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Brain protein burden is related to intravoxel incoherent motion: PET-MR imaging study.

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Paper

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The authors' code

Python · 57 lines · 1.9 KB · CC-BY-4.0

  1. # Configuration file for the Sphinx documentation builder.
  2. #
  3. # This file only contains a selection of the most common options. For a full
  4. # list see the documentation:
  5. # https://www.sphinx-doc.org/en/master/usage/configuration.html
  7. # -- Path setup --------------------------------------------------------------
  9. # If extensions (or modules to document with autodoc) are in another directory,
  10. # add these directories to sys.path here. If the directory is relative to the
  11. # documentation root, use os.path.abspath to make it absolute, like shown here.
  12. #
  13. # import os
  14. # import sys
  15. # sys.path.insert(0, os.path.abspath('.'))
  17. # -- Project information -----------------------------------------------------
  20. project = "ivim_fit"
  21. copyright = "2024, Dimuthu"
  22. author = "Dimuthu"
  25. # -- General configuration ---------------------------------------------------
  27. # Add any Sphinx extension module names here, as strings. They can be
  28. # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
  29. # ones.
  30. extensions = [
  31. "sphinx_rtd_theme",
  32. "sphinxarg.ext",
  33. ]
  35. # Add any paths that contain templates here, relative to this directory.
  36. templates_path = ["_templates"]
  38. # List of patterns, relative to source directory, that match files and
  39. # directories to ignore when looking for source files.
  40. # This pattern also affects html_static_path and html_extra_path.
  41. exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
  44. master_doc = "index"
  47. # -- Options for HTML output -------------------------------------------------
  49. # The theme to use for HTML and HTML Help pages. See the documentation for
  50. # a list of builtin themes.
  51. #
  52. html_theme = "sphinx_rtd_theme"
  54. # Add any paths that contain custom static files (such as style sheets) here,
  55. # relative to this directory. They are copied after the builtin static files,
  56. # so a file named "default.css" will overwrite the builtin "default.css".
  57. html_static_path = ["_static"]

conf.py, under CC-BY-4.0 · at the source

Overview

Authors: Dimuthu Hemachandra1, Kevin Zheng1, Sara A. Lorkiewicz1, Joseph Winer1, Hillary Vossler1, Guido A. Davidzon2, Elizabeth C. Mormino1,3,4, Tilman Schulte5,6, Kathleen L. Poston1,3,4,7, Eva M. Müller-Oehring1,5
ORCID iDs: Kathleen L. Poston
  1. Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
  2. Department of Radiology, Stanford University School of Medicine, Stanford, CA, United States
  3. Wu Tsai Neuroscience Institute, Stanford University, Stanford, CA, United States
  4. Phil and Penny Knight Initiative for Brain Resilience, Stanford University, Stanford, CA, United States
  5. Biosciences Division, SRI International, Menlo Park, CA, United States
  6. Department of Psychology, Palo Alto University, Palo Alto, CA, United States
  7. Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, United States
Institutions: Stanford Medicine (United States); Stanford University (United States); SRI International (United States); Palo Alto University (United States)
Journal: Frontiers in neuroscience, volume 20, article 1841093
Dates: received 27 March 2026; accepted 28 May 2026; published online 16 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3389/fnins.2026.1841093 · PMID 42382107 · PMCID PMC13314920 · OpenAlex W7164882293
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), PET / SPECT (modality), human (organism), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Smoothing, state filtering, decompositions, Machine learning, fMRI & imaging
Keywords: amyloid, brain waste clearance, glymphatic system, intravoxel incoherent motion (IVIM), neurodegeneration, PET/MRI, tau
Topic: Advanced Neuroimaging Techniques and Applications (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Citations: not cited yet (Europe PMC); 49 references in the paper

Abstract

Introduction: Dysfunction in brain protein clearance mechanisms is thought to contribute to many neurodegenerative diseases, yet non-invasive assessment of these mechanisms in humans remains challenging. This study is the first to examine whether intravoxel incoherent motion (IVIM) diffusion MRI metrics, measures of water diffusion and fluid dynamics, are associated with pathological protein accumulation and cognition in aging individuals, and hence whether they serve as a proxy for brain waste clearance function.

Methods: We analyzed data from 94 participants (n = 45 β-amyloid positive) who underwent simultaneous PET/MRI scans to calculate three key IVIM metrics: D (true diffusion coefficient), D* (pseudo-diffusion coefficient reflecting perfusion), and f (perfusion fraction) within 98 regions of interest. A machine learning model was trained to identify the most informative IVIM features for predicting β-amyloid (Aβ) status. Selected features were then evaluated for correlations with protein burden (Aβ and tau) and cognitive performance.

Results: The model identified a subset of 25 key features that effectively predicted Aβ status, achieving a predictive accuracy of 80.0% on unseen data. Regions with important IVIM features aligned with previously identified Aβ-affected regions and showed significant correlations with Aβ burden (r = 0.53, p < 0.0001) and tau burden (r = 0.61, p < 0.0001). A significant negative correlation was observed between IVIM features and cognitive decline (r = −0.60, p < 0.0001). When stratified by Aβ status, this correlation remained significant only in the Aβ-positive group (r = −0.61, p < 0.0001), but not in the Aβ-negative group.

Conclusion: IVIM-derived metrics (D, D*, and f), which measure water diffusion and perfusion dynamics in the brain, may be valuable non-invasive biomarkers of protein accumulation and associated cognitive decline in the aging human brain.

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

Repositories

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

Zenodo 20560360

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (14 files), NiBabel (11 files), pandas (9 files), ANTs (1 file), FSL (1 file), Matplotlib (1 file), Nilearn (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)
26 files
At the source:

PostonLab/IVIM_fit

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 9b1681cac3b695126548eddd8f75b1f39bbd8b39, 8 September 2026
Languages: Python (21), Shell (3)
Size: 68 files, 24 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, license file, environment (poetry.lock, pyproject.toml, docs/requirements.txt), continuous integration, documentation
Not found: CITATION.cff, tests
Tools: NumPy (14 files), NiBabel (11 files), pandas (9 files), ANTs (1 file), FSL (1 file), Matplotlib (1 file), Nilearn (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
26 files
At the source: github.com/PostonLab/IVIM_fit

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 48 scripts, 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

No dataset and no data link were found in the paper.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors pending final legal and ethical review, without undue reservation.

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 10 authors, 7 keywords, 44 references.

Cite

This paper

Hemachandra, D., Zheng, K., Lorkiewicz, S. A., Winer, J., Vossler, H., Davidzon, G. A., Mormino, E. C., Schulte, T., Poston, K. L., & Müller-Oehring, E. M. (2026). Brain protein burden is related to intravoxel incoherent motion: PET-MR imaging study. Frontiers in neuroscience, 20, 1841093. https://doi.org/10.3389/fnins.2026.1841093

BibTeX

@article{hemachandra2026brain,
author = {Hemachandra, Dimuthu and Zheng, Kevin and Lorkiewicz, Sara A. and Winer, Joseph and Vossler, Hillary and Davidzon, Guido A. and Mormino, Elizabeth C. and Schulte, Tilman and Poston, Kathleen L. and Müller-Oehring, Eva M.},
title = {{Brain protein burden is related to intravoxel incoherent motion: PET-MR imaging study}},
journal = {Frontiers in neuroscience},
year = {2026},
month = jun,
volume = {20},
pages = {1841093},
publisher = {Frontiers Media SA},
issn = {1662-4548},
doi = {10.3389/fnins.2026.1841093},
url = {https://doi.org/10.3389/fnins.2026.1841093},
pmid = {42382107},
pmcid = {PMC13314920}
}

RIS

TY - JOUR
AU - Hemachandra, Dimuthu
AU - Zheng, Kevin
AU - Lorkiewicz, Sara A.
AU - Winer, Joseph
AU - Vossler, Hillary
AU - Davidzon, Guido A.
AU - Mormino, Elizabeth C.
AU - Schulte, Tilman
AU - Poston, Kathleen L.
AU - Müller-Oehring, Eva M.
TI - Brain protein burden is related to intravoxel incoherent motion: PET-MR imaging study
T2 - Frontiers in neuroscience
J2 - Front Neurosci
PY - 2026
DA - 2026/06/16
VL - 20
SP - 1841093
SN - 1662-4548
PB - Frontiers Media SA
DO - 10.3389/fnins.2026.1841093
UR - https://doi.org/10.3389/fnins.2026.1841093
LA - en
ER -

CSL-JSON

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