OSCR

Pulmonary and cerebral damage in COVID-19 survivors: is there any association?

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 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Materials and methods › Brain MRI › MRI scans analysis ↔ utils/DSC_mri_getOptions.m, the whole file · a weak match · score 0.55 · stable spline, SVD, clustering, deconvolution, AIF, DSC
  2. [2] § Materials and methods › Brain MRI › MRI scans analysis ↔ utils/DSC_mri_stable_spline_v5.m, lines 1–114 · score 0.53 · stable spline, deconvolution, AIF, DSC, MRI

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

MATLAB · 91 lines · 3 KB · MIT · 1 match

  1. function [optionsOUT]=DSC_mri_getOptions()
  4. % OPZIONI DI VISUALIZZAZIONE
  5. optionsOUT.display=2; % 0:off, 1:notify (text), 2:notify (images), 3:debug
  6. optionsOUT.waitbar=1; % 0:off, 1:on
  9. % OPZIONI PER LA PREPARAZIONE DEI DATI
  10. optionsOUT.mask.npixel=300;
  11. % rappresenta il numero di pixel minimi di una componente connessa che ?
  12. % utilizzata come soglia per escludere dall'immagine lo scalpo e le zone
  13. % adiacenti all'esterno dell'encefalo
  15. optionsOUT.conc=0;
  16. % 0: i dati forniti sono segnale, 1: i dati forniti sono concentrazioni
  18. optionsOUT.S0.nSamplesMin=3;
  19. % Numero minimo di scansioni iniziali sulle quali calcolare S0
  21. optionsOUT.S0.nSamplesMax=12;
  22. % Numero massimo di scansioni iniziali sulle quali calcolare S0
  24. optionsOUT.S0.thresh=0.05;
  25. % Soglia utilizzata per scegliere l'istante di comparsa del tracciante
  27. % OPZIONI PER LA FASE DI INDIVIDUAZIONE DELL'AIF
  28. optionsOUT.aif.enable= 1;
  29. % 0: non calcola la AIF, 1: calcola la AIF
  31. optionsOUT.aif.ricircolo= 1;
  32. % 0: non tiene conto del ricircolo, 1: fitta il ricircolo
  34. optionsOUT.aif.nSlice= 0;
  35. % Slice sulla quale cercare la AIF (0: fa selezionare la slice
  36. % all'operatore)
  38. optionsOUT.aif.semiasseMaggiore= 0.3500;
  39. % Dimensione del semiasse maggiore per l'area di ricerca
  41. optionsOUT.aif.semiasseMinore= 0.1500;
  42. % Dimensione del semiasse minore per l'area di ricerca
  44. optionsOUT.aif.pArea= 0.4000;
  45. % Percentuali di voxel scartati a causa della AUC
  47. optionsOUT.aif.pTTP= 0.4000;
  48. % Percentuali di voxel scartati a causa del TTP
  50. optionsOUT.aif.pReg= 0.0500;
  51. % Percentuali di voxel scartati a causa della regolarit? dell'andamento
  53. optionsOUT.aif.diffPicco= 0.0400;
  54. % Soglia per decidere se selezionare il cluster sulla base del picco o del
  55. % TTP
  57. optionsOUT.aif.nVoxelMax= 6;
  58. % n? massimo di voxel scelti per la AIF
  60. optionsOUT.aif.nVoxelMin= 4;
  61. % n? minimo di voxel scelti per la AIF
  64. % Correzione della formula per il calcolo della concentrazione dal segnale
  65. % nel caso di calcolo della AIF
  66. optionsOUT.qr.enable= 0; % 0: non applica la correzione, 1: applica la correzione
  67. optionsOUT.qr.b= 5.7400e-004;
  68. optionsOUT.qr.a= 0.0076;
  69. optionsOUT.qr.r= 0.0440;
  71. % OPZIONI PER I METODI DI DECONVOLUZIONE
  72. optionsOUT.deconv.SVD.threshold= 0.2; % soglia della SVD
  73. optionsOUT.deconv.SVD.residual= 1; % 0: non salva i residui, 1: salva i residui
  75. optionsOUT.deconv.cSVD.threshold= 0.1; % soglia della cSVD (0.1 vale per dati ottenuti a 1.5T)
  76. optionsOUT.deconv.cSVD.residual= 1; % 0: non salva i residui, 1: salva i residui
  78. optionsOUT.deconv.oSVD.OIthres = 0.035; % threshold del 10% con in Ostergaard e Calamante
  79. optionsOUT.deconv.oSVD.OIcounter = 1;
  80. optionsOUT.deconv.oSVD.residual= 1; % 0: non salva i residui, 1: salva i residui
  82. %DA AGGIUNGERE PARAMETRI PER STABLE SPLINE
  83. optionsOUT.deconv.SS.residual = 1;
  85. optionsOUT.deconv.method={'SVD';'cSVD';'oSVD'}; % Metodi da applicare per il calcolo della perfusione
  87. % COSTANTI DI PROPORZIONALITA'
  88. optionsOUT.par.kh= 1;
  89. optionsOUT.par.rho= 1;
  90. optionsOUT.par.kvoi= 1;

DSC_mri_getOptions.m at commit 9e1e8cc, under MIT · at the source

Overview

Authors: Alberto Arrigoni1, Serena Capelli1, Gianluca Imeri2, Francesca Pennati3, Serena Venturelli4, Pietro Andrea Bonaffini5,6, Francesco Sanvito7, Paolo Marra5,6, Andrea Aliverti3, Fabiano Di Marco2, Simonetta Gerevini8, Anna Caroli1
ORCID iDs: Alberto Arrigoni, Serena Capelli, Gianluca Imeri, Francesca Pennati, Serena Venturelli, Pietro Andrea Bonaffini, Francesco Sanvito, Paolo Marra, Andrea Aliverti, Fabiano Di Marco, Simonetta Gerevini, Anna Caroli
  1. Bioengineering Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Ranica, BG, Italy
  2. Respiratory Unit, ASST Papa Giovanni XXIII, Bergamo, BG, Italy
  3. Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, MI, Italy
  4. Infectious Diseases Unit, ASST Papa Giovanni XXIII, Bergamo, BG, Italy
  5. Department of Radiology, ASST Papa Giovanni XXIII, Bergamo, BG, Italy
  6. Department of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy
  7. Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
  8. Department of Neuroradiology, ASST Papa Giovanni XXIII, Bergamo, BG, Italy
Institutions: Mario Negri Institute for Pharmacological Research (Italy); Ospedale Papa Giovanni XXIII (Italy); Politecnico di Milano (Italy); University of Milano-Bicocca (Italy); University of California, Los Angeles (United States)
Journal: Annals of medicine, volume 58, issue 1, article 2685416
Dates: received 18 March 2026; accepted 3 June 2026; published online 17 June 2026; in print December 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1080/07853890.2026.2685416 · PMID 42305018 · PMCID PMC13276816 · OpenAlex W7165013478
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), other (modality), human (organism), other condition (population), stroke (population), clinical / translational (subfield)
Methods: Machine learning, Statistics, Preprocessing, Spectral & time-frequency, Connectivity, fMRI & imaging
Keywords: COVID-19, Pulmonary function tests, Chest CT, Brain MRI, Vascular dysfunction, Lung–brain axis
MeSH: Brain*, COVID-19*, Lung*, Aged, Cerebrovascular Circulation, Female, Gray Matter, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Pandemics, Respiratory Function Tests, SARS-CoV-2, Survivors, Tomography, X-Ray Computed, White Matter (* major topic)
Topic: Long-Term Effects of COVID-19 (Neurology, Medicine), according to OpenAlex
Citations: not cited yet (Europe PMC); 55 references in the paper

Abstract

Background: COVID-19 primarily affects the respiratory system, with pulmonary function tests (PFTs) evaluating respiratory impairments and chest CT imaging revealing structural lung abnormalities. However, the disease’s multisystem impact – including vascular dysfunction and neurological complications associated with MRI neuroimaging alterations – raises the possibility of shared mechanisms underlying both pulmonary and cerebral involvement, potentially mediated by vascular damage.

Materials and Methods: The study included 27 consecutive COVID-19 patients (median age 59.0 [IQR 50.5 – 70.0] years, 37% female) presenting with concurrent respiratory and neurological symptoms. All patients underwent PFTs, chest CT, and brain MRI. CT images were processed to quantify lung parenchyma, airways, and vasculature, including small-vessel volume (SVV) and air-to-perfusion ratio (APR). Brain MRI analysis assessed gray matter (GM) integrity (volume and cortical thickness), white matter (WM) diffusion metrics (Apparent Diffusion Coefficient, ADC), and GM perfusion (cerebral blood volume, CBV; cerebral blood flow, CBF). Relationships between pulmonary and cerebral metrics were explored using Spearman correlations and multivariable linear regression.

Results: GM volume was positively associated with diffusing capacity for carbon monoxide (DLCO), and cortical thickness was positively associated with alveolar volume (VA). No associations were observed for WM ADC. In contrast, lower pulmonary function (DLCO, VA, forced expiratory volume in 1 s (FEV1), and forced vital capacity (FVC)) were associated with higher GM perfusion.

Conclusions: COVID-19-related pulmonary impairment is associated with reduced GM structural integrity and altered cerebral perfusion, suggesting a lung-brain interplay potentially driven by systemic vascular dysfunction and adaptive cerebrovascular mechanisms. These findings underscore the importance of integrated pulmonary and neurological assessment in post-COVID-19 patients and highlight the need for longitudinal studies to clarify the evolution and clinical consequences of these alterations.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

JoHof/lungmask

License: Apache-2.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 87fc9f2b701458a6d38f5e64d59a5e60bc68c884, 20 July 2026
Languages: Python (10)
Size: 26 files, 10 scripts
Software Heritage: not archived
Found in: the text, “CT scans analysis”
Holds: README, license file, environment (flake.nix, pyproject.toml, requirements.txt, setup.py, uv.lock), tests, continuous integration
Not found: CITATION.cff, documentation
Tools: NumPy (6 files), SimpleITK (5 files), PyTorch (4 files), scikit-image (2 files), pydicom (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
12 files
At the source: github.com/JoHof/lungmask

antonioguj/bronchinet

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 7da4c88dfde6f0dd2f8f181b2d3fd07dc2d28638, 31 August 2021
Languages: Python (89), Shell (6)
Size: 117 files, 95 scripts
Software Heritage: archived
Found in: the text, “CT scans analysis”
Holds: README, license file, environment (requirements_all.txt, requirements_keras.txt, requirements_torch.txt, setup.py), tests, continuous integration, documentation
Not found: CITATION.cff
Tools: NumPy (50 files), TensorFlow (8 files), Keras (7 files), Matplotlib (7 files), SciPy (6 files), PyTorch (5 files), scikit-image (2 files), h5py (1 file), imageio (1 file), NiBabel (1 file), pandas (1 file), pydicom (1 file), seaborn (1 file), SimpleITK (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
97 files
At the source: github.com/antonioguj/bronchinet

FAIR-Unipd/dsc-mri-toolbox

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 9e1e8cc7349d00ed269072d1fc85cbdb9f754339, 29 March 2022
Languages: MATLAB (29)
Size: 45 files, 29 scripts
Software Heritage: not archived
Found in: the text, “MRI scans analysis”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Image Processing Toolbox (4 files), Statistics and Machine Learning Toolbox (2 files), Curve Fitting Toolbox (1 file), Optimization Toolbox (1 file), Tools for NIfTI and ANALYZE image (MATLAB) (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
31 files
At the source: github.com/FAIR-Unipd/dsc-mri-toolbox

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:

  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 134 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

Datasets cited

Data availability statement

The data that supports the findings of this study are available from the corresponding author, upon reasonable request.

Reproduced under the paper's license (CC BY-NC), 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, 12 authors, 6 keywords, 17 MeSH terms, 55 references.

Cite

This paper

Arrigoni, A., Capelli, S., Imeri, G., Pennati, F., Venturelli, S., Bonaffini, P. A., Sanvito, F., Marra, P., Aliverti, A., Di Marco, F., Gerevini, S., & Caroli, A. (2026). Pulmonary and cerebral damage in COVID-19 survivors: is there any association? Annals of medicine, 58(1), 2685416. https://doi.org/10.1080/07853890.2026.2685416

BibTeX

@article{arrigoni2026pulmonary,
author = {Arrigoni, Alberto and Capelli, Serena and Imeri, Gianluca and Pennati, Francesca and Venturelli, Serena and Bonaffini, Pietro Andrea and Sanvito, Francesco and Marra, Paolo and Aliverti, Andrea and Di Marco, Fabiano and Gerevini, Simonetta and Caroli, Anna},
title = {{Pulmonary and cerebral damage in COVID-19 survivors: is there any association?}},
journal = {Annals of medicine},
year = {2026},
month = jun,
volume = {58},
number = {1},
pages = {2685416},
publisher = {Taylor \& Francis},
issn = {0785-3890},
doi = {10.1080/07853890.2026.2685416},
url = {https://doi.org/10.1080/07853890.2026.2685416},
pmid = {42305018},
pmcid = {PMC13276816}
}

RIS

TY - JOUR
AU - Arrigoni, Alberto
AU - Capelli, Serena
AU - Imeri, Gianluca
AU - Pennati, Francesca
AU - Venturelli, Serena
AU - Bonaffini, Pietro Andrea
AU - Sanvito, Francesco
AU - Marra, Paolo
AU - Aliverti, Andrea
AU - Di Marco, Fabiano
AU - Gerevini, Simonetta
AU - Caroli, Anna
TI - Pulmonary and cerebral damage in COVID-19 survivors: is there any association?
T2 - Annals of medicine
J2 - Ann Med
PY - 2026
DA - 2026/06/17
VL - 58
IS - 1
SP - 2685416
SN - 0785-3890
PB - Taylor & Francis
DO - 10.1080/07853890.2026.2685416
UR - https://doi.org/10.1080/07853890.2026.2685416
LA - en
ER -

CSL-JSON

{
"id": "10.1080/07853890.2026.2685416",
"type": "article-journal",
"title": "Pulmonary and cerebral damage in COVID-19 survivors: is there any association?",
"container-title": "Annals of medicine",
"author": [
{
"family": "Arrigoni",
"given": "Alberto"
},
{
"family": "Capelli",
"given": "Serena"
},
{
"family": "Imeri",
"given": "Gianluca"
},
{
"family": "Pennati",
"given": "Francesca"
},
{
"family": "Venturelli",
"given": "Serena"
},
{
"family": "Bonaffini",
"given": "Pietro Andrea"
},
{
"family": "Sanvito",
"given": "Francesco"
},
{
"family": "Marra",
"given": "Paolo"
},
{
"family": "Aliverti",
"given": "Andrea"
},
{
"family": "Di Marco",
"given": "Fabiano"
},
{
"family": "Gerevini",
"given": "Simonetta"
},
{
"family": "Caroli",
"given": "Anna"
}
],
"container-title-short": "Ann Med",
"volume": "58",
"issue": "1",
"page": "2685416",
"DOI": "10.1080/07853890.2026.2685416",
"PMID": "42305018",
"PMCID": "PMC13276816",
"ISSN": "0785-3890",
"publisher": "Taylor & Francis",
"URL": "https://doi.org/10.1080/07853890.2026.2685416",
"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:10.1002/hbm.70602 [code]
Neuroimaging Correlates of Post-Stroke Pain After Ischemic Stroke: Secondary Analysis of the INSPiRE-TMS Trial.
Journal: Human brain mapping
In common: pydicom, Curve Fitting Toolbox, Tools for NIfTI and ANALYZE image (MATLAB), 12 other tools, stroke, other, clinical / translational, 1 other category
[2] doi:10.1111/ene.70678 [code]
Who Falls After a Stroke? Evidence From a Prospective Stroke Cohort.
Journal: European journal of neurology
In common: pydicom, Curve Fitting Toolbox, Tools for NIfTI and ANALYZE image (MATLAB), 12 other tools, stroke, clinical / translational
[3] doi:10.1038/s41586-026-10631-3 [code]
A prognostic human brain network for diffuse midline glioma.
Journal: Nature
In common: pydicom, Curve Fitting Toolbox, Tools for NIfTI and ANALYZE image (MATLAB), 12 other tools, clinical / translational, other condition
[4] doi:10.1002/ana.78206 [code]
Multimodal Image Guidance in Subthalamic Deep Brain Stimulation for Parkinson's Disease.
Journal: Annals of neurology
In common: pydicom, Curve Fitting Toolbox, Tools for NIfTI and ANALYZE image (MATLAB), 12 other tools, clinical / translational
[5] doi:10.1016/j.celrep.2026.117404 [code]
Action and rest tremor map to distinct networks within the primary motor cortex.
Journal: Cell reports
In common: pydicom, Curve Fitting Toolbox, Tools for NIfTI and ANALYZE image (MATLAB), 12 other tools
[6] doi:10.3389/frai.2026.1771088 [code]
Few-shot deployment of pretrained MRI transformers in brain imaging tasks.
Journal: Frontiers in artificial intelligence
In common: pydicom, imageio, SimpleITK, 9 other tools, structural MRI / diffusion
[7] doi:10.1186/s12880-026-02481-2 [code]
Deep learning-based neuroanatomical profiling reveals population-specific brain changes in multiple sclerosis: a large-scale Middle Eastern study.
Journal: BMC medical imaging
In common: pydicom, imageio, Keras, 8 other tools, structural MRI / diffusion, clinical / translational
[8] doi:10.1364/boe.605322 [code]
Generalized plaque digitization framework for multi-dimensional mesoscopic images.
Journal: Biomedical optics express
In common: imageio, SimpleITK, Keras, 9 other tools
[9] doi:10.1038/s42003-026-10957-8 [code]
Brain defence by the extracellular matrix protein Cochlin.
Journal: Communications biology
In common: pydicom, Keras, Optimization Toolbox, 9 other tools
[10] doi:10.1162/imag.a.1262 [code]
Frame-wise multi-echo distortion correction for superior functional MRI.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: pydicom, Tools for NIfTI and ANALYZE image (MATLAB), Optimization Toolbox, 9 other tools

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.