Faculty Affairs and Academic Programs
Sandya Subramanian
Medical; Health Sciences; Nursing
Computational Precision Health
UCSF
Mapping Individual Central–Autonomic Signatures of Chronic Pain using a Novel Network AI Framework
Chronic pain affects over 20% of adults and costs over $600 billion annually in the US alone [1]. It is a highly heterogeneous disease; each patient’s response to any treatment is impossible to anticipate. From a basic science perspective, it is known that chronic pain evolves from acute pain, but this process, termed pain chronification, is still poorly understood. While pain chronification has traditionally been considered a process that occurs in the brain, I hypothesize that dysfunction in the feedback loops from the body back to the brain is a primary contributor to chronic pain [2]. Unfortunately, previous studies of chronic pain divide into brain- or body-focused, usually through the lens of only one organ system [3], which has severely limited our understanding of pain chronification as a joint brain-body process. In this project, I will tackle two major challenges limiting our ability to study and characterize chronic pain through the novel lens of joint brain-body networks: (1) we are limited in our ability to monitor continuous physiologic data that captures everyday triggers, activity from multiple organs, and lifestyle factors affecting an individual’s pain chronification, and (2) we are unable to map whole-body CNS-ANS network behavior of chronic pain for each patient. In this proposal, I will describe a bold and innovative research project spanning hardware development, computational modeling and AI, and human studies to produce the first ever joint mapping of brain-body interactions on an individual level in chronic pain. This work will establish my lab’s track record in hardware development and lead to our first major high-impact journal publication.
