First-in-human prediction of chronic pain state using intracranial neural biomarkers

Using the chronic brain recordings in the anterior cingulate cortex and orbitofrontal cortex in four participants with intractable chronic pain, this study identified objective neural biomarkers of subjective pain experience. Chronic pain syndromes are often refractory to treatment and cause substantial suffering and disability. Pain severity is often measured through subjective report, while objective biomarkers that may guide diagnosis and treatment are lacking. Also, which brain activity underlies chronic pain on clinically relevant timescales, or how this relates to acute pain, remains unclear. Here four individuals with refractory neuropathic pain were implanted with chronic intracranial electrodes in the anterior cingulate cortex and orbitofrontal cortex (OFC). Participants reported pain metrics coincident with ambulatory, direct neural recordings obtained multiple times daily over months. We successfully predicted intraindividual chronic pain severity scores from neural activity with high sensitivity using machine learning methods. Chronic pain decoding relied on sustained power changes from the OFC, which tended to differ from transient patterns of activity associated with acute, evoked pain states during a task. Thus, intracranial OFC signals can be used to predict spontaneous, chronic pain state in patients.

Automated summary

Using chronic brain recordings, this study identified objective neural biomarkers of subjective pain experience in individuals with intractable chronic pain. Chronic pain is often difficult to treat and causes significant suffering. Objective biomarkers for pain severity are lacking, and the neural activity underlying chronic pain on relevant timescales is unclear. The study successfully predicted chronic pain severity scores using machine learning methods based on sustained power changes in the orbitofrontal cortex.