Evidence map›Paper›PMID 42563554›Full record

ArticleAnnals of clinical and translational neurology2026

Arterial Spin-Labeling MRI at the Cortical-CSF Interface: A Novel Biomarker in Alzheimer Disease.

Mona Asghariahmadabad, Pouya Metanat, Michael Thomas, Luke W Bonham, Tyson Lam, Mehmet Kurt, Hong Nguyen, Tara Ellingson, Mauro Zucchelli, Stefano Casagranda and 13 more

Abstract read
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In one paragraph

Article in Annals of clinical and translational neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

23 authors.

Mona AsghariahmadabadDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA.ORCID https://orcid.org/0000-0002-0110-2987
Pouya MetanatDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA.
Michael ThomasDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA.
Luke W BonhamDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA.
Tyson LamDepartment of Mechanical Engineering, University of Washington, Seattle, Washington, USA.
Mehmet KurtDepartment of Mechanical Engineering, University of Washington, Seattle, Washington, USA.
Hong NguyenDepartment of Neurology, Edward and Pearl Fein Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA.ORCID https://orcid.org/0009-0009-5610-6748
Tara EllingsonDepartment of Neurology, Edward and Pearl Fein Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA.
Mauro ZucchelliDepartment of Research and Innovation of Olea Medical, La Ciotat, France.
Stefano CasagrandaDepartment of Research and Innovation of Olea Medical, La Ciotat, France.
Quin LuGE HealthCare, San Francisco, California, USA.
Andreas M RauscheckerDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA.
Leo SugrueDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA.
Luis E SavastanoDepartment of Neurological Surgery, University of California, San Francisco, San Francisco, California, USA.
Christopher P HessDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA.
Kamalini RanasingheDepartment of Neurology, Edward and Pearl Fein Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA.ORCID https://orcid.org/0000-0002-4217-8785
S Andrew JosephsonDepartment of Neurology, Edward and Pearl Fein Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA.
Bruce L MillerDepartment of Neurology, Edward and Pearl Fein Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA.
Jennifer S YokoyamaDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA.
Gil D RabinoviciDepartment of Neurology, Edward and Pearl Fein Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA.
Peter A LjubenkovDepartment of Neurology, Edward and Pearl Fein Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA.
Lawren VandeVredeDepartment of Neurology, Edward and Pearl Fein Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA.
Kambiz NaelDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA.ORCID https://orcid.org/0000-0002-4194-9488

Funding

TDP-43 Loss-of-Function: Biology to BiomarkersP01AG019724 · NIA · UNIVERSITY OF PENNSYLVANIA · PI Jennifer Merrilees · 2002 to 2026
$67.2M
Project 2: Biomarker Analysis, Non-Genetic Risk Factors, and Their Genetic InteractionsU19AG079774 · NIA · UNIVERSITY OF PENNSYLVANIA · PI HELENA Chang CHUI, Gyungah Jun · 2023 to 2026
$39.4M
Research Education ComponentP30AG062422 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Katherine P Rankin · 2019 to 2026
$36.9M
Tau Metabolism in FTD: From Gene Mutations to Molecular Chaperones and Lysosomal ProteasesU54NS123985 · NINDS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI KARCH, CELESTE MARIE · 2021 to 2025
$9.0M
US-South American Initiative for Genetic-Neural-Behavioral Interactions in Human Neurodegenerative ResearchR01AG057234 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Claudia Duran-Aniotz, Agustin M. Ibanez · 2019 to 2026
$6.1M
NIA NIH HHS P01 AG019724NIA NIH HHS P30 AG062422NIA NIH HHS R01 AG057234NIA NIH HHS U19 AG079774NINDS NIH HHS U54 NS123985
6 · The paper itself

Abstract

BACKGROUND/

objectiveArterial spin-labeling (ASL) MRI can measure perfusion signal adjacent to CSF spaces and may provide information regarding CSF-adjacent water transport physiology. We developed an automated pipeline to extract cortical-CSF interface (IF) perfusion for comparison between Alzheimer disease (AD) and cognitively normal controls.

methodsIn this retrospective study, participants with AD and cognitively normal controls with ASL and 3D T1-weighted MRI were included. Parenchymal cerebral blood flow (CBF) was evaluated globally (whole brain, gray matter, white matter) and across 12 brain regions. IF-perfusion values were quantified using an automated pipeline with tissue segmentation, registration, partial-volume-aware masking, and arterial-signal exclusion. Multivariable sensitivity analysis was performed, adjusting for age, sex, scanner vendor, and acquisition site. Predictive modeling was performed using LASSO-regularized logistic regression with nested repeated stratified cross-validation, and feature contributions were summarized using SHAP.

resultsA total of 51 patients with AD, age (median: 73 years) and 53 cognitively normal subjects (median age: 69) were included. Global CBF did not differ between groups. However, IF-perfusion values were significantly lower in AD (median 4.4, IQR 3.2-4.8) than in controls (median 5.2, IQR 4.5-6.3; p < 0.001). In multivariable analysis, AD remained independently associated with lower IF-perfusion after adjustment for age, sex, scanner vendor, and acquisition site (β = -1.88, 95% CI: -3.52 to -0.25; p = 0.025). In LASSO-regularized logistic regression model, only IF-perfusion and cuneus-CBF remained as significant contributing features for the prediction of AD probability with the highest contribution from IF. The final model showed mean ROC-AUC 0.81 in the training and AUC of 0.78 in the testing dataset.

conclusionASL-derived IF-perfusion is reduced in AD and may represent a promising imaging marker of altered CSF-adjacent water transport physiology. Further validation against established biomarkers of CSF dynamics and clearance pathways is warranted.

Indexed as

Alzheimer diseasearterial spin‐labeling MRIcortical‐CSF interfaceperfusion biomarker

Identifiers

PMID42563554
PMCPMC13448560

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.