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Brain dysconnectivity patterns associated with chronic back pain development.

Overview

Authors: Stephan Wunderlich1, Enrico Schulz1, Florian Ringel2, Veit M. Stoecklein2, Sophia Stoecklein1
ORCID iDs: Enrico Schulz
  1. Department of Radiology, LMU University Hospital, LMU Medizin, Ludwig-Maximilians-Universität München,Munich, Germany
  2. Department of Neurosurgery, LMU University Hospital, LMU Medizin, Ludwig-Maximilians-Universität München,Munich, Germany
Journal: Brain informatics, volume 13, issue 1, article 39
Dates: received 24 November 2025; accepted 7 August 2026; published online 21 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1186/s40708-026-00328-8 · PMID 42640485 · PMCID PMC13507019 · OpenAlex W7203859145
Open access: gold, a free copy (OpenAlex)
Status: code found, still to be checked (how the code is found and verified)
Categories: fMRI (modality), human (organism), pain (population), clinical / translational (subfield)
Methods: Spectral & time-frequency, Smoothing, state filtering, decompositions, Preprocessing, fMRI & imaging
Keywords: FMRI, Chronic Back Pain, Prediction
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Klinikum der Universität München (6933)
Citations: not cited yet (Europe PMC); 99 references in the paper

Abstract

Chronic back pain often emerges from a transitional period of subacute pain, yet no clinically applicable biomarker exists to identify which patients are at risk for chronification. Evidence suggests that this transition is driven not only by nociceptive input but by changes in brain networks involved in valuation, emotion regulation, and learning. Here, we used resting-state functional magnetic resonance imaging (rs-fMRI) and machine learning to explore whether dysconnectivity in these networks is associated with later development of chronic back pain. We analyzed functional connectivity in 46 patients with subacute back pain and 43 healthy controls from a publicly available longitudinal cohort, classifying patients one year later as either recovered or chronified based on pain outcomes. A data-driven model identified a set of six brain regions whose patterns of dysconnectivity distinguished the two patient trajectories with an area under the curve of 0.87. These regions encompass prefrontal, temporal, and somatosensory hubs implicated in reinforcement learning, avoidance behavior, and pain catastrophizing, suggesting a potential link between dysconnectivity patterns and psychological processes implicated in pain persistence. Based on these features, we introduced an exploratory rs-fMRI–based marker for pain chronification, suggesting potential prognostic relevance that requires independent validation before clinical stratification or targeted intervention can be considered.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

No file of the authors' code could be read here: it is described below, and read at its source.

openpain.org

License: none: the authors keep all their rights
State: unreachable at the last attempt, verified on 13 November 2026
Evidence: found in the paper
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 20 checks, the latest on 13 November 2026: unreachable at the last attempt
  • 13 November 2026: unreachable at the last attempt
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At the source: openpain.org

Code availability

Code is available on request.

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

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  • 0 scripts, each with its path and the digest of its content;
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

All data and code used in this study are publicly available via the OpenPain repository: https://openpain.org. The dataset includes neuroimaging, behavioral, and clinical information from the SBP and CBP cohorts used in the current analysis.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 3 keywords, 1 funder, 95 references.

Cite

This paper

Wunderlich, S., Schulz, E., Ringel, F., Stoecklein, V. M., & Stoecklein, S. (2026). Brain dysconnectivity patterns associated with chronic back pain development. Brain informatics, 13(1), 39. https://doi.org/10.1186/s40708-026-00328-8

BibTeX

@article{wunderlich2026brain,
author = {Wunderlich, Stephan and Schulz, Enrico and Ringel, Florian and Stoecklein, Veit M. and Stoecklein, Sophia},
title = {{Brain dysconnectivity patterns associated with chronic back pain development}},
journal = {Brain informatics},
year = {2026},
month = aug,
volume = {13},
number = {1},
pages = {39},
publisher = {Springer},
issn = {2198-4018},
doi = {10.1186/s40708-026-00328-8},
url = {https://doi.org/10.1186/s40708-026-00328-8},
pmid = {42640485},
pmcid = {PMC13507019}
}

RIS

TY - JOUR
AU - Wunderlich, Stephan
AU - Schulz, Enrico
AU - Ringel, Florian
AU - Stoecklein, Veit M.
AU - Stoecklein, Sophia
TI - Brain dysconnectivity patterns associated with chronic back pain development
T2 - Brain informatics
J2 - Brain Inform
PY - 2026
DA - 2026/08/21
VL - 13
IS - 1
SP - 39
SN - 2198-4018
PB - Springer
DO - 10.1186/s40708-026-00328-8
UR - https://doi.org/10.1186/s40708-026-00328-8
LA - en
ER -

CSL-JSON

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