Using a brain implant capable of recording neural signals for several months, my the research team and me discovered objective biomarkers of chronic pain severity in four patients suffering from chronic pain during their daily life.
Pain is one of the most important and fundamental subjective experiences a person can have. Although there is ample evidence that the perception of pain happens in the brainthere is also a significant lack of knowledge regarding where and how pain signals are processed in the brain.
Although pain is universal, there is no way to objectively measure its intensity.
Most previous studies of the brain signals responsible for pain have relied on laboratory experiments in artificial environments.
Until now, most chronic pain research has used indirect measures of brain activity, such as functional magnetic resonance imaging Or electroencephalography.
Additionally, while doctors widely recognize that chronic pain isn’t just an extension of acute pain — like stubbing your toe — it’s still unclear how the brain circuits behind acute and chronic pain are linked together. to each other.
Our study was part of a larger clinical trial aiming to develop a new brain stimulation therapy to treat severe chronic pain.
My team surgically implanted electrodes into the brains of four patients with post-stroke pain and phantom limb pain to record neural signals in their orbitofrontal cortexan area of the brain associated with planning and expectation, and cingulate cortexa domain associated with emotion.
We asked patients about their level of pain intensity several times a day for up to six months. We then built machine learning models to try to match and predict each patient’s self-reported pain intensity scores with snapshots of their brain activity signals.
These brain signals consisted of electrical waves that could be broken down into different frequencies, similar to a the musical chord can be broken down into individual sounds of different pitches.
From these models, we found that low frequencies in the orbitofrontal cortex corresponded to each of the patient’s subjective pain intensities, providing an objective measure of chronic pain.
The greater the change in low-frequency activity we measured, the more likely the patient is to experience severe pain.
Next, we wanted to compare the relationship between chronic pain and acute pain. We looked at how the brain responded to intense, short-term pain caused by the application of heat to patients’ bodies.
Based on data from two participants, we found that the anterior cingulate cortex was more involved in acute pain treatment than chronic pain.
This experiment provides the first direct evidence that chronic pain involves brain information processing areas distinct from those involved in acute pain.
why is it important
Chronic pain, defined as pain that lasts more than three months, affects up to 1 in 5 people in the United States In 2019, the incidence of chronic pain was more frequent than that of diabeteshigh blood pressure or depression.
Neuropathic pain resulting from damage to the nervous system, such as stroke and phantom limb pain, often does not respond to available treatments and can significantly impair physical and emotional function and quality of life.
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Better understanding how to measure brain activity to track pain could improve the diagnosis of chronic pain conditions and help develop new treatments such as deep brain stimulation.
What is not yet known
Although our study provides proof of concept that signals from specific brain regions can serve as an objective measure of chronic pain, it is more likely that pain signals are distributed over a vast cerebral network.
We still don’t know which other regions of the brain may harbor important pain signals that might more accurately reflect subjective pain. It’s also unclear whether the signals we found would apply to patients with other pain conditions.
And after
We hope to use these newly discovered neural biomarkers to develop personalized brain stimulation as a means of treating chronic pain disorders. This approach involves incorporating signals into custom algorithms that would govern the timing and location of on-demand brain stimulation, similar to how a thermostat operates.
Prasad Shirvalkarassociate professor of anesthesia, University of California, San Francisco
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