Experimental and Model-based Approaches to Directional Thalamic Deep Brain Stimulation

University of Minnesota Ph.D. dissertation. September 2016. Major: Biomedical Engineering. Advisor: Matthew Johnson. 1 computer file (PDF); xii, 181 pages.Deep brain stimulation (DBS) is an effective surgical procedure for the treatment of several brain disorders. However, the clinical successes of DBS hinges on several factors. Here, we describe the development of tools and methodologies in the context of thalamic DBS for essential tremor (ET) to address three key challenges: 1) accurate localization of nuclei and fiber pathways for stimulation, 2) model-based programming of high-density DBS electrode arrays (DBSA) and 3) in vivo assessment of computational DBS model predictions. We approached the first challenge through a multimodal imaging approach, utilizing high-field (7T) susceptibility-weighted imaging and diffusion-weighted imaging data. A nonlinear image deformation algorithm was used in conjunction with probabilistic fiber tractography to segment individual thalamic sub-nuclei and reconstruct their afferent fiber pathways. We addressed the second challenge by developing subject-specific computational model-based algorithms built on maximizing population activating function values within a target region using convex optimization principles. The algorithms converged within seconds and only required as many finite-element simulations as the number of electrodes on the DBSA being modeled. For the third challenge, we recorded (in two non-human primates) unit-spike data from neurons in the vicinity of chronically implanted thalamic DBSAs before, during and after high-frequency stimulation. A novel entropy-based method was developed to quantify the degree and significance of stimulation-induced changes in neuronal firing pattern. Results indicated that neurons modulated by thalamic DBS were distributed and not confined to the immediate proximity of the active electrode. For those that were modulated by DBS, their responses increasingly shifted from firing rate modulation to firing pattern modulation with increased stimulation amplitude. Additionally, strong low-pass filtering effect was observed where <4% of DBS pulses produced phase-locked spikes in cells exhibiting significant excitatory firing pattern modulation. Finally, we quantified the spatial distribution of neurons modulated by DBS by developing a novel spherical statistical framework for analysis. Together, these tools and methodologies are poised to improve our understanding of DBS mechanisms and improve the efficacy and efficiency of DBS therapy

Using Phase Response Curves to Optimize Deep Brain Stimulation

University of Minnesota Ph.D. dissertation. April 2016. Major: Neuroscience. Advisor: Theoden Netoff. 1 computer file (PDF); vii, 190 pages.Deep brain stimulation (DBS) is a neuromodulation therapy effective at treating motor symptoms of patients with Parkinson’s disease (PD). Currently, an open-loop approach is used to set stimulus parameters, where stimulation settings are programmed by a clinician using a time intensive trial-and-error process. There is a need for a systematic approach to tuning stimulation parameters based on a patient’s physiology. An effective biomarker in the recorded neural signal is needed for this approach. It is hypothesized that DBS may work by disrupting enhanced oscillatory activity seen in PD. In this thesis I propose and provide evidence for using a simple measure, called a phase response curve, to systematically tune stimulation parameters and develop novel approaches to stimulation to suppress pathological oscillations. In this work I show that PRCs can be used to optimize stimulus frequency, waveform, and stimulus phase to disrupt a pathological oscillation in a computational model of Parkinson’s disease and/or to disrupt entrainment of single neurons in vitro. This approach has the potential to improve efficacy and reduce post-operative programming time

Personalized computational models of deep brain stimulation

University of Minnesota Ph.D. dissertation. December 2016. Major: Biomedical Engineering. Advisor: Matthew Johnson. 1 computer file (PDF); xii, 138 pages.Deep brain stimulation (DBS) therapy is used for managing symptoms associated with a growing number of neurological disorders. One of the primary challenges with delivering this therapy, however, continues to be accurate neurosurgical targeting of the DBS lead electrodes and post-operative programming of the stimulation settings. Two approaches for addressing targeting have been advanced in recent years. These include novel DBS lead designs with more electrodes and computational models that can predict cellular modulation during DBS. Here, we developed a personalized computational modeling framework to (1) thoroughly investigate the electrode design parameter space for current and future DBS array designs, (2) generate and evaluate machine learning feature sets for semi-automated programming of DBS arrays, (3) study the influence of model parameters in predicting behavioral and electrophysiological outcomes of DBS in a preclinical animal model of Parkinson’s disease, and (4) evaluate feasibility of a novel endovascular targeting approach to delivering DBS therapy in humans. These studies show how independent current controlled stimulation with advanced machine learning algorithms can negate the need for highly dense electrode arrays to shift, steer, and sculpt regions of modulation within the brain. Additionally, these studies show that while advanced and personalized computational models of DBS can predict many of the behavioral and electrophysiological outcomes of DBS, there are remaining inconsistencies that suggest there are additional physiological mechanisms of DBS that are not yet well understood. Finally, the results show how computational models can be beneficial for prospective development of novel approaches to neuromodulation prior to large-scale preclinical and clinical studies