Examining neuroimaging biomarkers, plasma biomarkers and cognitive functions in patients with recovered COVID-19 infection: a multicentre study using 7T 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
Python · 31 lines · 794 B · no license
- from setuptools import setup, find_packages
- setup(
- name='wmh_seg',
- version='1.6.0',
- packages=find_packages(),
- include_package_data=True,
- package_data={'wmh_seg': ['nnunet_assets/*/*.json']},
- description='WMH segmentation for FLAIR images',
- author='Jinghang Li',
- author_email='[email hidden]',
- url='https://github.com/jinghangli98/wmh_seg', # Replace with your GitHub repo
- install_requires=[
- 'tqdm',
- 'torch',
- 'torchvision',
- 'nibabel',
- 'einops',
- 'torchio',
- "timm==0.6.12",
- 'numpy',
- 'segmentation-models-pytorch==0.3.2',
- 'huggingface_hub',
- 'nnunetv2',
- ],
- entry_points={
- 'console_scripts': [
- 'wmh_seg=wmh_seg.cli:main',
- ],
- },
- )
setup.py at commit a5ad55c, no license · at the source
Overview
and 11 other authors
Richard Bowtell3, Olivier Mougin3, Penny A Gowland3, Mohammad Zia Katshu3, Farhaan S Vahidy4, Timothy D Girard1, Heidi I L Jacobs5, Akram A Hosseini3, Sudha Seshadri2, Tamer S Ibrahim1, 7T MRI COVID Consortium- University of Pittsburgh, Pittsburgh, PA 15260, USA
- University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
- University of Nottingham, Nottingham NG7 2RD, UK
- Houston Methodist Research Institute, Houston, TX 77030, USA
- Massachusetts General Hospital, Harvard University, Boston, MA 02114, USA
Abstract
We examined the impact of COVID-19 hospitalization on neuroimaging biomarkers and the association of these neuroimaging biomarkers with cognitive measures and plasma biomarkers. A total of 179 dementia-free people, including 52 hospitalized COVID-19 patients, across four medical centres in the USA and UK underwent 7T brain MRI scans, cognitive tests and blood collection. We found that hospitalized patients exhibited a comparable white matter hyperintensity burden, lower total hippocampal volume and lower plasma glial fibrillary acidic protein concentration, along with poorer memory performance, compared to age-matched non-hospitalized participants. Higher white matter hyperintensity burden was associated with older age, worse cognitive scores and higher plasma biomarker levels; higher total hippocampal volume was associated with younger age, better cognitive scores and lower plasma phosphorylated tau levels. However, these correlation coefficients did not differ between the hospitalized and non-hospitalized groups. Longitudinal studies are needed to clarify the long-term impact of COVID-19-related hospitalization.
Reproduced under the paper's license (CC BY), from the paper cited above.
Repositories
Its files are read in the Code ↔ Paper reader above.
jinghangli98/wmh_seg
a5ad55c6dec7cb2d4bd7b419a3e7d0c14e72aaee, 25 May 2026Availability: 1 check, the latest on 30 September 2026: the link answers
- 30 September 2026: the link answers
9 files
- setup.py, Python, 31 lines
- wmh_pytorch.py, Python, 140 lines
- wmh_seg/
__init__.py , Python, 4 lines - wmh_seg/
cli.py , Python, 95 lines - wmh_seg/
model_loader.py , Python, 67 lines - wmh_seg/
nnunet_infer.py , Python, 224 lines - wmh_seg/
orientation.py , Python, 45 lines - wmh_seg/
wmh_pytorch.py , Python, 89 lines - README.md, Text, 143 lines
pyushkevich/ashs
8dc72a2b7e96c19d64ef494890093b92ec2a10df, 9 March 2026Availability: 1 check, the latest on 30 September 2026: the link answers
- 30 September 2026: the link answers
48 files
- bin/
ashs_atlas_bl_qsub.sh , Shell, 48 lines - bin/
ashs_atlas_bootstrap_qsu , Shell, 94 linesb.sh - bin/
ashs_atlas_initdir_qsub. , Shell, 75 linessh - bin/
ashs_atlas_lf_qsub.sh , Shell, 54 lines - bin/
ashs_atlas_loo_qsub.sh , Shell, 94 lines - bin/
ashs_atlas_pairwise_qsub , Shell, 62 lines.sh - bin/
ashs_atlas_resample_to_t , Shell, 89 linesemplate_qsub.sh - bin/
ashs_atlas_upgrade.sh , Shell, 77 lines - bin/
ashs_biascorr_qsub.sh , Shell, 74 lines - bin/
ashs_bootstrap_qsub.sh , Shell, 142 lines - bin/
ashs_common_master.sh , Shell, 50 lines - bin/
ashs_config.sh , Shell, 240 lines - bin/
ashs_default_hook.sh , Shell, 60 lines - bin/
ashs_extractstats_qsub.s , Shell, 170 linesh - bin/
ashs_finalqa_qsub.sh , Shell, 112 lines - bin/
ashs_function_qsub.sh , Shell, 37 lines - bin/
ashs_grid.sh , Shell, 242 lines - bin/
ashs_heuristic_qsub.sh , Shell, 92 lines - bin/
ashs_lib.sh , Shell, 2,563 lines - bin/
ashs_main.sh , Shell, 593 lines - bin/
ashs_multiaffine_qsub.sh , Shell, 64 lines - bin/
ashs_multiatlas_qsub.sh , Shell, 58 lines - bin/
ashs_template_qsub.sh , Shell, 286 lines - bin/
ashs_thickness_qsub.sh , Shell, 112 lines - bin/
ashs_train.sh , Shell, 500 lines - bin/
ashs_util_makepdf.sh , Shell, 127 lines - bin/
ashs_version.sh , Shell, 41 lines - bin/
ashs_voting_qsub.sh , Shell, 59 lines - ext/
Linux/ , Shell, 215 linesbin/ ants_1042/ ANTSpexec.sh - ext/
Linux/ , Shell, 160 linesbin/ ants_1042/ ants.sh - ext/
Linux/ , Shell, 684 linesbin/ ants_1042/ antsIntroduction.sh - ext/
Linux/ , Shell, 86 linesbin/ ants_1042/ antsaffine.sh - ext/
Linux/ , Shell, 1,061 linesbin/ ants_1042/ buildtemplateparallel.sh - ext/
Linux/ , Perl, 142 linesbin/ ants_1042/ waitForSGEQJobs.pl - src/
CorrectiveLearning/ , C/C++, 304 linesAdaBoost.h - src/
CorrectiveLearning/ , C/C++, 111 linesutil.h - src/
LabelFusion/ , C/C++, 255 linesWeightedVotingLabelFusio nImageFilter.h - src/
PatchSuperResolution/ , C/C++, 328 linesinclude/ CommandLineHelper.h - src/
PatchSuperResolution/ , C/C++, 11 linesinclude/ check.h - src/
PatchSuperResolution/ , C/C++, 6 linessrc/ MABONLM3D.h - src/
PatchSuperResolution/ , C/C++, 4 linessrc/ NLMUpsample.h - src/
PatchSuperResolution/ , C/C++, 271 linessrc/ util/ itkOrientedRASImage.h - src/
PointSet/ , C/C++, 216 linesitkOrientedRASImage.h - src/
PointSet/ , C/C++, 81 linesitk_to_nifti_xform.h - testing/
atlas_system_test/ , Shell, 17 linesconfig/ ashs_config_test.sh - testing/
atlas_system_test/ , Shell, 15 linesrunme.sh - LICENSE, License, 674 lines
- README.md, Text, 4 lines
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:
- 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
- 54 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
All data generated and analysed during this study are included in this article. The anonymized raw and processed MRI scans, along with group-level plasma biomarkers and cognitive data, are available upon reasonable request. All GitHub codes used in this work are cited in the methods section.
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, 30 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 31 authors, 5 keywords, 3 funders, 59 references, 1 RRID.
Cite
This paper
Liou, J.-J., Santini, T., Li, J., Gireud-Goss, M., Kautz, T. F., Parker-Garza, J., Guerrero, J. C., Patel, V., Adeyemi, O. F., de Erausquin, G. A., Garbarino, V. R., Habes, M., Himali, J. J., Karmonik, C., Snitz, B. E., Mettenburg, J. M., Wu, M., Aizenstein, H. J., Marsland, A. L., . . . 7T MRI COVID Consortium. (2026). Examining neuroimaging biomarkers, plasma biomarkers and cognitive functions in patients with recovered COVID-19 infection: a multicentre study using 7T MRI. Brain communications, 8(2), fcag045. https://
BibTeX
@article{liou2026examini
author = {Liou, Jr-Jiun and Santini, Tales and Li, Jinghang and Gireud-Goss, Monica and Kautz, Tiffany F and Parker-Garza, Julie and Guerrero, Juan Carlos and Patel, Vibhuti and Adeyemi, Oluwatobi F and de Erausquin, Gabriel A and Garbarino, Valentina R and Habes, Mohamad and Himali, Jayandra J and Karmonik, Christof and Snitz, Beth E and Mettenburg, Joseph M and Wu, Minjie and Aizenstein, Howard J and Marsland, Anna L and Gianaros, Peter J and Bowtell, Richard and Mougin, Olivier and Gowland, Penny A and Katshu, Mohammad Zia and Vahidy, Farhaan S and Girard, Timothy D and Jacobs, Heidi I L and Hosseini, Akram A and Seshadri, Sudha and Ibrahim, Tamer S and {7T MRI COVID Consortium}},
title = {{Examining neuroimaging biomarkers, plasma biomarkers and cognitive functions in patients with recovered COVID-19 infection: a multicentre study using 7T MRI}},
journal = {Brain communications},
year = {2026},
month = mar,
volume = {8},
number = {2},
pages = {fcag045},
publisher = {Oxford University Press},
issn = {2632-1297},
doi = {10.1093/
url = {https://
pmid = {41809434},
pmcid = {PMC12967851}
}
RIS
TY - JOUR
AU - Liou, Jr-Jiun
AU - Santini, Tales
AU - Li, Jinghang
AU - Gireud-Goss, Monica
AU - Kautz, Tiffany F
AU - Parker-Garza, Julie
AU - Guerrero, Juan Carlos
AU - Patel, Vibhuti
AU - Adeyemi, Oluwatobi F
AU - de Erausquin, Gabriel A
AU - Garbarino, Valentina R
AU - Habes, Mohamad
AU - Himali, Jayandra J
AU - Karmonik, Christof
AU - Snitz, Beth E
AU - Mettenburg, Joseph M
AU - Wu, Minjie
AU - Aizenstein, Howard J
AU - Marsland, Anna L
AU - Gianaros, Peter J
AU - Bowtell, Richard
AU - Mougin, Olivier
AU - Gowland, Penny A
AU - Katshu, Mohammad Zia
AU - Vahidy, Farhaan S
AU - Girard, Timothy D
AU - Jacobs, Heidi I L
AU - Hosseini, Akram A
AU - Seshadri, Sudha
AU - Ibrahim, Tamer S
AU - 7T MRI COVID Consortium
TI - Examining neuroimaging biomarkers, plasma biomarkers and cognitive functions in patients with recovered COVID-19 infection: a multicentre study using 7T MRI
T2 - Brain communications
J2 - Brain Commun
PY - 2026
DA - 2026/
VL - 8
IS - 2
SP - fcag045
SN - 2632-1297
PB - Oxford University Press
DO - 10.1093/
UR - https://
LA - en
ER -
CSL-JSON
{
"id": "10.1093/
"type": "article-journal",
"title": "Examining neuroimaging biomarkers, plasma biomarkers and cognitive functions in patients with recovered COVID-19 infection: a multicentre study using 7T MRI",
"container-title": "Brain communications",
"author": [
{
"family": "Liou",
"given": "Jr-Jiun"
},
{
"family": "Santini",
"given": "Tales"
},
{
"family": "Li",
"given": "Jinghang"
},
{
"family": "Gireud-Goss",
"given": "Monica"
},
{
"family": "Kautz",
"given": "Tiffany F"
},
{
"family": "Parker-Garza",
"given": "Julie"
},
{
"family": "Guerrero",
"given": "Juan Carlos"
},
{
"family": "Patel",
"given": "Vibhuti"
},
{
"family": "Adeyemi",
"given": "Oluwatobi F"
},
{
"family": "de Erausquin",
"given": "Gabriel A"
},
{
"family": "Garbarino",
"given": "Valentina R"
},
{
"family": "Habes",
"given": "Mohamad"
},
{
"family": "Himali",
"given": "Jayandra J"
},
{
"family": "Karmonik",
"given": "Christof"
},
{
"family": "Snitz",
"given": "Beth E"
},
{
"family": "Mettenburg",
"given": "Joseph M"
},
{
"family": "Wu",
"given": "Minjie"
},
{
"family": "Aizenstein",
"given": "Howard J"
},
{
"family": "Marsland",
"given": "Anna L"
},
{
"family": "Gianaros",
"given": "Peter J"
},
{
"family": "Bowtell",
"given": "Richard"
},
{
"family": "Mougin",
"given": "Olivier"
},
{
"family": "Gowland",
"given": "Penny A"
},
{
"family": "Katshu",
"given": "Mohammad Zia"
},
{
"family": "Vahidy",
"given": "Farhaan S"
},
{
"family": "Girard",
"given": "Timothy D"
},
{
"family": "Jacobs",
"given": "Heidi I L"
},
{
"family": "Hosseini",
"given": "Akram A"
},
{
"family": "Seshadri",
"given": "Sudha"
},
{
"family": "Ibrahim",
"given": "Tamer S"
},
{
"literal": "7T MRI COVID Consortium"
}
],
"container-title-short":
"volume": "8",
"issue": "2",
"page": "fcag045",
"DOI": "10.1093/
"PMID": "41809434",
"PMCID": "PMC12967851",
"ISSN": "2632-1297",
"publisher": "Oxford University Press",
"URL": "https://
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
9
]
]
}
}
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/alz.71649 [code]
- Postmortem brain MRI reveals differential associations of subcortical and limbic volumes with cortical thinning and neurodegenerative pathologies.Journal: Alzheimer's & dementia : the journal of the Alzheimer's AssociationIn common: nnU-Net, ANTs, FSL, 3 other tools, structural MRI / diffusion, other condition, 1 reference
- [2] doi:10.2463/mrms.mp.2024-0149 [code]
- Image Distortion Correction for Diffusion MR Imaging Using a Transformer-based U-Net.Journal: Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in MedicineIn common: nnU-Net, ANTs, FSL, 3 other tools, stroke, structural MRI / diffusion, clinical / translational
- [3] doi:10.1002/epi.70296 [code]
- Fully automated three-dimensional deep learning-based magnetic resonance imaging segmentation of brain cavities in epilepsy surgery.Journal: EpilepsiaIn common: nnU-Net, ANTs, FSL, 2 other tools, structural MRI / diffusion, clinical / translational
- [4] doi:10.1038/s41467-026-71555-0 [code]
- A deep representation learning model to predict response to vagus nerve stimulation.Journal: Nature communicationsIn common: ANTs, FSL, NiBabel, 2 other tools, structural MRI / diffusion, clinical / translational, 1 reference
- [5] doi:10.1073/pnas.2603114123 [code]
- The human hippocampus can pattern separate memories by meaning.Journal: Proceedings of the National Academy of Sciences of the United States of AmericaIn common: FSL, NiBabel, NumPy, 3 references
- [6] doi:10.1371/journal.pcbi.1014555 [code]
- Body surface potential driven personalisation of electrophysiological digital twins in hypertrophic cardiomyopathy.Journal: PLoS computational biologyIn common: nnU-Net, ANTs, NiBabel, 2 other tools, other, structural MRI / diffusion
- [7] doi:10.1162/imag.a.1164 [code]
- Bias and generalizability of brain age prediction models: A multi-cohort evaluation with anatomical and interpretability insights.Journal: Imaging neuroscience (Cambridge, Mass.)In common: ANTs, FSL, NiBabel, 2 other tools, structural MRI / diffusion, 1 reference
- [8] doi:10.3389/fnins.2026.1841093 [code]
- Brain protein burden is related to intravoxel incoherent motion: PET-MR imaging study.Journal: Frontiers in neuroscienceIn common: ANTs, FSL, NiBabel, 1 other tool, structural MRI / diffusion, 2 references
- [9] doi:10.1002/alz.71530 [code]
- Differential associations of plasma biomarkers with Alzheimer's disease and small vessel disease: A multimodal imaging study.Journal: Alzheimer's & dementia : the journal of the Alzheimer's AssociationIn common: nnU-Net, NiBabel, PyTorch, 1 other tool, stroke, other, structural MRI / diffusion, 1 other category
- [10] doi:10.1002/hbm.70497 [code]
- A Digital Anatomical Atlas of the Human Cerebellum at Subfolial Resolution.Journal: Human brain mappingIn common: nnU-Net, ANTs, NiBabel, 2 other tools, structural MRI / diffusion
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.
Claim this paper
Correct its record
Say what each link of this record is, remove the ones that are not the paper's, add the ones that are missing. The correction becomes a new version of the record, in its Versions section.
Validate its tracing map
You validate the map as this page shows it: 2 repositories of the authors' code, each at its verified commit and with its license, 54 scripts, and 0 matches between paragraphs and code (see the Code and Map sections). It then receives a DOI on Zenodo, with you (your ORCID iD) and OSCR as its creators; the code itself is not deposited.
The map's fingerprint: sha256:3ee9fc602c584994…
Add the badge to its README
The badge links the code to this page. Copy one of these into the README of the paper's code: only you decide where it goes, and nothing is changed for you.
Markdown
[, paste the snippet at the top, then “Commit changes…” and, to review it first, “Create a new branch and start a pull request”. You open the pull request; OSCR asks for no permission.
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.
