Optimized Targeting in Deep Brain Stimulation for Movement Disorders.

Deep Brain Stimulation;Movement Disorders;Biomedical Engineering;Engineering;Biomedical Engineering

Houshmand, LaylaChestek, Cynthia Anne ;

University of Michigan

关键词: Deep Brain Stimulation;    Movement Disorders;    Biomedical Engineering;    Engineering;    Biomedical Engineering;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/111402/hlayla_1.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Deep brain stimulation (DBS) is the dominant surgical therapy for medically-refractory Parkinson’s Disease (PD) and Essential Tremor (ET). Despite its success in treating the physical symptoms of many movement disorders, optimal targeting protocols are unknown. The success of the surgery is highly dependent upon proper placement of the electrode in the brain. However, the anatomical targets for PD and ET DBS—the subthalamic nucleus (STN) and ventral intermediate (Vim) nucleus of the thalamus, respectively—are not distinguishable on conventional magnetic resonance imaging. Neurosurgeons typically locate these structures using imprecise atlas-based indirect targeting methods requiring several attempts, increasing the risk of intracranial hemorrhage. The purpose of this work was to optimize targeting in DBS for PD and ET. First, we evaluated the most common indirect STN targeting methods with our validated 3-Tesla MRI protocol optimized for STN visualization. We calculated indirect targets as prescribed by midcommissural point (MCP) -based and red nucleus-based (RN) methods, and compared those coordinates to the position of the STN. We found that RN-based targeting is statistically superior to MCP-based targeting and should be routinely used in the absence of direct STN visualization.In our next study, we investigated the volume of tissue activated (VTA) in thalamic DBS. First, we developed a k-means clustering algorithm that operates on diffusion tensor imaging data to segment the thalamus into its functionally-distinct nuclei. We segmented individual patient thalami and an atlas thalamus in an existing VTA model, and created an individualized VTA model by utilizing each patient’s own anatomy and tissue conductivity. We measured stimulation overlaps with relevant nuclei for clinically efficacious stimulation settings. Our preliminary results indicated that individualized VTA modeling may provide more precise modeling results than existing atlas-based VTA modeling.Next, we investigated the ability of atlas-based and individualized VTA modeling methods to explain common side effects from thalamic DBS. We found that individualized VTA modeling is superior to atlas-based modeling in the prediction of side effects. The results of this work advance the understanding of proper DBS targeting for movement disorders, and our VTA modeling system represents the most individualized approach for ET DBS surgical planning.