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Myo-inositol concentration in the medial prefrontal cortex is associated with changes in brain white matter microstructure in early psychosis.

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] § Methods › Microstructure estimation ↔ matlab/WMTI_Watson_maps.m, lines 1–96 · score 0.88 · axonal water fraction, perpendicular diffusivities, parameter maps, axonal diffusivity, extra axonal, orientation
  2. [2] § Methods › Microstructure estimation ↔ WMTI_Watson.py, lines 164–248 · score 0.87 · axonal water fraction, perpendicular diffusivities, axonal diffusivity, parameter maps, extra axonal, orientation

Paper

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The authors' code

MATLAB · 124 lines · 3.7 KB · no license · 1 match

  1. function [f,Da,Depar,Deperp,c2,exitflags] = WMTI_Watson_maps(md,ad,rd,mk,ak,rk,mask,invivo_flag)
  3. % given md, ad, rd, mk, ak, rk (mean,axial,radial diffusivity, mean,axial,radial kurtosis) maps,
  4. % calculate WM model parameter maps:
  5. % f (axonal water fraction), Da (axonal diffusivity), Depar, Deperp (extra-axonal
  6. % parallel and perpendicular diffusivities), c2 (mean cos^2 of the axon
  7. % orientation dispersion: c2=1/3 fully isotropic, c2=1 perfectly parallel)
  8. % c2 is directly related to the Watson distribution concentration parameter
  9. % kappa (same as in NODDI)
  11. % All diffusivities in um2/ms
  12. % md, ad, rd should also be in um2/ms, otherwise converted here
  14. % mask: brain or ROI mask
  15. % invivo_flag: boolean, flag for in vivo (true) or ex vivo (false)
  17. % I. Jelescu, July 2021
  19. lb = [0 0 0 0 0]; % fit lower bound for model parameters [f, Da, Depar, Deperp, kappa]
  20. if invivo_flag
  21. ub = [1 3 3 3 128]; % fit upper bound for model parameters, in vivo
  22. x0 = [0.9 2.2 1.6 0.7 7]; % initial guess, in vivo
  23. md_ub = 2.5;
  24. else
  25. ub = [1 2 2 2 128]; % fit upper bound for model parameters, ex vivo
  26. x0 = [0.9 1.6 1 0.4 7]; % initial guess, ex vivo
  27. md_ub = 1.8; % upper bound on md to avoid CSF contamination
  28. end
  30. % Check md in um2/ms and not mm2/s, if not, convert
  31. if max(md(:))<1e-2
  32. md = md*1e3; ad = ad*1e3; rd = rd*1e3;
  33. end
  35. % filter out voxels with unrealistic tensor values
  36. filter = (md<md_ub) & (rk>0) & (rk<10) & (mk>0) & (mk<10);
  37. roi = mask & filter; % exclude voxels with unphysical values from calculation
  39. % calculate signal moments
  41. Wpar = ak.*(ad./md).^2;
  42. Wperp = rk.*(rd./md).^2;
  44. D0 = md; % mean diffusivity
  45. D2 = 2/3*(ad-rd);
  46. W0 = mk;
  47. W2 = 1/7*(3*Wpar + 5*mk - 8*Wperp);
  48. W4 = 4/7*(Wpar - 3*mk + 2*Wperp);
  50. % initialize model parameter maps
  51. f = NaN*ones(size(md));
  52. Da = f; Depar = f; Deperp = f; kappa = f; exitflags = f;
  54. options = optimoptions('lsqnonlin','MaxIterations',5000,'Display','off');
  56. cnt = sum(~isnan(f(:)));
  58. for k=1:size(md,3)
  60. disp(cnt/sum(roi(:)))
  62. for j = 1:size(md,2)
  64. parfor i=1:size(md,1)
  66. if roi(i,j,k)>0
  68. cnt = cnt+1;
  70. moments = [D0(i,j,k) D2(i,j,k) W0(i,j,k) W2(i,j,k) W4(i,j,k)];
  72. [x,~,~,exitflag] = lsqnonlin(@(x) wmti_watson(x,moments),x0,lb,ub,options);
  74. f(i,j,k) = x(1);
  75. Da(i,j,k) = x(2);
  76. Depar(i,j,k) = x(3);
  77. Deperp(i,j,k) = x(4);
  78. kappa(i,j,k) = x(5);
  79. exitflags(i,j,k) = exitflag;
  81. else
  82. continue
  83. end
  86. end
  87. end
  88. end
  90. % from kappa, calculate c2, the mean cos^2 of the angle between axons and
  91. % main bundle orientation (an easier metric of orientations dispersion, c2
  92. % varies between 1/3 (isotropic) and 1 (perfectly parallel axons)
  93. Fs = sqrt(pi)./2*exp(-kappa).*erfi(sqrt(kappa));
  94. c2 = 1./(2*sqrt(kappa).*Fs)-1./(2*kappa);
  96. end
  99. function F = wmti_watson(x,moments)
  101. % moments
  102. D0 = moments(1);
  103. D2 = moments(2);
  104. W0 = moments(3);
  105. W2 = moments(4);
  106. W4 = moments(5);
  108. f = x(1);
  109. Da = x(2);
  110. Depar = x(3);
  111. Deperp = x(4);
  112. k = x(5);
  114. dawsonf = 0.5*exp(-k)*sqrt(pi).*erfi(sqrt(k));
  115. p2 = 1/4*(3/(sqrt(k)*dawsonf)-2-3/k);
  116. p4 = 1/(32*k^2)*(105+12*k*(5+k)+(5*sqrt(k)*(2*k-21))/dawsonf);
  118. F(1) = 3*D0 - f*Da - (1-f)*(2*Deperp+Depar);
  119. F(2) = 3/2*D2 - p2*(f*Da + (1-f)*(Depar-Deperp));
  120. F(3) = D2^2+5*D0^2*(1+W0/3) - f*Da^2 - (1-f)*(5*Deperp^2+(Depar-Deperp)^2+10/3*Deperp*(Depar-Deperp));
  121. F(4) = 1/2*D2*(D2+7*D0)+7/12*W2*D0^2 - p2*(f*Da^2+(1-f)*((Depar-Deperp)^2+7/3*Deperp*(Depar-Deperp)));
  122. F(5) = 9*D2^2/4+35/24*W4*D0^2 - p4*(f*Da^2 + (1-f)*(Depar-Deperp)^2);
  124. end

WMTI_Watson_maps.m at commit 97d9c02, no license · at the source

Overview

Authors: Tommaso Pavan1, Qiaochu Wang2, Yasser Alemán-Gómez1, Raoul Jenni3, Martine Cleusix3, Luis Alameda4,5,6, Kim Q Do3, Philippe Conus4, Patric Hagmann1, Pascal Steullet3, Paul Klauser3,7, Lijing Xin8,9, Ileana Jelescu1
ORCID iDs: Tommaso Pavan, Qiaochu Wang, Raoul Jenni, Luis Alameda, Kim Q Do, Patric Hagmann, Pascal Steullet, Paul Klauser, Lijing Xin, Ileana Jelescu
  1. Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
  2. Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
  3. Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
  4. Service of General Psychiatry, Treatment and Early Intervention in Psychosis Program. Lausanne University Hospital (CHUV), Lausanne, Switzerland
  5. Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience. King’s College of London, London, UK
  6. Centro Investigacion Biomedica en Red de Salud Mental (CIBERSAM); Instituto de Biomedicina de Sevilla (IBIS), Hospital Universitario Virgen del Rocio, Departamento de Psiquiatria, Universidad de Sevilla, Sevilla, Spain
  7. Division of Child and Adolescent Psychiatry, Department of Psychiatry, Lausanne University Hospital and the University of Lausanne, Lausanne, Switzerland
  8. Center for Biomedical Imaging (CIBM), Lausanne, Switzerland
  9. Institute of Physics (IPHYS), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
Institutions: Centre Hospitalier Universitaire Vaudois (Switzerland); University of Lausanne (Switzerland); École Polytechnique Fédérale de Lausanne (Switzerland); King's College London (United Kingdom); Centro de Investigación Biomédica en Red de Salud Mental (Spain); Instituto de Biomedicina de Sevilla (Spain); Hospital Universitario Virgen del Rocío (Spain); Universidad de Sevilla (Spain); Centre d'Imagerie BioMedicale (Switzerland)
Journal: Translational psychiatry, volume 16, issue 1, article 393
Dates: received 8 September 2025; accepted 30 April 2026; published online 2 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41398-026-04072-9 · PMID 42230558 · PMCID PMC13444330 · OpenAlex W4413394904
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.08.15.25333577
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), schizophrenia / psychosis (population), systems (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Physiology & signal measures
Keywords: Schizophrenia, Diagnostic markers, Molecular neuroscience
MeSH: Inositol*, Prefrontal Cortex*, Psychotic Disorders*, Schizophrenia*, White Matter*, Adult, Case-Control Studies, Choline, Diffusion Magnetic Resonance Imaging, Diffusion Tensor Imaging, Female, Humans, Magnetic Resonance Spectroscopy, Male, Young Adult (* major topic)
Topic: Advanced Neuroimaging Techniques and Applications (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: Swiss National Science Foundation (185897, 320030, 51NF40_185897, 213769, 320030_197787, 194260, PCEFP2_194260)
Citations: not cited yet (Europe PMC); 101 references in the paper

Abstract

Recent research highlights the critical role of white matter (WM) alterations in psychosis and schizophrenia (SZ), reporting volumetric and structural brain changes in affected individuals. In this study, we explored the role of astroglia in SZ, which is believed to play a role in white matter integrity. We investigated for the first time the associations between advanced diffusion Magnetic Resonance Imaging (dMRI) measures of WM microstructure and Magnetic Resonance Spectroscopy (MRS)-derived glial markers in 30 subjects with early psychosis (EP, mean age 24 ± 6) versus 49 healthy controls (HC, mean age 25 ± 6). We focused on two metabolites involved in glia: myo-Inositol (myo-Ins) and total Choline (tCho), measured in the medial prefrontal cortex (mPFC), relating them to quantitative dMRI metrics derived from Diffusion Kurtosis Imaging (DKI) and WM Tract Integrity-Watson (WMTI-W) biophysical model, including mean diffusivity and kurtosis, axonal water fraction and extra-axonal diffusivities in the whole white matter. Our findings reveal a difference between EP and HC in WM diffusivities, specifically in the extra-axonal parallel direction, but not in MRS metabolites. However, we found that the mPFC myo-Ins concentrations in EP are exclusively and strongly associated with proximal WM microstructure features, in the form of a positive correlation with axonal water fraction, a proxy for axonal density, and a negative correlation with extra-axonal parallel diffusivity, suggesting the white matter alterations could be linked to astrocytic changes in early psychosis.

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 2 matches between paragraphs and lines of code.

Mic-map/WMTI-Watson_Python

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 97d9c024824655945007fe4a9d1a6b2d729e0d03, 9 July 2024
Languages: MATLAB (2), Python (1)
Size: 4 files, 3 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Optimization Toolbox (1 file), NiBabel (1 file), Tools for NIfTI and ANALYZE image (MATLAB) (1 file), NumPy (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
4 files
At the source: github.com/Mic-map/WMTI-Watson_Python

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;
  • 3 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

The data supporting the findings of this study are available from the corresponding author upon reasonable request. The code for WMTI-W can be found here: https://github.com/Mic-map/WMTI-Watson_Python.

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, issue, pages, dates, 13 authors, 3 keywords, 15 MeSH terms, 1 funder, 98 references.

Cite

This paper

Pavan, T., Wang, Q., Alemán-Gómez, Y., Jenni, R., Cleusix, M., Alameda, L., Q Do, K., Conus, P., Hagmann, P., Steullet, P., Klauser, P., Xin, L., & Jelescu, I. (2026). Myo-inositol concentration in the medial prefrontal cortex is associated with changes in brain white matter microstructure in early psychosis. Translational psychiatry, 16(1), 393. https://doi.org/10.1038/s41398-026-04072-9

BibTeX

@article{pavan2026myo,
author = {Pavan, Tommaso and Wang, Qiaochu and Alemán-Gómez, Yasser and Jenni, Raoul and Cleusix, Martine and Alameda, Luis and Q Do, Kim and Conus, Philippe and Hagmann, Patric and Steullet, Pascal and Klauser, Paul and Xin, Lijing and Jelescu, Ileana},
title = {{Myo-inositol concentration in the medial prefrontal cortex is associated with changes in brain white matter microstructure in early psychosis}},
journal = {Translational psychiatry},
year = {2026},
month = jun,
volume = {16},
number = {1},
pages = {393},
publisher = {Nature Publishing Group},
issn = {2158-3188},
doi = {10.1038/s41398-026-04072-9},
url = {https://doi.org/10.1038/s41398-026-04072-9},
pmid = {42230558},
pmcid = {PMC13444330}
}

RIS

TY - JOUR
AU - Pavan, Tommaso
AU - Wang, Qiaochu
AU - Alemán-Gómez, Yasser
AU - Jenni, Raoul
AU - Cleusix, Martine
AU - Alameda, Luis
AU - Q Do, Kim
AU - Conus, Philippe
AU - Hagmann, Patric
AU - Steullet, Pascal
AU - Klauser, Paul
AU - Xin, Lijing
AU - Jelescu, Ileana
TI - Myo-inositol concentration in the medial prefrontal cortex is associated with changes in brain white matter microstructure in early psychosis
T2 - Translational psychiatry
J2 - Transl Psychiatry
PY - 2026
DA - 2026/06/02
VL - 16
IS - 1
SP - 393
SN - 2158-3188
PB - Nature Publishing Group
DO - 10.1038/s41398-026-04072-9
UR - https://doi.org/10.1038/s41398-026-04072-9
LA - en
ER -

CSL-JSON

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