Research Description
Pain is one of the most fundamental experiences a person can have, but also one of the most complex in terms of brain representation. The Pain Neuromodulation Lab (Shirvalkar Lab) focuses on studying basic brain mechanisms of acute and chronic pain in humans using psychology, behavior, neurophysiology (invasive recordings and stimulation, EEG), computational neuroscience, imaging (DTI, fMRI), signal processing and machine learning. Our short term goal is to understand of how pain circuits live in the brain and how these circuits go awry to produce chronic pain states. The long term goal is to use this knowledge to develop new brain and spine stimulation technology as a viable therapy.
Current Projects
- Developing personalized brain stimulation therapy for chronic pain
Intracranial electrical brain stimulation (aka deep brain stimulation (DBS)) for treating severe chronic pain has been tested since the 1960's, but the optimal brain targets are unknown for most patients. DBS could be significantly improved if we could pinpoint which targets work best for each individual or by using neural biomarkers of pain to selectively control stimulation when it is needed (“closed-loop” DBS). We are running human clinical trials to test the feasibility of personalized targeting of brain regions that support multiple pain dimensions and to develop new technology for “closed-loop” DBS for pain. In collaboration with neurosurgeon colleagues, we perform a temporary 10 day brain mapping period in the hospital, by stimulating and recording across hundreds of temporarily implanted electrical contacts. If patients experience significant relief during this trial, we implant them with permanent wireless DBS devices at personalized targets that are capable of recording brain activity at home. We follow them for up to 2 years to try to optimize DBS algorithms customized for each patient. In the future, we hope to study emerging noninvasive brain stimulation using focused ultrasound for chronic pain. - Brain network mechanisms of acute and chronic pain.
Human chronic pain experience involves integration of somatosensory, emotional, and cognitive dimensions which requires coordinated activity across many brain regions. Leveraging the clinical trials above, our lab uses multiple experimental (acute) pain tasks to understand how nociception and natural pain is encoded by direct neural recordings that are sampled across pain-relevant brain circuits. We use machine learning and graph theory methods to build computational models of brain activity and study how experimental and spontaneously fluctuating pain states (e.g., high vs. low pain) relate to network dynamics. Further, we use targeted stimulation to study causal links between specific network nodes (i.e., brain regions) and behavior. Emerging areas in the lab are using functional MRI and diffusion imaging to identify brain signatures that predict pain. - Brain mechanisms and personalized biomarkers of spinal cord stimulation therapy
Spinal cord stimulation (SCS) is a popular device technology to treat chronic low back and leg pain. However, there is no good way to optimize therapy settings for each patient nor predict which patients will maintain future benefit. We study brain signatures of SCS and chronic pain using electroencephalograhy (EEG) in patients to develop precision-medicine algorithms to guide optimal, patient-tailored device programming and predict which patients may be good candidates for SCS implantation. Using classification, source localization and feature engineering on human EEG signals, we aim to study basic mechanisms of action of SCS and try to improve patient outcomes. - Influence of expectation and emotion on pain representations
The experience of pain is strongly influenced by expectation and emotion. Expectations involve predictions of the future and can either amplify (nocebo) or dampen (placebo) perceived pain intensity. To isolate the effect of expectancy on pain perception, we use a sensory conditioning task where sensory cues are contingently paired with either high pain or low pain stimuli. Similarly, emotions such as joy/happiness can lessen pain while fear/anxiety can amplify pain. To influence mood, we use a task with emotionally charged movie clips based in affective science. By systematically controlling expectation and mood together with direct brain recording, we use computational tools to dissect circuit mechanisms that influence pain. Specifically, we are interested in how bottom-up pain pathways interact with top-down circuits across the brain.
Lab Members
1. Catherine Borror - Senior Clinical Research Coordinator ([email protected])
2. Ryan Bijan Leriche - Clinical Research Coordinator / Data Scientist ([email protected])
3. Chad Sitgraves - Engineer / Clinical Research Coordinator ([email protected])
4. Jeremy Saal - Neuroscience PhD Student ([email protected])
5. Lucy Johnston - Neuroscience PhD Student ([email protected])
6. Yiyuan Han, PhD - Postdoctoral Scholar ([email protected])
7. Ritwik Vatsyayan, PhD - Postdoctoral Scholar (starting Dec 2024)
8. Julian Motzkin, MD, PhD - Assistant Professor in Neurology ([email protected])
9. Tom Wozny, MD - Neurosurgery Resident / Postdoctoral Scholar ([email protected])
Lab Website
Academic community service and committee membership
Co-Director of Neuromodulation Division of PARC (Pain Addiction Resource Consortium at UCSF), Anesthesia Residency Research Scholars Admission Committee (T32), Anesthesia AI in research committee, UCSF/Berkeley Center for Neural Engineering and Prosthetics member, recurring study section member for NINDS NSD-C study section, NIH HEAL Initiative Scientific Programming Committee, NIH HEAL Nonaddictive pain therapeutics subcommittee member, UC wide EPPIC-NET member.