Do EEG and MEG Have Diagnostic Utility in Chronic Pain?

A systematic review published in Pain found no clear relationship between pain intensity and resting electroencephalography (EEG) or magnetoencephalography (MEG) measures among individuals with chronic pain.

Pain is a subjective experience that is difficult to quantify, which makes evaluating the efficacy of therapy to guide treatment decisions challenging. In the setting of chronic pain, developing objective pain markers has been a key research objective. In recent decades, investigators have focused on alterations in brain structure and function in the setting of pain. As such, noninvasive brain scans, such as EEG and MEG, have gained attention as potential objective pain measurement tools.

To evaluate their role as a marker of pain, investigators from the Technical University of Munich in Germany searched publication databases through September 2021 for studies of chronic pain that used EEG and/or MEG. A total of 76 studies were included in this review.

In cross-sectional studies (n=44), theta power was reported to be higher among patients with chronic pain compared with patients without pain in most studies. Conversely, 3 studies reported that theta power was decreased in the setting of chronic pain. Nearly half of studies reported higher beta power, 37% reported no difference, and 17% reported lower beta power in the setting of chronic pain compared with no pain. For gamma power, the majority of studies (66%) reported no relationship with pain, and one-third of studies reported elevated gamma power in the setting of pain. No clear relationship between alpha power and chronic pain was reported. The findings about frequency-specific power were unclear; however, more studies reported higher connectivity.

For descriptive studies (n=25), most reported no correlation between pain intensity and theta bands (91%), beta bands (75%), gamma bands (60%), and alpha bands (50%). Similarly, studies that evaluated connectivity tended to report nonsignificant trends.

In longitudinal studies (n=22), no trends were observed for any frequency band.

Review authors noted that it has been increasingly observed that brain activity varies over short time intervals, and this variation conveys physiologically relevant information. For EEG, resting state activity generally comprises microstates that remain stable for tens of milliseconds before abrupt transition. These microstates are similar across participants. Focusing on these kinds of data, individual studies reported trends that differed in the setting of pain. Additional study about microstates using machine learning approaches is needed.

Review authors concluded, “The development of biomarkers has been recognized as a crucial step towards optimizing the treatment of chronic pain. […] In cross-sectional studies, we found evidence for increased theta and beta power as a diagnostic biomarker in chronic pain patients. With respect to monitoring or predictive M/EEG biomarkers, evidence from longitudinal and descriptive studies is sparse and inconsistent so far. To proceed and better exploit the potential of M/EEG as biomarkers of pain, large-scale studies which differentiate biomarker types, assess biomarker specificity, investigate brain network connectivity, adhere to highest standards of transparency/reproducibility and explore the potential of composite biomarkers are most promising.”