| Title: | Deep phenotyping of patients with a range of chronic pain conditions in the UK Biobank using a machine-learning approach. |
| Lead Institution: | University of Oxford |
| Principal investigator: | Dr Andrew Segerdahl |
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About
Chronic pain is a common and disabling problem which affects up to half of adults in the UK. The current treatments for chronic pain are often not very effective. This is partly because we do not fully understand how the pain is generated. We know that part of the problem is due to how pain signals are processed in the brain. By doing brain scans on patients with chronic pain, we can see how the different areas of the brain react to pain and how they are connected to one another. Studies involving brain scans in patients with chronic pain usually only involve a small number of patients. This means that we cannot be completely sure that what has been found so far is definitely correct. In addition we might have missed some important differences between patients that might exist even if they have been diagnosed with the same painful condition. The UK biobank study aims to image 100,000 volunteers by 2020. Of these participants, a significant percentage are likely to have one of the painful conditions that we would like to study. This means that we will be able to conduct a very powerful study to investigate the how the brain reacts to pain. We will also use a technique called 'machine-learning' to help analyse the data. This approach uses computer programming to find new patterns in the data which we may not otherwise be aware of. In doing so, we may uncover a better understanding of how the brain is working in people with chronic pain and we hope that this will help to shape future research looking at the best ways to help improve the quality of life for these people.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 14955 | The number of central nervous system-driven symptoms predicts subsequent chronic primary pain: evidence from UK Biobank | Eoin Kelleher (+6) | 2025 | British Journal of Anaesthesia |
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