Many pain syndromes are notoriously refractory to almost all treatment and pose significant costs to patients and society. Deep brain stimulation (DBS) for refractory pain disorders showed early promise but demonstration of long-term efficacy is lacking. Current DBS devices provide ?open-loop? continuous stimulation and thus are prone to loss of effect owing to nervous system adaptation and a failure to accommodate natural fluctuations in chronic pain states. DBS could be significantly improved if neural biomarkers for relevant disease states could be used as feedback signals in ?closed-loop? DBS algorithms that would selectively provide stimulation when it is needed. This approach may help avert the development of tolerance over time and enable the dynamic features of chronic pain to be targeted in a personalized fashion. Optimizing the brain targets for both biomarker detection and stimulation delivery may also markedly impact efficacy. Recent imaging studies in humans point to the key role of frontal cortical regions in supporting the affective and cognitive dimensions of pain, which may be more effective DBS targets than previous targets involved in basic somatosensory processing. Pathological activity in the anterior cingulate (ACC) and orbitofrontal cortex (OFC) is correlated with the higher-order processing of pain, and recent clinical trials have identified ACC as a promising stimulation target for the neuromodulation of pain. In this study we will target ACC and OFC for biomarker discovery and closed-loop stimulation. We will develop data-driven stimulation control algorithms to treat chronic pain using a novel neural interface device (Medtronic Activa PC+S) that allows longitudinal intracranial signal recording in an ambulatory setting. By building and validating this technological capacity in an implanted device, we will empower DBS for chronic pain indications and advance personalized, precision methods for DBS more generally. We will enroll ten patients with post-stroke pain, phantom limb syndrome and spinal cord injury pain in our three-phase clinical trial. We will first identify biomarkers of low and high pain states to define optimal neural signals for pain prediction in individuals (Aim 1). We will then use these pain biomarkers to develop closed-loop algorithms for DBS and test the feasibility and efficacy of performing closed-loop DBS for chronic pain in a single-blinded, sham controlled clinical trial (Aim 2). Our main outcome measures will be a combination of pain, mood and functional scores together with quantitative sensory testing. In the last phase, we will assess the efficacy of closed-loop DBS algorithms against traditional open-loop DBS (Aim 3) and assess mechanisms of DBS tolerance in response to chronic stimulation. Successful completion of this study would result in the first algorithms to predict real-time fluctuations in chronic pain states for the delivery of analgesic stimulation and would prove the feasibility of closed-loop DBS for pain-relief by advancing implantable device technology.

Public Health Relevance

Deep brain stimulation (DBS) holds promise as a new option for patients suffering from treatment-resistant chronic pain, but we currently lack the technology to reliably achieve long-term pain symptom relief. A ?one-size- fits-all? approach of continuous, 24/7 brain stimulation has helped patients with some movement disorders, but the key to reducing pain may be the activation of stimulation only when needed, as this may help keep the brain from adapting to stimulation effects. By expanding the technological capabilities of an investigative brain stimulation device, we will enable the delivery of stimulation only when pain signals in the brain are high, and then test whether this more personalized stimulation leads to reliable symptom relief for chronic pain patients over extended periods of time.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Exploratory/Developmental Cooperative Agreement Phase II (UH3)
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Special Emphasis Panel (ZNS1)
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Ashmont, Kari Rich
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University of California San Francisco
Schools of Medicine
San Francisco
United States
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