Evidence map›Paper›PMID 42818797›Full record

ArticlemedRxiv : the preprint server for health sciences2026

Machine learning-derived Alzheimer's disease dimensions: neuroanatomical, cognitive, clinical, functional, and genetic risk heterogeneity signatures.

Hoang Nam Le, Saba Ishrat, Bahram Yaghooti, Taslim Murad, Ram Sapkota, Deepa S Thakuri, Dean F Wong, Carlos Cruchaga, for Alzheimer’s Disease Neuroimaging Initiative, Ganesh B Chand

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 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

10 authors.

Hoang Nam LeNeuroimaging Informatics and Artificial Intelligence (NeuroAI) Laboratory, Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.ORCID 0000-0003-2556-7612
Saba IshratNeuroimaging Informatics and Artificial Intelligence (NeuroAI) Laboratory, Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
Bahram YaghootiNeuroimaging Informatics and Artificial Intelligence (NeuroAI) Laboratory, Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
Taslim MuradNeuroimaging Informatics and Artificial Intelligence (NeuroAI) Laboratory, Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
Ram SapkotaNeuroimaging Informatics and Artificial Intelligence (NeuroAI) Laboratory, Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
Deepa S ThakuriDepartment of Medicine, University of Missouri, School of Medicine, Columbia, MO, USA.
Dean F WongDepartments of Neurosurgery, Radiology, Psychiatry and Neurology, Kahlert Institute for Addiction Medicine, University of Maryland Baltimore School of Medicine, Baltimore, MD, USA.
Carlos CruchagaKnight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA.ORCID 0000-0002-0276-2899
for Alzheimer’s Disease Neuroimaging Initiative
Ganesh B ChandNeuroimaging Informatics and Artificial Intelligence (NeuroAI) Laboratory, Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Alzheimer's disease (AD) is characterized by substantial clinical and biological heterogeneity, with diverse neuroanatomical, cognitive, clinical, functional, and genetic risk profiles that are poorly captured by current diagnostic frameworks. Identifying reproducible disease dimensions is critical for understanding divergent pathophysiological mechanisms and designing future stratified therapeutic interventions. Herein, we applied a semi-supervised machine learning clustering framework and robust reproducibility validation strategies to structural MRI data from 1,757 participants, comprising 1,108 cognitively normal controls (CN) and 649 patients with mild cognitive impairment (MCI) or dementia due to AD. We identified two MRI-based neuroanatomical dimensions of AD: Dimension 1 (

Indexed as

Alzheimer’s diseasedisease heterogeneitymachine learningmild cognitive impairmentsemi-supervised clusteringstructural MRI

Identifiers

PMID42818797
PMCPMC13622705

What OpenQuestion holds

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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.