Resting-state electroencephalography and magnetoencephalography as biomarkers of chronic pain: a systematic review

Abstract

Reliable and objective biomarkers promise to improve the assessment and treatment of chronic pain. Resting-state electroencephalography (EEG) is broadly available, easy to use, and cost efficient and, therefore, appealing as a potential biomarker of chronic pain. However, results of EEG studies are heterogeneous. Therefore, we conducted a systematic review (PROSPERO CRD42021272622) of quantitative resting-state EEG and magnetoencephalography (MEG) studies in adult patients with different types of chronic pain. We excluded populations with severe psychiatric or neurologic comorbidity. Risk of bias was assessed using a modified Newcastle-Ottawa Scale. Semiquantitative data synthesis was conducted using modified albatross plots. We included 76 studies after searching MEDLINE, Web of Science Core Collection, Cochrane Central Register of Controlled Trials, and EMBASE. For cross-sectional studies that can serve to develop diagnostic biomarkers, we found higher theta and beta power in patients with chronic pain than in healthy participants. For longitudinal studies, which can yield monitoring and/or predictive biomarkers, we found no clear associations of pain relief with M/EEG measures. Similarly, descriptive studies that can yield diagnostic or monitoring biomarkers showed no clear correlations of pain intensity with M/EEG measures. Risk of bias was high in many studies and domains. Together, this systematic review synthesizes evidence on how resting-state M/EEG might serve as a diagnostic biomarker of chronic pain. Beyond, this review might help to guide future M/EEG studies on the development of pain biomarkers.

Figure 1.

PRISMA flow diagram of study selection. CP, chronic pain; EEG, electroencephalography; PRISMA, preferred reporting items for systematic reviews and meta-analyses; RS, resting state; qM/EEG, quantitative M/EEG.

Figure 2.

Pain type, recording modality, and study designs of included studies. The charts show absolute numbers of studies. EEG, electroencephalography; MEG, magnetoencephalography.

Figure 3.

Risk of bias of included studies. M/EEG, magneto-/electroencephalography.

Figure 4.

Open science practices of included studies. The charts show absolute numbers of studies.

Figure 5.

Explanatory albatross plot based on fictional data. P values on the x-axis are displayed on a logarithmic scale (log10). n.s., not significant.

Figure 6.

Peak alpha frequency differences between patients and healthy participants in cross-sectional studies. P values on the x-axis are displayed on a logarithmic scale (log10). n.s., not significant.

Figure 7.

Power and connectivity differences between patients and healthy participants in cross-sectional studies. P values on the x-axis are displayed on a logarithmic scale (log10). n.s., not significant.

Figure 8.

Power changes with pain relief in longitudinal studies. On the right (“[higher]”) part of each albatross plot, studies with postinterventional power increases are displayed. On the left ([“lower”]) part of each plot, studies with postinterventional power decreases are displayed. Only studies that found a postinterventional pain relief were included. P values on the x-axis are displayed on a logarithmic scale (log10). n.s., not significant.

Figure 9.

Correlations of power and connectivity with pain intensity in descriptive studies. On the right (“[pos.]”) part of each albatross plot, studies with positive correlations of pain intensity and power/connectivity are displayed. On the left ([“neg.”]), part of each plot, studies with negative correlations of pain intensity and power/connectivity are displayed. P values on the x-axis are displayed on a logarithmic scale (log10). n.s., not significant.