Background & Aims
Resting state electroencephalography (rsEEG) is an upcoming tool for biomarker development in chronic pain, as it is pathophysiologically meaningful, easy to use and scalable to clinical populations. Recently, evidence was found for increased oscillatory brain activity at theta (3-8 Hz) and beta (13-30 Hz) frequencies in chronic pain patients compared to healthy participants (1). Besides oscillatory brain activity, peak alpha frequency and connectivity/network measures are other biomarker candidates. However, the specificity of those findings remains unclear, and for frequent comorbidities of chronic pain like depression and fatigue, similar changes have been described (2,3). In this preregistered study (ClinicalTrials.gov Identifier: NCT05261243), we aimed to disentangle the effects of neuropsychiatric symptoms on canonical rsEEG biomarker candidates of chronic pain.
Methods
RsEEG was recorded using a 32-channel dry-EEG system in a sample of chronic pain patients with diverse pain etiologies. EEG data was preprocessed and analyzed with DISCOVER-EEG (4) and MATLAB. Depression and fatigue were assessed using PROMIS-questionnaires (5). For analyses, we used Bayesian multivariate models to evaluate the effects of depressive symptoms and fatigue on alpha peak frequency and oscillatory power in different frequency bands (theta, alpha, beta, gamma). For all models, age and pain intensity were considered as covariates.
Results
Findings in n=88 patients (mean numeric rating scale pain intensity 0-10: 5.3, 58% female gender) showed weak evidence for effects of depression (BF10 = 2.52) and fatigue (BF10 = 1.46) scores on gamma power. For theta power, alpha power, beta power, and peak alpha frequency, we found moderate evidence against effects of depression and fatigue (all BF10 >0.1<0.33). Furthermore, we found strong evidence (BF10 = 11.01) for a negative association between age and peak alpha frequency. Analyses will be extended to a larger sample of n=130 patients (ongoing study, data collection to be completed in May 2024). Also, effects on connectivity-based network measures will be evaluated.
Conclusions
In this study, we found evidence for effects of depressive and fatigue symptomatology on gamma power in chronic pain patients. Neuropsychiatric comorbidities and subclinical neuropsychiatric symptoms need to be considered when conducting and interpreting rsEEG studies in chronic pain populations.
References
1.Zebhauser, P. T., Hohn, V. D. & Ploner, M. Resting-state electroencephalography and magnetoencephalography as biomarkers of chronic pain: a systematic review. Pain 164, 1200–1221 (2023).
2.Heitmann, H., Zebhauser, P. T., Hohn, V. D., Henningsen, P. & Ploner, M. Resting-state EEG and MEG biomarkers of pathological fatigue – A transdiagnostic systematic review. Neuroimage Clin 39, 103500 (2023).
3.Newson, J. J. & Thiagarajan, T. C. EEG Frequency Bands in Psychiatric Disorders: A Review of Resting State Studies. Frontiers in Human Neuroscience 12, (2019).
4.Gil Ávila, C. et al. DISCOVER-EEG: an open, fully automated EEG pipeline for biomarker discovery in clinical neuroscience. Sci Data 10, 613 (2023).
5.Cella, D. et al. The Patient-Reported Outcomes Measurement Information System (PROMIS): Progress of an NIH Roadmap Cooperative Group During its First Two Years. Medical Care 45, S3 (2007).
Presenting Author
Paul Theo Zebhauser
Poster Authors
Paul Theo Zebhauser
Dr. med.
University Hospital of the Klinikum rechts der Isar, Technical University Munich
Lead Author
Henrik Heitmann
Technische Universität München
Lead Author
Cristina Gil-Avila
Dr.
Department of Neurology, TUM School of Medicine and Health, Technical University of Munich, Germany
Lead Author
Felix Bott
Dr.
Department of Neurology, TUM School of Medicine and Health, Technical University of Munich, Germany
Lead Author
Enayatullah Baki
Dr.
Department of Neurology, TUM School of Medicine and Health, Technical University of Munich, Germany
Lead Author
Laura Bok
Dr.
Department of Neurology, TUM School of Medicine and Health, Technical University of Munich, Germany
Lead Author
Elisabeth May
Dr.
Department of Neurology, TUM School of Medicine and Health, Technical University of Munich, Germany
Lead Author
Markus Ploner
Technische Universitaet Munchen
Lead Author
Topics
- Pain Imaging