryant, K. L., Le Troter, A., Meunier, D., Becker, Y., Love, S. A., Bouziane, S., Loh, K. K., Sein, J., Renaud, L., Coulon, O., & Meguerditchian, A. (2026). Longitudinal MRI template of the baboon brain from birth to adolescence. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1316. https://doi.org/10.1162/imag.a.1316

BibTeX

@article{bryant2026longitudinal,
author = {Bryant, Katherine L and Le Troter, Arnaud and Meunier, David and Becker, Yannick and Love, Scott A and Bouziane, Siham and Loh, Kep Kee and Sein, Julien and Renaud, Luc and Coulon, Olivier and Meguerditchian, Adrien},
title = {{Longitudinal MRI template of the baboon brain from birth to adolescence}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = jul,
volume = {4},
pages = {IMAG.a.1316},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1316},
url = {https://doi.org/10.1162/imag.a.1316},
pmid = {42569348},
pmcid = {PMC13449925}
}

RIS

TY - JOUR
AU - Bryant, Katherine L
AU - Le Troter, Arnaud
AU - Meunier, David
AU - Becker, Yannick
AU - Love, Scott A
AU - Bouziane, Siham
AU - Loh, Kep Kee
AU - Sein, Julien
AU - Renaud, Luc
AU - Coulon, Olivier
AU - Meguerditchian, Adrien
TI - Longitudinal MRI template of the baboon brain from birth to adolescence
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/07/30
VL - 4
SP - IMAG.a.1316
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1316
UR - https://doi.org/10.1162/imag.a.1316
LA - en
ER -

CSL-JSON

{
"id": "10.1162/imag.a.1316",
"type": "article-journal",
"title": "Longitudinal MRI template of the baboon brain from birth to adolescence",
"container-title": "Imaging neuroscience (Cambridge, Mass.)",
"author": [
{
"family": "Bryant",
"given": "Katherine L"
},
{
"family": "Le Troter",
"given": "Arnaud"
},
{
"family": "Meunier",
"given": "David"
},
{
"family": "Becker",
"given": "Yannick"
},
{
"family": "Love",
"given": "Scott A"
},
{
"family": "Bouziane",
"given": "Siham"
},
{
"family": "Loh",
"given": "Kep Kee"
},
{
"family": "Sein",
"given": "Julien"
},
{
"family": "Renaud",
"given": "Luc"
},
{
"family": "Coulon",
"given": "Olivier"
},
{
"family": "Meguerditchian",
"given": "Adrien"
}
],
"container-title-short": "Imaging Neurosci (Camb)",
"volume": "4",
"page": "IMAG.a.1316",
"DOI": "10.1162/imag.a.1316",
"PMID": "42569348",
"PMCID": "PMC13449925",
"ISSN": "2837-6056",
"publisher": "MIT Press",
"URL": "https://doi.org/10.1162/imag.a.1316",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
30
]
]
}
}

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:10.21203/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: statannotations, ANTs, FSL, 7 other tools, 1 reference
[2] doi:10.1002/alz.71649 [code]
Postmortem brain MRI reveals differential associations of subcortical and limbic volumes with cortical thinning and neurodegenerative pathologies.
Journal: Alzheimer's & dementia : the journal of the Alzheimer's Association
In common: statannotations, ANTs, FSL, 7 other tools, structural MRI / diffusion
[3] doi:10.1038/s42003-026-10276-y [code]
The cellular correlates and adolescent reorganisation of cortical myelination networks in the common marmoset.
Journal: Communications biology
In common: statannotations, NiBabel, seaborn, 4 other tools, non-human primate, structural MRI / diffusion, 2 references
[4] doi:10.1162/imag.a.1276 [code]
High-resolution whole-brain magnetic resonance spectroscopic imaging in youth at risk for psychosis.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: ANTs, FSL, Pillow, 6 other tools, 1 reference
[5] 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: ANTs, FSL, Pillow, 6 other tools, 1 reference
[6] doi:10.7554/elife.108408 [code]
Frequency and laminar profile of feature-specific visual activity revealed by interleaved EEG-fMRI.
Journal: eLife
In common: ANTs, FSL, NiBabel, 5 other tools, 2 references
[7] doi:10.1002/epi.70296 [code]
Fully automated three-dimensional deep learning-based magnetic resonance imaging segmentation of brain cavities in epilepsy surgery.
Journal: Epilepsia
In common: ANTs, FSL, Pillow, 5 other tools, structural MRI / diffusion, 1 reference
[8] doi:10.64898/2026.08.18.26360725 [code]
Temporal pole blurring in hippocampal sclerosis reflects seizure-disrupted myelination
Journal: medRxiv (preprint)
In common: statannotations, Pillow, NiBabel, 5 other tools, structural MRI / diffusion, 1 reference
[9] doi:10.1162/imag.a.1252 [code]
Does the brain's E:I balance really shape long-range temporal correlations? Lessons learned from 3T MRI.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: ANTs, FSL, NiBabel, 5 other tools, structural MRI / diffusion, 1 reference
[10] doi:10.1038/s41467-026-71555-0 [code]
A deep representation learning model to predict response to vagus nerve stimulation.
Journal: Nature communications
In common: ANTs, FSL, NiBabel, 5 other tools, structural MRI / diffusion, 1 reference

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.