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Scientists have found a way to measure chronic pain by brain signals

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Scientists have found a way to measure chronic pain by brain signals

With the help of brain implants and machine learning, scientists have been able to predict the level of chronic pain in patients and identify areas of the brain responsible for different types of pain.

A team of researchers from the University of California, San Francisco (UCSF) was able to measure chronic pain for the first time on brain signals in four patients with neuropathic pain caused by stroke or amputation. To do this, they used implants in the brain that recorded neural activity over several months, and machine learning algorithms that predicted the degree of pain from these signals.

Pain is one of the most important and basic human subjective experiences. Although there is a lot of evidence that pain perception occurs in the brain, it is still unknown where and how pain signals are processed in the brain. Although pain is a universal phenomenon, there is no way to objectively measure its intensity.

Most of the previous research on pain signals in the brain was based on laboratory experiments in artificial conditions. So far, most research on chronic pain has used indirect methods to measure brain activity, such as functional magnetic resonance imaging or electroencephalography. In addition, while doctors widely acknowledge that chronic pain is not simply an extension of acute pain—such as a bruised finger—it remains unknown how the brain circuits for acute and chronic pain are related to each other.

The study was part of a large clinical trial to develop a new brain stimulation therapy for severe chronic pain. The team surgically implanted electrodes in the brains of four patients with post-stroke pain and phantom limb pain to record neural activity in their orbitofrontal cortex (OFC), a brain area associated with planning and anticipation, and the cingulate cortex (PC), a brain area associated with emotions.

They asked patients about their pain levels several times a day for six months. They then built machine learning models to try to correlate and predict each patient’s self-reported pain intensity with snapshots of their brain activity. These brain signals consisted of electrical waves that could be broken down into different frequencies, much like a musical chord can be broken down into individual sounds of different pitches. From these models, they found that the low-frequency signals in the OFC corresponded to each patient’s subjective pain level, providing an objective measure of chronic pain. The greater the change in low-frequency activity that we measured, the more likely the patient was in intense pain.

Next, they wanted to compare the relationship between chronic and acute pain. They studied how the brain reacted to short-term intense pain caused by the heating of the patient’s body. Based on data from two participants, they found that PC was more involved in the processing of acute pain than chronic pain. This experiment provides the first direct evidence that chronic pain involves areas of the brain that process information differently from those involved in acute pain.

The value of the discoveries

Chronic pain is defined as pain lasting more than three months. In 2019, the incidence of chronic pain was more common than diabetes, high blood pressure, or depression.

Neuropathic pain resulting from damage to the nervous system, such as stroke or phantom limb pain, often does not respond to available treatments and can significantly impair physical and emotional well-being and quality of life. A better understanding of how to measure brain activity to track pain can improve the diagnosis of chronic pain conditions and help develop new therapies such as deep brain stimulation.

What is still unknown

While the study provides evidence that signals from specific brain regions can serve as an objective measure of chronic pain, it is likely that pain signals are distributed across a wide brain network.

Researchers do not yet know which other areas of the brain may contain important pain signals that may more accurately reflect subjective pain. It is also unclear whether the signals found apply to patients with other pain conditions.

What’s next

The authors hope to use these newly discovered neural markers to develop personalized brain stimulation as a treatment for chronic pain disorders. This approach involves incorporating signals into individual algorithms that will determine the time and place of on-demand brain stimulation, similar to how a thermostat works.

This is not the first study to try to measure pain from brain signals. Formerly scientists from Carnegie Mellon University (USA) used data analysis method called algebraic topology to create a map of neural connections in the cerebral cortex. They found that different types of pain have different patterns of activity in these connections. This allowed them to classify pain according to its intensity and nature (physical or emotional) with an accuracy of 97%.

Also interesting is a study from the University of California at Los Angeles (USA), which showed that stimulation of a certain area of ​​the brain – the premotor cortex – can reduce the sensation of pain in patients with chronic back pain. Stimulation was performed using transcranial direct current stimulation (tDCS), which delivers a weak electrical current through the scalp. Scientists suggest that stimulation of the premotor cortex can activate a system of mirror neurons, which are responsible for imitating the actions of other people. This can help patients experience their pain as someone else’s and thereby reduce its impact on their lives.



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