Brian Litt, M.D. - US grants

DESCRIPTION (provided by applicant): The goal of this proposal is to develop and validate a practical method to predict epileptic seizures in human temporal and extratemporal epilepsy. Recent data from our laboratory suggest that human partial seizures are associated with a build-up of electrical activity minutes to hours prior to their onset on intracranial EEG (IEEG). The three most promising measures of this pre-ictal build-up are accumulating energy, subclinical seizure-like bursts (chirps), and high frequency epileptiform oscillations. In addition to increasing before seizures, these parameters wax and wane at other times, suggesting that recurrent changes in brain excitability occur repetitively and only proceed to seizures at critical times. By tracking the above three measures in continuous, long-term, multi-channel intracranial EEG (IEEG) data we plan to develop a practical model of how seizures are generated in the epileptic network and will prospectively validate the model's ability to identify periods of increased probability of seizure onset (our definition of "seizure prediction"). Algorithms developed in our laboratory based upon the quantitative features above are currently operating in first generation responsive brain stimulation devices for epilepsy being implanted in about 200 patients in an ongoing clinical trial, with encouraging results. These devices stimulate the brain in response to build-ups of the above quantitative measures in single channels. Optimal performance of these devices will require understanding how these measures develop and spread in the entire epileptic network, and the mechanisms underlying this process. Motivated by the above developments, the specific aims of this proposal are: (1) To meticulously collect, mark and archive a digital database of IEEG studies from a representative population of adults and children with medically resistant temporal and extra-temporal epilepsy, (2) to study the occurrence and duration of the above 3 quantitative measures in all intracranial electrode contacts and prospectively determine their relationship to electrographic seizure onset in continuous, undipped patient data sets; (3) to develop a practical model of seizure generation based upon these findings and prospectively validate its ability to predict seizures. Accomplishing these aims will yield important insight into the mechanisms underlying seizure generation and will be critical to improving the performance of 1st generation reactive epilepsy devices. Our lab will lead an established team of collaborators in this project for data collection, processing and interpretation, with expertise in electrical engineering, neuroscience, clinical epilepsy, neuropathology and statistics at The University of Pennsylvania, The Mayo Clinic and The Georgia Institute of Technology.

DESCRIPTION (provided by applicant): One of the most exciting developments in treating people with epilepsy, since the turn of the century, is a paradigm shift in our understanding of how epileptic seizures are generated. Rather than starting as abrupt, random events, new evidence suggests that seizure generation is probabilistic, with precursors that wax and wane before some synchronizing event triggers clinical seizures. This line of research has given rise to devices to warn of and pre-empt seizures, some now in clinical trials, and promises exciting therapeutic benefits to patients on the horizon. The research also has great potential to dramatically improve our understanding of the mechanisms underlying seizure generation and epileptogenesis, with even more profound clinical implications. Unfortunately, research in this field is significantly hindered by limited access to continuous, high quality, broad-band recordings from humans implanted with intracranial electrodes, and spontaneously seizing animal models of epilepsy. This is because these data are very expensive to acquire, extremely labor intensive, and the process of filtering, removing artifacts, and annotating recordings spanning weeks to months alone is prohibitive for all but the largest and best-funded investigative teams to undertake. This leaves literally hundreds of qualified scientists who would be actively working in this area unable to engage in this research. We propose to construct an international, collaborative database of broad-band, high quality, annotated intracranial data, from humans and spontaneously seizing animal models of epilepsy, centered at the University of Pennsylvania and Mayo Clinic. Data will be collected from the highest quality facilities worldwide, and made available to all investigators: academic, private and industry, for analysis. The database will be presided over by an international Scientific Advisory Board, and will eventually be a self-sustaining facility, funded by fees charged for data access. This effort will be the centerpiece of The International Collaborative Seizure-Prediction Group, a well-established international collaboration between the top laboratories in the world that study seizure generation, and whose meetings are supported by the National Institutes of Health, American Epilepsy Society, and European EEG Societies. This project will coordinate and collaborate openly with a European database of human intracranial recordings for clinical research. We aim to make this database outlined in this proposal a focal point for collaborative research in epilepsy, both basic science and translational, worldwide. Public Health Relevance: This project will make expensive, difficult-to-acquire, high quality data collected from electrodes implanted in patients during clinical care available to researchers world-wide who work on epilepsy. It will allow them to develop new sensors, devices and treatments for epilepsy. It will also help them understand how seizures and epilepsy begin, so that they can develop new treatments to prevent or cure them.

DESCRIPTION (provided by applicant): The objective of this project is to purchase a state of the art system to collect high- bandwidth multi-scale neurophysiologic data to understand functional networks in the human brain in health and disease. The equipment will form the core of a multi- disciplinary team of investigators in the fields of Neuroscience, Neurology, Neurosurgery, Psychology, Computational Neuroscience, and Engineering at the University of Pennsylvania. It will also dramatically increase the productivity of an international, multi-university collaborative research network we have established to collect and share human neurophysiologic data for research worldwide through the NINDS-funded International Epilepsy Neurophysiology Database. Major areas of research focus in our investigative team include epilepsy, memory and cognition, hearing, cortical dysplasia and movement disorders. Our collaborative group has been extremely productive to date, but relies on data gathered primarily at other sites, such as the Mayo Clinic, and Thomas Jefferson University Hospital. The equipment consists of a 256 channel, Neuralynx data acquisition and processing system, and 48 Terabytes of RAID storage to archive, process and share data. The project is a close collaboration between the Penn School of Medicine, where patients implanted with intracranial electrodes during evaluation for epilepsy surgery will be cared for, investigators throughout the university who craft studies to look at data coming from these patients, and the School of Engineering, where expertise for collecting, processing, archiving and analyzing these types of large, high-resolution data sets resides. Purchasing this equipment will dramatically expand our collaborative group at UPenn, and our research output, as it is currently limited by the availability of microelectrode data from other institutions. We are also poised to test prototypes of exciting new flexible, active electrode devices we have developed here at Penn, and the timing of this NCRR award will be synchronized with some of these first new human implants.

? DESCRIPTION (provided by applicant): The aim of our new, multidisciplinary institutional training program, based in the Schools of Medicine and Engineering at the University of Pennsylvania, is to train pre-doctoral engineering students and physician postdoctoral fellows in Neuroengineering and its clinical translation. Disorders of the nervous system, such as stroke, epilepsy, Parkinson's disease, depression, dementia, and head trauma, constitute 35% of all disease and disability, and the burden is rising. There is an explosion of promising therapies for these disorders in new technologies to image, analyze, and modulate neural circuits, but translating these therapies from the laboratory to patients is a challenge. It requires talented engineers educated in clinical science and technically proficient physicians who speak the same language. Together these investigators must navigate a complex scientific, regulatory, and clinical landscape. Despite the huge demand, few formal, interdisciplinary Neuroengineering training programs exist. We propose a new program shared by the Penn Schools of Medicine and Engineering focused on Neuroengineering and Clinical Translation. Physician and Engineering trainees will together become fluent in cutting-edge technologies at the forefront of Neuroengineering, such as devices, neurostimulation, machine learning, and algorithm development; cloud computing, nanotechnology and materials science. They will innovate new therapies for human disease and gain a thorough understanding of the clinical, regulatory, and developmental environments necessary to safely bring new technologies to patients. The program's core is a group of collaborative, multidisciplinary faculty mentors in engineering and the clinical neurosciences. In addition to dedicated neuroengineering research, our training program includes: (1) a longitudinal mentored clinical experience for PhD candidates, (2) engineering lab immersion and tutorial for MD postdoctoral fellows, (3) formal courses and seminars in Engineering, Neuroscience and Medicine, (4) training in the proper conduct of research, and (5) workshops on professional and career development, including scientific writing, public presentations, grant writing, and laboratory management. The program will recruit from an excellent pool of ~50 MD fellows and 80-90 PhD students each year. This effort formalizes a collaboration that has trained an impressive array of Neuroengineering investigators over the past twelve years. The program, unique at Penn, leverages a superb research and training environment, including a compact campus where robust centers for Engineering, Medicine and Neuroscience all reside within two blocks of each other, united through Penn's new Center for Neuroengineering and Therapeutics.

This workshop will explore key challenges and opportunities for the development of an open data ecosystem for neuroscience. It will bring together experts from academic, government, and other organizations, and is organized around three themes:
1. Incentives to Share, and What We Could Accomplish
2. Discoverability to Discovery
3. Sustainability: How to Fundamentally Change Science

The workshop is scheduled for July 25-26, 2016, in Washington, D.C. A resulting roadmap will facilitate alignment and coevolution of new and existing data sharing efforts, and powerful new modes of interdisciplinary discovery.

Generous co-funding is provided by the Divisions of Advanced Cyberinfrastructure (CISE/ACI), Behavioral and Cognitive Sciences (SBE/BCS), and Emerging Frontiers (BIO/EF).

Epilepsy affects 65 million people worldwide. While medications control many, over 20 million patients continue to have seizures despite maximal medical therapy. New surgical techniques, laser thermal ablation and responsive devices are exciting options for these patients, but their effectiveness is limited by our inability to accurately map which brain regions should be removed or treated with electrical stimulation. Currently, this mapping is done manually, but seizure onset patterns on intracranial EEG (IEEG) are frequently not well localized, and clinicians often disagree on seizure onset time, location, and what regions should be targeted. Finally, most patient evaluations present a number of viable options for surgery and device placement. There is currently no way to test the effects of a specific therapeutic approach- an operation or device placement- on outcome other than actually doing the procedure. A technique that could simulate these interventions and pick the best approach for individual patients would be a tremendous step forward in clinical care. In this proposal we develop and validate exciting new methods to localize epileptic networks from intracranial EEG that: (1) replace manual marking by clinicians with automated, objective tools, (2) remove the need for precipitating acute seizures during evaluation to localize them and (3) allow clinicians to simulate the effects of different brain surgeries or device placements for individual patients to select the treatment that will work best for them. This work marries new graph theoretical computational methods to model brain networks from IEEG with state of the art neuroimaging techniques to precisely localize implanted electrodes, devices and brain structure. Adult and pediatric patients undergoing brain implants during evaluation for epilepsy surgery or NeuroPace Responsive Neurostimulator (RNS) device placement will be enrolled at the Hospital of the University of Pennsylvania and Children's Hospital of Philadelphia. We will obtain high-resolution brain imaging before and after electrode implant and after surgery or device placement. Our models, recently published, will be applied to each patient's data and brain regions that drive seizures will be quantitatively identified and mapped to their brain images. Patients will undergo standard invasive therapy, either resection or device implant, and outcome- reduction in seizure frequency- will be compared to the amount of the epileptic network that is removed or stimulated by an implanted device. Finally, we will test our ?virtual resection? technique against each patient's data to predict which therapeutic intervention will be most effective, and compare this prediction to the performed procedure and patient outcome. This work differs from many computational studies in that its focus is on developing practical tools to guide invasive treatment for medication resistant epilepsy. It leverages an established collaboration between experienced clinicians in adult and pediatric epilepsy with experts in neuroimaging, bioengineering, functional neurosurgery and a MacArthur-award-winning computational neuroscientist at the University of Pennsylvania.

Project Summary The objective of this project is to purchase a state of the art system to collect high- bandwidth multi-scale neurophysiologic data to understand functional networks in the human brain in health and disease. The equipment will form the core of a multi- disciplinary team of investigators in the fields of Neuroscience, Neurology, Neurosurgery, Psychology, Computational Neuroscience, and Engineering at the University of Pennsylvania. It will also dramatically increase the productivity of an international, multi-university collaborative research network we have established to collect and share human neurophysiologic data for research worldwide through the NINDS-funded International Epilepsy Neurophysiology Database. Major areas of research focus in our investigative team include epilepsy, memory and cognition, hearing, olfaction and movement disorders. Our collaborative group has been extremely productive to date using a 256 channel Neuralynx recording system that was funded through an S10 grant awarded in 2011. The equipment being requested in this submission consists of a 512 channel, Blackrock Neuroport data acquisition and processing system for neural recordings. The project is a close collaboration between the Penn School of Medicine, where patients implanted with intracranial electrodes during evaluation for epilepsy surgery will be cared for and the School of Engineering, where expertise for collecting, processing, archiving and analyzing these types of large, high-resolution data sets resides. Investigators throughout the university have current research projects and will craft studies to look at neurophysiological data coming from these patients. Purchasing this equipment will dramatically expand our collaborative group at UPenn, and our research output, as it is currently limited to recording 256 channels from the Neuralynx. We are poised to test prototypes of exciting new flexible, active electrode devices we have developed here at Penn, and the timing of this award will be synchronized with some of these first new human implants.

Implantable devices are playing a greater role in neurologic care, but their effectiveness is limited, because they are blind to human thoughts, feelings, and behavior ? factors that most dramatically affect our health. Coupling peripheral sensors to implants might help, but wouldn?t it be easier if the devices just asked us? Armed with this knowledge, next generation machines will more effectively drive neural activity in the brain to healthy states. They will also quickly learn behaviors that worsen health and guide us to better choices. Though DARPA, the NIH, and Neuralink are spending millions of dollars on new hardware for brain-computer interfaces, none focus on reciprocal, natural communication between host and machine. There is a desperate need for novel, practical methods that enable devices to learn from and guide human behavior. In this application I propose to develop a new generation of autonomous brain-machine interfaces ? devices that can question, record, act - and combine learning algorithms applied to neurosignals with teaching by their human hosts. Life with these implants will entail a subtle human- machine dialogue in which devices and humans teach and learn from each other. Humans will inform intelligent algorithms about what we are doing and feeling, while machines will incorporate this information into therapy and guide us to optimize quality of life in personalized ways. This is a paradigm shift from today?s simple devices, which are programmed by physicians during occasional office visits. I propose to demonstrate this paradigm in a practical, scalable way using current epilepsy implants that is rapidly translatable to many neurological disorders. To achieve this goal, I will meld several cutting-edge technologies in novel ways, including: (1) State-of-the-art, high bandwidth implantables that sample neural activity, link to vast cloud- based computational power to process it, and intervene to modulate brain, spinal cord or peripheral neural activity. This work utilizes my experience from the past 20 years; (2) I will deploy powerful new computer science tools in novel ways. I will use convolutional neural nets (a.k.a. Deep Learning) to learn patterns from vast streams of continuous high-bandwidth neural data, build a two way human-machine interface using Natural Language Processing (NLP)., and probe networks with changes in human behavior and electrical stimulation and guide interventions toward therapeutic goals using Reinforcement Learning. Combining these computer science, machine learning techniques and measurements of human behavior is a new area of investigation for me that will leverage my unique background in clinical neurology and engineering to build a new class of interactive, human therapeutic devices.