Evidence map›Paper›PMID 41484947›Full record

ArticleBMC medical imaging2026

Dynamic network dysregulation in post-stroke aphasia: analysis of dALFF and dynamic functional connectivity.

Li Wang, Xingang Wang, Fengjie He, Linqiong Sang, Najing Zhang, Qiannan Wang, Ye Zhang, Mingguo Qiu, Chen Liu, Rubin Yan

Abstract read
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In one paragraph

Article in BMC medical imaging, 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

10 authors.

Li WangDepartment of Medical Imaging, College of Biomedical Engineering, Army Medical University, Chongqing, China.
Xingang WangDepartment of Radiology, Southwest Hospital, Army Medical University, Chongqing, China.
Fengjie HeChongqing Academy of Metrology and Quality Inspection, Chongqing, China.
Linqiong SangDepartment of Medical Imaging, College of Biomedical Engineering, Army Medical University, Chongqing, China.
Najing ZhangDepartment of Medical Imaging, College of Biomedical Engineering, Army Medical University, Chongqing, China.
Qiannan WangDepartment of Medical Imaging, College of Biomedical Engineering, Army Medical University, Chongqing, China.
Ye ZhangDepartment of Medical Imaging, College of Biomedical Engineering, Army Medical University, Chongqing, China.
Mingguo QiuDepartment of Medical Imaging, College of Biomedical Engineering, Army Medical University, Chongqing, China.
Chen LiuDepartment of Radiology, Southwest Hospital, Army Medical University, Chongqing, China.
Rubin YanDepartment of Rehabilitation, Southwest Hospital, Army Medical University, Chongqing, China. robinyan@tmmu.edu.cn.

Funding

the General Project of Chongqing Natural Science Foundation No. CSTB2022NSCQ-MSX0164the General Project of Chongqing Natural Science Foundation No. cstc2020jcyj-msxmX0311
6 · The paper itself

Abstract

Post-stroke aphasia involves dynamic disruptions in brain networks that are not fully captured by static analyses. This study employs dynamic amplitude of low-frequency fluctuations (dALFF) and dynamic functional connectivity (dFC) analyses combined with machine learning to comprehensively characterize these changes. Using a sliding-window approach, 22 key regions were identified based on dALFF alterations, and recurring dFC states were extracted via K-means clustering. Aphasia patients showed prolonged dwell times and fewer transitions in weak connectivity states, indicating reduced network flexibility. Network topology analysis revealed increased local and nodal efficiency alongside decreased global efficiency and modularity, suggesting a compensatory shift favoring local integration over global coordination. A support vector machine classifier, incorporating feature selection and leave-one-out cross-validation, effectively distinguished patients from controls with an AUC of 0.75, emphasizing critical dynamic temporal features associated with network dysfunction. These dynamic features correlated with language deficits and provided a more comprehensive temporal characterization than static metrics. Our findings reveal a dynamic balance between local compensation and impaired global integration in aphasia brain networks. Combining dynamic neuroimaging and machine learning offers a robust framework for capturing these alterations and supports the development of personalized rehabilitation strategies.

Indexed as

AphasiaBrainNerve NetStrokeAgedConnectomeFemaleHumansMachine LearningMagnetic Resonance ImagingMaleSupport Vector MachineDynamic amplitude of low-frequency fluctuationsDynamic functional connectivityMachine learningPost-stroke aphasia

Identifiers

PMID41484947
PMCPMC12866412

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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.