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    Investigations of spontaneous pain and modulation with spinal cord stimulation

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    IDLETTALI-DISSERTATION-2020.pdf (16.86Mb)
    Date
    2020-03-27
    Author
    Idlett Ali, Shaquia L.
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    Abstract
    Chronic pain is the leading cause of long-term disability. Heterogeneity in etiology and manifestation of neuropathic pain contribute to difficulties finding broadly effective pain management strategies. In cases where pharmacological treatment has failed to provide relief, epidural spinal cord stimulation (SCS) has emerged as an alternative intervention for intractable pain. This technology has been in clinical use for over 50 years, yet efficacy rates have remained stagnant and etiology-dependent. A barrier to improved efficacy is an absence of knowledge identifying the mechanism by which SCS can selectively inhibit chronic, spontaneous pain. The objective of this dissertation was to generate knowledge that leads to a better understanding of both spontaneous neuropathic pain and SCS pain relief. To do this, I first identified links between spontaneous sensory hyperexcitability and stimulus-independent physio-behavioral indices of pain, using a contusion model of spinal cord injury. Next, I used an ex vivo adult mouse spinal cord preparation to assess axonal recruitment with SCS. A computational model was utilized to inform parameter selection for examining clinically-analogous SCS with our model system. Finally, I tested the gate control theory by examining SCS modulation in an ex vivo model of spontaneous pain. I employed a threshold-based approach to examine SCS modulation of spontaneous nociceptive activity using traditional frequencies of SCS. Results indicate that afferent recruitment alone is not sufficient to replicate aspects of SCS modulation observed clinically. Together, these findings provide greater insight into the identification of spontaneous neuropathic pain and the underlying mechanisms leading to pain relief with SCS.
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    http://hdl.handle.net/1853/62801
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    • Department of Biomedical Engineering Theses and Dissertations [575]
    • Georgia Tech Theses and Dissertations [23877]

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