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  <title><![CDATA[PhD Proposal by Mark Conolly]]></title>
  <body><![CDATA[<p><strong>PhD Proposal Presentation</strong></p>

<p>&nbsp;</p>

<p><strong>Date:</strong>&nbsp;January 11, 2019</p>

<p><strong>Time:</strong> 9:00&nbsp;-&nbsp;11:00&nbsp;AM</p>

<p><strong>Location:&nbsp;</strong></p>

<p>Emory University Main Campus</p>

<p>Woodruff Memorial Research Building</p>

<p>Room 5101</p>

<p>&nbsp;</p>

<p><strong>Committee Members:</strong></p>

<p>&nbsp;Robert E. Gross, Neurosurgery, Biomedical Engineering, Georgia Tech/Emory (Advisor)</p>

<p>Annaelle Devergnas, Neurology, Emory University</p>

<p>Svjetlana Miocinovic, Neurology, Biomedical Engineering, Georgia Tech/Emory</p>

<p>Babak Mahmoudi, Biomedical Informatics, Biomedical Engineering, Georgia Tech/Emory&nbsp;</p>

<p>Chris Rozell, Biomedical Engineering, Georgia Tech/Emory</p>

<p>&nbsp;</p>

<p><strong>Title: </strong>A framework for optimizing neural modulation based on electrophysiological biomarkers&nbsp;</p>

<p>&nbsp;</p>

<p><strong>Abstract:</strong>&nbsp;Neural modulation is a fundamental tool for understanding and treating neurological and psychiatric diseases including Parkinson&rsquo;s and epilepsy. Like most neurological tools, accurately measuring the effect of an intervention is time-consuming and imprecise. However, neural modulation poses an additional problem where stimulation parameters have many degrees of freedom including amplitude, location/contact, frequency, etc. Black-box optimization techniques have the potential to automate the process of tuning stimulation parameters, efficiently search higher dimensional parameter spaces, and identify more effective parameter combinations. The objective of this proposal is to characterize the behavior different optimization algorithms for tuning stimulation parameters, and demonstrate closed-loop optimization <em>in vivo</em>. First, this project&nbsp;establishes a framework for prototyping and designing optimization algorithms&nbsp;<em>in silico</em>&nbsp;based on previously collected data. The platform is developed in the context of modulating of hippocampal gamma (33-50Hz) power through optogenetic stimulation of the medial septum. Next,&nbsp;the optimization system is implemented&nbsp;<em>in vivo</em>&nbsp;in real-time to directly learn the medial septum optogenetic stimulation parameters that maximize hippocampal gamma power in real-time<em>.</em>&nbsp;Finally, the&nbsp;closed-loop optimization framework&nbsp;is extended to&nbsp;characterize functional differences in the septohippocampal circuit in normal and epileptic rats, and for optimizing DBS therapy for patients with Parkinson&rsquo;s disease.</p>

<p>&nbsp;</p>
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