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Article

Fibromyalgia Detection Based on EEG Connectivity Patterns

1
Unidad de Corta Estancia, Hospital Psiquiátrico Román Alberca, National Service of Health, 30120 Murcia, Spain
2
Research Support Service, University of Murcia, 30100 Murcia, Spain
3
Regenerative Medicine and Advanced Therapies Lab., Instituto de Investigación Sanitaria San Carlos (IdIISC), Hospital Clínico San Carlos, 28040 Madrid, Spain
4
Biological and Health Psychology, Autonomous University of Madrid (UAM), 28049 Madrid, Spain
*
Author to whom correspondence should be addressed.
Academic Editors: Yasin Temel, Giovanni Ricevuti and Lorenzo Lorusso
J. Clin. Med. 2021, 10(15), 3277; https://doi.org/10.3390/jcm10153277
Received: 13 April 2021 / Revised: 21 July 2021 / Accepted: 22 July 2021 / Published: 25 July 2021
Objective: The identification of a complementary test to confirm the diagnosis of FM. The diagnosis of fibromyalgia (FM) is based on clinical features, but there is still no consensus, so patients and clinicians might benefit from such a test. Recent findings showed that pain lies in neuronal bases (pain matrices) and, in the long term, chronic pain modifies the activity and dynamics of brain structures. Our hypothesis is that patients with FM present lower levels of brain activity and therefore less connectivity than controls. Methods: We registered the resting state EEG of 23 patients with FM and compared them with 23 control subjects’ resting state recordings from the PhysioBank database. We measured frequency, amplitude, and functional connectivity, and conducted source localization (sLORETA). ROC analysis was performed on the resulting data. Results: We found significant differences in brain bioelectrical activity at rest in all analyzed bands between patients and controls, except for Delta. Subsequent source analysis provided connectivity values that depicted a distinct profile, with high discriminative capacity (between 91.3–100%) between the two groups. Conclusions: Patients with FM show a distinct neurophysiological pattern that fits with the clinical features of the disease. View Full-Text
Keywords: fibromyalgia; EEG; fast Fourier transform; diagnosis; ROC curve fibromyalgia; EEG; fast Fourier transform; diagnosis; ROC curve
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MDPI and ACS Style

Martín-Brufau, R.; Gómez, M.N.; Sanchez-Sanchez-Rojas, L.; Nombela, C. Fibromyalgia Detection Based on EEG Connectivity Patterns. J. Clin. Med. 2021, 10, 3277. https://doi.org/10.3390/jcm10153277

AMA Style

Martín-Brufau R, Gómez MN, Sanchez-Sanchez-Rojas L, Nombela C. Fibromyalgia Detection Based on EEG Connectivity Patterns. Journal of Clinical Medicine. 2021; 10(15):3277. https://doi.org/10.3390/jcm10153277

Chicago/Turabian Style

Martín-Brufau, Ramón, Manuel N. Gómez, Leyre Sanchez-Sanchez-Rojas, and Cristina Nombela. 2021. "Fibromyalgia Detection Based on EEG Connectivity Patterns" Journal of Clinical Medicine 10, no. 15: 3277. https://doi.org/10.3390/jcm10153277

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