Multielectrode arrays may have several advantages compared to the traditional single macroelectrode brain electrical stimulation technique including less tissue damage due to implantation and the ability to deliver several spatio-temporal patterns of stimulation. Prior work on cell cultures has shown that multielectrode arrays are capable of completely stopping seizure-like spontaneous bursting events through a distributed asynchronous multi-site approach. In my studies, I used a similar approach for controlling seizures in a rat model of temporal lobe epilepsy. First, I developed a new method of electroplating in vivo microelectrode arrays for durably improving their impedance. I showed that microelectrode arrays electroplated through the new technique called sonicoplating, required the least amount of voltage in current controlled stimulation studies and also produced the least amplitude and duration of stimulation artifact compared to unplated, DC electroplated or pulse-plated microelectrodes. Second, using c-fos immunohistochemistry, I showed that 16-electrode sonicoplated microelectrode arrays can activate 5.9 times more neurons in the dorsal hippocampus compared to a single macroelectrodes while causing < 77% the tissue damage. Next, through open-loop multisite asynchronous microstimulation, I reduced seizure frequency by ~50% in the rodent model of temporal lobe epilepsy. Preliminary studies aimed at using the same stimulation protocol in closed-loop responsive and predictive seizure control did not stop seizures. Finally, through an internship at Medtronic Neuromodulation, I worked on developing and implementing a rapid algorithm prototyping research tool for closed-loop human deep brain stimulation applications.
Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/50384 |
Date | 13 January 2014 |
Creators | Arcot Desai, Sharanya |
Contributors | Potter, Steve, Gross, Robert |
Publisher | Georgia Institute of Technology |
Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
Language | en_US |
Detected Language | English |
Type | Dissertation |
Format | application/pdf |
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