Evidence map›Paper›PMID 31176295›Full record

ArticleNeuroImage. Clinical2019

Multivariate resting-state functional connectivity predicts responses to real and sham acupuncture treatment in chronic low back pain.

Yiheng Tu, Ana Ortiz, Randy L Gollub, Jin Cao, Jessica Gerber, Courtney Lang, Joel Park, Georgia Wilson, Wei Shen, Suk-Tak Chan and 6 more

Erratum issuedAbstract readControlled Clinical Trial
PubMed Full text at PMC Publisher
In one paragraph

Article in NeuroImage. Clinical, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 66 papers, 9 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
66citing papers in PubMed, 9 pooled it
–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

66 citing papers in PubMed, 9 syntheses or guidelines pooled it.

  1. Pooled it
  2. Moving towards the use of artificial intelligence in pain management.European journal of pain (London, England) · 2025
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  8. Acupuncture for chronic nonspecific low back pain.The Cochrane database of systematic reviews · 2020
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  13. Manipulating placebo analgesia and nocebo hyperalgesia by changing brain excitability.Proceedings of the National Academy of Sciences of the United States of America · 2021
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6 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

16 authors.

Yiheng TuDepartment of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA; Department of Radiology, Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA.
Ana OrtizDepartment of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA.
Randy L GollubDepartment of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA.
Jin CaoDepartment of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA.
Jessica GerberDepartment of Radiology, Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA.
Courtney LangDepartment of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA.
Joel ParkDepartment of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA.
Georgia WilsonDepartment of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA.
Wei ShenDepartment of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA.
Suk-Tak ChanDepartment of Radiology, Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA.
Ajay D WasanDepartment of Anesthesiology, Center for Pain Research, University of Pittsburgh, Pittsburgh, PA, USA.
Robert R EdwardsDepartment of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Vitaly NapadowDepartment of Radiology, Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA.
Ted J KaptchukBeth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Bruce RosenDepartment of Radiology, Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA.
Jian KongDepartment of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA; Department of Radiology, Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA. Electronic address: jkong2@mgh.harvard.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite the high prevalence and socioeconomic impact of chronic low back pain (cLBP), treatments for cLBP are often unsatisfactory, and effectiveness varies widely across patients. Recent neuroimaging studies have demonstrated abnormal resting-state functional connectivity (rsFC) of the default mode, salience, central executive, and sensorimotor networks in chronic pain patients, but their role as predictors of treatment responsiveness has not yet been explored. In this study, we used machine learning approaches to test if pre-treatment rsFC can predict responses to both real and sham acupuncture treatments in cLBP patients. Fifty cLBP patients participated in 4 weeks of either real (N = 24, age = 39.0 ± 12.6, 16 females) or sham acupuncture (N = 26, age = 40.0 ± 13.7, 15 females) treatment in a single-blinded trial, and a resting-state fMRI scan prior to treatment was used in data analysis. Both real and sham acupuncture can produce significant pain reduction, with those receiving real treatment experiencing greater pain relief than those receiving sham treatment. We found that pre-treatment rsFC could predict symptom changes with up to 34% and 29% variances for real and sham treatment, respectively, and the rsFC characteristics that were significantly predictive for real and sham treatment differed. These results suggest a potential way to predict treatment responses and may facilitate the development of treatment plans that optimize time, cost, and available resources.

Indexed as

Acupuncture TherapyMachine LearningAdultBasal GangliaCerebral CortexChronic PainConnectomeFemaleHumansLow Back PainMagnetic Resonance ImagingMaleMiddle AgedOutcome Assessment, Health CarePlacebosPrefrontal CortexPlacebosAcupunctureChronic low back painMachine learning analysisResting-state functional connectivityTreatment responses

Identifiers

PMID31176295
PMCPMC6551557

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.