Anterior insula-based network biomarkers of experimental pain in humans

The 2024 MSC Emerging Science Contest for Early-Career Investigators took place on December 9, 2024. Below is a written summary of one of the presentations from the contest. (A recording of the event is available here).

Category: Pain

Runner up: Yiyuan Han, PhD, UCSF

Title: Anterior insula-based network biomarkers of experimental pain in humans.

Hypothesis, methodology, findings and conclusions
This study explored how the brain processes pain by analyzing activity from 76 brain stereo- electroencephalography (sEEG) contacts in a chronic pain patient during thermal pain stimulation. Using sEEG recordings and machine learning, we found that communication between the anterior insula and the somatosensory cortex was crucial for predicting pain intensity, with 98.43% accuracy. Theta, alpha, and high-gamma brain wave patterns were the most significant. These findings provide new insights into how pain is processed in the brain, offering potential pathways for objective pain assessment and targeted treatments for chronic pain conditions.

Implications for understanding migraine disease and/or its comorbidities, or how the research holds promise as a new avenue of future migraine study.
This research highlights the potential of machine learning for assessing pain, offering a potential tool for diagnosing migraine and monitoring changes in migraine patients. Such models could enable clinicians to detect exceptional pain-related states more rapidly and accurately. Additionally, the methodologies applied to chronic pain patients in general can be adapted to study chronic migraine patients specifically, helping to uncover both the similarities and differences in pain mechanisms across various chronic pain conditions.