Christopher R. Butson, Ph.D. - US grants

DESCRIPTION (provided by applicant): The goal of the proposed studies is to develop two complementary animal models to advance deep brain stimulation (DBS) of the central thalamus (CT) as a therapeutic strategy for the treatment of acquired cognitive disabilities resulting from traumatic brain injury (TBI). Each day of the year approximately 4,000 Americans suffer a traumatic brain injury (TBI), leaving as many as 100,000 persons/year with long-term cognitive disabilities. We will form a multidisciplinary research program utilizing systems neuroscience and bioengineering methods to improve the efficacy of central thalamic brain stimulation (CT/DBS). The research team will be lead by investigators at Weill- Cornell Medical in partnership with researchers at The Rockefeller University and Medical College of Wisconsin. Dr. Nicholas Schiff (Weill-Cornell), a leading neurologist/neuroscientist in the fields of CT/DBS and human brain injury studies, will act as P.I. of the R01 along with Dr. Keith Purpura (Weill-Cornell), an expert systems neurophysiologist, to carry out a series of experimental studies in intact alert, behaving monkeys. The work with monkeys will examine the influence of different patterns of electrical stimulation on rostral central thalamic neurons. These neurons link the brain stem centers that control arousal with the cerebral cortex, and play a crucial role in integrating cortex, striatum and thalamus. Behavioral effects of continuous stimulation and of brief pulses applied at specific times will be evaluated during the performance of two elementary cognitive tasks. Neural activity in the monkey's frontal lobe during and following stimulation will also be examined. Dr. Donald Pfaff (Rockefeller), a world- renowned expert on the cellular basis of behavior, will adapt a vetted set of arousal assays he developed for the mouse to studies of CT/DBS. Preliminary results in his laboratory have shown that CT/DBS in the mouse can facilitate behavioral performance following induced traumatic brain injury. Dr. Christopher Butson (Medical College of Wisconsin), a bioengineer and expert in computational modeling of brain electrical stimulation will develop detailed models of the volume of tissue activated in the animal experiments at Weill-Cornell and Rockefeller. He will also supervise the development and analysis of a probabilistic atlas to identify the sites of optimal application of CT/DBS. This atlas will assist in the construction of a human atlas that could be used in the treatment of non-progressive brain injuries. Thus, the long-range goal of this work is to optimize neuromodulation strategies employing electrical stimulation of the central thalamus to treat cognitive impairment following TBI. PUBLIC HEALTH RELEVANCE: Acquired cognitive impairment following severe brain injury leave as many as 100,000 Americans each year with devastating disabilities. The studies proposed here will help to advance the necessary knowledge to advance and further develop a novel application of electrical brain stimulation aimed at improving the lives of patients suffering with these lifelong challenges.

? DESCRIPTION (provided by applicant): Severe to moderate traumatic brain injury (smTBI) annually afflicts many hundreds of thousands of Americans producing chronic cognitive disabilities that lack effective treatments. The present proposal will develop a critical first-in-an early clinical feasibility study to support a next generation device to provide central thalamic deep brain stimulation (CT-DBS). CT-DBS is proposed as a therapy for the survivors of smTBI who recover to independent functional levels but remain significantly limited in their activities b chronic cognitive impairment (difficulties with sustained attentional effort, working memory, processing speed and fatigue). Stakeholders, including patients identifying their cognitive difficulties as matched to the functions proposed to be supported by CT-DBS, have shown support for this approach and willingness to consider participation after having the concepts and risks of this approach presented to them. The working hypothesis for the present study is that the pattern of cognitive deficits seen after smTBI takes origin in a broad reduction of neuronal connections and cell loss produced by smTBI that will on average produce disproportionate down-regulation of frontostriatal systems and deafferentation of the central thalamus (which collectively support the range of executive cognitive functions typically impaired in smTBI), and that CT-DBS can activate these systems sufficiently to provide effective functional improvements. Preliminary studies including evidence of CT-DBS facilitation of cognitive function in a different, more severely brain-injured population of patients with traumatic brain injuries as well as pre-clinical behavioral, electrophysiological, and computational modeling studies in intact non-human primates (NHP) support the hypothesis and the approach. The present study will use bilateral placement of a research single- electrode system with sensing and recording capabilities to aid the electrophysiological mapping of the central thalamus. Our supporting data demonstrate that behavioral facilitation can be achieved with a single electrode system in both the human and NHP. In NHP studies we have found that a more reliable and robust therapeutic response can be achieved through the use of a multiple electrode system capable of targeted delivery of electric fields across a specific fiber tract in the central thalams. Here we will obtain and analyze neuroimaging, computational modeling, behavioral, and electrophysiological data from human subjects to advance the development of a next-generation system that may allow more flexibility and reliability of for the application of CT-DBS in patients with traumatic brain injuries. These studies will be carried out by an investigative team with multiple, long-standing collaborations aimed at the development of CT-DBS technologies and treatment of cognitive impairment following TBI; the team spans expertise in clinical trials, neurology, neurosurgery, neurophysiology, neurorehabilitation, neuropsychology, radiology, and computational modeling. The early feasibility study proposed has been through a presubmission review for an Investigational Device Exemption with the Food and Drug Administration.

? DESCRIPTION (provided by applicant): Deep brain stimulation (DBS) has tremendous potential to improve the lives of patients with a wide range of chronic illnesses. Good outcomes from DBS for Parkinson's disease (PD) are strongly correlated to accurate electrode placement and to careful post-operative selection of stimulation parameters (voltage, pulse width, frequency, active electrode contact(s), among others). Although DBS is beneficial for a variety of disorders, a persistent problem has been extensive, costly programming time after the electrode leads are implanted. This is largely because there are very few tools available to assist clinicians in this process, and as a result DBS programming can require a significant degree of experience and expertise, as well as a substantial amount of time for the clinician to search for optimal device settings. Over the last few years computational models have been developed to predict and visualize the effects of DBS based on the neuroanatomy of individual patients. Recently these models have shown promise for improving the efficiency of DBS programming, and have been incorporated into a clinical decision support system. The long-term goal of this research is to improve the lives of patients with neurological disease that are treated with DBS. The objective of this application is to prospec- tively test the use of DBS clinical decision support tool in post-operative clinical care. The central hypothesis is that the use of a DBS clinical decision support system for individual patient management will enable consider- able time savings and reduced burden on patients and caregivers. This hypothesis has been formulated from pilot studies that have shown dramatic decreases in DBS programming time compared to standard care for clinicians who used an iPad-based decision support system (99% time savings from over 4 hours to 2 minutes). The rationale for the proposed research is that computational models, clinical informatics, and mobile computing devices can be used to enable DBS management in a way that has never before been possible. Guided by strong preliminary data, this hypothesis will be tested in two specific aims: 1) Measure the effective- ness of DBS decision support system in an established PD clinic; 2) Measure the effectiveness of DBS deci- sion support system by home health nurses. Under the first aim we will compare programming time and clinical outcomes for patients managed using the clinical decision support system compared to standard care. Under the second aim we will assess the effects of the system on patient and caregiver strain when used by home health nurses. This approach is innovative because it provides an iPad-based clinical decision support applica- tion (app) to enable nurses and physicians to quickly focus on stimulation settings that are likely to be most effective. The proposed research is significant because it will provide powerful tools to the health care provid- ers who will be able to provide the greatest benefit for DBS patients. The knowledge gained could enable a future model for DBS management where care is provided in both clinical and home settings by skilled nurses who use expert systems for guidance.

Deep brain stimulation (DBS) is a therapy that has been shown to be effective for the treatment of Parkinson's disease and essential tremor, and is now being assessed for a wide range of other disorders such as Alzheimer's disease, depression and traumatic brain injury. Hence, patients with a wide range of neurological disorders could benefit from DBS. However, these patients face an access problem because DBS devices are almost exclusively implanted and managed in major cities at academic medical centers. While it is reasonable for a patient to travel once or twice for surgery, it can be infeasible for them to travel long distances for post-operative management of their DBS devices in the months and years following surgery. We envision a new model in which patients travel once or twice for surgery and then are managed in their home area by community neurologists or family practice physicians who use expert decision support tools to choose DBS device settings. The purpose of this grant is to test the use of an app-based decision support platform that runs on iOS devices, and provides predictive, patient-specific computational models over a high-bandwidth network that was developed for healthcare applications. We believe that this system can drastically reduce the amount of time necessary for DBS programming, and in the future it may enable patients to be post-operatively managed without the need to travel to DBS surgical centers. We anticipate that if this study is successful then it will achieve a critical step by providing a system that runs on mobile devices, and can be used to manage DBS patients across a wide range of neurological disorders. Hence, we feel that the technology developed and tested in this application could have transformative effects on large numbers of patients.

In recent years there has been substantial growth in the use of patient-specific computational models to predict and visualize the effects of neuromodulation therapies such as deep brain stimulation (DBS) to treat movement disorders including Parkinson's disease (PD) and essential tremor (ET). These models have been clinically validated, and their utility in DBS programming has been demonstrated in several studies. However, translating these models from a research environment to the everyday clinical workflow has been a major challenge, primarily due to the complexity of the models and the expertise required in specialized visualization software. In this application we propose to deploy an interactive visualization system, ImageVis3D Mobile (IV3Dm), which has been designed for mobile iOS computing devices such as the iPhone or iPad, to visualize patients-specific models of Parkinson's disease (PD) patients who received DBS therapy. Selection of DBS settings is a significant clinical challenge that requires considerable expertise to achieve optimal therapeutic response, and is often performed without any visual representation of the stimulation system in the patient. This issue is compounded by a catch-22 in the management of these patients: very few clinicians outside academic medical centers will manage DBS patients because they lack the tools and expertise to do so; no one has developed remote, mobile tools because there is a perception that providers outside academic medical centers will not use them. The purpose of this application is to break this deadlock by providing a decision support system that can provide clinicians with the tools necessary to manage DBS patients in rural areas. We have previously tested the utility of IV3Dm for programming DBS patients in a controlled clinical setting and have shown that it can drastically reduce the amount of time necessary to choose good therapeutic settings. In this application we proposed to add several key enabling technologies and test the use of IV3Dm on PD patients in remote areas of Utah. These include: integrating a previously developed GPU-based solver; adding remote volume rendering capability to IV3Dm to enable a wide range of possible DBS settings; testing IV3Dm over the Utah Telehealth Network (UTN), a broadband network in the State of Utah that is dedicated for use in healthcare. We anticipate that if this study is successful we will show that PD patients can receive care that is comparable to that provided by specialists at major medical centers but with far less patient burden (i.e. travel time). The intellectual merit of this application lies in the delivery of patient-specific computational models of DBS patients over a broadband telehealth network to improve the care of PD patients.

PROJECT SUMMARY Tourette syndrome (TS) is a continuous lifelong condition that is highly prevalent, socially disabling, and in some severe cases, physically injurious. DBS has emerged as a promising treatment option for addressing uncontrollable tics in medically resistant and severe cases of TS frequently involving self-injurious behavior. We have undertaken a major informatics initiative by establishing the International TS DBS Registry and Database, a multi-country consortium that has captured long term outcomes of 277 TS DBS patients representing 50-75% of all TS DBS cases worldwide. From these outcomes, two deep brain targets have emerged as potentially effective: the centromedian nucleus region (CM) of the thalamus, and the anterior globus pallidus internus (aGPi). However, our current understanding of tic generation is limited by many factors including a lack of animal models for TS, apparently normal brain structure on structural imaging, and the impracticality of studying involuntary motor tics with functional imaging. Next generation closed-loop DBS systems can record brain activity in patients with TS and identify the neurophysiological correlates of tics. Moreover, these devices can deliver stimulation in response to a patient's symptomatic state. Our overall goal is to develop neurophysiology driven and connectivity-guided closed-loop DBS systems for the improved treatment of TS. To this end, we will implant 8 medically resistant TS patients with bilateral leads in the CM and aGPi. In Aim 1, we will identify structural network projections from CM and aGPi to guide pre-operative surgical planning and post-operative selection of stimulation parameters. In Aim 2, we will identify neurophysiologic correlates of tic genesis in the CM and aGPi. We will also study thalamo-pallidal network interactions leading to and during tics. In Aim 3, we will test the feasibility, safety, and efficacy of closed-loop TS DBS. We expect that closed-loop stimulation will provide more effective and personalized treatment options with longer battery life and fewer adverse effects.