Background & Aims

Electroencephalogram (EEG) signatures for patients undergoing spinal cord stimulation (SCS) treatment for chronic pain have largely been studied for patients with relatively new implants [1]. Our understanding of cortical changes over the course of treatment for these patients is limited and requires further investigation. Within this study, we aimed to describe the cortical changes patients may sustain after undergoing SCS for an extended period.

Methods

We recorded 60-channel EEGs in 7 patients with chronic pain 2.04±0.53 years (mean±SEM). after successful SCS implantation. We processed the data using the custom MATLAB scripts and EEGLAB toolbox [2], and utilized the Multiple Artifact Rejection Algorithm (MARA)[3] to evaluate components derived from the independent component analysis. We investigated the stimulation OFF (baseline) and stimulation ON (clinically optimized settings) by computing relative subband power in theta (4-8Hz), alpha (8-12Hz), beta (13-30Hz), and gamma (30-70Hz) bands. Additionally, we investigated frequency changes and correlation of spectral features with the patient-reported outcome measures at the day of EEG recoding.

Results

Our cohort consisted of five females and two males, who were diagnosed with chronic neuropathic pain (n=2), failed back surgery syndrome (n=3), or complex pain regional syndrome (n=2). Four patients were implanted with percutaneous leads and three with paddle leads at thoracic level. The average Numeric Rating Scale (NRS) score at the day of EEG session was 2.93±0.68 and all patients confirmed that they were receiving pain relief. When the patients were grouped into tonic (n=2), HF10 (n=3), and burst (n=3) based on their clinically optimized SCS settings, the relative power spectra (ON-to-OFF) demonstrated slower alpha rhythms in tonic and faster alpha rhythms in HF10. Burst stimulation indicated comparable alpha rhythms with stronger beta activity. The statistical comparison between groups indicated that relative theta and alpha power in patients with burst and HF10 were significantly different than the ones with tonic stimulation (?2(2)=107.08, p<0.01; ?2(2)=59.36, p<0.01).

Conclusions

The trends noted in the EEG features demonstrate that there is a potential relationship between SCS treatment and these changes in cortical features. To our knowledge, this is one of only a few studies that has investigated EEG features among long-term SCS patients. These findings support the notion that there might be plastic changes that may influence changes in spectral features over the course of SCS treatment. The results of the present study point toward a need for future studies to include a larger sample size that could elucidate a significant overall trend for changes in EEG features in long-term SCS patients which can help us to design better neuromodulation therapies.

References

[1] Witjes, B., Ottenheym, L., Huygen, F., and de Vos, Cecile. A review of effects of spinal cord stimulation on spectral features in resting-state electroencephalography. Neuromodulation: Technology at the Neural Interface. 2023;26(1): 35-42. doi.org/10.1016/j.neurom.2022.04.036
[2] Delorme A & Makeig S (2004) EEGLAB: an open-source toolbox for analysis of single-trial EEG dynamics, Journal of Neuroscience Methods 134:9-21.
[3] Irene Winkler, Stefan Haufe and Michael Tangermann. Automatic Classification of Artifactual ICA-Components for Artifact Removal in EEG Signals. Behavioral and Brain Functions, 7:30, 2011.

Presenting Author

Ilknur Telkes

Poster Authors

Ilknur Telkes

PhD, MS

Florida Atlantic University

Lead Author

Tomas Swickley

Florida Atlantic University

Lead Author

Stacey Tevelev

Florida Atlantic University

Lead Author

Olga Khazen

Florida Atlantic University

Lead Author

Marisa DiMarzio

Florida Atlantic University

Lead Author

Julie Pilitsis

Florida Atlantic University

Lead Author

Topics

  • Treatment/Management: Interventional Therapies – Neuromodulation