Peak Alpha Frequency: Discovery and Validation of a Prolonged Pain Biomarker

Abstract

Chronic pain is a pervasive and debilitating disease that afflicts nearly one in five individuals. Given its frustrating treatment resistance, one avenue for combatting chronic pain is to develop interventions that can be used to prevent disease emergence. One barrier to realize these interventions, however, is that it is difficult to identify individuals at high risk for developing chronic pain or to identify those already in the early stages of disease development. Previous work has indicated that Peak Alpha Frequency (PAF), an EEG-derived measure of the frequency element demonstrating maximal power in the 8-12 Hz range, is abnormally slow in cases of chronic pain. This PAF slowness may reflect processes related to ongoing pain or factors that predispose an individual to being highly sensitive to pain, perhaps the best-known risk factor for developing chronic pain. Using experimental models of prolonged pain in healthy individuals, as well as ongoing EEG, the current work tests these two hypotheses and reveals several key findings. First, in support of the hypothesis that PAF reflects risk factors associated with heightened chronic pain risk, PAF recorded during a pain-free state is negatively correlated to pain experienced during a future noxious with slower PAF associated with greater pain sensitivity. Second, the relationship between pain-free PAF and pain sensitivity is reliable and can be observable across multiple models of prolonged pain and at multiple points of time. Third, in support of the hypothesis that PAF slowing is a consequence of ongoing pain, exposure to prolonged pain produces reliable PAF slowing that occurs through focal power reductions in the “fast” 10-12 Hz portion of the Alpha range. This latter finding represents the first mechanistic evidence through which PAF slowing occurs. In total, these findings strongly position PAF for use in the clinic as a diagnostic tool for identifying individuals at high risk for developing chronic pain and for identifying those individuals in the early stages of pain emergence.