Background & Aims

Chronic pain is one of the top five symptoms of Long COVID (LC), a novel multisystem clinical syndrome estimated to affect over 200 million individuals globally (1-2). New-onset chronic pain in LC is contributing to increased functional disability and reduced quality of life in patients, thus adding to the growing global burden of chronic pain. The current pharmacological interventions for chronic pain are often ineffective and unsafe. There is a need to explore newer nonpharmacological neuromodulatory interventions based on targets identified in diagnostic electroencephalography (EEG). A growing body of evidence demonstrates a reduction in EEG individual peak alpha frequency (IAF) in various chronic pain populations that correlates with pain intensity and duration (3-5). Whether findings of altered IAF extend to new-onset LC chronic pain is still unknown.   

This study aims to explore the relationship between intensity of pain and IAF in LC patients with new-onset chronic pain.  

Methods

We enrolled individuals with a clinical diagnosis of LC as per the NICE guidelines definition (6), who presented with new-onset chronic pain defined by the ICD-11 criteria (7). Participants completed questionnaires regarding their LC symptoms (8), including a severity subscale for pain, and psychological functioning prior to recording eyes-open resting-state EEG. IAF was estimated via the traditional method as the frequency in the alpha range (8–13Hz) that exhibits maximum power spectral density (max-IAF), and via the Fitting Oscillations and One Over F (FOOOF) algorithm as the strongest Gaussian peak in the alpha range (FOOOF-IAF) (9). IAF was entered as predictor in multiple linear regression analysis, with pain intensity as dependent variable, and results compared between methods. Of the 31 individuals enrolled, data from six participants was excluded from analysis due to missing/incomplete questionnaires.   

Results

Our results from 25 participants (16 females, 9 males) show a negative relationship between global IAF (averaged across all electrode sites) and pain intensity when controlling for age (F(2,22) = 3.852, p = 0.037, adj R squared = 0.259 for max-IAF However, this result did not replicate when using the FOOOF method. Regional analysis suggests that IAF over posterior electrode sites predicts pain intensity while also controlling for age and level of depression as measured by the Patient Health Questionnaire-9 (10), with lower IAF being associated with greater pain intensity. This finding was consistent across methods of estimation of IAF (F(3,20) = 7.114, p = 0.002, adj R squared = 0.444 for max-IAF; F(3,20) = 7.110, p = 0.002, adj R squared = 0.444 for FOOOF-IAF). The independent metrics (IAF, age, depression) were not statistically correlated and in combination were able to account for around 40% variability in pain intensity.  

Conclusions

Our preliminary findings provide evidence for posterior IAF as a promising biomarker of chronic pain in a new post-pandemic chronic pain syndrome.  IAF can thus be explored as a potential cortical target for EEG-based neuromodulation interventions for chronic pain, such as neurofeedback. Neurofeedback is a technique that trains the individual to self-regulate their own neural oscillatory activity that offers the opportunity of non-invasive personalised therapeutic strategies to alleviate pain-related outcomes. Our ongoing work is directed at testing the regression model on longitudinal data collected from the same group of LC patients with new-onset chronic pain at 6 and 12 months after baseline visit. We are also collecting post-covid no-long COVID control data that will be used for comparative analysis. Future work will focus on developing more advanced EEG analysis techniques to further investigate the neuropathophysiological mechanisms underlying chronic pain in LC.   

References

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Presenting Author

Barbara Silva-Passadouro

Poster Authors

Bárbara Silva-Passadouro

BSc

Leeds Institute of Rheumatology and Musculoskeletal Medicine, School of Medicine, University of Leeds, Leeds, UK  

Lead Author

Omar Khoja

MSc

Leeds Institute of Rheumatology and Musculoskeletal Medicine, School of Medicine, University of Leeds, Leeds, UK  

Lead Author

Ioannis Delis

PhD

School of Biomedical Sciences, Faculty of Biological Sciences, University of Leeds, Leeds, UK

Lead Author

Alexander J. Casson (MEng

PhD

School of Biomedical Sciences, Faculty of Biological Sciences, University of Leeds, Leeds, UK

Lead Author

Christopher Brown

PhD

Department of Psychology, Institute of Population Health, University of Liverpool, Liverpool, UK

Lead Author

Manoj Sivan

MD FRCP Edin

Leeds Institute of Rheumatology and Musculoskeletal Medicine, School of Medicine, University of Leeds, Leeds, UK  

Lead Author

Topics

  • Pain Imaging