"Pain Differs for Everyone"... Quantifying Chronic Pain Intensity with Brain Imaging [Reading Science]

A new method has been established to quantify the intensity of chronic pain using individual patients' brain imaging. Unlike previous approaches that sought common pain signals, this is the first case of predicting pain intensity by analyzing each patient’s unique ‘brain fingerprint’ with artificial intelligence (AI). This development is expected to mark a turning point in establishing personalized precision diagnostics and treatment strategies.

A joint research team led by Woongwan Woo, Associate Director of the Center for Neuroscience Imaging at the Institute for Basic Science (IBS) and Associate Professor at Sungkyunkwan University’s Department of Global Biomedical Engineering, along with Professor Seonggeun Cho of Chungnam National University, announced that they have succeeded in precisely predicting the pain intensity experienced by individual chronic pain patients solely through analysis of their unique brain function patterns using brain imaging.

Importance of brain regions as markers of chronic pain based on brain imaging. The color of each region reflects the importance value, representing how much the prediction accuracy decreases when that region is excluded. The top 5 regions with the highest importance are separately indicated, and these core regions varied among participants. Provided by the research team

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Chronic pain is a common condition affecting one in five adults, but unlike blood pressure or body temperature, there has been no objective indicator for measurement, resulting in limitations for diagnosis and treatment. In particular, because chronic pain can occur without external stimuli, it often appears ‘normal’ on tests, leading to a vicious cycle of relying on symptom relief rather than fundamental treatment.

The research team performed repeated functional magnetic resonance imaging (fMRI) scans over several months on patients with fibromyalgia, a disorder characterized by widespread, persistent pain throughout the body. fMRI is a device that measures activated areas of the brain based on changes in blood flow. The researchers analyzed this data using AI machine learning to derive each patient’s ‘functional connectome’ (a brain connectivity map).

As a result, the newly developed biomarker predicted changes in pain intensity experienced over several months with high accuracy using only brain imaging data. Notably, the pain pattern found in one patient could not be applied to another, scientifically demonstrating that pain responses are as unique as fingerprints for each individual.

The research team adopted a strategy of ‘collecting sufficient repeated data from a single person’ rather than using group averages. When the number of imaging sessions exceeded four to five, prediction accuracy improved significantly, demonstrating the effectiveness of a personalized approach.

Woongwan Woo, Associate Director at IBS, stated, “This research demonstrated that invisible chronic pain can be relatively objectified and quantified through brain imaging,” and added, “We have established a foundation for precision medicine that will lead to the development of personalized treatments.” First author Dr. Jaejoong Lee, postdoctoral researcher, emphasized, “The key point is that the brain connectivity related to pain appears uniquely in each patient,” and said, “We will develop this into a brain-based precision diagnostic tool usable in clinical practice.”

The results of this research were published online in the international journal Nature Neuroscience on February 26.

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