UC Irvine

Dystonia is a movement disorder characterized by involuntary muscle contractions leading to abnormal movements and postures. This movement disorder poses significant challenges to affected individuals. Treating children with dystonia is challenging, as the condition is a network disorder impacting the brain's entire signaling system, rather than being localized to a specific brain region. This involves dystonic signals propagating through the neuronal network, extending from the cortex to the muscles. This irregular or imbalanced signal transmission is believed to be the root cause of dystonic symptoms. These symptoms manifest as spasticity, abnormal postures, and involuntary muscle contractions, each contributing to the nature of this disorder. Although many have studied the pathophysiology of dystonia, the pattern of neural activity in dystonia still remains unclear. While dystonic symptoms can be managed or moderated through medication or other methods a definitive cure remains elusive. Therefore, further investigation on the mechanism of dystonia and its treatments are required. This dissertation aims to elucidate the complex mechanisms of dystonia and motor control in pediatric patients, with a primary emphasis on the effects of deep brain stimulation (DBS) on the deep brain regions, using principles of control theory and dynamic models. DBS has been widely used as an effective symptomatic treatment of dystonia. This treatment involves implantation of depth leads inside deep brain regions which allows us to study the neural behavior and transmission within the brain networks affected by dystonia. In addition to the focus on dynamic models of neural signal transmission, this study explores the potential of sensory awareness as a non-invasive therapeutic option to ameliorate motor symptoms associated with dystonia, included as additional chapters to this dissertation. Through comprehensive analysis and experimentation, this dissertation research contributes to the advancement of DBS as a treatment of various neurological disorders and understanding of dystonia's underlying neurophysiological mechanisms. The outcomes presented in this dissertation stem from my efforts in devising strategies to handle neurophysiological data, eliminate noise, and construct computational models informed by concepts of electrical engineering and my deep understanding of human neurophysiology and neuroscience. By employing these methodologies and knowledge, I aspire to make a small but lasting impact on the understanding of the mechanisms of dystonia and DBS. I am convinced that my doctoral investigation has advanced our comprehension of the effects of DBS and abnormal patterns of neural activity associated with dystonia, aiming to achieve optimal clinical outcomes for patients with dystonia and also other related movement and neurological disorders.