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Quantifying Cardiac, Respiratory, and Low Frequency Components of CSF Motion From fMRI Inflow Effects.

Code ↔ Paper

3 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 3 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › MRI Protocol ↔ get_protocol_settings.m, the whole file · a weak match · score 0.82 · dummy scans, flip angle, slice thickness, echo, MRI, TR
  2. [2] § Methods › MRI Protocol ↔ get_protocol_settings.m, the whole file · a weak match · score 0.70 · flip angle, slice thickness, echo, TR, space, scan
  3. [3] § Methods › Model Validation ↔ investigate_profile.m, lines 50–122 · score 0.59 · fitted plug, flow profile, plug flow, velocities, model, signals

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

MATLAB · 43 lines · 1.9 KB · no license · 2 matches

  1. function model_settings = get_protocol_settings(nslices)
  2. % Create a struct with model and protocol settings
  5. model_settings = struct;
  7. model_settings.theta = 80; % flip angle (degrees)
  8. model_settings.TR = 2; % repitition time (s)
  9. model_settings.TE = 30/1000; % echo time (s)
  10. model_settings.T1 = 4.7; % 4.2734; %4.3; % (s) Normal Values of Magnetic Relaxation Parameters of Spine
  11. model_settings.T2 = 1.6; % 1.5776; %2.00; % (s) Components with the Synthetic MRI Sequence
  13. model_settings.ds = 10; % Nr of dummy scans
  14. model_settings.n_slices = nslices; % Nr of excited slices of consideration
  16. model_settings.L = 3.4; % slice thickness (mm)
  17. model_settings.sp = 0.5; % spacing (mm)
  18. model_settings.stp = 20; % number of points per mm for mesh
  20. model_settings.xmin = -(model_settings.n_slices-1)*(model_settings.L+model_settings.sp) + model_settings.sp; % min position
  21. model_settings.xmax = model_settings.n_slices*(model_settings.L+model_settings.sp) - model_settings.sp; % max position
  24. model_settings.x0 = 0:(model_settings.L+model_settings.sp):(model_settings.n_slices-1)*(model_settings.L + model_settings.sp); % ground position slices (mm)
  25. model_settings.dt = 2/37; % Time per slice (s)
  26. model_settings.Nt = 170; %60; %170; % Nr of excitations per slice
  27. model_settings.t0 = 30; % time before excitations
  30. t_ex = zeros(model_settings.Nt + model_settings.ds, model_settings.n_slices); % time at excitation (s)
  31. for i = 1:model_settings.n_slices
  32. j = floor(i/2);
  33. if (mod(i, 2) == 0)
  34. t_ex(:,i) = model_settings.t0 + (18 + j)*model_settings.dt + model_settings.TR*(0:(model_settings.ds + model_settings.Nt-1));
  35. else
  36. t_ex(:,i) = model_settings.t0 + j*model_settings.dt + model_settings.TR*(0:(model_settings.ds + model_settings.Nt-1));
  37. end
  38. end
  40. model_settings.t_ex = t_ex;
  43. end

get_protocol_settings.m at commit 1a22169, no license · at the source

Overview

Authors: Pontus Söderström1, Cecilia Björnfot2,3, Britt M. Andersson1, Jan Malm4, Anders Eklund2,3, Anders Wåhlin1,2,3
ORCID iDs: Pontus Söderström
  1. Department of Applied Physics and Electronics Umeå University Umeå Sweden
  2. Department of Diagnostics and Intervention, Biomedical Engineering and Radiation Physics Umeå University Umeå Sweden
  3. Umeå Center for Functional Brain Imaging (UFBI), Umeå University Umeå Sweden
  4. Department of Clinical Science, Neurosciences Umeå University Umeå Sweden
Institutions: Umeå University (Sweden)
Journal: Magnetic resonance in medicine, volume 96, issue 4, pages 1916-1928
Dates: received 19 December 2025; accepted 5 May 2026; published online 17 May 2026; in print October 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/mrm.70438 · PMID 42143758 · PMCID PMC13418971 · OpenAlex W7161483616
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), methods / tools (subfield)
Methods: Spectral & time-frequency, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Connectivity, fMRI & imaging, Physiology & signal measures
Keywords: cerebrospinal fluid flow, glymphatic system, inflow effect, quantitative flow assessment, resting‐state fMRI
MeSH: Brain*, Cerebrospinal Fluid*, Magnetic Resonance Imaging*, Aged, Aged, 80 and over, Female, Humans, Male, Motion, Phantoms, Imaging, Reproducibility of Results, Respiration (* major topic)
Journal subjects: Computer Processing and Modeling
Topic: Cerebrospinal fluid and hydrocephalus (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: Hjärt-Lungfonden (20210653); Vetenskapsrådet (2021‐00711_VR/JPND, 2022‐04263); Stiftelsen för Strategisk Forskning (RMX18‐0152)
Citations: not cited yet (Europe PMC); 66 references in the paper

Abstract

Purpose: Cerebrospinal fluid (CSF) flow oscillations have emerged as a potentially important marker related to brain clearance, but their acquisition often relies on specialized imaging MRI sequences. The purpose of this work was to enable quantitative assessment of CSF flow associated with cardiac, respiratory, and low‐frequency cycles using widely available functional magnetic resonance imaging (fMRI) acquisitions.

Methods: A method was developed to translate fMRI‐derived CSF inflow signals into quantitative flow rates. This approach modeled the spin‐history of an oscillating ensemble of molecules. Validation was performed using phantom experiments with cardiac‐, respiratory‐, and low‐frequency‐like oscillatory flow. The method was further applied to resting‐state data from 48 older adults (68–82 years, 19 women) to characterize CSF flow at the foramen magnum.

Results: Phantom experiments demonstrated excellent correlations between estimated and true velocities for cardiac‐ and respiratory‐like frequencies (r = 0.94 and 0.97, respectively) and moderate correlation for the low‐frequency‐like oscillation (r = 0.58). In the population cohort, median CSF stroke volumes were 0.77 [0.57, 1.09] mL for the cardiac cycle, 0.38 [0.26, 0.88] mL for the respiratory cycle, and 0.26 [0.14, 0.39] mL for the low‐frequency cycle.

Conclusion: The proposed spin‐history modeling method enabled quantitative estimation of CSF flow components using a conventional fMRI dataset and showed that the cardiac cycle dominates CSF motion at the foramen magnum.

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 3 matches between paragraphs and lines of code.

Buntess/fMRI-Flow-Quantification

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 1a221699fed6b9035f1772c0bf7893aa26914c99, 5 May 2026
Languages: MATLAB (12)
Size: 21 files, 12 scripts
Software Heritage: not archived
Found in: the text, “Simulations of CSF Inflow Signal”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Optimization Toolbox (3 files)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
13 files
At the source: github.com/Buntess/fMRI-Flow-Quantification

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;
  • 12 scripts, each with its path and the digest of its content;
  • 3 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 Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

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

  • Publisher: n/a → Wiley

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 5 keywords, 12 MeSH terms, 3 funders, 62 references.

Cite

This paper

Söderström, P., Björnfot, C., Andersson, B. M., Malm, J., Eklund, A., & Wåhlin, A. (2026). Quantifying Cardiac, Respiratory, and Low Frequency Components of CSF Motion From fMRI Inflow Effects. Magnetic resonance in medicine, 96(4), 1916-1928. https://doi.org/10.1002/mrm.70438

BibTeX

@article{soderstrom2026quantifying,
author = {Söderström, Pontus and Björnfot, Cecilia and Andersson, Britt M. and Malm, Jan and Eklund, Anders and Wåhlin, Anders},
title = {{Quantifying Cardiac, Respiratory, and Low Frequency Components of CSF Motion From fMRI Inflow Effects}},
journal = {Magnetic resonance in medicine},
year = {2026},
month = may,
volume = {96},
number = {4},
pages = {1916--1928},
publisher = {Wiley},
issn = {0740-3194},
doi = {10.1002/mrm.70438},
url = {https://doi.org/10.1002/mrm.70438},
pmid = {42143758},
pmcid = {PMC13418971}
}

RIS

TY - JOUR
AU - Söderström, Pontus
AU - Björnfot, Cecilia
AU - Andersson, Britt M.
AU - Malm, Jan
AU - Eklund, Anders
AU - Wåhlin, Anders
TI - Quantifying Cardiac, Respiratory, and Low Frequency Components of CSF Motion From fMRI Inflow Effects
T2 - Magnetic resonance in medicine
J2 - Magn Reson Med
PY - 2026
DA - 2026/05/17
VL - 96
IS - 4
SP - 1916
EP - 1928
SN - 0740-3194
PB - Wiley
DO - 10.1002/mrm.70438
UR - https://doi.org/10.1002/mrm.70438
LA - en
ER -

CSL-JSON

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"container-title": "Magnetic resonance in medicine",
"author": [
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"family": "Söderström",
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"container-title-short": "Magn Reson Med",
"volume": "96",
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"DOI": "10.1002/mrm.70438",
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17
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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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