Evidence map›Paper›PMID 41636698›Full record

ArticleCNS neuroscience & therapeutics2026

Altered Salience-Default Mode Network Dynamics in Subclinical Depression: A Preclustering-Based Co-Activation Pattern Analysis.

Bo Zhang, Zhinan Yu, Feifan Yan, Yiwei Sun, Jiao Ye, Xiaoya Liu, Shouliang Qi, Xinhua Wei, Shuang Liu, Dong Ming

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in CNS neuroscience & therapeutics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Bo ZhangTianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Medical School, Tianjin University, Tianjin, China.ORCID 0000-0002-9324-7074
Zhinan YuTianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Medical School, Tianjin University, Tianjin, China.ORCID 0009-0000-8987-1002
Feifan YanTianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Medical School, Tianjin University, Tianjin, China.
Yiwei SunTianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Medical School, Tianjin University, Tianjin, China.
Jiao YeTianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Medical School, Tianjin University, Tianjin, China.
Xiaoya LiuTianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Medical School, Tianjin University, Tianjin, China.
Shouliang QiCollege of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.ORCID 0000-0003-0977-1939
Xinhua WeiDepartment of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China.ORCID 0000-0002-6801-4826
Shuang LiuTianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Medical School, Tianjin University, Tianjin, China.ORCID 0000-0002-4372-8443
Dong MingTianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Medical School, Tianjin University, Tianjin, China.

Funding

Autonomous Project of Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration 24HHNJSS00016National Key Research and Development Program of China 2023YFF1203700National Natural Science Foundation of China 62376187
6 · The paper itself

Abstract

backgroundNeuroimaging studies frequently report aberrant spontaneous brain activity and functional connectivity within core functional networks, including the default mode network (DMN), frontoparietal network (FPN), and salience network (SN) in subclinical depression (SD). However, the dynamic coordination among these networks remains poorly understood, impeding comprehensive elucidation of the underlying neuropathology of SD.

methodsResting-state functional magnetic resonance imaging (fMRI) data were collected from subjects with SD (n = 26) and healthy controls (HCs, n = 33). A preclustering-based co-activation pattern method was developed to investigate the dynamic patterns of network coordination. Finally, machine learning analysis was conducted to evaluate the potential of network dynamics for clinical diagnosis.

resultsSubjects with SD exhibited decreased dwell time in the SN and increased transition frequency from the SN to DMN, which was positively correlated with depressive severity. Furthermore, an ensemble learning model based on SN-DMN dynamic features achieved a classification accuracy of 96.44% in distinguishing SD from HC.

conclusionThese findings underscore the potential of altered SN-DMN dynamics as candidates for future neuroimaging markers of SD and support a neurocognitive model whereby altered SN-DMN dynamic coordination makes subjects with SD more prone to internal directed attention biases, thereby contributing to self-related depressive symptoms like rumination.

Indexed as

BrainDefault Mode NetworkDepressionNerve NetAdultBrain MappingFemaleHumansMachine LearningMagnetic Resonance ImagingMaleNeural PathwaysOxygenYoung AdultOxygendefault mode networkfunctional magnetic resonance imagingsalience networksubclinical depression

Identifiers

PMID41636698
PMCPMC12871089

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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.