Demystifying Chronic Pain: Researchers Map Brain Activity to Pain
Researchers are looking for ways to understand chronic pain and treat it better. One way is to examine how different chronic pain types manifest in brain activity.
A team of scientists and clinicians, led by Dr. Prasad Shirvalkar, at the University of San Francisco, California, conducted a study on four patients. Three of these individuals felt chronic pain while recovering from a stroke, while the fourth was experiencing phantom limb pain after undergoing an above-knee amputation on their right leg. All four patients have central pain syndrome, a type of chronic pain caused by dysfunction in the central nervous system.
Treating this type of chronic pain is known to be challenging, as an external source or a damaged body part does not cause it. Furthermore, the lack of concrete knowledge regarding how different types of pain—both acute and chronic—are represented in the nervous system further complicates the matter.
Deep brain stimulation
A treatment option is available for those suffering from chronic neuropathic pain, such as central pain syndrome. This treatment is called deep brain stimulation, which can manipulate neural activity to alleviate painful sensations. Deep brain stimulation has also successfully treated other neurological conditions like Parkinson's and severe depression. However, in cases of chronic pain, it can be challenging to determine where to place the electrodes for deep brain stimulation to effectively target the brain activity causing pain.
To better understand how central pain arises from brain function, researchers analyzed the neural activity of the four patients. The data was collected through intracranial electrodes placed in the patients' anterior cingulate cortices and orbitofrontal cortices—two brain regions previously linked to pain sensations.
Every patient received a surgical procedure to implant a neural recording and stimulation platform into their brain. The portable device allowed patients to carry on with their daily routines while recording data that accurately represented their central pain experiences. Patients were also periodically reminded by a phone app to note their pain level and record 30 seconds of brain-wave data from the neural implant.
The researchers then analyzed the neural activity in relation to the questionnaires and pain scores ranging from 0 to 10, which each patient reported.
Machine learning
Shirvalkar and the team used machine learning to establish a connection between the characteristics of neural activity and the pain levels reported by each patient just before each brain recording. These characteristics consistently corresponded to high and low pain states within each person. However, the associations differed significantly from one patient to another in terms of which brain locations had the most power at which frequencies.
Despite the differences they observed, the researchers found some commonalities. Specifically, Shirvalkar and his team observed an increase in low-frequency power in the lower regions of the patients' orbitofrontal cortices. These areas were located on the opposite side of the brain from where the patients experienced their pain.
Shirvalkar stated in an interview with IEEE Spectrum that the fact that all patients experienced pain in only one part of their body presents an opportunity for further research. "We had limited knowledge about brain laterality in relation to pain," he stated.
Kate Nicholson, the founder and executive director of the National Pain Advocacy Center, believes that this research could improve the measurement of pain and enhance treatment options for individuals with central pain disorder and other conditions that result in chronic pain.
Shirvalkar intends to use the results of this study to enhance chronic pain stimulation treatments. He also expressed that this research is only the start, and he hopes to have a stimulator that can modify frequencies and stimulation locations in future studies.
