Evidence map›Paper›PMID 42129136›Full record

ArticleTranslational psychiatry2026

Functional system-specific brain aging across the Alzheimer's disease continuum.

Yanxi Huo, Weijie Huang, Zhenzhao Liu, Haojie Chen, Tianyu Bai, Yichen Wang, Kexin Wang, Daoqiang Zhang, Jian Cheng, Yu Sun and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Translational psychiatry, 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

14 authors.

Yanxi Huo *State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.ORCID http://orcid.org/0009-0002-8448-3275
Weijie Huang *College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, the Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing, China.
Zhenzhao LiuState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Haojie ChenState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Tianyu BaiState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Yichen WangState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Kexin WangState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Daoqiang ZhangCollege of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, the Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing, China.ORCID http://orcid.org/0000-0002-5658-7643
Jian ChengSchool of Computer Science and Engineering, Beihang University, Beijing, China.
Yu SunDepartment of Radiology, China-Japan Friendship Hospital, Beijing, China.
Guolin MaDepartment of Radiology, China-Japan Friendship Hospital, Beijing, China.
Cui ZhaoDepartment of Geriatrics, Affiliated Hospital of Chengde Medical University, Chengde, China. zhaocui1805@163.com.
Zhanjun ZhangState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China. zhang_rzs@bnu.edu.cn.ORCID http://orcid.org/0000-0001-7266-4218
Ni ShuState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China. nshu@bnu.edu.cn.ORCID http://orcid.org/0000-0003-2420-2910

Funding

National Natural Science Foundation of China (National Science Foundation of China) 82301608, 32271145, 81871425, 210510238Natural Science Foundation of Beijing Municipality (Beijing Natural Science Foundation) L252087
6 · The paper itself

Abstract

Accelerated brain aging is implicated in Alzheimer's disease (AD). However, the spatial heterogeneity of brain aging patterns across different functional systems along the AD continuum remains largely unexplored. We developed functional system-specific brain age models derived from structural magnetic resonance imaging in a healthy adult cohort (n = 22,672) and applied them to 1478 participants across the AD continuum. Using up to 6 years of retrospective longitudinal data before clinical AD conversion, we quantified predicted age differences (PADs) and their change rates, characterized heterogeneous brain aging trajectories, and examined their associations with AD biomarkers, cognitive performance, and clinical progression. Progressive mild cognitive impairment (MCI) individuals showed early PAD deviations in the default mode network and accelerated changes in attention and control networks. System-wise PAD dynamics mediated the effects of AD-related biomarkers on cognitive decline. Integrating PAD features can improve predictive accuracy of MCI-to-AD conversion (AUC = 0.95). Functional system-specific PADs can be sensitive biomarkers for early detection and monitoring of individualized AD risk.

Indexed as

AgingAlzheimer DiseaseBrainCognitive DysfunctionAgedAged, 80 and overBiomarkersDisease ProgressionFemaleHumansLongitudinal StudiesMagnetic Resonance ImagingMaleMiddle AgedRetrospective StudiesBiomarkers

Identifiers

PMID42129136
PMCPMC13338047

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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.