Evidence map›Paper›PMID 41070945›Full record

ArticleEuropean journal of neurology2025

Abnormal Enhanced Gamma Synchronization in the Default Mode Network Associated With Chronic Pain.

Zhaoshun Jiang, Fei Tao, Feidong Lv, Zhibo Xu, Xiaolei Wang, Yuxi Cai, Xiyu Du, Fafa Sun, Lina Yi, Songbin Liu and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in European journal of neurology, 2025. 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

13 authors.

Zhaoshun JiangDepartment of Anesthesiology, Huadong Hospital, Fudan University, Shanghai, China.ORCID 0000-0003-4284-8539
Fei TaoDepartment of Anesthesiology, Huadong Hospital, Fudan University, Shanghai, China.
Feidong LvDepartment of Anesthesiology, Huadong Hospital, Fudan University, Shanghai, China.
Zhibo XuDepartment of Anesthesiology, Huadong Hospital, Fudan University, Shanghai, China.
Xiaolei WangDepartment of Pain Management, Huadong Hospital, Fudan University, Shanghai, China.
Yuxi CaiDepartment of Anesthesiology, Huadong Hospital, Fudan University, Shanghai, China.
Xiyu DuDepartment of Anesthesiology, Huadong Hospital, Fudan University, Shanghai, China.
Fafa SunDepartment of Anesthesiology, Huadong Hospital, Fudan University, Shanghai, China.
Lina YiDepartment of Anesthesiology, Huadong Hospital, Fudan University, Shanghai, China.
Songbin LiuDepartment of Anesthesiology, Huadong Hospital, Fudan University, Shanghai, China.
Xixue ZhangDepartment of Anesthesiology, Huadong Hospital, Fudan University, Shanghai, China.
Yongjun ZhengDepartment of Pain Management, Huadong Hospital, Fudan University, Shanghai, China.
Weidong GuDepartment of Anesthesiology, Huadong Hospital, Fudan University, Shanghai, China.ORCID 0000-0002-3014-5839

Funding

Huadong Hospital Excellent Project (Youth) ZRPY016YHuadong Hospital Key Discipline Project ZDXK2210National Natural Science Foundation of China 82271286Natural Science Foundation of Tibet Autonomous Region XZ2023ZR-ZY44(Z)Shanghai Municipal Health Commission 2022JC025
6 · The paper itself

Abstract

backgroundChronic pain is characterized by persistent and often debilitating symptoms, yet its underlying neural mechanisms remain poorly understood. This study investigates alterations in brain oscillations and connectivity in chronic pain patients using electroencephalography (EEG), aiming to identify potential neural signatures of chronic pain.

methodsThis cross-sectional study analyzed EEG data from 42 chronic pain patients and 42 healthy controls to identify differences in oscillatory activity and network connectivity. Connectivity analyses were corrected for multiple comparisons using network-based statistics. Machine learning techniques were employed to evaluate the potential of these neural signatures as biomarkers for chronic pain.

resultsChronic pain patients exhibited decreased power in high-frequency bands. Conversely, functional connectivity analysis revealed widespread enhancements in gamma synchronization in chronic pain patients. Dynamic connectivity analysis demonstrated that chronic pain patients had significantly increased gamma synchronization within the default mode network (DMN), particularly in a dominant state characterized by stronger intra-cingulate connections. A machine learning model effectively differentiated patients from controls, achieving robust accuracy of 75.5% ± 6.9%, with sensitivity of 75.3% ± 12.6% and specificity of 80.0% ± 10.5%, primarily driven by the DMN connectivity features. Correlation analysis indicated that the connection between the left posterior cingulate and caudal anterior cingulate within the DMN was positively correlated with pain duration (p = 0.021, r = 0.354).

conclusionEnhanced gamma synchronization within the DMN plays a critical role in the pathophysiology of chronic pain. DMN gamma synchronization may serve as a valuable neural marker for chronic pain, providing new insights into its underlying mechanisms.

Indexed as

Chronic PainDefault Mode NetworkGamma RhythmNerve NetAdultCross-Sectional StudiesElectroencephalographyFemaleHumansMachine LearningMaleMiddle Agedchronic paindefault mode networkdynamic functional connectivityelectroencephalographygamma synchronization

Identifiers

PMID41070945
PMCPMC12512199

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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.