June 6, 2023

Brain activity predicts chronic pain

At a Glance

  • In a small study of four people, researchers identified brain activity patterns associated with chronic pain.
  • A better understanding of the brain activity underlying chronic pain could help to improve diagnosis and treatment.
X-ray shows recording devices in both shoulders with electrodes at the top of the brain and above the eye sockets. This X-ray shows two recording devices implanted in the shoulders and recording electrodes placed in the brain. Dr. Prasad Shirvalkar, UCSF

Chronic pain—pain that lasts for more than three months—is a major cause of disability. Some types of chronic pain are difficult to treat and lead to considerable suffering. Pain is often measured by patients’ subjective reports. A lack of objective measures for chronic pain hinders the development of better ways to diagnose and treat it.

Previous work to develop objective pain measures has looked at brain activity associated with pain. But most of this work was done on acute pain in healthy people. It has not been clear if the findings apply to people with chronic pain. Moreover, studies in people with chronic pain have only looked at short time frames that may not be clinically relevant.

To investigate, an NIH-funded team of researchers led by Dr. Prasad Shirvalkar at the University of California, San Francisco studied four people with chronic neuropathic pain (caused by nerve damage or disease). Three of the participants had chronic pain that developed after a stroke. The fourth had phantom limb pain—pain that seems to come from an amputated limb.

The participants had electrodes surgically implanted in two brain regions— the anterior cingulate cortex (ACC) and the orbitofrontal cortex (OFC)—to study an approach called deep brain stimulation. These regions have been implicated in different aspects of the pain response. The electrodes recorded activity in these brain regions over several months. During this time, the participants rated the intensity of their pain multiple times a day. Results from the study appeared in Nature Neuroscience on May 22, 2023.

The researchers used machine learning to develop models for predicting pain severity from activity in the ACC and OFC. The models were able to distinguish between high- and low-intensity pain states. But they could only rarely predict the participants’ exact pain ratings. OFC activity was more consistent across patients for predicting chronic pain than ACC activity.

The researchers also looked at activity associated with acute pain. They induced acute pain by applying heat to different parts of the body. Acute pain prediction depended more on the ACC, but the models could only predict acute pain in two of the four participants. Activity changes predicting chronic pain tended to be sustained, lasting on the order of seconds. Those for acute pain occurred in shorter but more frequent bursts.

The results suggest that OFC signals may be an objective marker of chronic pain. These signals might be used to diagnose chronic pain and measure the effectiveness of new treatments. The findings also highlight the different brain mechanisms underlying chronic versus acute pain. This may help guide the development of interventions like deep brain stimulation for relieving pain.

“When you think about it, pain is one of the most fundamental experiences an organism can have,” Shirvalkar says. “Despite this, there is still so much we don’t understand about how pain works. By developing better tools to study and potentially affect pain responses in the brain, we hope to provide options to people living with chronic pain conditions.”

The researchers note that larger studies will be needed to determine how general the findings are. Also, brain regions outside the ACC and OFC are likely to be important for the pain response as well. Further research will be needed to identify these.

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References: First-in-human prediction of chronic pain state using intracranial neural biomarkers. Shirvalkar P, Prosky J, Chin G, Ahmadipour P, Sani OG, Desai M, Schmitgen A, Dawes H, Shanechi MM, Starr PA, Chang EF. Nat Neurosci. 2023 May 22. doi: 10.1038/s41593-023-01338-z. Online ahead of print. PMID: 37217725.

Funding: NIH’s Brain Research Through Advancing Innovative Neurotechnologies (BRAIN) Initiative, Helping to End Addiction Long-term Initiative (HEAL) Initiative, and National Institute of Neurological Disorders and Stroke (NINDS); Defense Advanced Research Projects Agency.