Network Dynamics of Chronic Pain
Uncovering directed brain network signatures of chronic pain from human EEG and ECoG recordings
Chronic pain is a debilitating condition affecting over 20% of adults globally, yet its neural basis remains poorly understood. A key open question is how chronic pain reorganizes the brain’s functional circuitry — and whether these changes differ across its qualitative (affective) and quantitative (intensity) dimensions.
The data. We leverage a unique dataset from the UCSF Chronic Pain Study (in collaboration with Dr. Prasad Shirvalkar, UCSF), comprising simultaneous EEG and ECoG recordings from human subjects with chronic pain, enabling multi-scale analysis of neural dynamics at both scalp and cortical levels.
Our approach. Using nonparametric causal inference methods (CITS), we characterize:
- Multi-scale directed network dynamics underlying chronic pain states
- How causal network structure differs across the qualitative (affective) and quantitative (intensity) dimensions of pain
- Dynamic changes in network connectivity during pain fluctuations
Significance. Identifying reliable neural biomarkers of chronic pain could inform the development of closed-loop neurostimulation therapies and personalized pain management strategies. This work is conducted in close collaboration with clinicians at UCSF’s Department of Neurology and Pain Center.