Evidence map›Paper›PMID 40868327›Full record

ArticleBioengineering (Basel, Switzerland)2025

Unveiling Early Signs of Preclinical Alzheimer's Disease Through ERP Analysis with Weighted Visibility Graphs and Ensemble Learning.

Yongshuai Liu, Jiangyi Xia, Ziwen Kan, Jesse Zhang, Sheela Toprani, James B Brewer, Marta Kutas, Xin Liu, John Olichney

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioengineering (Basel, Switzerland), 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

9 authors.

Yongshuai LiuDepartment of Computer Science, University of California, Davis, CA 95616, USA.ORCID 0000-0002-9629-0393
Jiangyi XiaCenter for Mind and Brain and Neurology Department, University of California, Davis, CA 95618, USA.
Ziwen KanDepartment of Computer Science, University of California, Davis, CA 95616, USA.
Jesse ZhangDepartment of Computer Science, University of Southern California, Los Angeles, CA 90089, USA.
Sheela TopraniDepartment of Neurology, Division of Epilepsy, University of California, Davis, CA 95817, USA.
James B BrewerDepartments of Radiology and Neurosciences, University of California, San Diego, CA 92037, USA.
Marta KutasDepartment of Cognitive Science, University of California, San Diego, CA 92037, USA.ORCID 0000-0002-3689-5825
Xin LiuDepartment of Computer Science, University of California, Davis, CA 95616, USA.ORCID 0000-0002-5379-8269
John OlichneyCenter for Mind and Brain and Neurology Department, University of California, Davis, CA 95618, USA.

Funding

UCSD Shiley-Marcos Alzheimer's Disease Research Center P30P30AG062429 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI DOUGLAS R GALASKO · 2019 to 2026
$34.9M
UC Davis Alzheimer's Disease Research CenterP30AG072972 · NIA · UNIVERSITY OF CALIFORNIA AT DAVIS · PI Charles DeCarli, Rachel A Whitmer · 2021 to 2026
$25.2M
Longitudinal Cognitive ERP studies: Advancement for AD Clinical TrialsR01AG048252 · NIA · UNIVERSITY OF CALIFORNIA AT DAVIS · PI BREWER, JAMES B, OLICHNEY, JOHN M · 2015 to 2020
$4.2M
NIA NIH HHS P30 AG062429NIA NIH HHS P30 AG072972NIA NIH HHS R01 AG048252NIH HHS P30 AG062429NIH HHS P30 AG072972NIH HHS RO1-AG048252
6 · The paper itself

Abstract

The early detection of Alzheimer's disease (AD) is important for effective therapeutic interventions and optimized enrollment for clinical trials. Recent studies have shown high accuracy in identifying mild AD by applying visibility graph and machine learning methods to electroencephalographic (EEG) data. We present a novel analytical framework combining Weighted Visibility Graphs (WVG) and ensemble learning to detect individuals in the "preclinical" stage of AD (preAD) using a word repetition EEG paradigm, where WVG is an advanced variant of natural Visibility Graph (VG), incorporating weighted edges based on the visibility degree between corresponding data points. The EEG signals were recorded from 40 cognitively unimpaired elderly participants (20 preclinical AD and 20 normal old) during a word repetition task. Event-related potential (ERP) and oscillatory signals were extracted from each EEG channel and transformed into a WVG network, from which relevant topological features were extracted. The features were selected using

Indexed as

Alzheimer’s diseaseEEGensemble learningevent-related potentialweighted visibility graph

Identifiers

PMID40868327
PMCPMC12383961

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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.