Cerebral peak alpha frequency predicts individual differences in pain sensitivity

&NA; The identification of neurobiological markers that predict individual predisposition to pain are not only important for development of effective pain treatments, but would also yield a more complete understanding of how pain is implemented in the brain. In the current study using electroencephalography (EEG), we investigated the relationship between the peak frequency of alpha activity over sensorimotor cortex and pain intensity during capsaicin‐heat pain (C‐HP), a prolonged pain model known to induce spinal central sensitization in primates. We found that peak alpha frequency (PAF) recorded during a pain‐free period preceding the induction of prolonged pain correlated with subsequent pain intensity reports: slower peak frequency at pain‐free state was associated with higher pain during the prolonged pain condition. Moreover, the degree to which PAF decreased between pain‐free and prolonged pain states was correlated with pain intensity. These two metrics were statistically uncorrelated and in combination were able to account for 50% of the variability in pain intensity. Altogether, our findings suggest that pain‐free state PAF over relevant sensory systems could serve as a marker of individual predisposition to prolonged pain. Moreover, slowing of PAF in response to prolonged pain could represent an objective marker for subjective pain intensity. Our findings potentially lead the way for investigations in clinical populations in which alpha oscillations and the brain areas contributing to their generation are used in identifying and formulating treatment strategies for patients more likely to develop chronic pain. Graphical abstract Figure. No caption available. HighlightsRelationship between EEG peak alpha frequency and prolonged pain is examined.PAF during pain‐free state correlated with prolonged pain intensity 40 min later.PAF change from pain‐free to prolonged pain correlated with reported pain intensity.PAF and PAF changes could represent distinct mechanisms predicting pain sensitivity.

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