Browsing by Subject "Deep Brain Stimulation"

Now showing 1 - 8 of 8
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    Characterization of evoked compound action potentials in targets of deep brain stimulation for Parkinson’s disease
    (2022-10) Rosing, Joshua
    Deep brain stimulation (DBS) for Parkinson’s disease relies on accurate targeting of stimulation to provide the best therapeutic outcomes for patients. Current clinical practices typically rely on a brute force approach to finding the ideal stimulation electrode, and despite improvements to lead geometries such as the inclusion of rows of directional electrodes for precise targeting of stimulation, time constraints often prevent clinicians from making good use of these advancements. Additionally, although research has uncovered specific stimulation targets in common implant areas that are ideal for the treatment of specific symptoms or the avoidance of certain side effects, the clinical capacity for localizing a lead after implantation is not sufficient for confident declarations of implant location, and even the best imaging techniques can only be confirmed with post-mortem histology. Evoked compound action potentials (ECAPs) have been shown to vary by brain region, and to be linked to therapeutic outcomes, but a detailed investigation of their spatiotemporal properties has not yet been conducted. Through a series of experiments, in parkinsonian non-human primates instrumented with scaled-down clinical DBS leads, ECAP responses to changes in stimulation amplitudes, pulse widths, and electrode configurations were systematically investigated. Additionally, a novel DBS lead with a liquid crystal polymer (LCP) substrate and a high-density array of electrodes with a rough platinum-iridium site coating was evaluated for improved spatial resolution in ECAP and local field potential recordings in DBS targets. Project 1: One challenge with optimizing DBS therapy for a given patient is knowing where electrodes are located relative to the neural pathways around the DBS lead. We tested the hypothesis that ECAP features would differ between electrodes within gray matter (subthalamic nucleus, STN) and white matter (lenticular fasciculus, LF) for STN-DBS implants. ECAPs in these targets were characterized by short-latency ‘primary’ features (within 1.6 ms of stimulus pulse onset) and longer-latency ‘secondary’ features (>1.6 ms after stimulus pulse onset). We observed that ECAP primary feature responses were significantly larger in amplitude for LF/LF stimulation/record sites than for STN/STN stimulation/record sites. Furthermore, the number of secondary features detected in the STN (for STN or LF stimulation) was higher than that in LF (for LF stimulation). This supports the concept that ECAP primary features derive from direct axonal activation and secondary features result from post-synaptic axonal activation in the basal ganglia network. Primary feature amplitude was able to accurately predict electrode location at the border of the lenticular fasciculus and STN within and across all four subjects. Project 2: Another challenge with optimizing DBS therapy for Parkinson’s disease has been finding biomarkers that align with the seconds to minutes wash-in effects of DBS therapy on parkinsonian motor signs. ECAP features were found to adapt over the duration of the applied high-frequency DBS pulse train. Primary features habituated over time, while secondary features increased in latency over the first 30 seconds of stimulation, and trended toward earlier latencies at higher stimulation amplitudes. The total increase in secondary feature latency over the 30 seconds following stimulation onset also increased with increasing stimulation amplitude. In comparison to the instantaneous changes in spectral local field potential (LFP) power observed during STN-DBS, the temporal wash-in dynamics of ECAP responses seem to better align with the temporal wash-in profiles of DBS therapy on parkinsonian motor signs, and future studies will need to further investigate correlations between ECAPs and motor signs. Project 3: With the advent of microfabricated technology come opportunities to create bidirectional DBS lead technology to sense and modulate neural activity with higher spatial resolution. To further investigate the spatial features of ECAPs in the basal ganglia, we designed, developed, and evaluated a novel high-density LCP substrate DBS array. The arrays provided improvements in electrode longevity over previous high-density DBS arrays while also providing increased spatial resolution for both ECAP responses and LFP activity compared to state-of-the-art clinical electrodes.
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    Dissociating Cortico-Striato-Thalamo-Cortical Neural Circuitry Using Rodent Models of Cognitive Flexibility
    (2021-04-12) Cooper, Dawson C
    Mental illness is the single largest cause of disability worldwide. These disorders are characterized by breakdowns in neuronal communication between and among different areas of the brain. In order to restore proper functioning, treatment strategies have increasingly focused on modulating specific neuronal circuits. Deep brain stimulation (DBS) allows for targeted circuit-based neuromodulation and has shown to be a promising treatment for mental disorders. Despite its success, the mechanisms underlying its therapeutic effects remain unclear. Further investigating cortico-striato-thalamo-cortical (CSTC) circuits, often impaired in those with major depressive (MDD) or obsessive-compulsive disorder (OCD), may provide mechanistic clues. MDD and OCD can be characterized by impairments in cognitive control—the ability to organize, plan, and schedule mental operations in different environments. Cognitive control depends on distinct subregions of the prefrontal cortex (PFC) which project into the striatum. Here we show that DBS applied to the mid-striatum in an attentional set-shifting task improves cognitive flexibility in outbred rats (n=12) by significantly decreasing reaction time (p < 0.01). Furthermore, we developed a novel touchscreen two-armed bandit task which may help in determining which parts of the PFC are responsible for DBS’ effects on cognitive flexibility. Our results demonstrate that DBS is able to modulate the neural circuitry underlying cognitive flexibility and that Long-Evans rats can serve as a viable animal model in translating the two-armed bandit behavioral paradigm. Our future study will evaluate the effects of DBS in both set-shifting and the two-armed bandit. Behavioral paradigms with an increased dependency on more ventral parts of the PFC, involved in the two-armed bandit, are hypothesized to not benefit from mid-striatum DBS treatment. Our results may translate to human behavioral tasks and serve as a predictor for DBS’ effectiveness.
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    Experimental and Model-based Approaches to Directional Thalamic Deep Brain Stimulation
    (2016-09) Xiao, Joe
    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.
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    The Inferior Colliculus: A Target for Deep Brain Stimulation for Tinnitus Suppression
    (2015-08) Offutt, Sarah
    Tinnitus is a neurological condition that manifests as a phantom auditory perception in the absence of an external sound source. Tinnitus is often caused by hearing loss associated with noise exposure or aging and as such, the prevalence is only expected to rise in the coming years. Currently there is no cure for tinnitus and available treatment options have only shown limited success, thus there is an ever present need for continued research into new treatments. In this thesis we propose a new approach to treating tinnitus that uses deep brain stimulation to target the inferior colliculus (IC) with the goal of altering tinnitus-related neural activity, such as hyperactivity and increased neural synchrony, to suppress the tinnitus percept. We hypothesize that stimulation of the outer cortices of the inferior colliculus will modulate the tinnitus-affected neurons in the central region of the inferior colliculus (ICC) and in turn, these neural changes will be carried throughout the central auditory system by the extensive projection network originating in the IC, and will induce modulation in other tinnitus-affected auditory nuclei. The research of this thesis is aimed at determining the feasibility of this tinnitus treatment by assessing the IC as a potential neuromodulation target and identifying optimal stimulation locations and stimulation strategies for achieving maximal suppression. The first study was completed to better understand the auditory coding properties of the IC and to create a three dimensional reconstruction of these functional properties across the entire IC. These results narrowed down the stimulation target to the dorsal cortex of the inferior colliculus (ICD) and produced a tool that could be used to consistently place stimulating and recording electrodes in correct regions in the IC. The second and third studies focused on assessing the best stimulation locations and stimulation paradigms within the ICD, respectively, by stimulating throughout and measuring changes in neural activity in the ICC. These results show that maximal suppression is achieved by stimulation of the rostral-medial region of the ICD using either electrical stimulation only or electrical stimulation paired with acoustic stimulation with an 18 ms delay. These results will guide implementation in human patients. There are already deaf patients who suffer from tinnitus that are being implanted with a deep brain stimulator for hearing restoration called the auditory midbrain implant. Hardware modifications to the auditory midbrain implant have been completed that will allow us to stimulate the ICD and evaluate the effects on the tinnitus percept directly in patients.
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    Machine Learning for Deep Brain Stimulation
    (2020-02) Grado, Logan
    Deep brain stimulation (DBS) is an effective treatment for a variety of neurological disorders, including Parkinson’s disease (PD). However, the success of DBS relies on selecting stimulation parameters which relieve symptoms while simultaneously avoiding stimulation-induced side-effects. Currently, DBS is programmed through a time-intensive trial-and-error process in which the clinician systematically evaluates stimulation settings, requiring hours of effort and multiple patient visits. Additionally, advances in DBS lead technology and stimulation algorithms are adding additional free parameters, further increasing the difficulty of programming these devices. This doctoral thesis advanced the programming of DBS arrays by: (1) developing the slid- ing windowed infinite Fourier transform (SWIFT), an efficient method of extracting oscillatory neural features which can be used to program DBS systems, (2) developing the Bayesian adaptive dual controller (ADC), a type of Active Learning DBS which can be used to learn optimal stimulation parameters, and (3) demonstrating the ef- ficacy of the Bayesian ADC in an animal model of PD. The primary findings of this dissertation suggest that the Bayesian ADC is capable of efficiently and autonomously learning stimulation parameters for DBS in order to optimize a selected biomarker. Furthermore, it was demonstrated that parameters learned by the Bayesian ADC performed as well as control parameters identified through a standard trial-and-error programming process. Together, these results suggest that the Bayesian ADC should be clinically translatable for tuning DBS in future studies.
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    Optimization Algorithms for Spatially Targeted Deep Brain Stimulation
    (2017-12) Peña, Edgar
    Optimization algorithms hold significant promise for precision medicine. This dissertation focuses on the application of optimization algorithms to improve the efficacy and efficiency of deep brain stimulation (DBS) therapy for treating brain disorders. Targeting of DBS therapy for a given patient involves neurosurgical implantation of one or more leads of electrodes within the brain and then identifying a set of electrode configurations and stimulation amplitudes that most robustly suppress clinical symptoms. One approach to improve spatial targeting of DBS therapy has been the development of DBS leads with multiple electrodes positioned around and along the lead implant. However, with the additional number of electrodes, brute-force determination of stimulation settings that can most selectively modulate the brain pathways of interest becomes a time-consuming challenge. This is especially relevant since DBS can also induce adverse side effects such as involuntary muscle contractions and mood changes if stimulation is not delivered correctly. In this dissertation, I will show how convex and particle swarm optimization techniques using multi-objective contexts can be applied to subject-specific computational models of DBS to address this challenge. These optimization algorithms leverage complex bioelectric tissue models as well as detailed anatomical and biophysical computational models of the motor thalamus for treating Essential Tremor and the subthalamic nucleus region for treating Parkinson’s disease. Both convex and particle swarm optimization demonstrated robust and efficient performance in generating spatially targeted solutions consisting of non-trivial combinations of active electrodes and stimulation amplitudes. These optimization algorithms have important applications for both targeting specific brain pathways to understand their role in behavior as well as in helping clinicians reduce the dimensionality of the stimulation parameter feature space to evaluate.
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    Orientation-Selective Programming Strategies for Targeted Deep Brain Stimulation
    (2019-12) Slopsema, Julia
    Deep Brain Stimulation (DBS) is a neurosurgical intervention that can be highly effective for treating several movement disorders, including Parkinson’s disease and Essential Tremor. However, the degree to which this therapy works depends on precisely targeting stimulation to key neural pathways within the brain and avoiding activation of neural pathways that produce side effects when stimulated. This thesis developed the theoretical and experimental framework for a novel ‘orientation selective stimulation’ (OSS) approach to more selectively target neural pathways within the brain. The approach was investigated in the context of directional DBS leads with electrodes segmented both along and around the lead body. Computational models revealed that steering the primary direction of the electric field along the axonal pathways of interest in patient-specific models of Parkinson’s disease increased the therapeutic window between activation of a therapeutic hyperdirect pathway while limiting activation of the internal capsule, which is known to produce involuntary muscle contractions. The OSS approach was investigated in a swine model of DBS where OSS was applied through a 16-channel segmented DBS lead implanted in the ventral lateral (VL) thalamus while the swine were imaged with whole-brain fMRI. The results showed that BOLD activity in motor and premotor cortex were tuned to the orientation of the electric field adjacent to the lead with maximal activation occurring when the electric field was aligned to the cortico-thalamocortical pathway. Finally, patient-specific models of ventral intermediate nucleus (VIM) DBS for Essential Tremor were developed and revealed that portions of the superior cerebellar peduncle terminating in the external and internal regions of the VIM were differentially associated with therapy and side effects, respectively. OSS paradigms increased activation of the external VIM afferents and reduced activation of the internal VIM afferents. An OSS approach to programming has important clinical significance to enhance patient care by increasing therapeutic windows and could provide the ability to more selectively activate an individual pathway and evaluate its role in aspects of DBS therapy on individual symptoms.
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    Programming and Sensing with Deep Brain Stimulation Arrays
    (2017-08) Zhang, Simeng
    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.