Evidence map›Paper›PMID 41258002›Full record

ArticleTranslational psychiatry2025

Individualized functional connectome biomarkers predict clinical symptoms after rTMS treatment in Alzheimer's disease.

Chengxiao Yang, Pan Wang, Ziyan Zhu, Hao Wu, Qin Su, Shuxiang Zhu, Yuxuan Shao, Hua Lin, Bharat B Biswal

Abstract read
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In one paragraph

Article in Translational psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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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.

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Chengxiao YangThe Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformatics, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Pan WangThe Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformatics, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China. wpjoepan@163.com.ORCID http://orcid.org/0000-0003-4074-885X
Ziyan ZhuDepartment of Neurology, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing, 100053, China.
Hao WuThe Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformatics, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Qin SuDepartment of Neurology, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing, 100053, China.
Shuxiang ZhuDepartment of Neurology, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing, 100053, China.
Yuxuan ShaoDepartment of Neurology, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing, 100053, China.
Hua LinDepartment of Neurology, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing, 100053, China. linhua@ccmu.edu.cn.
Bharat B BiswalThe Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformatics, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China. bbiswal@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pharmacological treatments for Alzheimer's disease (AD) often show limited effectiveness, prompting growing interest in non-drug approaches such as repetitive transcranial magnetic stimulation (rTMS). However, the effects of rTMS can vary widely between individuals with AD, highlighting the need to better understand brain characteristics that may influence treatment response. In this study, we applied a personalized method to divide each participant's brain cortex into functionally meaningful regions based on their brain activity patterns, rather than relying on a standard brain template. Using this individualized brain mapping approach, we examined how rTMS changes functional connectivity (FC) across the brain. We further used support vector regression to estimate whether these individualized FC patterns could predict the severity of clinical symptoms. The results showed that rTMS significantly increased whole-brain individualized FC strength during resting state, with the most prominent effects observed in the default mode and visual networks (Cohen's d > 0.27, corrected p < 0.05). Importantly, the personalized FC features served as predictive biomarkers, demonstrating greater accuracy in forecasting clinical outcomes compared to the conventional group-based approach. These findings suggest that individualized brain connectivity holds significant potential for guiding personalized therapeutic strategies and improving treatment efficacy in AD.

Indexed as

Alzheimer DiseaseBrainCerebral CortexConnectomeNerve NetTranscranial Magnetic StimulationAgedBiomarkersFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedBiomarkers

Identifiers

PMID41258002
PMCPMC12660808

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