Evidence map›Paper›PMID 40081846›Full record

ArticleAJNR. American journal of neuroradiology2025

Predicting White Matter Hyperintensity: Leveraging Portable MRI for Accessible Brain Health Screening.

Ian P Johnson, Hailey Brigger, Joel Smith, Emma Peasley, Alison Champagne, Lauren Littig, Dheeraj Lalwani, Gordon Sze, Seyedmehdi Payabvash, Basmah Safdar and 8 more

Abstract readValidation Study
In one paragraph

Article in AJNR. American journal of neuroradiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Review
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

18 authors.

Ian P JohnsonFrom the Department of Neurology (I.P.J., H.B., J.S., E.P., A.C., L.L., D.L., K.N.S., A.d.H.), Yale New Haven Hospital and Yale School of Medicine, New Haven, Connecticut.ORCID https://orcid.org/0000-0002-0834-7194
Hailey BriggerFrom the Department of Neurology (I.P.J., H.B., J.S., E.P., A.C., L.L., D.L., K.N.S., A.d.H.), Yale New Haven Hospital and Yale School of Medicine, New Haven, Connecticut.ORCID https://orcid.org/0009-0007-9075-7662
Joel SmithFrom the Department of Neurology (I.P.J., H.B., J.S., E.P., A.C., L.L., D.L., K.N.S., A.d.H.), Yale New Haven Hospital and Yale School of Medicine, New Haven, Connecticut.ORCID https://orcid.org/0009-0005-4599-6723
Emma PeasleyFrom the Department of Neurology (I.P.J., H.B., J.S., E.P., A.C., L.L., D.L., K.N.S., A.d.H.), Yale New Haven Hospital and Yale School of Medicine, New Haven, Connecticut.
Alison ChampagneFrom the Department of Neurology (I.P.J., H.B., J.S., E.P., A.C., L.L., D.L., K.N.S., A.d.H.), Yale New Haven Hospital and Yale School of Medicine, New Haven, Connecticut.
Lauren LittigFrom the Department of Neurology (I.P.J., H.B., J.S., E.P., A.C., L.L., D.L., K.N.S., A.d.H.), Yale New Haven Hospital and Yale School of Medicine, New Haven, Connecticut.ORCID https://orcid.org/0009-0005-7389-0328
Dheeraj LalwaniFrom the Department of Neurology (I.P.J., H.B., J.S., E.P., A.C., L.L., D.L., K.N.S., A.d.H.), Yale New Haven Hospital and Yale School of Medicine, New Haven, Connecticut.
Gordon SzeDepartment of Radiology (G.S., S.P.), Yale New Haven Hospital and Yale School of Medicine, New Haven, Connecticut.ORCID https://orcid.org/0000-0002-1143-8556
Seyedmehdi PayabvashDepartment of Radiology (G.S., S.P.), Yale New Haven Hospital and Yale School of Medicine, New Haven, Connecticut.ORCID https://orcid.org/0000-0003-4628-0370
Basmah SafdarEmergency Medicine (B.S., G.D., C.W.), Yale New Haven Hospital and Yale School of Medicine, New Haven, Connecticut.
Gail D'OnofrioEmergency Medicine (B.S., G.D., C.W.), Yale New Haven Hospital and Yale School of Medicine, New Haven, Connecticut.
Charles WiraEmergency Medicine (B.S., G.D., C.W.), Yale New Haven Hospital and Yale School of Medicine, New Haven, Connecticut.ORCID https://orcid.org/0000-0002-1041-4165
Juan Eugenio IglesiasAthinoula A. Martinos Center for Biomedical Imaging (J.E.I., M.S.R.), Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.
Matthew S RosenAthinoula A. Martinos Center for Biomedical Imaging (J.E.I., M.S.R.), Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.ORCID https://orcid.org/0000-0002-7194-002X
Annabel Sorby-AdamsDepartment of Neurology (A.S.-A., W.T.K.), Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.
W Taylor KimberlyDepartment of Neurology (A.S.-A., W.T.K.), Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.ORCID https://orcid.org/0000-0002-2519-8530
Kevin N ShethFrom the Department of Neurology (I.P.J., H.B., J.S., E.P., A.C., L.L., D.L., K.N.S., A.d.H.), Yale New Haven Hospital and Yale School of Medicine, New Haven, Connecticut.ORCID https://orcid.org/0000-0003-2003-5473
Adam de HavenonFrom the Department of Neurology (I.P.J., H.B., J.S., E.P., A.C., L.L., D.L., K.N.S., A.d.H.), Yale New Haven Hospital and Yale School of Medicine, New Haven, Connecticut adam.dehavenon@yale.edu.

Funding

Acquisition-independent machine learning for morphometric analysis of underrepresented aging populations with clinical and low-field brain MRIRF1AG080371 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI Juan Eugenio Iglesias Gonzalez · 2023 to 2026
$4.0M
Portable, Low Field Brain Magnetic Resonance Imaging (MRI) for Acute StrokeR01EB031114 · NIBIB · YALE UNIVERSITY · PI William Taylor Kimberly, Matthew Scot Rosen · 2022 to 2026
$3.5M
CAPTIVA-MRIUH3NS130228 · NINDS · YALE UNIVERSITY · PI Sepideh Amin-Hanjani, Arindam Rano Chatterjee · 2025 to 2026
$2.8M
Diagnosing the undiagnosable: studies of Alzheimer disease mimics and confounders via "neuropathometry" of dissection photos with 3D scanningR01AG070988 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI IGLESIAS GONZALEZ, JUAN EUGENIO · 2021 to 2025
$2.7M
Open-source software for multi-scale mapping of the human brainRF1MH123195 · NIMH · MASSACHUSETTS GENERAL HOSPITAL · PI FISCHL, BRUCE, IGLESIAS GONZALEZ, JUAN EUGENIO · 2020 to 2020
$2.0M
CAPTIVA-MRIUG3NS130228 · NINDS · YALE UNIVERSITY · PI AMIN-HANJANI, SEPIDEH, CHATTERJEE, ARINDAM R. · 2023 to 2023
$1.9M
Measuring Brain Health Using Low-Field Portable MRIR21NS138995 · NINDS · YALE UNIVERSITY · PI DE HAVENON, ADAM H. · 2024 to 2024
$477k
NIA NIH HHS R01 AG070988NIA NIH HHS RF1 AG080371NIBIB NIH HHS R01 EB031114NIMH NIH HHS RF1 MH123195NINDS NIH HHS R21 NS138995NINDS NIH HHS UG3 NS130228NINDS NIH HHS UH3 NS130228
6 · The paper itself

Abstract

background and purposePortable MRI (pMRI) has emerged as a cost-effective and accessible tool for the identification of white matter hyperintensities (WMH), an independent risk factor for stroke and dementia. Our objective was to confirm that pMRI can produce accurate WMH measurements and to develop and validate a risk model to predict WMH on pMRI for the purpose of identifying patients who may benefit from pMRI screening. MATERIALS AND

methodsThe development (

resultsThe final model, which included age, systolic blood pressure >140, atrial fibrillation, and tobacco use, achieved an area under the curve (AUC) of 0.83 (95% CI, 0.75-0.90) in the development cohort (

conclusionsThe WMH risk score demonstrated accurate performance and reproducibility across cohorts, supporting its potential as a screening tool for identifying patients at risk of moderate WMH burden. Appropriately targeted pMRI screening in high-risk individuals could allow providers and patients to proactively manage vascular risk factors and improve neurologic outcomes.

Indexed as

Magnetic Resonance ImagingWhite MatterAgedFemaleHumansMaleMiddle AgedProspective StudiesRetrospective StudiesRisk Factors

Identifiers

PMID40081846
PMCPMC12406303

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Registered trials

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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.