First-in-human prediction of chronic pain state using intracranial neural biomarkers.
Prasad ShirvalkarJordan ProskyGregory ChinParima AhmadipourOmid G SaniMaansi DesaiAshlyn SchmitgenHeather DawesMaryam M ShanechiPhilip A StarrEdward F ChangPublished in: Nature neuroscience (2023)
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.
Keyphrases
- chronic pain
- neuropathic pain
- pain management
- liver failure
- functional connectivity
- endothelial cells
- end stage renal disease
- spinal cord
- spinal cord injury
- ejection fraction
- chronic kidney disease
- drug induced
- multiple sclerosis
- physical activity
- depressive symptoms
- intensive care unit
- peritoneal dialysis
- blood brain barrier
- optical coherence tomography
- sleep quality