Evidence map›Paper›PMID 42033998›Full record

ArticleNeurobiology of aging2026

Predicting future cognitive impairment in preclinical Alzheimer's disease using amyloid PET and MRI: A multisite machine learning study.

Braden Yang, Tom Earnest, Murat Bilgel, Marilyn S Albert, Sterling C Johnson, Christos Davatzikos, Guray Erus, Colin L Masters, Susan M Resnick, Michael I Miller and 7 more

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in Neurobiology of aging, 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

5 · Who and what money

Authors and funding

17 authors.

Braden YangMallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA. Electronic address: b.y.yang@wustl.edu.
Tom EarnestMallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA.
Murat BilgelLaboratory of Behavioral Neuroscience, National Institute on Aging Intramural Research Program, National Institutes of Health, Baltimore, MD 21224, USA.
Marilyn S AlbertDepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
Sterling C JohnsonWisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53726, USA.
Christos DavatzikosCenter for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Guray ErusCenter for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Colin L MastersFlorey Institute of Neuroscience and Mental Health, Parkville, VIC 3052, Australia.
Susan M ResnickLaboratory of Behavioral Neuroscience, National Institute on Aging Intramural Research Program, National Institutes of Health, Baltimore, MD 21224, USA.
Michael I MillerCenter of Imaging Science and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
Arnold BakkerDepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
John C MorrisDepartment of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA.
Tammie L S BenzingerMallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA.
Brian A GordonMallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA.
Aristeidis SotirasMallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA. Electronic address: aristeidis.sotiras@pennmedicine.upenn.edu.
Alzheimer’s Disease Neuroimaging Initiative
Preclinical Alzheimer’s Disease Consortium

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Predicting the likelihood of developing Alzheimer's disease (AD) dementia in at-risk individuals is important for the design of and optimal recruitment for clinical trials of disease-modifying therapies. Machine learning (ML) has been shown to excel in this task; however, there remains a lack of models developed specifically for the preclinical AD population, who display early signs of abnormal brain amyloidosis but remain cognitively unimpaired. Here, we trained and evaluated ML classifiers to predict whether individuals with preclinical AD will progress to mild cognitive impairment or dementia within multiple fixed time windows, ranging from one to five years. Models were trained on regional imaging features extracted from amyloid positron emission tomography and magnetic resonance imaging pooled across seven independent sites and from two amyloid radiotracers ([

Indexed as

Alzheimer DiseaseAmyloidCognitive DysfunctionMachine LearningMagnetic Resonance ImagingPositron-Emission TomographyAgedBrainClassification AlgorithmsDisease ProgressionFemaleHumansMalePrediction AlgorithmsPredictive Learning ModelsAmyloidAmyloid positron emission tomographyClinical trial cohort enrichmentMachine learningPatient stratificationPreclinical Alzheimer’s diseaseStructural magnetic resonance imagingSupport vector machine

Identifiers

PMID42033998

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

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.