OSCR

Human fMRI at 11.7T: Assessing feasibility, stability, and reliability on the Iseult scanner.

Code ↔ Paper

11 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 11 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Results ↔ scripts/plotting/disp_atlas.py, lines 1–54 · score 0.88 · superior precentral sulcus, posterior Sylvian fissure, superior temporal sulcus, language network, lateralized, Broca
  2. [2] § Results ↔ scripts/plotting/atlas_v2.py, lines 20–37 · score 0.78 · superior precentral sulcus, posterior Sylvian fissure, superior temporal sulcus, Broca, maps
  3. [3] § Results ↔ scripts/plotting/disp_atlas.py, lines 1–54 · score 0.78 · posterior Sylvian fissure, frontal gyrus, IPS, intraparietal, Broca, precentral
  4. [4] § Results ↔ scripts/plotting/atlas_v2.py, lines 20–37 · score 0.73 · posterior Sylvian fissure, frontal gyrus, IPS, intraparietal, Broca, precentral
  5. [5] § Methods › Analysis ↔ scripts/analysis/first_level_glm.py, lines 26–90 · score 0.63 · design matrix, GLM, fMRIPrep, model, masked, FPR
  6. [6] § Methods › Analysis ↔ scripts/analysis/first_level_glm.py, lines 26–90 · score 0.62 · noise model, GLM, fMRIPrep, mask, FPR, scoring
  7. [7] § Methods › Analysis ↔ scripts/analysis/first_level_glm_caro.py, lines 25–82 · score 0.55 · design matrix, GLM, model, masked, FPR, scores
  8. [8] § Methods › Analysis ↔ scripts/analysis/first_level_glm_caro.py, lines 25–82 · score 0.55 · noise model, GLM, mask, FPR, scoring, scans
  9. [9] § Methods › Preprocessing ↔ scripts/preproc/bin/fmriprep_bash.sh, the whole file · a weak match · score 0.52 · MNI152NLin2009cAsym, FreeSurfer, fMRIPrep, preprocessing
  10. [10] § Methods › Metrics ↔ scripts/plotting/surface_projected_zmaps.py, lines 50–99 · score 0.51 · surface projected, Destrieux, Atlas, parcels, contours, native
  11. [11] § Methods › Study design ↔ utils/glm_utils.py, lines 39–62 · score 0.50 · vertical checkerboard, horizontal checkerboard, sentences

Paper

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The authors' code

Python · 188 lines · 9.9 KB · MIT · 2 matches

  1. import os
  2. import numpy as np
  3. import nibabel as nib
  4. import matplotlib.pyplot as plt
  5. from tristan_pipeline.utils.plotting_utils import *
  6. from tristan_pipeline.utils.analysis_utils import *
  7. from tristan_pipeline.io.params import *
  8. from nilearn.glm import threshold_stats_img
  9. from nilearn import surface, plotting, datasets
  10. from nilearn.datasets import fetch_surf_fsaverage, fetch_atlas_surf_destrieux
  11. from matplotlib import cm
  12. import pyvista as pv
  13. import nibabel.freesurfer as fs
  14. from mpl_toolkits.mplot3d import proj3d
  16. space = "T1w"
  17. contrasts_names = ['phrases']
  18. hemis = {'lh': 'left',
  19. #'rh': 'right'
  20. }
  21. PARCEL_NAME_MAP = {
  22. # ---------- LANGUAGE ----------
  23. "G_front_inf-Opercular": "Inferior frontal gyrus (Broca)", #Speech production, articulation, grammar (core Broca’s area) (ok)
  24. "G_front_inf-Triangul": "Inferior frontal gyrus (Broca)", #Sentence structure, controlled language output (Broca’s area) (ok)
  25. "S_precentral-inf-part": "Inferior precentral Sulcus", #Motor planning for speech (mouth, tongue, lips) (ok)
  26. #"G_temp_sup-Lateral": "Superior temporal gyrus", #Understanding spoken language, phoneme processing (ok)
  27. "G_temp_sup-Plan_tempo": "Planum temporale", #Auditory–language integration, phonology (Planum temporale) (ok)
  28. #"G_temp_sup-Plan_polar": "Superior temporal pole", #High-level speech and voice processing (ok)
  29. #"G_temporal_middle": "Middle temporal gyrus", #Word meaning, lexical–semantic processing (ok)
  30. #"Pole_temporal": "Temporal pole", #Conceptual knowledge, semantic memory (ok)
  31. "S_temporal_sup": "Superior temporal sulcus", #Linking sounds to meaning, social language cues (ok)
  32. #"S_temporal_inf": "Inferior temporal sulcus", #Visual–semantic associations (words, objects) (ok)
  33. "G_pariet_inf-Supramar": "Supramarginal gyrus", #Phonological working memory, reading, sound–symbol mapping (ok)
  34. "Lat_Fis-post": "Posterior sylvian fissure", #Anatomical hub of the perisylvian language network
  35. #"G_front_inf-Orbital": "Inferior frontal gyrus",
  36. #"G_temp_sup-Lateral": "Superior temporal gyrus",
  37. #"G_temporal_inf": "Inferior temporal gyrus",
  38. # ---------- MATH / NUMBER ----------
  39. "S_intrapariet_and_P_trans": "Intraparietal sulcus (IPS)", #Core number sense, quantity comparison, calculation (IPS) (ok)
  40. "G_parietal_sup": "Superior parietal lobule", #Spatial attention, mental calculation, number manipulation (ok)
  41. #"S_parietal_inf": "Inferior parietal sulcus", #Numeric operations, visuospatial processing
  42. #"S_parietal_sup": "Superior parietal sulcus", #Spatial reasoning, magnitude manipulation
  43. "G_pariet_inf-Angular": "Angular gyrus", #Arithmetic facts, symbolic meaning, number–word links
  44. #"S_front_middle": "Middle frontal sulcus", #Executive control during calculations (ok)
  45. #"S_front_sup": "Superior frontal sulcus", #Attention and monitoring during complex math (ok)
  46. "S_precentral-sup-part": "Superior Precentral Sulcus", #Motor planning for hand and speech actions (ok)
  47. "S_central": "Central Sulcus", #Boundary integrating perception and action (ok)
  48. # "S_precentral-sup-part": "Superior Precentral Sulcus",
  49. "G_front_middle": "Middle frontal gyrus", #Working memory, multi-step problem solving
  50. #"G_precentral": "Primary Motor Cortex",
  52. }
  53. TARGET_PARCELS = set(PARCEL_NAME_MAP.keys())
  54. subjects=[2]
  56. for subj in subjects:
  57. subj_color = subject_colors.get(subj, 'black')
  58. for ses in sessions:
  59. for contrast in contrasts_names:
  60. for m_idx, moco in enumerate(mocos.keys()):
  61. print(moco)
  62. FMRIPREP_PATH = os.path.join(DATA_DIR, 'derivatives', 'fmriprep')
  63. FREESURFER_PATH = os.path.join(
  64. DATA_DIR, 'derivatives', 'freesurfer', f'sub-{subj:02}'
  65. )
  66. zmap_path = os.path.join(
  67. FMRIPREP_PATH,
  68. f"sub-{subj:02}",
  69. f"ses-{ses}",
  70. "stats",
  71. f"sub-{subj:02}_ses-{ses}_zmap_{contrast}_{space}_{moco}.nii"
  72. )
  73. zmap_vol = nib.load(zmap_path)
  74. for h in hemis.keys():
  75. native_pial = os.path.join(FREESURFER_PATH, 'surf', f"{h}.pial")
  76. native_inflated = os.path.join(FREESURFER_PATH, 'surf', f"{h}.inflated")
  77. native_sulc = os.path.join(FREESURFER_PATH, 'surf', f"{h}.sulc")
  78. labels_native_path = os.path.join(FREESURFER_PATH, 'label', f"{h}.aparc.a2009s.annot")
  79. labels_native, ctab, names = fs.read_annot(labels_native_path)
  80. names = [n.decode("utf-8") for n in names]
  81. coords, faces = fs.read_geometry(native_inflated)
  82. texture = surface.vol_to_surf(zmap_vol, native_pial)
  83. parcel_levels = np.unique(labels_native)
  84. parcel_levels = parcel_levels[parcel_levels > 0]
  85. colors_rgb = ctab[:, :3] / 255.0
  86. colors_rgba = np.hstack([colors_rgb, np.ones((colors_rgb.shape[0], 1))])
  87. colors_for_contours = colors_rgba[parcel_levels]
  88. fig = plotting.plot_surf_stat_map(
  89. surf_mesh=native_inflated,
  90. stat_map=texture,
  91. hemi=hemis[h],
  92. bg_map=native_sulc,
  93. threshold=6,
  94. alpha=0.3,
  95. vmax=6,
  96. colorbar=False,
  97. cmap="seismic")
  98. ax = fig.axes[0]
  100. target_levels = [lab for lab in parcel_levels if names[lab] in TARGET_PARCELS]
  101. colors_for_target_contours = colors_rgba[target_levels]
  103. plotting.plot_surf_contours(
  104. surf_mesh=native_inflated,
  105. roi_map=labels_native,
  106. hemi=hemis[h],
  107. #levels=parcel_levels,
  108. #colors=colors_for_contours,
  109. levels=target_levels,
  110. colors=colors_for_target_contours,
  111. linewidths=1,
  112. figure=fig)
  113. for lab in parcel_levels:
  114. #parcel_name = names[lab]
  115. #if parcel_name not in TARGET_PARCELS:
  116. # continue
  117. raw_name = names[lab]
  118. if raw_name not in TARGET_PARCELS:
  119. continue
  120. verts = np.where(labels_native == lab)[0]
  121. if len(verts) < 200:
  122. continue
  123. centroid = coords[verts].mean(axis=0)
  124. label_text = PARCEL_NAME_MAP[raw_name]
  126. #x, y, _ = proj3d.proj_transform(centroid[0], centroid[1], centroid[2],
  127. # """ ax.get_proj())
  128. """if label_text in ["Superior parietal lobule", "Intraparietal sulcus (IPS)"]:
  129. ax.text(centroid[0], centroid[1], centroid[2]+7,
  130. #names[lab],
  131. label_text,
  132. fontsize=15,
  133. color="black",
  134. ha="center",
  135. va="center")
  136. #elif label_text in ["Superior frontal sulcus"]:
  137. # ax.text(centroid[0], centroid[1], centroid[2]+3,
  138. # #names[lab],
  139. # label_text,
  140. # fontsize=15,
  141. # color="black",
  142. # ha="center",
  143. # va="center")
  145. elif label_text in ["Planum temporale"]:
  146. ax.text(centroid[0], centroid[1], centroid[2]-5,
  147. #names[lab],
  148. label_text,
  149. fontsize=15,
  150. color="black",
  151. ha="center",
  152. va="center")
  153. elif label_text in ["Superior temporal sulcus"]:
  154. ax.text(centroid[0], centroid[1], centroid[2]-10,
  155. #names[lab],
  156. label_text,
  157. fontsize=15,
  158. color="black",
  159. ha="center",
  160. va="center")
  161. else:
  162. ax.text(centroid[0], centroid[1], centroid[2],
  163. #names[lab],
  164. label_text,
  165. fontsize=15,
  166. color="black",
  167. ha="center",
  168. va="center")"""
  170. #x2d, y2d, _ = proj3d.proj_transform(centroid[0], centroid[1], centroid[2], ax.get_proj())
  171. #ax.text2D(
  172. # x2d, y2d,
  173. # label_text,
  174. # fontsize=6,
  175. # color="black",
  176. # ha="center",
  177. # va="center",
  178. # transform=ax.transData
  179. #)
  180. plt.gcf().set_size_inches(8.27, 11.69) # A4 portrait
  181. plt.savefig(
  182. os.path.join(
  183. grp_dir,
  184. 'figures',
  185. f'sub-{subj:02}_ses-{ses}_surf-{h}_Destrieux_{space}_{moco}_ISMRMNoLabels_.png'),
  186. )
  187. plt.show()
  188. #plt.close(fig)

disp_atlas.py at commit 7d4e948, under MIT · at the source

Overview

Authors: Joseph Obriot1, Zaineb Amor2,3, Matthias Serger4, Rüdiger Stirnberg4, Philipp Ehses4, Malte Riedel5, Tony Stöcker4, Klass P. Prüssmann5, Shajan Gunamony6, Son Chu6, Alexis Amadon1, Alexandre Vignaud1, Vincent Gras1, Florent Meyniel2,3, Franck Mauconduit1, Caroline Le Ster1, Nicolas Boulant1
ORCID iDs: Joseph Obriot, Zaineb Amor, Matthias Serger, Rüdiger Stirnberg, Philipp Ehses, Malte Riedel, Tony Stöcker, Shajan Gunamony, Son Chu, Alexis Amadon, Alexandre Vignaud, Vincent Gras, Florent Meyniel, Franck Mauconduit, Caroline Le Ster, Nicolas Boulant
  1. Université Paris-Saclay, CEA, CNRS, BAOBAB, Neurospin, Gif-sur-Yvette, France
  2. Institut de neuromodulation, GHU Paris, psychiatrie et neurosciences, centre hospitalier Sainte-Anne, pôle hospitalo-universitaire 15, Université Paris Cité, Paris, France
  3. Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin center, Gif-sur-Yvette, France
  4. German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
  5. Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
  6. Imaging Center for Excellence, University of Glasgow, Glasgow, United Kingdom
Institutions: Centre National de la Recherche Scientifique (France); Commissariat à l'Énergie Atomique et aux Énergies Alternatives (France); Université Paris-Saclay (France); CEA Paris-Saclay (France); Building large instruments for neuroimaging: from population imaging to ultra-high magnetic fields (France); Inserm (France); Université Paris Cité (France); Centre Hospitalier Sainte-Anne (France); German Center for Neurodegenerative Diseases (Germany); University of Zurich (Switzerland); ETH Zurich (Switzerland); Institute for Biomedical Engineering (Switzerland); University of Glasgow (United Kingdom)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1362
Dates: received 11 February 2026; accepted 9 August 2026; published online 17 September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1362 · PMID 42761695 · PMCID PMC13588317 · OpenAlex W7203836177
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), methods / tools (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Connectivity, Preprocessing, Evoked potentials, fMRI & imaging, Machine learning
Keywords: ultra-high field MRI, fMRI, motion correction, parallel transmission
MeSH: Brain*, Brain Mapping*, Magnetic Resonance Imaging*, Adult, Feasibility Studies, Female, Humans, Image Processing, Computer-Assisted, Male, Oxygen, Reproducibility of Results, Signal-To-Noise Ratio, Young Adult (* major topic)
Topic: Advanced MRI Techniques and Applications (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: H2020 Future and Emerging Technologies (885876); Agence Nationale de la Recherche (French National Research Agency) (ANR-21-ESRE-0006)
Citations: not cited yet (Europe PMC); 74 references in the paper

Abstract

Ultra-high-field magnetic resonance imaging (MRI) promises major gains in blood oxygen level dependent (BOLD) sensitivity but introduces severe challenges such as RF field inhomogeneity, B0 field inhomogeneity, motion and vibration-induced field variations. We report the first whole-brain resting-state and task-based fMRI experiments at 11.7T. A 3D-EPI sequence was optimized using tailored parallel-transmit RF pulses, gradient reshaping to suppress vibration-induced B0 fluctuations, and prospective motion correction using servo navigation combined with retrospective phase equalization (PEERS). Four participants were scanned at 11.7T with whole-brain 1.2 mm isotropic resolution with servo navigation and PEERS that considerably reduced residual motion and increased temporal signal-to-noise ratio (SNR). The Default Mode Network in resting state could be detected and a physiological-noise-dominated regime was identified. Four additional but different participants were scanned at 7T for tSNR comparison and validation of the protocol, data quality, and processing pipeline. Task-based fMRI likewise was conducted at 11.7T and yielded robust, spatially specific activations across motor, visual, mathematical, and language networks, again with improved sensitivity and cleaner BOLD responses when using servo navigation and PEERS. These results demonstrate the feasibility and reliability of whole-brain human fMRI at 11.7T and constitute a first-quality control milestone to further increase resolution at that field strength.

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 11 matches between paragraphs and lines of code.

Zaineb18/tristan_pipeline

License: MIT
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 7d4e9484292521ec58b612649620ff3cd205e95b, 15 July 2026
Languages: Python (39), Shell (3)
Size: 45 files, 42 scripts
Software Heritage: not archived
Found in: “Data and Code Availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Nilearn (25 files), NiBabel (22 files), NumPy (18 files), Matplotlib (17 files), pandas (9 files), ANTs (3 files), SciPy (3 files), fMRIPrep (2 files), seaborn (2 files), dcm2niix (1 file), FreeSurfer (1 file), h5py (1 file), Nipype (1 file), statsmodels (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
44 files
At the source: github.com/Zaineb18/tristan_pipeline

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;
  • 42 scripts, each with its path and the digest of its content;
  • 11 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

Unprocessed 11.7T MRI data available at https://doi.org/10.5281/zenodo.21993131. Preprocessing and analysis scripts available at https://github.com/Zaineb18/tristan_pipeline.

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 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 17 authors, 4 keywords, 13 MeSH terms, 2 funders, 71 references.

Cite

This paper

Obriot, J., Amor, Z., Serger, M., Stirnberg, R., Ehses, P., Riedel, M., Stöcker, T., Prüssmann, K. P., Gunamony, S., Chu, S., Amadon, A., Vignaud, A., Gras, V., Meyniel, F., Mauconduit, F., Le Ster, C., & Boulant, N. (2026). Human fMRI at 11.7T: Assessing feasibility, stability, and reliability on the Iseult scanner. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1362. https://doi.org/10.1162/imag.a.1362

BibTeX

@article{obriot2026human,
author = {Obriot, Joseph and Amor, Zaineb and Serger, Matthias and Stirnberg, Rüdiger and Ehses, Philipp and Riedel, Malte and Stöcker, Tony and Prüssmann, Klass P. and Gunamony, Shajan and Chu, Son and Amadon, Alexis and Vignaud, Alexandre and Gras, Vincent and Meyniel, Florent and Mauconduit, Franck and Le Ster, Caroline and Boulant, Nicolas},
title = {{Human fMRI at 11.7T: Assessing feasibility, stability, and reliability on the Iseult scanner}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = sep,
volume = {4},
pages = {IMAG.a.1362},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1362},
url = {https://doi.org/10.1162/imag.a.1362},
pmid = {42761695},
pmcid = {PMC13588317}
}

RIS

TY - JOUR
AU - Obriot, Joseph
AU - Amor, Zaineb
AU - Serger, Matthias
AU - Stirnberg, Rüdiger
AU - Ehses, Philipp
AU - Riedel, Malte
AU - Stöcker, Tony
AU - Prüssmann, Klass P.
AU - Gunamony, Shajan
AU - Chu, Son
AU - Amadon, Alexis
AU - Vignaud, Alexandre
AU - Gras, Vincent
AU - Meyniel, Florent
AU - Mauconduit, Franck
AU - Le Ster, Caroline
AU - Boulant, Nicolas
TI - Human fMRI at 11.7T: Assessing feasibility, stability, and reliability on the Iseult scanner
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/09/17
VL - 4
SP - IMAG.a.1362
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1362
UR - https://doi.org/10.1162/imag.a.1362
LA - en
ER -

CSL-JSON

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"title": "Human fMRI at 11.7T: Assessing feasibility, stability, and reliability on the Iseult scanner",
"container-title": "Imaging neuroscience (Cambridge, Mass.)",
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Journal: Biology of sex differences
In common: Nipype, ANTs, FreeSurfer, 8 other tools, fMRI, 1 reference
[10] 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: fMRIPrep, Nipype, ANTs, 8 other tools

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