Matthew D. Johnson - US grants

[unreadable] DESCRIPTION (provided by applicant): Deep brain stimulation (DBS) of the globus pallidus (GP) is an effective treatment for patients with advanced Parkinson's disease (PD). Despite recent successes, the precise site(s) within the pallidum and physiological mechanism(s) of the therapy remain uncertain due in part to our limited understanding of the neural response to DBS. We hypothesize that parkinsonian motor symptoms have therapeutically distinct targets within the GP. By identifying the mechanisms of GP-DBS, we can then develop new stimulation patterns and electrodes to selectively target the cell groups implicated in the therapeutic benefit while minimizing stimulation induced side-effects. In this study, we propose to develop anatomically and biophysically accurate models of DBS in the globus pallidus externus (GPe) and globus pallidus internus (GPi) for four MPTP-treated, hemi-parkinsonian rhesus macaques. Each animal has been or will be implanted with a monkey-scaled version of a clinical DBS lead such that the four electrode contacts span the sensorimotor regions of both GPe and GPi. The computational models will be applied retrospectively to determine the neural response during therapeutic and non-therapeutic DBS in two monkeys. Models developed in two additional monkeys will then be used to prospectively evaluate the effects of targeted stimulation of specific anatomical territories. Our working hypothesis is that direct stimulation of the posteroventral sensorimotor GPi will primarily improve rigidity and levodopa-induced dyskinesias, whereas targeted stimulation of the sensorimotor aspects of dorsal GPi and ventral GPe will primarily improve bradykinesia. If the results of this study support this hypothesis with both retrospective and prospective evaluation, it will provide two important contributions to the field: 1) substantiate the technique of using detailed computational models to guide DBS parameter selection, and more importantly 2) provide anatomical and electrical guidelines for the clinical programming of GP-DBS implants. [unreadable] [unreadable]

This Integrative Graduate Education and Research Traineeship (IGERT) award supports the development of a multi-disciplinary, integrative graduate education and training program in NeuroEngineering (NE) at the University of Minnesota at Twin Cities. Intellectual Merit: The purpose of this program is to train doctoral students to develop the skills to revolutionize technologies for interfacing with the brain and advance the fundamental understanding of neuroscience processes that arise when interfacing with and modulating the brain.

Broader impacts include the development of a multi-disciplinary training program that blurs the boundary between neuroscientists and engineers, thus enabling a new generation of scientists to competently and confidently take on the grand challenges in the interdisciplinary field of NeuroEngineering. The NE program includes major research themes in decoding brain signals, modulating brain dynamics, and bi-directional brain interfacing. The program is a "degree-plus" model in which pre-doctoral students are admitted to one of the participating graduate programs (Biomedical Engineering, Electrical Engineering, Mechanical Engineering, and Neuroscience), and are trained through a series of hands-on, modular neuroengineering courses. All NE Fellows will immerse themselves in a lab outside their major in the summer of their first year, engage in multiple lab rotations, and participate in at least one clinical lab rotation, summer internship at a neurotechnology company, or summer international research experience. NE Fellows will have co-advisors beginning in their first year, one from the engineering sciences and one from the basic or clinical neurosciences. The training program incorporates several outreach efforts to recruit women and underrepresented minorities, provide outreach to K-12 and industry, and train NE Fellows to be effective communicators.

IGERT is an NSF-wide program intended to meet the challenges of educating U.S. Ph.D. scientists and engineers with the interdisciplinary background, deep knowledge in a chosen discipline, and the technical, professional, and personal skills needed for the career demands of the future. The program is intended to catalyze a cultural change in graduate education by establishing innovative new models for graduate education and training in a fertile environment for collaborative research that transcends traditional disciplinary boundaries.

DESCRIPTION (provided by applicant): Essential tremor (ET) is the most common movement disorder in the United States, affecting 4% of all adults over the age of 40. For individuals whose motor symptoms are refractory to medication and significantly impair their daily living, deep brain stimulation (DBS) is considered to be the only therapeutic option. Despite recent advances in DBS technology, a significant portion of ET patients with DBS implants will receive inadequate tremor control because of poorly placed DBS leads, while others will lose efficacy of the therapy after 1-2 years due in part to inflexible neurostimulator programming options. There is a strong and growing clinical need for implantable DBS lead designs that can enable clinicians to better sculpt electric fields within the brain, especially in cases where stimulation through a poorly placed DBS lead results in low-threshold side-effects. Our recent studies with a radially-segmented DBS lead have shown promising results, but knowing how to program the stimulation settings on such a lead remains a critical challenge towards making these leads practical in a clinical setting. Our proposed study will integrate high-field magnetic resonance imaging, computational modeling, and electrophysiology to develop an experimentally-validated computational programming algorithm that facilitates clinical determination of subject-specific neurostimulator settings through high-dimensional DBS electrode arrays. Specifically, we will: 1) develop a computational algorithm that can simplify the programming process of thalamic deep brain stimulation leads with radially-segmented electrode arrays; 2) quantify the degree to which the computational algorithms can accurately predict current steering through poorly targeted DBS arrays in the thalamus in non-human primates; and 3) compare the layer-specific neuronal dynamics induced in primary motor cortex (M1) during stimulation of the cerebellothalamic versus thalamocortical pathway in non-human primates. PUBLIC HEALTH RELEVANCE: Deep brain stimulation (DBS) is a proven therapy for patients with medication-refractory essential tremor, but a significant portion of patients with these implants do not receive adequate tremor control because of poorly placed DBS leads or inflexible DBS programming options. There is a strong and growing clinical need for implantable DBS lead designs that can enable clinicians to better sculpt electric fields in the brain. Our research study will experimentally evaluate a computational modeling approach to program a novel DBS lead with radially-segmented electrodes for improved targeting of stimulation within thalamus so as to improve the functional outcome for all patients requiring DBS to manage their essential tremor.

PROJECT SUMMARY AND ABSTRACT The basal ganglia have a rich, functional topography composed of motor subcircuits and oscillatory networks that are thought to be critically important to the pathophysiology of Parkinson's disease (PD) and the successful application of deep brain stimulation therapy (DBS) in managing each cardinal motor sign of PD. There is a strong clinical need to better understand these processes and in turn harness them to deliver therapy that is tailored to an individual patient and a patient's own symptomatology. In this project, we seek to develop a novel spatiotemporally optimized DBS therapy and evaluate its efficacy in a non-human primate model of PD. The optimization approach leverages the unique capabilities of (1) high-field MR imaging (7T and 10.5T), (2) subject-specific computational models of DBS, (3) a high-density DBS lead with electrodes arranged along and around the lead shank, and (4) a real-time signal processing interface that can readily adapt stimulation parameters on the DBS array based on analysis of ongoing oscillatory activity at and downstream of the site of stimulation. High-density DBS arrays spanning the subthalamic nucleus (STN) and thalamic fasciculus (Array A) and the external and internal globus pallidus (GP) (Array B) will be implanted in each subject. Aim 1 will characterize the magnitude and time course of therapeutic effects on each parkinsonian motor sign when targeting electrical stimulation within and around the STN and GP. Aim 2 will investigate how targeted stimulation differentially affects oscillatory activity at and downstream of the site of stimulation and relates to improvement in each parkinsonian motor sign. Aim 3 will develop and apply a novel set of optimization algorithms, including chaotic desynchronization and real-time closed-loop phasic stimulation, to test the hypothesis that optimizing suppression of exaggerated phase amplitude coupling in the STN and GP will further increase the overall magnitude of DBS therapy. Together, this project will enhance our understanding of the pathophysiology of PD and provide critical data towards translating a patient-optimized DBS therapy that integrates high-density DBS leads with novel closed-loop stimulation.

Abstract: The overall goal of project 3 of the University of Minnesota (UMN) Udall Center is to investigate why deep brain stimulation (DBS) therapy for Parkinson's disease (PD) works better in some individuals than in others and to develop methods to decrease the variability of DBS therapy for individual motor signs of PD. To deliver DBS therapy at a level consistent with the best responders, it is critical to investigate the (1) emergence of DBS- induced side effects that impede the delivery of more effective stimulation parameters, (2) logistical challenges in optimizing stimulation settings for each parkinsonian motor sign on an individual basis, and (3) multi-scale neurophysiological differences across the basal ganglia, thalamus, and brainstem that underlie the individual variability to DBS therapy. This project will leverage the well-characterized non-human primate model of PD (systemic MPTP) implanted with two scaled-down versions of the human DBS lead (subthalamic nucleus, STN and globus pallidus, GP). The approach involves a novel combination of high-field imaging (7T/10.5T, Imaging Core), computational neuron modeling of DBS, development of optimization algorithms based on quantitative behavioral assessments, multi-parameter regression analysis techniques (Biostatistics Core), and multi-scale electrophysiological analysis of DBS therapy that spans single-cell, ensemble, and whole-brain levels. Aim 1 will investigate the ability for narrow DBS pulse widths to extend the therapeutic parameter space window between alleviating parkinsonian motor signs and evoking motor side-effects. This aim will further enhance our understanding of the functional relationships between DBS parameter settings and their resultant therapeutic effect sizes and wash-in/wash-out time constants on a subject-specific, pathway-specific basis. Aim 2 will develop a novel real-time, behavior-based optimization algorithm for automatic and efficient selection of DBS parameters that minimize the expression of individual parkinsonian motor signs including rigidity, bradykinesia, akinesia, and gait/posture. Aim 3 will identify the subject-specific electrophysiological features that most closely correlate with the temporal and steady-state behavioral responses to DBS found in the first two aims. The simultaneous recordings will include local field potentials in the STN and GP as well as unit-spike recordings in three nuclei innervated by pallidofugal projection neurons (i.e. motor thalamus, centromedian-parafascicular complex of thalamus, and pedunculopontine nucleus). At the conclusion of the experiments, whole-brain transcription factor analysis for two metabolic markers (c-fos and egr-1) will be conducted through histological techniques to provide single-cell resolution for the neural pathways modulated by behaviorally-optimized DBS therapy. Together, these aims will provide critical new insight into the pathophysiological basis for the expression of each parkinsonian motor sign and which specific targeted pathways and electrophysiological features are most relevant to delivering the most effective and efficient level DBS therapy for each individual.

This Research Experiences for Undergraduates (REU) Site from the University of Minnesota will support a diverse cohort of undergraduate students to pursue research in the interdisciplinary field of neural systems engineering. This program will highlight recent developments in neurotechnology for basic science research and healthcare. It is estimated that 100 million U.S. citizens will have a significant brain-related disorder in their lifetime. There is a need to train scientists and engineers who will research and understand the process for translating neural systems engineering research into human applications. This site will focus on recruiting a diverse group of students with an emphasis on recruiting women, underrepresented minorities, and undergraduates from colleges where opportunities to pursue STEM-based research are opportunities.

Over a three year period, this REU program will engage undergraduate students in a 10-week intensive, summer research experience aimed at further developing neurotechnology for brain imaging, decoding and modulation. Projects will focus on developing technology, running simulations or data analysis. Many of the projects will integrate multiple modalities of neurotechnology, such as multi-modal imaging, brain-computer interfaces and closed-loop neuromodulation. Students will have opportunities to investigate neuronal dynamics, plasticity, learning and attention as feedback metrics to optimize neurotechnologies. Research projects will provide opportunities for REU students to learn about large scale data analysis, computational modeling of the brain, and participate in experiments. The program will have group building projects to develop a cohesion between the students within the program and with their graduate student mentors to create lasting friendships and collaborations. The program will emphasize near-peer mentoring where the REU students will be directly mentored students in their second and third year of graduate school, and in turn will be offered the opportunity to meet and educate high school students about neural engineering. To broaden their perspective, students will participate in a bi-weekly neuroengineering seminar series hosted in conjunction with a NSF IGERT program and the Center for Neuroengineering. Multiple mechanisms will help guide students in their professional development and prepare them for graduate school. To develop communication skills, students will participate in outreach programs and present their research in a small forum at the end of the program.

PROJECT SUMMARY AND ABSTRACT Essential tremor (ET) is the most common movement disorder in the United States, affecting 4% of all adults over the age of 40. For individuals whose motor symptoms are refractory to medication and significantly impair their daily living, deep brain stimulation (DBS) is considered to be the only bilateral therapeutic option. Despite recent advances in DBS technology, a significant portion of ET patients with DBS implants will receive inadequate tremor control because of poorly placed DBS leads, while others will lose efficacy of the therapy after 1-2 years due in part to inflexible neurostimulator programming options. There is a strong and growing clinical need for implantable DBS lead designs and programming algorithms that can enable clinicians to better sculpt electric fields within the brain, especially in cases where stimulation through a poorly placed DBS lead results in low-threshold side-effects. Our proposed study will integrate high-field magnetic resonance imaging, histological neurotracing of fiber pathways, computational modeling of DBS, and single-cell electrophysiology methods to further develop and experimentally-validate a novel semi-automated machine learning algorithm that facilitates hypothesis-driven determination of subject-specific neurostimulator settings through directional DBS leads. Specifically, we will: 1) identify the neural pathways involved in the reduction of action and postural tremor using directional DBS leads and a novel particle swarm optimization algorithm based on subject-specific anatomy; 2) quantify how tremor-related information is modulated on the single-cell, population, and network levels by therapeutic DBS in a preclinical large-animal model of harmline-induced tremor; and 3) investigate how therapeutic windows (i.e. the threshold difference between postural and action tremor abolishment and side effect emergence) change over time with human DBS therapy targeting one or more pathways within the cerebello-thalamoc-cortical network. Together, this project will (a) experimentally evaluate and translate a novel DBS programming algorithm to human ET patients, (b) provide a much more detailed map of the neural pathways underlying the therapeutic effects of DBS (on postural and action tremor) and side effects of DBS (on dysarthria, paresthesia, ataxia), (c) rigorously investigate how DBS for treating tremor works mechanistically at the single cell and network levels within the brain, and (d) probe the neural pathways involved in the worsening of tremor symptoms for ET patients over time.

PROJECT SUMMARY AND ABSTRACT Existing neurotechnologies continue to make significant clinical impact, but challenges remain for scientists and engineers in moving new devices from the bench to the bedside. This grant will support the development of a comprehensive educational training program on the best practices for successfully navigating the translational and commercialization pathway for neural medical devices. The educational program will include (1) a series of publicly available video lectures that will be assembled and curated into a certificate program by a diverse group of program faculty with significant experience in moving neurotechnologies from the bench to the bedside. These short-course video lectures will cover the following topics: preclinical model systems, safety and efficacy studies, good laboratory practices, device testing, quality system processes, regulatory agency interactions, steps in developing an investigational device exemption (IDE) application, reimbursement agency interactions, clinical trial design with an emphasis on quantitative outcome measures of target engagement, bioethical considerations that are specific to neural medical devices, techniques for securing strong intellectual property claims, funding opportunities available for technology development and clinical trials, and advice on moving neurotechnology into successful commercial ventures. In addition, the educational program will provide (2) an annual three-day workshop in which participants will work with program faculty to think through and develop submissions to an institutional review board (IRB), the FDA, and/or funding groups for their own devices or devices inspired by relevant case studies. The workshop will be open to academic researchers, clinician scientists, and small-business entrepreneurs who are interested in gaining expertise that will help them translate their own neural medical devices. Participants of the workshop will be encouraged to continue interacting with program faculty through regular online follow-ups. The hands-on workshop will be offered for two years during the grant funding period with the goal of extending the workshop in future years through collaborations with our ongoing relationships with neural interfaces conferences.

ABSTRACT Chronic overactivity of renal nerves results in physiological and pathological changes in renal function that contribute to kidney-based diseases. Hypertension is correlated with increased activity of sympathetic nerves to the kidneys in preclinical models, and in most of these models, hypertension is attenuated by renal denervation (RDN). Clinical trials building on these models have demonstrated that catheter-based RDN is effective in lowering arterial pressure in hypertensive patients. The success of catheter-based RDN to treat hypertension has catalyzed the emerging field of electroceuticals, which is based on the concept of organ-specific neuromodulation (rather than ablation) for cardiometabolic diseases. Whereas ablation is non-reversible and non-tritratable, neuromodulation can be incorporated into a closed-loop feedback design to precisely regulate the activity of nerves as desired. Moreover, neuromodulation can be turned off and restarted as needed. Combined, our laboratories have extensive knowledge on the role of renal nerves in the pathogenesis of hypertension and the mechansims mediating the anti-hypertensive effect of RDN in rodent models (Co-PI Osborn) as well as experience in developing computational modeling tools to design neurotechnologies (Co-PI Johnson. We aim to translate this knowledge to the development of a novel implantable technology for neuromodulation of the kidney for treatment of neurally-mediated renal pathology in a translational large animal model of renal pathology (DOCA-salt sheep). In Specific Aim 1, we will develop a bidirectional renal nerve cuff interface, first in silico and then in the lab, to electrically block (E-Block) and sense (E-Sense) renal nerve activity in sheep. In Specific Aim 2, we will optimize stimulus parameters of renal E-block in vivo by comparing the acute renal responses to E-Block to those observed following surgical ablation in anesthetized DOCA-hypertensive sheep. Successful development of this neuromodulatory tool for treatment of renal disease can be translated to treat other chronic diseases associated with overactivity of renal nerves including chronic kidney disease and end-stage renal failure. Moreover, this same technology can potentially be used to modulate other organs (e.g. liver, pancreas, spleen) for the treatment of chronic cardiometabolic diseases that are linked to excessive nerve activity.