Abstract
Pain is a subjective experience that alerts an individual to actual or potential tissue damage. Through mechanisms that are still unclear, normal physiological pain can lose its adaptive value and evolve into pathological chronic neuropathic pain. Chronic pain is a multifaceted experience that can be understood in terms of somatosensory, affective, and cognitive dimensions, each with associated symptoms and neural signals. While there have been many attempts to treat chronic pain, in this article we will argue that feedback-controlled ‘closed-loop’ deep brain stimulation (DBS) offers an urgent and promising route for treatment. Contemporary DBS trials for chronic pain use “open-loop” approaches in which tonic stimulation is delivered with fixed parameters to a single brain region. The impact of key variables such as the target brain region and the stimulation waveform is unclear, and long-term efficacy has mixed results. We hypothesize that chronic pain is due to abnormal synchronization between brain networks encoding the somatosensory, affective and cognitive dimensions of pain, and that multisite, closed-loop DBS provides an intuitive mechanism for disrupting that synchrony. By (1) identifying biomarkers of the subjective pain experience and (2) integrating these signals into a state-space representation of pain, we can create a predictive model of each patient's pain experience. Then, by establishing how stimulation in different brain regions influences individual neural signals, we can design real-time, closed-loop therapies tailored to each patient. While chronic pain is a complex disorder that has eluded modern therapies, rich historical data and state-of-the-art technology can now be used to develop a promising treatment.
Highlights
Chronic pain is a major healthcare problem, and estimates by the CDC suggest that it affects more people in the US than heart disease, diabetes and cancer combined (CDC, 2016)
Each patient’s pain is a multifaceted experience that can be understood in terms of somatosensory, affective, and cognitive dimensions, each correlated with activity in different brain regions (Melzack and Casey, 1968; Melzack, 1999; Figure 1)
We argue that using local field potentials (LFP) signals from three brain regions—S1, dACC, and orbitofrontal cortex (OFC)—could be used to calculate multidimensional, patient-specific pain states
Summary
Chronic pain is a major healthcare problem, and estimates by the CDC suggest that it affects more people in the US than heart disease, diabetes and cancer combined (CDC, 2016). Common pharmacological therapies have marginal analgesic benefit, and so far, modern neuromodulation therapies such as spinal cord or deep brain stimulation have had limited efficacy over time. We hypothesize that enduring analgesia will be best achieved by identifying patients’ unique neurophysiological biomarkers of pain perception across multiple brain regions and providing tailored, feedback-controlled deep brain stimulation across those target regions. We outline prior approaches to DBS for chronic pain, an approach to identifying neural biomarkers of pain, and propose strategies to develop a framework for closed-loop DBS based on control theory and state-space paradigms
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