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

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. 

Introduction

Fibromyalgia (FM) is a multifaceted chronic pain disorder presenting with extensive pain throughout the musculoskeletal system, alongside chronic fatigue, disruptions in sleep patterns, and cognitive impairments [1]. This disorder affects between 0.2% and 6.6% of the global population, while its prevalence among women has been reported to range from 2.4% to 6.8%. [2]. The multifaceted clinical manifestations of FM present substantial diagnostic and management challenges for both patients and healthcare providers. Despite the widespread occurrence of this disorder, its exact cause remains unknown, as it is shaped by a complex interaction of environmental, biological, and genetic influences [3].

Materials and methods

Ethics statement

The present study employed the UCLA Consortium for Neuropsychiatric Phenomics (CNP) dataset, which is publicly available from the OpenNeuro repository (https://openneuro.org/datasets/ds004144/versions/1.0.2/file-display/README). The research was conducted in accordance with the ethical requirements and protocols approved by the Institutional Review Board (IRB) of the University of California, Los Angeles (UCLA).Written informed consent was obtained from all participants prior to their enrollment in the study.

Results

The information from 62 women, including 32 individuals diagnosed with FM and 30 healthy individuals, alongside clinical information for individuals aged between 30 and 50 years (mean age: 41.73), has been studied (Table 2). The mean age of participants in both groups is very similar, with a comparable standard deviation, indicating age-matched groups. Most participants in both groups are married (HC: 63.6%, FM: 51.5%). The FM group has a slightly higher percentage of singles and widows compared to HC and Divorced/separated percentages are similar. Marital status distribution is broadly similar, with minor differences. 

Discussion

This study utilized machine learning techniques and advanced analysis of functional magnetic resonance imaging (fMRI) using graph theory to differentiate between individuals with FM and healthy individuals. After preprocessing and processing the fMRI data using MATLAB, a genetic algorithm was employed to select practical and significant features for disease diagnosis. These selected features were subsequently used as input for various models to enhance diagnostic accuracy. Machine learning algorithms exhibit distinct strengths and limitations in classification tasks, with their accuracy being contingent upon factors such as sample size and dataset complexity.

Conclusion

Among the evaluated models, the SVM-MK demonstrated the best overall performance, achieving the highest AUC, along with high accuracy and sensitivity, moderate specificity, and a strong F1 score. These results highlight its superior ability to accurately distinguish individuals with FM from HC. Other SVM variants, particularly the polynomial kernel SVM, also showed strong performance, highlighting the effectiveness of kernel-based methods in capturing complex patterns in the data. In contrast, models like Gradient Boosting and RF exhibited lower sensitivity or specificity, resulting in reduced overall effectiveness. 

Acknowledgments

The authors sincerely thank all researchers and participants who contributed to the original study and data collection.

Citation: Solouki L, Shahsavari S, Sharini H, Hashemian AH, Johann Schmid V, Jafari S, et al. (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

Editor: Hadeel K. Aljobouri, Al-Nahrain University, IRAQ

Received: November 20, 2025; Accepted: August 25, 2026; Published: October 1, 2026

Copyright: © 2026 Solouki et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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.

Funding: This work was supported in part by the Research Deputy of Kermanshah University of Medical Sciences (KUMS), project grant No. Of 4040419. There was no additional external funding received for this study.

Competing interests: The authors have declared that no competing interests exist.