Background & Aims

Fibromyalgia syndrome (FMS) is a chronic musculoskeletal condition that affects between 0.2 and 6.6% of the general population, showing a higher prevalence among women, between 2.4 and 6.8%. The present study aimed to investigate clinical and physiological predictors of brain oscillatory activity in patients with FMS, by assessing resting-state power, event-related desynchronization (ERD), and event-related synchronization (ERS) during the tasks of movement observation (MO), movement imagery (MI), and motor execution (ME).

Methods

We performed a cross-sectional data analysis from an ongoing randomized double-blinded trial, including clinical, demographic, and neurophysiological data from 78 subjects with FMS diagnosis. Multivariate regression models were built to explore predictors of electroencephalography bands in resting state and during tasks.

Results

Our findings showed that high levels of beta oscillation in the frontal and parietal regions are associated with less pain symptoms. Models related to ERS showed that a longer duration of fibromyalgia was associated with increased activity of Delta, Theta, Beta, and Gamma oscillations. Higher theta ERS in the frontal region is correlated to lower pain interference levels, and higher alpha ERS in the frontal region is associated with less fatigue and temporal pain summation. Moreover, fatigue levels presented a significant association with resting state and alpha ERS oscillatory activity.

Conclusions

Clinical variables such as pain intensity and fatigue levels are associated with alpha and beta oscillations during the resting state. At the same time, FMS duration is a significant predictor in most of the bands assessed during motor tasks. Resting-state oscillations and motor-related ERS seem to provide potential biomarkers for FMS, for which further studies are needed to elucidate their roles in its pathophysiology.

References

1.Lima D, Pacheco-Barrios K, Slawka E, Camargo L, Castelo-Branco L, Cardenas-Rojas A, et al. The role of symptoms severity, heart rate, and central sensitization for predicting sleep quality in patients with fibromyalgia. Pain Med. 2023;24(10):1153-60.
2.Marques AP, Santo A, Berssaneti AA, Matsutani LA, Yuan SLK. Prevalence of fibromyalgia: literature review update. Rev Bras Reumatol Engl Ed. 2017;57(4):356-63.
3.Wolfe F, Clauw DJ, Fitzcharles MA, Goldenberg DL, Häuser W, Katz RL, et al. 2016 Revisions to the 2010/2011 fibromyalgia diagnostic criteria. Semin Arthritis Rheum. 2016;46(3):319-29.
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5.Chen H, Koubeissi MZ. Electroencephalography in Epilepsy Evaluation. Continuum (Minneap Minn). 2019;25(2):431-53.
6.Pinheiro ES, de Queirós FC, Montoya P, Santos CL, do Nascimento MA, Ito CH, et al. Electroencephalographic Patterns in Chronic Pain: A Systematic Review of the Literature. PLoS One. 2016;11(2):e0149085.

Presenting Author

Kevin Pacheco-Barrios

Poster Authors

Kevin Pacheco-Barrios

MD, MPH, MSc

Spaulding Rehabilitation Hospital, Harvard Medical School.

Lead Author

E-mail

Lucas Camargo

Spaulding Rehabilitation Hospital, Harvard Medical School.

Lead Author

E-mail

Lucas M. Marques

Universidade de São Paulo

Lead Author

E-mail

Daniela Martinez-Magallanes

Spaulding Rehabilitation Hospital, Harvard Medical School.

Lead Author

E-mail

Elly A. Pichardo

Spaulding Rehabilitation Hospital, Harvard Medical School.

Lead Author

E-mail

Marianna Daibes

Spaulding Rehabilitation Hospital, Harvard Medical School.

Lead Author

E-mail

Wolnei Caumo

Universidade Federal do Rio Grande do Sul

Lead Author

E-mail

Felipe Fregni

Spaulding Rehabilitation Hospital, Harvard Medical School.

Lead Author

E-mail

Topics

  • Pain Imaging