Evidence map›Paper›PMID 34335444›Full record

ArticleFrontiers in neurology2021

Functional Disruptions of the Brain in Low Back Pain: A Potential Imaging Biomarker of Functional Disability.

Bidhan Lamichhane, Dinal Jayasekera, Rachel Jakes, Wilson Z Ray, Eric C Leuthardt, Ammar H Hawasli

Abstract read
In one paragraph

Article in Frontiers in neurology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 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

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Artificial Intelligence and Computer Aided Diagnosis in Chronic Low Back Pain: A Systematic Review.International journal of environmental research and public health · 2022
    Pooled it
  2. Review
  3. Dual task function differences in chronic low back pain: a narrative review.The Journal of the Canadian Chiropractic Association · 2025
    Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. 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

6 authors.

Bidhan LamichhaneDepartment of Neurosurgery, Washington University School of Medicine, St. Louis, MO, United States.
Dinal JayasekeraDepartment of Biomedical Engineering, Washington University in St. Louis McKelvey School of Engineering, St. Louis, MO, United States.
Rachel JakesDepartment of Biomedical Engineering, Washington University in St. Louis McKelvey School of Engineering, St. Louis, MO, United States.
Wilson Z RayDepartment of Neurosurgery, Washington University School of Medicine, St. Louis, MO, United States.
Eric C LeuthardtDepartment of Neurosurgery, Washington University School of Medicine, St. Louis, MO, United States.
Ammar H HawasliDepartment of Neurosurgery, Washington University School of Medicine, St. Louis, MO, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chronic low back pain (LBP) is one of the leading causes of disability worldwide. While LBP research has largely focused on the spine, many studies have demonstrated a restructuring of human brain architecture accompanying LBP and other chronic pain states. Brain imaging presents a promising source for discovering noninvasive biomarkers that can improve diagnostic and prognostication outcomes for chronic LBP. This study evaluated graph theory measures derived from brain resting-state functional connectivity (rsFC) as prospective noninvasive biomarkers of LBP. We also proposed and tested a hybrid feature selection method (Enet-subset) that combines Elastic Net and an optimal subset selection method. We collected resting-state functional MRI scans from 24 LBP patients and 27 age-matched healthy controls (HC). We then derived graph-theoretical features and trained a support vector machine (SVM) to classify patient group. The degree centrality (DC), clustering coefficient (CC), and betweenness centrality (BC) were found to be significant predictors of patient group. We achieved an average classification accuracy of 83.1% (

Indexed as

chronic low back painelastic netfeature selectiongraph theorysupport vector machine

Identifiers

PMID34335444
PMCPMC8317987

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.