American scientists have discovered that brain signals can be used to predict a person's level of pain. The research findings are the first direct detection of chronic pain in the human body, or may help develop therapies for patients with chronic pain, such as post stroke pain or phantom limb pain. The relevant research was recently published in the journal Nature Neuroscience.

Long term chronic pain is a major public health issue that can cause significant disabilities and economic burden. The current treatment methods are usually insufficient to manage chronic pain, and frequently prescribed opioid drugs also pose a risk of overdose for patients. The severity of pain in patients is mainly evaluated through self-reported indicators, but pain perception is subjective and varies among individuals, so this evaluation method is not perfect. Search for objective biomarkers of pain or help guide the diagnosis of chronic pain and identify potential therapies.

Prasad Shirvalkar and colleagues at the University of California, San Francisco implanted recording electrodes in the anterior cingulate cortex and orbitofrontal cortex (pain related brain regions) of four patients with chronic pain. Over the next 3 to 6 months, these patients will self report their pain levels, while electrodes will record their brain activity.

They used machine learning technology to successfully predict the severity score of pain through highly sensitive brain activity, and found that participants were able to distinguish between chronic pain (more correlated with the orbitofrontal cortex) and acute, thermal pain imposed by the experimenter (more correlated with the anterior cingulate cortex).The author believes that these results may contribute to the development of systems that can immediately detect brain pain and implement interventions in thefuture. 

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

Journal：Nature Neuroscience

Corresponding Author：Prasad Shirvalkar

Abstract:

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.