Mark A. Kramer - US grants

DESCRIPTION (provided by applicant): PROJECT SUMMARY / ABSTRACT Across the world, nearly 50 million people suffer from epilepsy. For an estimated 30% of these individuals, seizures remain poorly controlled despite maximal medical management. Moreover, treatment of epilepsy through medication often results in significant - sometimes debilitating - side effects. To advance the therapeutic management of epilepsy, we must understand the physiological mechanisms which support this disease. Unraveling these mechanisms is especially difficult. Like many neural processes, the seizure involves spatial scales spanning many orders of magnitude, from the individual neuron to the entire nervous system. Moreover, typical clinical recordings provide only a limited view of the vast mechanisms underlying the seizure. In this project, an interdisciplinary research group consisting of clinicians, statisticians, and mathematicians will study the dynamical mechanisms that support epilepsy and, in doing so, suggest novel therapies to treat the disease. The research will focus on three fundamental aspects of the seizure - how it begins, how it spreads over the brain, and how it ends - observed in voltage recordings from human patients with epilepsy. These recordings will span multiple spatial scales and include activity generated by individual neurons, small neural populations, and large brain regions. To characterize these data, sophisticated analysis techniques will be applied that link the activity across spatial scales. In addition, computational models of the neural activity will be developed and constrained using the multiscale data. These models will then be applied to suggest the mechanisms that support multiscale interactions during seizure and to propose novel therapies to treat epilepsy. Completion of the proposed research will represent a significant step forward toward a deeper and more complete understanding of epileptic physiology, and toward a system for exploring and testing innovative methods for seizure control. PUBLIC HEALTH RELEVANCE: PROJECT NARRATIVE Epilepsy is a devastating and poorly understood illness that evolves on multiple spatial scales. During a seizure, multiscale interactions are prominent yet the mechanisms that support these interactions remain unknown. To address (and eventually treat) these mechanisms, an interdisciplinary team of researchers will record, analyze and model multiscale voltage data from human patients with epilepsy.

? DESCRIPTION (provided by applicant): Advances in technology for measuring neuronal activity at ever-larger scales and with increasing spatial and temporal resolution, concomitant with a decrease in costs of data storage, are driving a revolution in neuroscience. The era of big data is reflected in a number of major funding initiatives that have begun worldwide to support neuroscience research, and especially neuronal data collection. As a flood of neuronal data accumulates worldwide, a new challenge faces the global neuroscience community: how to make sense of these complex data to drive basic biological insight and to shed new light on neurological and neuropsychiatric disorders. This new, data-driven era of neuroscientific research demands that investigators master the fundamental methods in time series and image analysis and know when and how to appropriately apply these methods, either in custom applications or in existing software packages. Accessible - yet rigorous - resources to develop hands-on experience with modern data analysis techniques are lacking in neuroscience. To address directly this current and growing worldwide challenge, we propose to develop an innovative open online course (or OOC). To reach the largest target audiences - the biologists, psychologists, and clinicians immersed in neuronal data - we will assume only a basic mathematics background and limited familiarity with computer programming, common to those trained in biological sciences. The proposed OOC will target investigators at all career levels - spanning from the beginning undergraduate researcher to the established PI - to analyze and understand neuronal data. Through an interdisciplinary case-study approach, we will use real-world neurophysiological data (including data available from large, emerging public repositories) to motivate the study of modern quantitative analysis methods. The OOC will comprise 15 independent modules. The first two modules will emphasize programming in MATLAB for neuroscientists and computational techniques relevant for large datasets. Each additional module will focus on one category of neuroscience case-study data, and will consist of multimedia material combining video lectures, MATLAB-based examples, and quantitative assessments. The modular format will provide multiple coherent learning paths through the online content, and thereby allow personalized learning for individuals with varying quantitative backgrounds and research interests. The OOC format will also permit the developed resources to be widely available, disseminated, and discoverable. The proposed OOC will prepare researchers with the fundamental skills required for the analysis of neuronal big data, and elevate the general competencies in data usage and analysis across the research workforce.

Epilepsy - the condition of recurrent unprovoked seizures - is a brain disorder that affects 3 million people in the United States. Although the symptoms of epilepsy have been observed for millennia, the brain processes that support human seizures remain poorly understood. This lack of understanding has a profound clinical impact; in one-third of patients with epilepsy, seizures are not adequately controlled. Animal studies provide powerful methods to uncover the potential mechanisms for epilepsy, yet how the results from these studies relate to human epilepsy remains unclear. Although some mechanisms of epilepsy may be consistent in animal models and humans, differences occur, and these differences are critical to understanding and treating human epilepsy. The PI's goal is to improve understanding of the mechanisms that drive human seizures and thereby advance therapeutic management of this disease. To do so, brain voltage recordings made directly from human patients will be analyzed. Motivated by these patient data, mathematical models will be developed that describe the activity of individual neurons and small populations of interacting neurons. The mathematical models will then be used to study the biological mechanisms that support the different brain voltage rhythms that appear during seizure, and how these rhythms move across the surface of the brain. Ultimately, these mathematical models will provide new insights into human epilepsy, and help identify novel approaches to improve patient care. The PI will also include integration of research data and methods into an undergraduate course in computational neuroscience, publish a textbook and online course in neuronal data analysis, and provide undergraduate and graduate research training in computational neuroscience, with a specific emphasis on clinical data and computational modeling.

The PI aims to improve understanding of the ionic and neuronal mechanisms that govern the brain's stereotyped spatiotemporal dynamics during human seizure. To do so, a computational modeling framework will be developed that incorporates individual neuron dynamics in cortical and subcortical structures and ion concentration dynamics in the extracellular space. Model behavior will be explored through simulation and dynamical systems techniques, and model features will be constrained to match microelectrode array recordings of seizures in human patients. The modeling framework will be used to test the hypothesized scenario that a class of cortical interneurons serve as the first line of defense against the outbreak of seizure, but eventually fails upon entering depolarization block. Concomitant with this failure, another circuit activates to the support large amplitude, spike-and-wave dynamics, which appear as traveling waves that sweep across the cortical surface. Two main research goals are the focus of the project. First the modeling of human seizure data will provide new insights into the mechanisms of medically refractory epilepsy, and help identify biological targets for novel pharmacological approaches to improve patient care. Second, to understand brain function and dysfunction, a deeper knowledge of cortical and subcortical neuronal dynamics combined with ion concentration dynamics is required. In this project, the stereotyped dynamical state of seizure motivates models that implement these dynamics to examine principles that support spatiotemporal patterns in the human brain. Educationally the PI will develop new interdisciplinary training in computational neuroscience. This will be done through integration of research data, analysis methods and computational technology in the undergraduate classroom, publication of a textbook and development of an online course describing cases studies in neural data analysis, and directed graduate and undergraduate research in computational neuroscience.

? DESCRIPTION (provided by applicant): Epilepsy is one of the most common neurological syndromes, affecting an estimated 3 million people in the United States. In one-third of these patients, seizures cannot be controlled despite maximal medication management. The complexity of the neuronal network dynamics that define the epileptogenic cortex and drive seizure initiation and spread makes understanding and treating epilepsy a unique challenge. In this proposal, an interdisciplinary research team will address this challenge. The assembled researchers integrate clinical expertise and data recording capabilities with sophisticated network analysis and statistical modeling techniques. Utilizing invasive brain voltage recordings, dynamic functional networks will be inferred from a population of patients during spontaneous seizures. To characterize these dynamic networks, new data analysis and statistical modeling techniques tailored to address the unique challenges of the clinical human data will be developed. These techniques will be applied to understand the sudden, explosive emergence of well-connected subsets of nodes (a.k.a., communities) in the noisy, real-world environment of human cortical seizure dynamics. Understanding the rapid network organization at seizure onset and termination will inspire new treatment strategies for epilepsy, and motivate developments and applications in the emerging theoretical research field of explosive percolation. The proposed research will advance scientific knowledge and understanding in three ways. First, the development and application of novel dynamic network analysis techniques to clinical seizure data will provide a deeper understanding of human epilepsy and the network interactions that underlie seizure initiation and termination. Second, the proposed research requires new tools to characterize and track community structure in noisy, dynamic networks. Development of these tools will help to address open questions and unexplored directions in the study of transient and recurrent community patterns emergent in dynamic networks. All dynamic network analysis tools developed in this proposal will be made freely available for other researchers to apply and develop. Third, by utilizing complex neurophysiological data, the proposed research will ground the field of explosive percolation in noisy real-world phenomena, and motivate new developments and applications critical to this emerging science. There are three broader impacts of the proposed research. First, the dynamic network analysis and statistical modeling of human seizure data will provide new approaches to improve patient care of medically refractory epilepsy. In particular, through prospective and retrospective studies, the dynamic network analysis and modeling techniques will be applied to identify principled surgical targets, and predict which patients will - and will not - benefit from surgery. Second, the dynamic community detection tools and statistical models developed will have general applicability across many domains of science. These tools can be applied broadly within systems neuroscience - to elucidate brain dynamics underlying healthy brain function and present in pathology - and in many other scientific fields (e.g., cell biology, ecology, social sciences, distributed computing, to name a few) in which dynamic networks appear. Third, the proposed research will provide unique training opportunities for graduate students in translational neuroscience, with a specific emphasis on clinical data, network inference and dynamical network analysis, and statistical modeling. These trainees will develop unique interdisciplinary skills in clinical, statistical, and computational neuroscience.

PROJECT SUMMARY Although rhythms are a prominent feature of brain activity, the role of rhythms in brain function (and dysfunction) remains elusive. Rhythms have been proposed to organize information transfer within and between brain regions by modulating neural excitability at different time scales. Rhythms have also been proposed to interact across these different time scales, a phenomenon labeled cross-frequency coupling or CFC. Clinical and experimental observations have identified many different types of CFC, such as coupling between the phase of a low frequency rhythm and the amplitude of a high frequency rhythm (phase-amplitude coupling), or between the phases of two different frequency rhythms (phase-phase coupling). Many functional roles for CFC have been proposed, including in working memory, neuronal computation, communication, learning and emotion. Despite the mounting experimental evidence for CFC, three important challenges remain that limit understanding of this phenomenon. First, many different data analysis methods have been developed to characterize CFC, with each method typically focused on one type of CFC. Choosing an inappropriate method weakens statistical power and introduces opportunities for confounding effects. Second, analysis of CFC typically occurs post hoc, prohibiting opportunities to modulate CFC during an experiment. New methods are needed to assess CFC in real time while limiting the impacts of potential confounds. Third, the mechanisms that produce CFC are not known. While computational models developed to explore these mechanisms provide important insights, these models have been mainly restricted to synaptic mechanisms of rhythm generation and associations between two types of rhythms. New models are needed to examine the role of other rhythms and rhythm generating mechanisms in CFC. Inclusion of more realistic biological features in simulations of neural rhythms facilitates exploration of a new challenge: how electrical stimulation modulates CFC. In this project, an interdisciplinary research group consisting of a statistician, a mathematician, and a psychiatrist-engineer will analyze, model, and modulate cross-frequency coupling. To do so, the team will develop and apply a statistical inference framework suitable for real time analysis of CFC, and apply this framework to analyze - and modulate with electrical stimulation - in vivo recordings from rat cortex and subcortex. The team will also develop computational models of CFC, to link the observed data to cellular mechanisms, and create hypotheses testable in the in vivo experiments. Completion of the proposed research will represent a significant step forward toward a more complete understanding of cross-frequency coupling, and toward a system for exploring and testing innovative methods for its modulation.

PROJECT SUMMARY Epilepsy is the world?s most prominent serious brain disorder, affecting nearly 50 million people worldwide. For an estimated 30% of these patients, seizures remain poorly controlled despite maximal medical management, with significant financial costs and effects on health and quality of life. To advance the therapeutic management of epilepsy requires a more detailed understanding of the spatiotemporal dynamics that drive seizures. Characterizing these dynamics is especially difficult because, like many brain functions, the processes span spatial and temporal scales, from the fast activity of small neural populations to the slow evolution from seizure onset to termination of large brain regions. How brain signals at one scale relate to those at other scales is a significant and poorly understood issue. While animal models of epilepsy provide powerful techniques to investigate detailed neural activity within and between spatial scales, the relationship of these models to human epilepsy is unclear. An alternative to animal models of epilepsy is to study spontaneously occurring seizures in vivo from a population of human patients. However, typical in vivo clinical recordings provide only a limited view of a seizure?s multiscale dynamics. In this project, an interdisciplinary research group consisting of epileptologists and clinical neurophysiologists, a statistician, and a mathematician will study the spatiotemporal dynamics of human seizures. To do so, the team will analyze simultaneous microelectrode and macroelectrode recordings from human patients during seizures, with a particular focus on the organized spatiotemporal patterns and high frequency oscillations common in epilepsy. To make sense of these data, the team will develop and apply new methods to characterize these patterns, and link these activities to candidate mechanisms in computational models. Completion of the proposed research will represent significant progress towards a deeper understanding of human seizures, new methods to analyze and model the spatiotemporal dynamics of seizures observed in complex multiscale data, new methods to estimate model parameters and variables from brain voltage recordings, and new candidate targets for surgical treatment of epilepsy.