Evidence map›Paper›PMID 40599906›Full record

ArticleBrain communications2025

An artificial intelligence-derived metabolic network predicts psychosis in Alzheimer's disease.

Nha Nguyen, Jesus J Gomar, Jack N Truong, Janos Barbero, Patrick P Do, Andrea Rommal, Alice Oh, David Eidelberg, Jeremy Koppel, An Vo

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Brain communications, 2025. 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.

Nha NguyenCenter for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA.
Jesus J GomarThe Litwin-Zucker Research Center for the Study of Alzheimer's Disease, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA.ORCID https://orcid.org/0000-0002-6995-4270
Jack N TruongDepartment of Computer Science, Erik Jonsson School of Engineering and Computer Science at The University of Texas at Dallas, Dallas, TX 75080, USA.
Janos BarberoDepartment of Molecular Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY 11549, USA.
Patrick P DoManning College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA 01003, USA.
Andrea RommalDepartment of Molecular Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY 11549, USA.
Alice OhCenter for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA.
David EidelbergCenter for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA.ORCID https://orcid.org/0000-0002-0854-864X
Jeremy KoppelThe Litwin-Zucker Research Center for the Study of Alzheimer's Disease, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA.
An VoCenter for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA.ORCID https://orcid.org/0000-0002-4961-536X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The delusions and hallucinations that characterize Alzheimer's disease psychosis (AD + P) are associated with violence towards caregivers and an accelerated cognitive and functional decline whose management relies on the utilization of medications developed for young people with schizophrenia. The development of novel therapies requires biomarkers that distinguish AD + P from non-psychotic Alzheimer's disease. We investigated whether there might exist a brain metabolic network that distinguishes AD + P from non-psychotic Alzheimer's disease that could be used as a biomarker to predict and track the course of AD + P for use in clinical trials. Utilizing F-18 fluorodeoxyglucose positron emission tomography scans from cohorts of cognitively healthy elderly (

Indexed as

Alzheimer’s disease psychosisbiomarkerconvolutional neural networkexplainable AImetabolic brain networks

Identifiers

PMID40599906
PMCPMC12209852

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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.