OSCR

High-resolution whole-brain magnetic resonance spectroscopic imaging in youth at risk for psychosis.

Code ↔ Paper

8 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 8 matches
  1. [1] § Methods › 3D-MRSI data pre-processing › Spatial filtering ↔ mrsitoolbox/filters/biharmonic.py, lines 130–172 · score 0.84 · smooth_img, spatial filtering, full width, spatially smoothed, nilearn, FWHM
  2. [2] § Methods › 3D-MRSI data pre-processing › Spatial filtering ↔ mrsitoolbox/filters/biharmonic.py, lines 21–81 · score 0.78 · biharmonic inpainting, NaNs, brain mask, median, spike, zero
  3. [3] § Methods › 3D-MRSI data pre-processing › Partial volume effect (PVE) correction ↔ mrsitoolbox/filters/pve.py, lines 38–96 · score 0.68 · CSF partial volume, T1w space, PVE, inverse, CAT12, WM
  4. [4] § Methods › 3D-MRSI reconstruction ↔ scripts/create_BIDS_gui.py, lines 457–473 · score 0.59 · GPC, PCh, PCr, GSH, GABA, NAAG
  5. [5] § Methods › 3D-MRSI data pre-processing ↔ experiments/Preprocessing/preprocess.py, lines 1562–1677 · score 0.54 · partial volume, MRSI map, PVE, preprocessing, T1w, filtering
  6. [6] § Methods › 3D-MRSI data pre-processing › Spatial filtering ↔ mrsitoolbox/filters/biharmonic.py, lines 21–81 · score 0.54 · brain mask, brain regions, inpainting, spikes, filtering, voxels
  7. [7] § Methods › 3D-MRSI data pre-processing › Normalization ↔ experiments/MetSiM_pipeline/map_parcel_image_to_mrsi.py, lines 28–129 · score 0.53 · MNI space, T1w space, MNI152, transformations, maps, MRSI
  8. [8] § Methods › 3D-MRSI data pre-processing › Co-registration with T1w ↔ experiments/Preprocessing/skull_strip_hdbet.py, lines 86–172 · score 0.50 · skull striped, FSL, T1w

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 · 244 lines · 11 KB · other · 3 matches

The registry keeps no copy of this file: the license of its repository (other) is not one it has verified to allow it. Your browser shows it from its source, with JavaScript.

It can be read at the source: mrsitoolbox/filters/biharmonic.py.

Overview

Authors: Edgar Céléreau1, Federico Lucchetti1, Yasser Alemán-Gómez2, Daniella Dwir1, Martine Cleusix1, Jean-Baptiste Ledoux2,3, Raoul Jenni1, Caroline Conchon4, Meritxell Bach Cuadra2,3, Zoé Schilliger1,5, Alessandra Solida6, Marco Armando5, Kerstin Jessica Plessen5, Patric Hagmann2, Philippe Conus4, Antoine Klauser7, Paul Klauser1,5
ORCID iDs: Edgar Céléreau, Federico Lucchetti, Yasser Alemán-Gómez, Daniella Dwir, Jean-Baptiste Ledoux, Raoul Jenni, Meritxell Bach Cuadra, Zoé Schilliger, Alessandra Solida, Marco Armando, Kerstin Jessica Plessen, Patric Hagmann, Philippe Conus, Antoine Klauser, Paul Klauser
  1. Centre for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
  2. Service of Diagnostic and Interventional Radiology, Department of Medical Radiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
  3. CIBM Center for BioMedical Imaging, Lausanne, Switzerland
  4. Service of General Psychiatry, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
  5. Division of child and adolescent psychiatry, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
  6. Centre Neuchatelois de Psychiatrie, Neuchatel, Switzerland
  7. Swiss Innovation Hub, Siemens Healthineers International AG, Lausanne, Switzerland
Institutions: Centre Hospitalier Universitaire Vaudois (Switzerland); University of Lausanne (Switzerland); Centre d'Imagerie BioMedicale (Switzerland)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1276
Dates: received 18 June 2025; accepted 21 May 2026; published online 17 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1276 · PMID 42326559 · PMCID PMC13277781 · OpenAlex W4411576030
Open access: diamond, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.06.22.660965
Status: code verified
Categories: human (organism), schizophrenia / psychosis (population), clinical / translational (subfield)
Methods: Connectivity, Statistics, Machine learning, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: magnetic resonance spectroscopic imaging, clinical high risk, schizotypal personality disorder, N-acetylasparate, inositol, voxel-based analyses
Topic: Advanced MRI Techniques and Applications (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: Faculté de Biologie et de Médecine, Université de Lausanne; Swiss National Science Foundation (215728); Adrian & Simone Frutiger Foundation; Fondation Leenaards
Citations: cited by 1 paper (Europe PMC); 105 references in the paper

Abstract

Advances in three-dimensional magnetic resonance spectroscopic imaging (3D-MRSI) allow for the high-resolution mapping of multiple neurometabolites throughout the entire brain in vivo and within clinically compatible time frames. Leveraging this capability, we created a voxel-based pipeline that corrects and spatially normalizes whole-brain maps of total N-acetylaspartate (tNAA), myo-inositol (Ins), choline compounds (Cho), glutamate + glutamine (Glx) and creatine + phosphocreatine (tCr). We examined 2 different 3D-MRSI dataset: first, a clinical sample of adolescents and young adults at risk for psychosis (n = 21) meeting DSM-5 criteria for Attenuated Psychosis Syndrome (APS) or Schizotypal Personality Disorder (SCZT), and age-/sex-matched healthy controls (n = 13); and second, a non-clinical sample of adolescents (n = 61) scanned on a different site. The objective of the study was threefold: first, to assess the reproducibility of 3D-MRSI measures across datasets and scanning sites; second, to validate the feasibility of whole-brain, voxel-based analyses on 3D-MRSI data; and third, to test the sensitivity of this approach. Metabolite distributions showed reproducible regional variation in standard space between the two independent samples and scanning sites (r ranging from 0.82 to 0.99). Relative to controls, at-risk participants exhibited higher tNAA levels in frontal grey matter; the SCZT subgroup additionally displayed widespread cortical and subcortical elevations of Ins levels compared with both APS and controls. Voxel-based analyses of structural (i.e., gray and white matter volumes or densities) and diffusion (i.e., generalized fractional anisotropy) parameters yielded no significant differences between patients and controls. These preliminary findings suggest that high-resolution 3D-MRSI may be sensitive enough to detect subtle neurometabolic alterations at the group level in the early stages of psychotic disorders when structural or diffusion measures show no difference. High-resolution whole-brain metabolic mapping may have the potential to help with early identification of young people at risk for psychosis or other mental disorders.

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 8 matches between paragraphs and lines of code.

MRSI-Psychosis-UP/MRSI-Metabolic-Connectome

License: other
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 20b82a12a75e690f559c91b948d839d2ebd2d23b, 4 September 2026
Languages: Python (52), Shell (2)
Size: 150 files, 54 scripts
Software Heritage: not archived
Found in: “Data and Code Availability”
Holds: README, license file, environment (environment.yaml, pyproject.toml, requirements.txt, setup.py)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (39 files), NiBabel (25 files), Matplotlib (15 files), Nilearn (15 files), SciPy (11 files), pandas (8 files), NetworkX (7 files), scikit-learn (5 files), DIPY (3 files), FSL (3 files), seaborn (3 files), Brain Connectivity Toolbox (2 files), CuPy (2 files), UMAP (2 files), ANTs (1 file), BrainSpace (1 file), MNE-Python (1 file), Numba (1 file), Pillow (1 file), Plotly (1 file), scikit-image (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
56 files, not copied: shown from their source

OSCR keeps no copy of these files: the license of this repository (other) is not one it has verified to allow it. The reader above shows each one from its source, fetched by your browser at commit 20b82a1, when its fingerprint is the one OSCR verified. How this works.

At the source: github.com/MRSI-Psychosis-UP/MRSI-Metabolic-Connectome

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;
  • 54 scripts, each with its path and the digest of its content;
  • 8 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 and Code Availability

Because of the highly sensitive nature of the clinical data used in this study, researchers interested in accessing the dataset should contact the authors with a well-motivated request detailing their research aims and the intended use of the data. The code used to correct and normalize 3D-MRSI maps presented in this paper is available at the following GitHub repository: https://github.com/MRSI-Psychosis-UP/MRSI-Metabolic-Connectome.

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

  • Authors: added Jean-Baptiste Ledoux (0000-0003-0447-5073); Alessandra Solida (0000-0001-5080-366X); removed Jean-Baptiste Ledoux; Alessandra Solida

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 17 authors, 6 keywords, 4 funders, 102 references.

Cite

This paper

Céléreau, E., Lucchetti, F., Alemán-Gómez, Y., Dwir, D., Cleusix, M., Ledoux, J.-B., Jenni, R., Conchon, C., Bach Cuadra, M., Schilliger, Z., Solida, A., Armando, M., Plessen, K. J., Hagmann, P., Conus, P., Klauser, A., & Klauser, P. (2026). High-resolution whole-brain magnetic resonance spectroscopic imaging in youth at risk for psychosis. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1276. https://doi.org/10.1162/imag.a.1276

BibTeX

@article{celereau2026high,
author = {Céléreau, Edgar and Lucchetti, Federico and Alemán-Gómez, Yasser and Dwir, Daniella and Cleusix, Martine and Ledoux, Jean-Baptiste and Jenni, Raoul and Conchon, Caroline and Bach Cuadra, Meritxell and Schilliger, Zoé and Solida, Alessandra and Armando, Marco and Plessen, Kerstin Jessica and Hagmann, Patric and Conus, Philippe and Klauser, Antoine and Klauser, Paul},
title = {{High-resolution whole-brain magnetic resonance spectroscopic imaging in youth at risk for psychosis}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = jun,
volume = {4},
pages = {IMAG.a.1276},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1276},
url = {https://doi.org/10.1162/imag.a.1276},
pmid = {42326559},
pmcid = {PMC13277781}
}

RIS

TY - JOUR
AU - Céléreau, Edgar
AU - Lucchetti, Federico
AU - Alemán-Gómez, Yasser
AU - Dwir, Daniella
AU - Cleusix, Martine
AU - Ledoux, Jean-Baptiste
AU - Jenni, Raoul
AU - Conchon, Caroline
AU - Bach Cuadra, Meritxell
AU - Schilliger, Zoé
AU - Solida, Alessandra
AU - Armando, Marco
AU - Plessen, Kerstin Jessica
AU - Hagmann, Patric
AU - Conus, Philippe
AU - Klauser, Antoine
AU - Klauser, Paul
TI - High-resolution whole-brain magnetic resonance spectroscopic imaging in youth at risk for psychosis
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/06/17
VL - 4
SP - IMAG.a.1276
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1276
UR - https://doi.org/10.1162/imag.a.1276
LA - en
ER -

CSL-JSON

{
"id": "10.1162/imag.a.1276",
"type": "article-journal",
"title": "High-resolution whole-brain magnetic resonance spectroscopic imaging in youth at risk for psychosis",
"container-title": "Imaging neuroscience (Cambridge, Mass.)",
"author": [
{
"family": "Céléreau",
"given": "Edgar"
},
{
"family": "Lucchetti",
"given": "Federico"
},
{
"family": "Alemán-Gómez",
"given": "Yasser"
},
{
"family": "Dwir",
"given": "Daniella"
},
{
"family": "Cleusix",
"given": "Martine"
},
{
"family": "Ledoux",
"given": "Jean-Baptiste"
},
{
"family": "Jenni",
"given": "Raoul"
},
{
"family": "Conchon",
"given": "Caroline"
},
{
"family": "Bach Cuadra",
"given": "Meritxell"
},
{
"family": "Schilliger",
"given": "Zoé"
},
{
"family": "Solida",
"given": "Alessandra"
},
{
"family": "Armando",
"given": "Marco"
},
{
"family": "Plessen",
"given": "Kerstin Jessica"
},
{
"family": "Hagmann",
"given": "Patric"
},
{
"family": "Conus",
"given": "Philippe"
},
{
"family": "Klauser",
"given": "Antoine"
},
{
"family": "Klauser",
"given": "Paul"
}
],
"container-title-short": "Imaging Neurosci (Camb)",
"volume": "4",
"page": "IMAG.a.1276",
"DOI": "10.1162/imag.a.1276",
"PMID": "42326559",
"PMCID": "PMC13277781",
"ISSN": "2837-6056",
"publisher": "MIT Press",
"URL": "https://doi.org/10.1162/imag.a.1276",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
17
]
]
}
}

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/s41398-026-04072-9 [code]
Myo-inositol concentration in the medial prefrontal cortex is associated with changes in brain white matter microstructure in early psychosis.
Journal: Translational psychiatry
In common: NiBabel, SciPy, NumPy, schizophrenia / psychosis, 9 references, 3 authors
[2] 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: Brain Connectivity Toolbox, ANTs, UMAP, 11 other tools, 1 reference
[3] doi:10.1038/s42003-026-10957-8 [code]
Brain defence by the extracellular matrix protein Cochlin.
Journal: Communications biology
In common: CuPy, UMAP, Numba, 10 other tools
[4] doi:10.1016/j.isci.2026.116825 [code]
Social hierarchy shapes behavioral and transcriptional responses to chronic stress and ketamine in male mice.
Journal: iScience
In common: CuPy, UMAP, Numba, 10 other tools
[5] 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: ANTs, Numba, FSL, 10 other tools
[6] doi:10.1038/s41467-026-71719-y [code]
Brain functional-structural gradient coupling reflects development, behavior and genetic influences.
Journal: Nature communications
In common: DIPY, BrainSpace, ANTs, 8 other tools, 1 reference
[7] 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, Brain Connectivity Toolbox, ANTs, 9 other tools
[8] 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, 6 other tools, 4 references
[9] doi:10.3389/fnsys.2026.1822122 [code]
Convergence-divergence circuits for multimodal integration of innate and learned opponent valences.
Journal: Frontiers in systems neuroscience
In common: UMAP, Numba, NetworkX, 10 other tools
[10] doi:10.1038/s41586-026-10631-3 [code]
A prognostic human brain network for diffuse midline glioma.
Journal: Nature
In common: BrainSpace, ANTs, FSL, 8 other tools, clinical / translational, 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.