Scientists track brain activity associated with chronic pain

Following the implantation, the volunteers were asked to fill out details about the intensity of their pain. The study participants did this activity for several months, noting pain scores based on pain severity about two-three times a day. Each time they did this, they were then required to record their brain activity for 30 seconds using the remote that controlled the implanted device.

After the collection of data from study participants, the team used machine learning algorithms to predict the reported pain levels linked to the part of the brain. The analysis revealed that the OFC brain signals were more strongly associated with episodes of chronic pain than the ACC signals. Moreover, they were also able to determine each patient's unique neural activity signature associated with their pain level. 

“This is a big milestone because it’s the first time that neural activity related to chronic pain has been measured in the real world over a clinically relevant time period. And while the biomarkers we found were specific to each individual, their location in the OFC appeared to be common across subjects,” said Prasad Shirvalkar, who led this study, in an official release. 

The new study paves the way for patients to be tracked for severe pain, and OFC could be a reliable biomarker for this medical procedure.

The results have been published in the journal Nature Neuroscience. 

Study 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.