UCSF Researchers Uncover Brain Biomarkers for Pain

by James Cavuoto, editor

May 2023 issue

One of the greatest challenges confronting pain clinicians and healthcare firms targeting chronic pain has been the lack of an objective measure of pain intensity. Although subjective tests such as the VAS score are available to patients and pain doctors, there could be wide variability in the perceived level of pain among different subjects and at different times. This situation not only makes it difficult for clinicians to prescribe and dose appropriate therapies, it also deprives neuromodulation firms of valuable data that could be used in a closed-loop therapeutic regimen.

There have been previous efforts to devise biomarkers that offer an objective pain score. The Israeli firm Medasense Biometrics Ltd., founded in 2008, developed a pain measuring system that uses a multi-parametric sensor platform and advanced AI algorithms to convert complicated data into a patient’s “Signature of Pain.” At the 2014 meeting of the International Association of Pain in Argentina, investigators from Medasense presented data from a study testing the predictive power of the company’s PMD100 device in assessing pain relief from spinal cord stimulation. The technology is now used largely in operating rooms and high-acuity settings, where patients are under anesthesia and unable to communicate, enables clinicians to personalize pain management, control pain and avoid overmedication.

At the 2019 meeting of the North American Neuromodulation Society, Ali Rezai from West Virginia University discussed his institution’s efforts to develop biomarkers for chronic pain and to identify physiological, cognitive, and psychological parameters that would contribute to improved neuromodulation response. Rezai’s team is developing digital health tools based on tablet and smartphone use that would complement future neuromodulation therapies.

But perhaps the most significant advance in the effort to develop brain biomarkers for pain occurred earlier this month when investigators at UCSF reported their findings. For the first time, researchers have recorded pain-related data from inside the brain of individuals with chronic pain disorders caused by stroke or phantom limb pain. Data were collected over months while patients were at home, and they were analyzed using machine learning tools. Doing so, the researchers identified an area of the brain associated with chronic pain and objective biomarkers of chronic pain in individual patients. These findings, published in Nature Neuroscience, represent a first step towards developing novel methods for tracking and treating chronic pain.

“This is a great example of how tools for measuring brain activity originating from the BRAIN Initiative have been applied to the significant public health problem of relieving persistent, severe chronic pain,” said NINDS director Walter Koroshetz. “We are hopeful that building from these preliminary findings could lead to effective, non-addictive pain treatments.”

“When you think about it, pain is one of the most fundamental experiences an organism can have,” said Prasad Shirvalkar, associate professor of anesthesia and neurological surgery at UCSF and lead author of this study. “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.”

This study looked directly at changes in brain activity in two regions where pain responses are thought to occur—the anterior cingulate cortex and the orbitofrontal cortex—as participants reported their current levels of chronic pain.

“Functional MRI studies show that the ACC and OFC regions of the brain light up during acute pain experiments. We were interested to see whether these regions also played a role in how the brain processes chronic pain,” said Shirvalkar. “We were most interested in questions like how pain changes over time, and what brain signals might correspond to or predict high levels of chronic pain?”

Four participants, three with post-stroke pain and one with phantom limb pain, were surgically implanted with electrodes targeting their ACC and OFC. Several times a day, each participant was asked to answer questions related to how they would rate the pain they were experiencing, including strength, type of pain, and how their level of pain was making them feel emotionally. They would then initiate a brain recording by clicking a remote-control device, which provided a snapshot of the activity in the ACC and OFC at that exact moment. Using machine learning analyses, the research team was able to use activity in the OFC to predict the participants’ chronic pain state.

In a separate study, the researchers looked at how the ACC and OFC responded to acute pain, which was caused by applying heat to areas of the participants’ bodies. In two of the four patients, brain activity could again predict pain responses, but in this case the ACC appeared to be the region most involved. This suggests that the brain processes acute vs. chronic pain differently, though more studies are needed given that data from only two participants were used in this comparison.

This study represents an initial step towards uncovering the patterns of brain activity that underly our perception of pain. Identifying such a pain signature will enable the development of new therapies that can alter brain activity to relieve suffering due to chronic pain. The most immediate benefit may be in informing ongoing studies in HEAL and BRAIN to use DBS to treat chronic pain. Ongoing and future work involving more participants will be key in determining whether different pain conditions share the OFC activity seen in these patients or how the signatures differ among persons with different pain conditions.

More modern approaches to DBS that fine-tune the stimulation based on activity biomarkers from the brain have been used to successfully treat some brain disorders including Parkinson’s disease and major depressive disorder, but those successes have required well-established brain biomarkers. For conditions such as chronic pain, the identification of biomarkers is in the early stages.