Spontaneous pain dynamics characterized by stochasticity in awake human LFP with chronic pain

Chronic pain involves persistent fluctuations lasting seconds to minutes, yet there are limited studies on spontaneous pain fluctuations utilizing high-temporal-resolution electrophysiological signals in humans. This study addresses the gap, capturing data during awake deep brain stimulation (DBS) surgery in five chronic pain patients. Patients continuously reported pain levels using the visual analog scale (VAS), and local field potentials (LFP) from key pain-processing structures (ventral parietal medial of the thalamus, VPM; subgenual cingulate cortex, SCC; periaqueductal gray, PVG) were recorded. Our novel AMI analysis revealed that regular spike-like events in the theta/alpha band was associated with higher pain; and regular events in the gamma band was associated with opioid effects. We demonstrate a novel methodology that successfully characterizes spontaneous pain dynamics with human electrophysiological signals, holding potential for advancing closed-loop DBS treatments for chronic pain.