Evidence map›Paper›PMID 40766179›Full record

ArticleFrontiers in aging neuroscience2025

Uncovering abnormal gray and white matter connectivity patterns in Alzheimer's disease spectrum: a dynamic graph theory analysis for early detection.

Juanjuan Jiang, Tao Kang, Ronghua Ling, Yingqian Liu, Jiuai Sun, Yiming Li, Xiaoou Li, Hui Yang, Bingcang Huang, Alzheimer’s Disease Neuroimaging Initiative

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In one paragraph

Article in Frontiers in aging neuroscience, 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
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0citing papers in PubMed
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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

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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

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5 · Who and what money

Authors and funding

10 authors.

Juanjuan Jiang *School of Medical Imaging, Shanghai University of Medicine and Health Science, Shanghai, China.
Tao Kang *AVIC Huadong Optoelectronics (Shanghai) Co., Ltd, Shanghai, China.
Ronghua LingSchool of Medical Imaging, Shanghai University of Medicine and Health Science, Shanghai, China.
Yingqian LiuSchool of Electrical Engineering, Shandong University of Aeronautics, Binzhou, China.
Jiuai SunSchool of Medical Imaging, Shanghai University of Medicine and Health Science, Shanghai, China.
Yiming LiCollege of Medical Instrument, Shanghai University of Medicine & Health Sciences, Shanghai, China.
Xiaoou LiSchool of Medical Imaging, Shanghai University of Medicine and Health Science, Shanghai, China.
Hui YangCollege of Medical Instrument, Shanghai University of Medicine & Health Sciences, Shanghai, China.
Bingcang HuangDepartment of Radiology, Shanghai Pudong New Area Gongli Hospital, Shanghai, China.
Alzheimer’s Disease Neuroimaging Initiative

Funding

Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0M
NIA NIH HHS U01 AG024904
6 · The paper itself

Abstract

Background: Alzheimer's disease (AD) requires early intervention at preclinical stages like subjective memory complaints (SMC). Traditional static brain network analyses lack sensitivity to detect early functional disruptions in SMC. This study aimed to improve preclinical AD stratification by integrating dynamic gray-white matter functional connectivity (DFC) and machine learning. Methods: Using multi-cohort ADNI data [ Results: DFC with short sliding windows (20-50 TRs, 98% overlap) demonstrated greater sensitivity than SFC in detecting early functional disruptions in gray-white matter networks, identifying 34 CN-SMC [ Conclusion: This study confirms that dynamic gray-white matter connectivity serves as a sensitive biomarker for preclinical Alzheimer's disease. The WM4-WM5 disruption hub and machine learning framework provide effective tools for early stratification of SMC, facilitating timely intervention within the disease's therapeutic window.

Indexed as

Alzheimer’s diseasebrain imagingdynamic networkfunctional networkgray and white matter

Identifiers

PMID40766179
PMCPMC12321872

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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.