Graph theory-based machine learning for automated fibromyalgia diagnosis using fMRI during an emotional task.

Overview

Authors: Leila Solouki^1,2, Soodeh Shahsavari^2,3, Hamid Sharini^2,4, Amir Hossein Hashemian^2,3, Volker Johann Schmid⁵, Samira Jafari⁶, Saleh Rouhi⁷, Behzad Mahaki^2,3

ORCID iDs: Volker Johann Schmid, Samira Jafari, Behzad Mahaki

Student Research Committee, Kermanshah University of Medical Sciences, Kermanshah, Iran
Health Data Science Research Center, Health Research Institute, Kermanshah University of Medical Sciences, Kermanshah, Iran
Department of Biostatistics, School of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran
Department of Biomedical Engineering, Faculty of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran
Bioimaging Group, Department of Statistics, Ludwig Maximimilians, Munich, Germany
Modeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
Department of Medical Physics, Faculty of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran

Institutions: Kermanshah University of Medical Sciences (Iran); Ludwig-Maximilians-Universität München (Germany); Kerman University of Medical Sciences (Iran)

Journal: PloS one, volume 21, issue 10, article e0357964

Dates: received 20 November 2025; accepted 25 August 2026; published online 1 October 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1371/journal.pone.0357964 · PMID 42821602 · PMCID PMC13630208 · OpenAlex W7215067142

Open access: gold, a free copy (OpenAlex)

Status: data only (how the code is found and verified)

Categories: fMRI (modality), human (organism), pain (population)

Methods: Machine learning, Statistics, Graphs, fMRI & imaging

MeSH: Emotions*, Fibromyalgia*, Machine Learning*, Magnetic Resonance Imaging*, Adult, Algorithms, Boosting Machine Learning Algorithms, Brain, Classification Algorithms, Female, Humans, Middle Aged, Random Forest, Soft Computing, Support Vector Machine (* major topic)

Topic: Fibromyalgia and Chronic Fatigue Syndrome Research (Psychiatry and Mental health, Medicine), according to OpenAlex

Funding: Kermanshah University of Medical Sciences (4040419)

Citations: not cited yet (Europe PMC); 37 references in the paper

Abstract

Background: Fibromyalgia (FM) is a multifaceted chronic pain disorder presenting with pain throughout the musculoskeletal system, alongside chronic fatigue,sleep disturbances, and cognitive impairments. Current diagnostic criteria primarily rely on symptom questionnaires, with no specific laboratory markers or definitive tests, making diagnosis subjective and often delayed. Moreover, the overlap with other central pain syndromes further complicates accurate diagnosis. Neuroimaging studies, particularly functional magnetic resonance imaging (fMRI), have shown promise in elucidating brain abnormalities underlying FM. However, translating these findings into reliable diagnostic tools remains challenging, especially given small sample sizes. Therefore, this study aims to enhance the diagnostic accuracy of FM based on fMRI data by developing an intelligent model. This model employs machine learning algorithms designed to perform effectively with limited dataset sizes and incorporates features extracted through graph theory methods. We analyzed fMRI data from 32 FM patients and 30 healthy controls. Graph theory metrics were extracted from 13 regions of the emotion regulation network. A genetic algorithm was employed to select the most effective features, which were then used to train multiple machine learning classifiers—including Support Vector Machine (SVM) with polynomial and Radial Basis Function (RBF) kernels, Random Forest, Gradient Boosting Machine, Adaptive Boosting, and SVM with Mixture Kernels—within a cross-validation framework to ensure robustness.

Result: The genetic algorithm selected 84 features as inputs for machine learning algorithms. The polynomial SVM achieved the highest overall accuracy, whereas the SVM with mixture kernels demonstrated superior sensitivity and AUC. Sensitivity and AUC were prioritized as the main criteria for model selection due to their clinical relevance in reducing false negatives.

Conclusion: The study identified the SVM with Mixture Kernels as the most effective model for distinguishing FM patients from healthy controls. This approach demonstrates promising potential for enhancing diagnostic accuracy and supporting psychological and psychiatric interventions.

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

Code

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Data

Datasets cited

doi:10.18112/openneuro.ds004144.v1.0.2, at OpenNeuro; found in “Data Availability”
openneuro:ds004144, at OpenNeuro; found in the text, “Ethics statement”

Data Availability

The dataset used in this study is publicly available without restriction. Data were obtained from the UCLA Consortium for Neuropsychiatric Phenomics (CNP) and are hosted on the OpenNeuro repository under accession number ds004144. The dataset can be accessed via the following DOI: doi:10.18112/openneuro.ds004144.v1.0.2.

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

Versions

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Version 1, 6 October 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 15 MeSH terms, 1 funder, 29 references.

Cite

This paper

Solouki, L., Shahsavari, S., Sharini, H., Hashemian, A. H., Johann Schmid, V., Jafari, S., Rouhi, S., & Mahaki, B. (2026). Graph theory-based machine learning for automated fibromyalgia diagnosis using fMRI during an emotional task. PloS one, 21(10), e0357964. https://doi.org/10.1371/journal.pone.0357964

BibTeX

@article{solouki2026graph, author = {Solouki, Leila and Shahsavari, Soodeh and Sharini, Hamid and Hashemian, Amir Hossein and Johann Schmid, Volker and Jafari, Samira and Rouhi, Saleh and Mahaki, Behzad}, title = {{Graph theory-based machine learning for automated fibromyalgia diagnosis using fMRI during an emotional task}}, journal = {PloS one}, year = {2026}, month = oct, volume = {21}, number = {10}, pages = {e0357964}, publisher = {PLOS}, issn = {1932-6203}, doi = {10.1371/journal.pone.0357964}, url = {https://doi.org/10.1371/journal.pone.0357964}, pmid = {42821602}, pmcid = {PMC13630208} }

RIS

TY - JOUR AU - Solouki, Leila AU - Shahsavari, Soodeh AU - Sharini, Hamid AU - Hashemian, Amir Hossein AU - Johann Schmid, Volker AU - Jafari, Samira AU - Rouhi, Saleh AU - Mahaki, Behzad TI - Graph theory-based machine learning for automated fibromyalgia diagnosis using fMRI during an emotional task T2 - PloS one J2 - PLoS One PY - 2026 DA - 2026/10/01 VL - 21 IS - 10 SP - e0357964 SN - 1932-6203 PB - PLOS DO - 10.1371/journal.pone.0357964 UR - https://doi.org/10.1371/journal.pone.0357964 LA - en ER -

CSL-JSON

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