Evidence map›Paper›PMID 41806384›Full record

ArticleJournal of neuropathology and experimental neurology2026

Clinical and pathologic correlations of machine learning quantification of Aβ deposits across 3 brain regions of decedents with Alzheimer disease.

David Garcia, Shivam Rajendra Rai Sharma, Naomi Saito, Laurel Beckett, Louise Nicole C Sevilla, La Rissa Vasquez, Charles S DeCarli, David Gutman, Juan Vizcarra, David G Coughlin and 5 more

Abstract read
In one paragraph

Article in Journal of neuropathology and experimental 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

15 authors.

David GarciaDepartment of Pathology and Laboratory Medicine, University of California, Davis, Sacramento, CA, United States.ORCID 0009-0000-4021-1927
Shivam Rajendra Rai SharmaDepartment of Pathology and Laboratory Medicine, University of California, Davis, Sacramento, CA, United States.ORCID 0009-0003-8550-8443
Naomi SaitoDepartment of Public Health Sciences, University of California, Davis, Davis, CA, United States.
Laurel BeckettDepartment of Public Health Sciences, University of California, Davis, Davis, CA, United States.
Louise Nicole C SevillaDepartment of Pathology and Laboratory Medicine, University of California, Davis, Sacramento, CA, United States.
La Rissa VasquezDepartment of Pathology and Laboratory Medicine, University of California, Davis, Sacramento, CA, United States.
Charles S DeCarliDepartment of Neurology, Alzheimer's Disease Research Center, University of California, Davis, School of Medicine, Sacramento, CA, United States.
David GutmanDepartment of Neurology, Emory University School of Medicine, Atlanta, GA, United States.
Juan VizcarraDepartment of Neurology, Emory University School of Medicine, Atlanta, GA, United States.
David G CoughlinDepartment of Neurology, University of California, San Diego, La Jolla, CA, United States.
Andrew F TeichDepartment of Neurology, Taub Institute for Research on Alzheimer's Disease and Aging Brain, Columbia University Medical Center, New York, NY, United States.
Lorena GarciaDepartment of Public Health Sciences, University of California, Davis, Davis, CA, United States.
Dan M MungasDepartment of Neurology, Alzheimer's Disease Research Center, University of California, Davis, School of Medicine, Sacramento, CA, United States.
Chen-Nee ChuahDepartment of Electrical and Computer Engineering, University of California, Davis, Davis, CA, United States.ORCID 0000-0002-2772-387X
Brittany N DuggerDepartment of Pathology and Laboratory Medicine, University of California, Davis, Sacramento, CA, United States.ORCID 0000-0003-2141-8855

Funding

Satellite Diagnostic and Treatment Clinic CoreP50AG008702 · NIA · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI SMALL, SCOTT A · 1989 to 2019
$46.3M
UCSD Shiley-Marcos Alzheimer's Disease Research Center P30P30AG062429 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI JAMES B BREWER · 2019 to 2026
$34.9M
Research Education CoreP30AG066462 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI PHILIP L DE JAGER · 2020 to 2026
$30.1M
Epidemiology of Age-related Dementia, Mild Cognitive Impairment and Brain Pathology in Cohort of Oldest-OldR01AG056519 · NIA · UNIVERSITY OF CALIFORNIA AT DAVIS · PI Maria Corrada, Paola Gilsanz · 2022 to 2026
$25.8M
UC Davis Alzheimer's Disease Research CenterP30AG072972 · NIA · UNIVERSITY OF CALIFORNIA AT DAVIS · PI Charles DeCarli, Rachel A Whitmer · 2021 to 2026
$25.2M
Lifecourse health, cerebral pathology and ethnic disparities in dementia (KHANDLE Study)R01AG052132 · NIA · UNIVERSITY OF CALIFORNIA AT DAVIS · PI GILSANZ, PAOLA, GLYMOUR, MEDELLENA MARIA · 2021 to 2025
$18.0M
The Neuropathologic Landscape of Alzheimer's Disease in Hispanic DecedentsR01AG062517 · NIA · UNIVERSITY OF CALIFORNIA AT DAVIS · PI Brittany Nicole Dugger · 2019 to 2026
$10.5M
Brain Digital Slide Archive: An Open Source Platform for data sharing and analysis of digital neuropathologyU24NS133949 · NINDS · EMORY UNIVERSITY · PI Lee Cooper, Brittany Nicole Dugger · 2023 to 2026
$4.3M
Alzheimer's Disease Research Center U01AG024904National Institute of Justice 2014-R2-CX-0012National Institute on Aging (NIA) of the National Institutes of Health (NIH) R01AG062517, P30AG072972, P30AG062429, P50AG008702, P30AG066462, and U24NS133949NIA NIH HHS R01 AG062517NIH HHS P30AG062429NIH HHS P30AG066462NIH HHS P30AG072972NIH HHS P50AG008702NIH HHS R01AG052132NIH HHS R01AG056519NIH HHS R01AG062517NIH HHS U01AG061357-S1NIH HHS U24NS133949
6 · The paper itself

Abstract

Machine learning enables scalable quantification of neuropathology, offering deeper phenotyping of Alzheimer's disease (AD). In this validation study, we quantified amyloid-beta (Aβ) deposits, evaluating multiple brain regions across institutions, and evaluated associations with clinical, demographic, and genetic factors in persons pathologically diagnosed with AD. All linear models were adjusted for sex, age of death, ethnicity, and center. We analyzed densities (#/mm2) of cored plaques, diffuse plaques, and cerebral amyloid angiopathy (CAA) in 273 individuals from 3 Alzheimer's Disease Research Centers. Formalin-fixed paraffin-embedded sections of frontal, temporal, and parietal cortices were immunostained and digitized, generating 799 whole-slide images (WSIs). Following log transformation, mixed-effects modeling revealed the parietal cortex had the highest cored plaque densities (P < .001); the temporal cortex had the highest diffuse plaque (P < .001); CAA showed no regional differences. Wilcoxon rank-sum test, and covariates adjusted linear models showed ApoE ε4- status was associated with higher cored plaque densities in the temporal lobe (P = .04). ApoE ε4+ status was associated with diffuse plaques in the temporal lobe (P = .001), and CAA in the frontal lobe (P = .004). These findings provide further validation and provide exploratory associations advancing deeper phenotyping of AD.

Indexed as

Alzheimer DiseaseAmyloid beta-PeptidesBrainMachine LearningPlaque, AmyloidTemporal LobeAgedAged, 80 and overCerebral Amyloid AngiopathyFemaleHumansMaleAmyloid beta-PeptidesAlzheimer’s diseaseamyloid-betaconvolutional neural networkdigital pathologymachine learningneurodegeneration

Identifiers

PMID41806384
PMCPMC13197125

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.