Evidence map›Paper›PMID 35585847›Full record

ArticleFrontiers in neurology2022

Brain Mechanism of Acupuncture Treatment of Chronic Pain: An Individual-Level Positron Emission Tomography Study.

Jin Xu, Hongjun Xie, Liying Liu, Zhifu Shen, Lu Yang, Wei Wei, Xiaoli Guo, Fanrong Liang, Siyi Yu, Jie Yang

Open access · goldAbstract read
In one paragraph

Article in Frontiers in neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 4 pooled it
2.5field-weighted citation impact, top 12% of its field
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

9 citing papers in PubMed, 4 syntheses or guidelines pooled it, 9 citations in OpenAlex.

  1. Pooled it
  2. Moving towards the use of artificial intelligence in pain management.European journal of pain (London, England) · 2025
    Pooled it
  3. Pooled it
  4. Pooled it
  5. Review
  6. Review
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  8. Article
  9. Review
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 at 3 institutions in 1 country.

Jin XuDepartment of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Hongjun XieDepartment of Nuclear Medicine, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, China.
Liying LiuDepartment of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Zhifu ShenDepartment of Traditional Chinese and Western Medicine, North Sichuan Medical College, Nanchong, China.
Lu YangDepartment of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Wei WeiDepartment of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Xiaoli GuoDepartment of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Fanrong LiangDepartment of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Siyi YuDepartment of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Jie YangDepartment of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Chengdu University of Traditional Chinese Medicine · CNNorth Sichuan Medical University · CNSichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Acupuncture has been shown to be effective in the treatment of chronic pain. However, their neural mechanism underlying the effective acupuncture response to chronic pain is still unclear. We investigated whether metabolic patterns in the pain matrix network might predict acupuncture therapy responses in patients with primary dysmenorrhea (PDM) using a machine-learning-based multivariate pattern analysis (MVPA) on positron emission tomography data (PET). Methods: Forty-two patients with PDM were selected and randomized into two groups: real acupuncture and sham acupuncture (three menstrual cycles). Brain metabolic data from the three special brain networks (the sensorimotor network (SMN), default mode network (DMN), and salience network (SN)) were extracted at the individual level by using PETSurfer in fluorine-18 fluorodeoxyglucose positron emission tomography ( Results: Paired Conclusion: This study indicates that the individual-level metabolic patterns in DMN is associated with real acupuncture treatment response in chronic pain. The present findings advanced the knowledge of the brain mechanism of the acupuncture treatment in chronic pain.

Indexed as

acupuncturebiomarkermachine learningmetabolicprimary dysmenorrhea

Identifiers

PMID35585847
PMCPMC9108276
OpenAlexW4225289141

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.