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" Developing Non-Linear and Adaptive Neuronal Synchrony and Connectivity Analysis to Personalize Closed-Loop Dbs Therapy for Treating Epilepsy "
Farahmand, Sina
Mogul, David J.
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Latin Dissertation
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English
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| Record Number
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1051847
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TL50964
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| Main Entry
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Farahmand, Sina
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| Title & Author
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Developing Non-Linear and Adaptive Neuronal Synchrony and Connectivity Analysis to Personalize Closed-Loop Dbs Therapy for Treating Epilepsy\ Farahmand, SinaMogul, David J.
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| College
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Illinois Institute of Technology
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| Date
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2019
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| Degree
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Ph.D.
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2019
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| Note
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117 p.
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| Abstract
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Epilepsy disease afflicts more than seventy million people worldwide. In approximately one third of the cases, antiepileptic medications fail to control seizures. Over the last few decades, electrical stimulation of the brain has been evaluated as a potential alternative to treat surgically and medically refractory epilepsy patients. Despite some successes, most of the devices using this protocol operate based on pre-determined stimulation parameters (e.g. frequency and location of stimulation) that have little or no relationship to the individuals’ underlying brain dynamics, which we hypothesize may explain their low clinical efficacy in preventing or terminating seizures. In this study, a non-linear adaptive neuronal synchrony and connectivity analysis was developed in order to extract stimulation parameters from endogenous, multi-site brain dynamics of epilepsy patients. A non-linear analytical methodology was proposed to assess phase-synchrony dynamics in epilepsy patients as seizures evolve. This study revealed a desynchronization around seizure onset. However, the synchrony level started to increase gradually towards seizure end and reached its maximum at seizure termination. This results reveal that hyper-synchronization of the epileptic network may be a critical self-regulatory mechanism by which the brain terminates seizures. In the other phase of this study, a non-linear adaptive phase-connectivity analysis was developed in order to extract frequency and locations of stimulation that match the synchronized network dynamics at seizure termination. Matching these parameters to the endogenous brain dynamics of epilepsy patients as seizure naturally terminates may not only terminate seizures prior to their development, but it may also lead to a personalized deep brain stimulation (DBS) therapy with higher clinical efficacy.
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Bioengineering
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Neurosciences
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| Added Entry
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Mogul, David J.
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Illinois Institute of Technology
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