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Researchers: A/Prof Sylvia Gustin, Dr Negin Hesam-Shariati, Dr Wei-Ju Chang, A/Prof James McAuley, Dr Andrew Booth, A/Prof Toby Newton-John, Prof Chin-Teng Lin, A/Prof Zina Trost
Chronic pain is a global health problem, affecting around one in five individuals in the general population. The understanding of the key role of functional brain alterations in the generation of chronic pain has led researchers to focus on pain treatments that target brain activity. Electroencephalographic (EEG) neurofeedback attempts to modulate the power of maladaptive EEG frequency powers to decrease chronic pain. Although several studies provide promising evidence, the effect of EEG neurofeedback on chronic pain is uncertain. This systematic review aims to synthesise the evidence from randomised controlled trials (RCTs) to evaluate the analgesic effect of EEG neurofeedback.
The search strategy will be performed on five electronic databases (Cochrane Central, MEDLINE, Embase, PsycInfo, and CINAHL) for published studies and on clinical trial registries for completed unpublished studies. We will include studies that used EEG neurofeedback as an intervention for people with chronic pain. Risk of bias tools will be used to assess methodological quality of the included studies. RCTs will be included if they have compared EEG neurofeedback with any other intervention or placebo control. The data from RCTs will be aggregated to perform a meta-analysis for quantitative synthesis. In addition, non-randomised studies will be included for a narrative synthesis. The data from non-randomised studies will be extracted and summarised in a descriptive table. The primary outcome measure is pain intensity assessed by self-report scales. Secondary outcome measures include depressive symptoms, anxiety symptoms, and sleep quality measured by self-reported questionnaires. Further, we will investigate the non-randomised studies for additional outcomes addressing safety, feasibility, and resting-state EEG analysis.
Pain is the single most common reason for seeking medical attention. Under normal circumstances, pain acts to signal injury and is a protective response that prevents further damage and promotes tissue healing. People differ not only in their ability to detect and tolerate pain, but also in their ability to recover from an injury, with some people experiencing pain that outlasts the duration of tissue healing. Interventions to treat or cure chronic pain have had limited success.
Recent research has identified a novel cortical biomarker that could identify individuals at risk of developing chronic pain, which could be used to identify individuals at high risk of transitioning from acute to chronic pain (PREDICT project). However, whether a causal relationship exists between this cortical biomarker and pain is unknown.
The pain biomarker is based on rhythmic patterns of electrical activity in the brain and is measured using electroencephalography (EEG). Previous research suggests that the speed of this rhythmic activity can be altered through the administration of nicotine. MODULATE will attempt to alter the speed of the brain’s rhythmic activity, using nicotine gum, and observe the impact on pain. The project will help determine whether a causal relationship exists between the biomarker and pain.
Temporomandibular disorder (TMD) is the second most common musculoskeletal pain condition and is associated with pain and tenderness of the jaw. Although a number of biological factors have shown an association with chronic TMD in cross-sectional and case control studies, there are currently no biomarkers that can predict the development of chronic symptoms. Because of the difficulty in treating chronic pain, development of brain signal predictive biomarkers is of growing interest.
The PREDICT project will aim to develop a predictive biomarker signature of pain severity and duration using two commonly available techniques – electroencephalogram (EEG) and transcranial magnetic stimulation (TMS) – and perform initial clinical validation in first onset TMD. The biomarker could have utility in identifying patients at high risk of transitioning from acute to chronic pain and has additional potential for clinical application in the treatment and prevention of chronic pain.
This project will be carried out in collaboration with a team at the University of Maryland, Baltimore lead by A/Prof David Seminowicz (see more information here).
Seminowicz DA, Bilska K, Chowdhury NS, Skippen P, Millard SK, Chiang A, Chen S, Furman AJ, & Schabrun SM. (2020). A novel cortical biomarker signature for predicting pain sensitivity: protocol for the PREDICT longitudinal analytical validation study. Pain Reports, 5(4), e833. doi: 10.1097/PR9.0000000000000833