Spontaneous pain dynamics characterized by stochasticity in neural recordings of awake humans with chronic pain

Abstract Chronic pain is characterized by spontaneous fluctuations in pain intensity, a phenomenon that remains poorly understood. The aim of this study is to elucidate the neural mechanisms underlying pain fluctuations in patients with chronic pain undergoing deep brain stimulation surgery. We recorded local field potentials (LFPs) from pain-processing hub structures, including the ventral posteromedial nucleus of the thalamus, subgenual cingulate cortex, and periventricular and periaqueductal gray, while patients continuously reported their pain levels. Using novel auto-mutual information metrics to analyze LFP stochastic patterns, we found that pain intensity correlated with both increased regularity of spike-like events and greater past-dependency of neural oscillations in the 4- to 15-Hz frequency band. In addition, during periods of higher pain states, we observed enhanced functional connectivity between the examined hub structures and the prefrontal cortex, suggesting a more focused flow of pain-related information within the pain circuit. By characterizing the dynamic nature of pain fluctuations, this study bridges the gap in understanding moment-to-moment pain variations and their underlying neural mechanisms, paving the way for improved chronic pain management strategies.