Programming and Sensing with Deep Brain Stimulation Arrays
2017-08
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Programming and Sensing with Deep Brain Stimulation Arrays
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2017-08
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Deep brain stimulation (DBS) is a neurosurgical therapy for movement disorders that relies on both precise neurosurgical implantation of a DBS lead of electrodes and systematic optimization (or programming) of stimulation settings to achieve beneficial clinical outcomes. One recent advance to improve the targeting of stimulation is the development of DBS arrays with electrodes segmented both along and around the DBS lead. However, increasing the number of independent electrodes creates the logistical challenge of how to efficiently optimize stimulation parameters to target one or more pathways within the brain. This doctoral dissertation advanced the programming capabilities of DBS arrays by: (1) developing a multi-objective Particle Swarm Optimization (PSO) algorithm to program DBS arrays in the motor thalamus, and (2) investigating the PSO algorithm’s capacity to target DBS therapy within the globus pallidus (GP) and subthalamic nucleus (STN) for treating parkinsonian motor signs. With increased numbers of electrodes and decreased inter-electrode spacing, DBS arrays also open up opportunities to record local field potential (LFP) activity with improved spatial resolution, which could expand the feature set of neurophysiological feedback for closed-loop approaches to DBS therapy. This dissertation also (3) investigated the spatial resolution of local field potential (LFP) signals in the GP and STN in both resting state and during an active task in two non-human primates in both naïve and parkinsonian conditions. The primary findings of this dissertation suggest that the PSO algorithm produced solutions with accuracy, robustness, and consistency, and further that the PSO algorithm applied to targeting the STN in a parkinsonian non-human primate showed superior performance in reducing rigidity in comparison to conventional clinical monopolar review settings. Furthermore, it was demonstrated that segmented DBS arrays with higher density and smaller contacts were able to provide more spectral information within both GP and STN than could be sensed using grouped macroelectrode configurations consistent with commercial DBS leads. Together these results suggest that future translation of DBS array technology to the clinical setting will benefit both spatial and temporal optimization of DBS therapy on a patient-specific basis.
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University of Minnesota Ph.D. dissertation.August 2017. Major: Biomedical Engineering. Advisor: Matthew Johnson. 1 computer file (PDF); x, 117 pages.
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Zhang, Simeng. (2017). Programming and Sensing with Deep Brain Stimulation Arrays. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/201119.
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