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Cerebral venous blood flow regulates intracerebral pressure and brain clearance via meningeal lymphatic vessels.

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The authors' code

Python · 130 lines · 4.8 KB · no license

  1. import numpy as np
  2. import nibabel as nib
  3. import matplotlib.pyplot as plt
  4. from skimage import io
  5. from scipy.integrate import cumtrapz
  6. from scipy.optimize import curve_fit
  7. import openpyxl
  8. import os
  10. def run_analysis(nifti_path, roi_path, aif_path, group, mouse_number, start_frame):
  11. print(f"Running DCE analysis from frame {start_frame}...")
  13. # === Load Data ===
  14. nifti_img = nib.load(nifti_path)
  15. data = nifti_img.get_fdata()
  16. repetition_time_s = 0.038
  17. corrected_time_s = repetition_time_s * 74
  18. TR = 0.038
  19. flip = 15
  20. r1 = 3
  21. time_points_s_full = np.arange(data.shape[3]) * corrected_time_s
  22. time_points_s = time_points_s_full[start_frame:]
  24. # === ROI Signal ===
  25. roi_mask = io.imread(roi_path, as_gray=True).T
  26. roi_slice_index = int(os.path.basename(roi_path)[5])
  27. roi_signal = data[:, :, roi_slice_index, :].reshape(-1, data.shape[3])
  28. roi_signal = roi_signal[roi_mask.flatten() > 0]
  29. roi_signal = roi_signal[:, start_frame:]
  30. mean_roi_signal = np.mean(roi_signal, axis=0)
  32. # === AIF Signal ===
  33. aif_mask = io.imread(aif_path, as_gray=True).T
  34. aif_slice_index = int(os.path.basename(aif_path)[5])
  35. if aif_slice_index >= data.shape[2]:
  36. raise ValueError(f"AIF slice index {aif_slice_index} exceeds available slices in NIfTI ({data.shape[2]}).")
  37. aif_signal = data[:, :, aif_slice_index, :][aif_mask > 0]
  38. mean_aif_signal = np.mean(aif_signal, axis=0)[start_frame:]
  40. # === Plot Signal Intensity ===
  41. plt.figure("Signal Intensity")
  42. plt.plot(time_points_s, mean_roi_signal, label="ROI Signal")
  43. plt.plot(time_points_s, mean_aif_signal, label="AIF Signal")
  44. plt.xlabel("Time (s)")
  45. plt.ylabel("Signal Intensity")
  46. plt.title("Raw Signal Intensity Curves")
  47. plt.grid(True)
  48. plt.legend()
  49. plt.show()
  51. # === Convert to Concentration ===
  52. def convert_signal_to_concentration(signal, s0, T1, R1, flip):
  53. A = (signal / s0) * ((1 - np.exp(-TR * R1)) / (1 - np.cos(flip) * np.exp(-TR * R1)))
  54. R1t = (-1 / TR) * np.log((1 - A) / (1 - np.cos(flip) * A))
  55. return (R1t - R1) / r1
  57. s0 = np.min(roi_signal)
  58. T1 = 2
  59. R1 = 1 / T1
  60. tissue_conc = convert_signal_to_concentration(roi_signal, s0, T1, R1, flip)
  61. mean_tissue_conc = np.mean(tissue_conc, axis=0)
  63. s0b = np.min(mean_aif_signal)
  64. T1b = 2.249
  65. R1b = 1 / T1b
  66. hct = 0.45
  67. blood_conc = convert_signal_to_concentration(mean_aif_signal, s0b, T1b, R1b, flip)
  68. plasma_conc = blood_conc * (1 - hct) + np.min(mean_tissue_conc)
  70. # === Plot Concentration Curves ===
  71. plt.figure("Concentration Curves")
  72. plt.plot(time_points_s, mean_tissue_conc, label="Tissue Conc.")
  73. plt.plot(time_points_s, plasma_conc, label="Plasma Conc.", linestyle='dotted')
  74. plt.xlabel("Time (s)")
  75. plt.ylabel("Concentration (mM)")
  76. plt.title("Tissue & Plasma Concentration Curves")
  77. plt.grid(True)
  78. plt.legend()
  79. plt.show()
  81. # === Fit Tofts Model ===
  82. def tofts_integral(t, Cp, Kt=0.1, ve=0.2):
  83. Ct_out = np.zeros(len(t))
  84. for k in range(len(t)):
  85. Ct_out[k] = cumtrapz(np.exp(-(Kt / ve) * (t[k] - t[:k+1])) * Cp[:k+1], t[:k+1], initial=0.0)[-1]
  86. return Kt * Ct_out
  88. def fit_tofts(Ct, Cp, t):
  89. N = Ct.shape[0]
  90. Kt_vals, ve_vals = [], []
  91. for i in range(N):
  92. try:
  93. popt, _ = curve_fit(lambda t, Kt, ve: tofts_integral(t, Cp, Kt, ve),
  94. t, Ct[i, :], p0=[0.01, 0.01])
  95. Kt_vals.append(popt[0])
  96. ve_vals.append(popt[1])
  97. except RuntimeError:
  98. continue
  99. return round(np.mean(Kt_vals), 5), round(np.mean(ve_vals), 5)
  101. Kt, ve = fit_tofts(tissue_conc, plasma_conc, time_points_s)
  103. # === Final Plot ===
  104. plt.figure("Final Fit")
  105. plt.plot(time_points_s, np.mean(tissue_conc, axis=0), label="Tissue Conc.")
  106. plt.plot(time_points_s, plasma_conc, label="Plasma Conc.", linestyle='dotted')
  107. plt.title(f"Final Model Fit\nKtrans = {Kt}, Ve = {ve}")
  108. plt.xlabel("Time (s)")
  109. plt.ylabel("Concentration (mM)")
  110. plt.grid()
  111. plt.legend()
  112. plt.show()
  114. # === Save Results ===
  115. roi_region = os.path.splitext(os.path.basename(roi_path))[0][-2:]
  116. aif_region = {3: "CA", 4: "CA", 5: "SSS"}.get(aif_slice_index, "Unknown")
  118. results_file = "results.xlsx"
  119. if not os.path.exists(results_file):
  120. wb = openpyxl.Workbook()
  121. ws = wb.active
  122. ws.append(["Group", "Mouse", "ROI", "Ktrans", "Ve"])
  123. else:
  124. wb = openpyxl.load_workbook(results_file)
  125. ws = wb.active
  127. ws.append((group, mouse_number, roi_region, Kt, ve, aif_region, tissue_conc.shape[0]))
  128. wb.save(results_file)
  130. print("✅ Ktrans and Ve saved to results.xlsx")

DCE.py, no license · at the source

Overview

Authors: Marie-Renee El Kamouh1, Myriam Spajer1,2, Ruchith Singhabahu1,2, Anne-Laure Joly Marolany1, Kurt A Sailor3, Laura Mouton1, Diana Doukhi4, Sunil Koundal5, Tanner Metcalfe5, Dominique Langui1, Abel Grine1,6, Kevin Boyé7, Felipe Saceanu Leser7,8, Justus Ninnemann3,9, Joshua Gottschalk1, Cyrus Sadeghi1,10, Han Xu11, Ligia Simoes Braga Boisserand11, David Akbar1, Jerome Van Wassenhove1
and 13 other authorsAnthony Ruze1, Amelle Nasri1, Marie-Charlotte Bourrienne12,13, Elora Buscher1, Young-Kwon Hong14, Mikael Mazighi12,15, Pierre-Marie Lledo3, Helene Benveniste5, Mathieu Santin1, Anne Eichmann7,8,16, Stéphane Lehericy1,6, Jean-Léon Thomas1,11,17, Stéphanie Lenck1,6,17
ORCID iDs: Myriam Spajer, Ruchith Singhabahu, Laura Mouton, Diana Doukhi, Felipe Saceanu Leser, Justus Ninnemann, Joshua Gottschalk, Cyrus Sadeghi, Jerome Van Wassenhove, Elora Buscher, Pierre-Marie Lledo, Helene Benveniste, Jean-Léon Thomas, Stéphanie Lenck
17 affiliations
  1. Paris Brain Institute, Université Pierre et Marie Curie Paris 06, INSERM UMRS 1127, Sorbonne Université, Paris, France
  2. These authors contributed equally: Myriam Spajer, Ruchith Singhabahu
  3. Institut Pasteur, Université de Paris, CNRS UMR 3571, Paris, France
  4. Université Paris Cité, Department of Neurology, AP-HP, Hôpital Lariboisière, FHU NeuroVasc, Paris, France
  5. Department of Anesthesiology, Yale School of Medicine, New Haven, CT, USA
  6. Department of Neuroradiology, AP-HP, Pitié-Salpêtrière Hospital, Paris, France
  7. Paris Cardiovascular Research Center, Université Paris Cité, INSERM U970, Paris, France
  8. Department of Internal Medicine, Cardiovascular Research Center, Yale University School of Medicine, New Haven, CT, USA
  9. Institut Pasteur, Université de Paris Cité, INSERM U1224, Paris, France
  10. Yale University, New Haven, CT, USA
  11. Department of Neurology, Yale University School of Medicine, New Haven, CT, USA
  12. Optimisation Thérapeutique en Neuropsychopharmacologie, INSERM U1144, Paris, France
  13. Laboratoire Hématologie, AP-HP, Hôpital Bichat-Claude Bernard, Paris, France
  14. Department of Surgery, Beth Israel Deaconess Medical center, Harvard Medical School, Boston, MA, USA
  15. Université Paris Cité, Department of Neurology, AP-HP, Hôpital Lariboisière, FHU NeuroVasc 2030, Institut Universitaire de France, Paris, France
  16. Department of Cellular and Molecular Physiology, Yale University School of Medicine, New Haven, CT, USA
  17. These authors jointly supervised this work: Jean-Léon Thomas, Stéphanie Lenck
Institutions: Inserm (France); Sorbonne Université (France); Institut du Cerveau (France); Université Pierre-et-Marie-Curie (France); Centre National de la Recherche Scientifique (France); Institut Pasteur (France); Université Paris Cité (France); Gènes, synapses et cognition (France); Assistance Publique – Hôpitaux de Paris (France); Hôpital Lariboisière (France); FHU Neurovasc (France); Yale University (United States); Pitié-Salpêtrière Hospital (France); Paris Cardiovascular Research Center (France); Hôpital Bichat-Claude-Bernard (France); Optimisation Thérapeutique en Neuropsychopharmacologie (France); Laboratoire d'hématologie (France); Beth Israel Deaconess Medical Center (United States); Harvard University (United States); Institut Universitaire de France (France)
Journal: Nature neuroscience, volume 29, issue 9, pages 2190-2203
Dates: published online 22 July 2026; in print September 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41593-026-02358-1 · PMID 42487032 · PMCID PMC13533833 · OpenAlex W7170086960
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), mouse (organism), stroke (population)
Methods: Statistics, Smoothing, state filtering, decompositions, fMRI & imaging, Physiology & signal measures, Machine learning
MeSH: Brain*, Cerebral Veins*, Cerebrovascular Circulation*, Intracranial Pressure*, Lymphatic Vessels*, Meninges*, Pseudotumor Cerebri*, Animals, Brain Edema, Disease Models, Animal, Female, Humans, Jugular Veins, Magnetic Resonance Imaging, Male, Mice, Mice, Inbred C57BL (* major topic)
Topic: Cerebral Venous Sinus Thrombosis (Neurology, Medicine), according to OpenAlex
Funding: NHLBI NIH HHS (R01 HL141857)
Citations: not cited yet (Europe PMC); 61 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above.

Zenodo 17453925

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: Python (1)
Size: 1 file, 1 script
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (1 file), NiBabel (1 file), NumPy (1 file), scikit-image (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
1 file
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Code availability statement

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  • no repository, dataset or request procedure was recognized in it

Read it in the paper: doi.org/10.1038/s41593-026-02358-1.

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.

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 1 script, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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

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Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41593-026-02358-1.

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

  • Publisher: n/a → Nature Portfolio
  • Authors: added Felipe Saceanu Leser (0000-0002-7499-2765); removed Felipe Saceanu Leser

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 33 authors, 17 MeSH terms, 1 funder, 61 references.

Cite

This paper

El Kamouh, M.-R., Spajer, M., Singhabahu, R., Joly Marolany, A.-L., Sailor, K. A., Mouton, L., Doukhi, D., Koundal, S., Metcalfe, T., Langui, D., Grine, A., Boyé, K., Leser, F. S., Ninnemann, J., Gottschalk, J., Sadeghi, C., Xu, H., Boisserand, L. S. B., Akbar, D., . . . Lenck, S. (2026). Cerebral venous blood flow regulates intracerebral pressure and brain clearance via meningeal lymphatic vessels. Nature neuroscience, 29(9), 2190-2203. https://doi.org/10.1038/s41593-026-02358-1

BibTeX

@article{elkamouh2026cerebral,
author = {El Kamouh, Marie-Renee and Spajer, Myriam and Singhabahu, Ruchith and Joly Marolany, Anne-Laure and Sailor, Kurt A and Mouton, Laura and Doukhi, Diana and Koundal, Sunil and Metcalfe, Tanner and Langui, Dominique and Grine, Abel and Boyé, Kevin and Leser, Felipe Saceanu and Ninnemann, Justus and Gottschalk, Joshua and Sadeghi, Cyrus and Xu, Han and Boisserand, Ligia Simoes Braga and Akbar, David and Van Wassenhove, Jerome and Ruze, Anthony and Nasri, Amelle and Bourrienne, Marie-Charlotte and Buscher, Elora and Hong, Young-Kwon and Mazighi, Mikael and Lledo, Pierre-Marie and Benveniste, Helene and Santin, Mathieu and Eichmann, Anne and Lehericy, Stéphane and Thomas, Jean-Léon and Lenck, Stéphanie},
title = {{Cerebral venous blood flow regulates intracerebral pressure and brain clearance via meningeal lymphatic vessels}},
journal = {Nature neuroscience},
year = {2026},
month = jul,
volume = {29},
number = {9},
pages = {2190--2203},
publisher = {Nature Portfolio},
issn = {1097-6256},
doi = {10.1038/s41593-026-02358-1},
url = {https://doi.org/10.1038/s41593-026-02358-1},
pmid = {42487032},
pmcid = {PMC13533833}
}

RIS

TY - JOUR
AU - El Kamouh, Marie-Renee
AU - Spajer, Myriam
AU - Singhabahu, Ruchith
AU - Joly Marolany, Anne-Laure
AU - Sailor, Kurt A
AU - Mouton, Laura
AU - Doukhi, Diana
AU - Koundal, Sunil
AU - Metcalfe, Tanner
AU - Langui, Dominique
AU - Grine, Abel
AU - Boyé, Kevin
AU - Leser, Felipe Saceanu
AU - Ninnemann, Justus
AU - Gottschalk, Joshua
AU - Sadeghi, Cyrus
AU - Xu, Han
AU - Boisserand, Ligia Simoes Braga
AU - Akbar, David
AU - Van Wassenhove, Jerome
AU - Ruze, Anthony
AU - Nasri, Amelle
AU - Bourrienne, Marie-Charlotte
AU - Buscher, Elora
AU - Hong, Young-Kwon
AU - Mazighi, Mikael
AU - Lledo, Pierre-Marie
AU - Benveniste, Helene
AU - Santin, Mathieu
AU - Eichmann, Anne
AU - Lehericy, Stéphane
AU - Thomas, Jean-Léon
AU - Lenck, Stéphanie
TI - Cerebral venous blood flow regulates intracerebral pressure and brain clearance via meningeal lymphatic vessels
T2 - Nature neuroscience
J2 - Nat Neurosci
PY - 2026
DA - 2026/07/22
VL - 29
IS - 9
SP - 2190
EP - 2203
SN - 1097-6256
PB - Nature Portfolio
DO - 10.1038/s41593-026-02358-1
UR - https://doi.org/10.1038/s41593-026-02358-1
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41593-026-02358-1",
"type": "article-journal",
"title": "Cerebral venous blood flow regulates intracerebral pressure and brain clearance via meningeal lymphatic vessels",
"container-title": "Nature neuroscience",
"author": [
{
"family": "El Kamouh",
"given": "Marie-Renee"
},
{
"family": "Spajer",
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{
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{
"family": "Joly Marolany",
"given": "Anne-Laure"
},
{
"family": "Sailor",
"given": "Kurt A"
},
{
"family": "Mouton",
"given": "Laura"
},
{
"family": "Doukhi",
"given": "Diana"
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"family": "Koundal",
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{
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{
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},
{
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{
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"given": "Felipe Saceanu"
},
{
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},
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},
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"family": "Sadeghi",
"given": "Cyrus"
},
{
"family": "Xu",
"given": "Han"
},
{
"family": "Boisserand",
"given": "Ligia Simoes Braga"
},
{
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"given": "David"
},
{
"family": "Van Wassenhove",
"given": "Jerome"
},
{
"family": "Ruze",
"given": "Anthony"
},
{
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},
{
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"given": "Marie-Charlotte"
},
{
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"given": "Elora"
},
{
"family": "Hong",
"given": "Young-Kwon"
},
{
"family": "Mazighi",
"given": "Mikael"
},
{
"family": "Lledo",
"given": "Pierre-Marie"
},
{
"family": "Benveniste",
"given": "Helene"
},
{
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"given": "Mathieu"
},
{
"family": "Eichmann",
"given": "Anne"
},
{
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{
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{
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],
"container-title-short": "Nat Neurosci",
"volume": "29",
"issue": "9",
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"DOI": "10.1038/s41593-026-02358-1",
"PMID": "42487032",
"PMCID": "PMC13533833",
"ISSN": "1097-6256",
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"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
22
]
]
}
}

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