Bruce Gluckman - US grants

IBN 97-27739 SO, SCHIFF, GLUCKMANN. An understanding of synchronous activities within an ensemble of neurons is essential in the study of neuroscience. It is important to understand and characterize both the computation within an ensemble, as well as the information flow between different ensembles within the brain. In the so called "binding problem", when spatially disparate neurons must coordinate to compute aspects of sensory perception, synchrony is essential. Traditionally, these issues have been addressed using the concept of identical synchrony (IS) which assumes that two or more ensembles of the brain are performing the same activities in locked time step with each other. However, in ensembles with generic nonlinear components, of which neuronal ensembles are most certainly included, more complex coherent behaviors should arise. Consequently, our concept of dynamical coherence beyond identical synchrony must be broadened. Chaos theory broadly encompasses the study of such nonlinear dynamical systems. A major theoretical advance in this field was the recognition that seeming erratic behaviors from these nonlinear systems could be effectively characterized by a set of special unstable equilibrium states. In a cartoonist view, these so called unstable periodic orbits (UPOs) are hills and valleys of an abstract dynamical landscape. As the system progresses in time, the state of the systems can be described by a trajectory within this dynamical landscape constructed with the UPOs. For coupled systems (neurons), the arrangement and symmetry of these hills and valleys reflect the varying degree of dynamical coherence exhibited within the system. Most importantly, analogous to statistical mechanics in physics, these UPOs form a framework of microscopic states for the system and their structural changes afford a description for the topographical changes within this dynamical landscape. A thermodynamical description based on these UPOs for the various possible dynamical coherent states might then be constructed. Theoretical tools developed will be applied to quintessential examples of neuronal coupling from our archived biological data: two coupled neurons and two ensembles of neurons. Results from this project will both theoretically broaden our understanding of coupled nonlinear oscillators, including neurons, coupled mechanical and electronic devices, etc., and will serve as the initial attempt to experimentally characterize the grammatical code used between ensembles of neurons.

DESCRIPTION (provided by applicant): An applied electric field can be used to modulate a neuron' s activity. Although the advantage of employing fields for implementation in the design of neural prosthetics was recognized from invertebrate research, the field lay fallow for nearly a decade. Over the past five years, we have moved this field forward significantly. In recent papers, we have demonstrated that a DC electric field could be used to suppress or enhance epileptiform activity in hippocampal slices (Gluckman, et al., 1996a), and, when applied adaptively, could turn off seizures indefinitely (Gluckman, et al., 2001). One advantage of using electric fields to interact with neuronal networks is that, if properly instrumented, it can be done simultaneously with ongoing measurement of neuronal activity. Therefore, feedback can be easily implemented. But, one of the reasons electric fields have not been pursued is that readily available measurement and stimulation electronics are not easily adaptable for use with electric field stimulation. In addition, until recently, biocompatible electrode materials with sufficient charge passing capacity to produce sustained electric fields were not available. The aims of this project are to translate our existing seizure control techniques for chronic in vivo animal use. This will require design of instrumentation for simultaneously stimulating with electric fields and recording neuronal activity in intact brain, to establish safety limits for biocompatible electrodes under electric field stimulation, and to develop and test feedback seizure control algorithms. The instrumentation and methods developed will be prototypes of a novel neuronal interface based on electric field stimulation. Such an interface would lay the groundwork for a new generation of medical devices to treat dynamical diseases of the brain such as epilepsy, to provide an interface for neuronal prosthetics, as well as provide an arsenal of new tools for probing the complex dynamics of neuronal systems.

Project Summary Nearly 30% of the two million Americans suffering from epilepsy continue to have seizures despite treatment. There is now a growing acceptance of stimulation devices as a mean for therapeutic neuromodulation. To provide sophisticated feedback stimulation, one can either respond early into the seizure in order to minimize its impact and spread, or one can respond before the seizure to some other state. Identification of a suitable preseizure state has been a central theme for quite a while that now falls under the rubric of seizure prediction. Identification of such a preseizure state could both indicate when to stimulate in order to avert an oncoming seizure. But, the experience in the seizure prediction community has been that all measures used so far yield a significant false detection rate for significant levels of sensitivity. This work will be performed in the tetanus toxin model for temporal lobe epilepsy. We have developed a system for applying low frequency electrical stimulation for modulation of neuronal activity without interfering with our ability to record neural activity in chronically implanted animals (Sunderam, et al, 2006), and therefore can apply feedback stimulation. With head acceleration measurements, we are able to determine state of vigilance (Sunderam, et al, 2007). The aims of this grant are three fold. First, to investigate if the addition of state of vigilance as a discrimination feature improves identification of preseizure states. Second, to implement and test an active probe of brain state through small amplitude stimulations to detect changes in brain state indicative of a preseizure state. We expect after implementing both the passive and active prediction methods that we will still observe significant false prediction rates. The third aim is to probe through stimulation the nature of these detections (a) if the false predictions are simply misclassifications OR (b) if the identified preseizure state is seizure permissive - a state that only sometimes transitions to seizure - and the 'false predictions' are correct identifications of this state. From a basic science standpoint, this should give insight into the seizure generation process and long-term treatment. From a more practical short-term application standpoint, detection of a seizure permissive state and the relevant transition probabilities will have great utility in the development of a useful feedback intervention. Specifically, one then targets intervention - for example electrical stimulation - in response to detection of the state to modify this transition probability. The extension of this work in future years will be to test a range of responsive stimuli to preseizure detections for their ability to prevent seizure.

DESCRIPTION (provided by applicant): This CRCNS application derives from work performed in a current DAAD (Deutscher Akademischer Austausch Dienst, German Academic Exchange Service) Grant between the Technical University of Berlin and Penn State University entitled: "Feedback control of spreading depolarizations in neural systems: Theory and Experiments". The design of this CRCNS proposal, and all preliminary data, were generated during the course of German Faculty and PhD students coming to Penn State University, and the synergistic collaborative efforts to establish the feasibility of feedback control of spreading depression. Spreading depression (SD) is a dramatic depolarization of brain that propagates slowly and is the physiological underpinning of the initial aura in migraines. The following hypothesis is posed: SD can be represented in computational models of the underlying neuronal biophysics, and can therefore be controlled using model-based control strategies. The project starts by developing an experimental preparation using a tangential 2-dimensional visual cortex rodent brain slice. SD is triggered with a perfusate potassium perturbation, and SD is imaged using a sensitive CCD camera that detects the intrinsic optical imaging signal associated with index of refraction changes from cellular swelling. A model-based strategy similar to that used in autonomous robotics such as airframe autolanders is employed. A hardware and software control system takes the optical image in real-time, fuses it with a model of SD, reconstructs the underlying physiological processes, calculates needed control, and modulates an electrical field to modulate SD. Both biophysically accurate models of the neuronal compartments and ion flows, and reduced models that reflect the dynamics of the wave propagation, will be used as observation and control models. Intellectual merit: This will be the first experimental demonstration of model-based control of a neuronal network. Similar engineering strategies have revolutionized advanced robotics, and the fundamentals learned from a fusion of computational neuroscience with control engineering will have wide ranging adaptations in other areas of neuronal modulation. Furthermore, this will be the first model-based control of a physiological mechanism that underlies a dynamical disease of the brain - migraine auras. The control models will further serve as probes to gain increased understanding of the mechanisms of SD. The team assembled has a substantial track record in the range of disciplines required to carry out this project: neurophysiology, experimental and theoretical physics, computational neuroscience, control theory and neural engineering. The preliminary work shown in the proposal suggests that this project is feasible given the resources requested. Broader impact: Fusing computational neuroscience models with modern model-based control theory will lay the foundation for a transformational paradigm for the observation of activity within the brain, as well as access to a more optimal technology for the control of pathological processes in the brain. A transdisciplinary German-American educational collaboration will be formed where the graduate students trained (and the PIs) will synergistically work together within the interface between computational neuroscience, control theory, experimental neurophysiology, and control system engineering. The PIs have a track record in training and mentoring women and underrepresented minorities, and they will make every effort to seek such trainees for the mentoring opportunities of this project. As a collaborative partnership, the PIs anticipate that what is learned in controlling SD may provide a set of testable strategies for electrical control of migraines in people who suffer from severe migraine attacks and are pharmacologically intractable. Furthermore, based upon this CRCNS, the same science and engineering will be applicable to the modulation of oscillatory waves and rhythms in both in vitro (e.g. Schiff et al 2007) and in vivo (e.g. Sunderam et al 2009) systems. They plan to widely disseminate the algorithms and hardware design developed as described in the Data Management Plan.

DESCRIPTION (provided by applicant): The overall objective of this effort is to convene the Sixth International Workshop on Seizure Prediction, a forum that brings together an international interdisciplinary group involving epileptologists, engineers, physicists, mathematicians, neurosurgeons and neuroscientists focused on the development of treatments for epilepsy especially based on closed loop prediction and interventions. While this goal has not changed, the group has recognized that the focus of attention needs to shift away from a biological mechanism-free algorithmic approach to EEG and related electrophysiology recordings, and toward a better understanding of underlying physiological mechanisms as they specifically relate to clinical recordings. At a fundamental level, the goal of seizure prediction is to identif the underlying mechanisms of seizure generation and to engineer systems that will detect those dynamics and provide for intervention. This meeting will focus attention on basic questions at the intersection of engineering, computational and epilepsy neuroscience necessary for predicting and intervening with seizures: how seizures arise in the neocortex, how to identify sites of seizure origination, how seizures spread, and how they terminate. We have identified these topics as key roadblocks to further advances in treatment of pharmacoresistant epilepsy syndromes. Through these questions we will focus attention on bridging clinical and physiological metrics to arrive at new approaches to understand epileptic seizure generation mechanisms across all scales of brain from neuron to organism, and especially close the loop between models of seizure generation and physiological observations.

? DESCRIPTION (provided by applicant): The objective is to convene the Seventh International Workshop on Seizure Prediction (IWSP). This meeting focuses on the development of treatments for epilepsy especially based on closed loop seizure prediction, detection and interventions. The IWSP series is a unique forum that brings together an international interdisciplinary group involving epileptologists, engineers, physicists, mathematicians, neurosurgeons and neuroscientists. While this goal has not changed over the previous six workshops, the group has recognized that there needs to be a balance in developing an understanding of the biological mechanisms underlying epilepsy in parallel with treatment oriented approaches in order to improve the performance of seizure prediction, detection and control algorithms, and with the ultimate objective of improving quality of life for the epileptic patient. At a fundamental level, the goal of seizure prediction is to identify the underlying mechanisms of seizure generation and to engineer systems that will detect those dynamics and provide for intervention. Activity in the field of seizure prediction and control is reinvigorated.The clinical trial of the NeuroVista seizure advisory system produced months-to-years long ambulatory intra-cranial EEG from human subjects, and demonstrated that seizure prediction is feasible. The recent FDA approval of the Neuropace RNS closed loop seizure control device has further galvanized the field. These two thrusts provide realistic and relevant data streams to advance this field. This seventh meeting will address three key themes: (1) Seizure prediction from months-to-years long recordings; (2) Model-based data assimilation for time series analysis, neuro-intervention and feedback control; and (3) Multi-modal neuroimaging and computational modeling to assess the spatial origin of seizures and the epileptic network at multiple scales. These themes will be complemented by the prior topics in the IWSP series. In line with theme (1) there will be a human-data seizure prediction contest using recordings from the long-term NeuroVista Seizure Advisory System clinical trial. Recent access to ultra long-term high-quality data means there is now greater ability to evaluate both seizure prediction algorithms and computational modeling in ways that were previously constrained by the limited physiological measurements. Through the modeling themes we aim to build bridges between theoretical, computational, physiological and clinical metrics to arrive at new approaches to understand epileptic seizure mechanisms across all scales of the brain, and thereby develop improved methods for seizure prediction, detection and control. NIH funding is sought for travel support to encourage US participation.

DESCRIPTION (provided by applicant): Sleep is a fundamental biological cycle that is coupled into every aspect of body function from behavior and information processing to metabolic storage and release. Sleep-wake patterns correlate with, and sleep disruptions are comorbid with, many neurological and mental health disease dynamics including epilepsy. Abnormal sleep can be disruptive to quality of life and further exacerbate the primary disorders. Within the past decade a number of groups have developed mathematical and computational dynamical models for the network of brain nuclei and cell groups that regulate sleep-wake dynamics. But their validation to date has been substantially limited to reproduction of statistic of cycle time and dwell time durations, and their application to understanding and control of diseases limited. The first objective of this project is to validate and optimize these models for reconstruction, forecasting, and control of sleep-wake regulation. This involves experimentally recording activity from select cell groups of the sleep-wake regulatory system (SWRS) along with cortical, hippocampal, and behavioral activity. The mathematical models will be incorporated into model-based data assimilation (DA). The parameters and models will be optimized for reconstruction and forecasting, and performance will be used to establish the 'best' model. Experimental perturbation of sleep state and sleep cycle dynamics will be done with both sensory and direct neural stimulation. The models will then be modified to account for and predict changed dynamics from such perturbations. The second objective of this project is to apply these models and framework to understand and control sleep-cycle dis-regulation in a model of temporal lobe epilepsy. This involves experimentally recording activity from the SWRS in epileptic animals, modifying and optimizing the models to reconstruct and forecast the observed sleep cycle dynamics. The models will then be used in closed feedback form to prescribe control perturbations to regularize the sleep cycles of the epileptic animals. The project embodies a paradigm shift for neuroscience and neural-engineering in which computational models are validated and optimized through their capacity to reconstruct and forecast detailed time series from real neurological measurements, that such model-based reconstruction is used to observe detailed state dynamics from less costly (invasive or damaging) measurements, and in which such biologically based models are used to control neurological systems and treat neurological disorders. The approach of this proposed research will have a major impact in diagnosing, monitoring, and controlling neurological disorders by both incorporating detailed biologically based models into the measurement or observation process, and by allowing remote observation through measurement of identified less costly measurements. The specific validation and improvement of computational models and observation methodology of the sleep-wake regulatory system will allow detailed investigation of its role in a host of neurological diseases in which sleep regulatin is implicated either as a cause or consequence, such as epilepsy and schizophrenia, and thereby the development of interventions or therapy. In addition to the theoretical and experimental advances, educational and outreach will be served through this project, including development of new course materials and enhancing underrepresented participation in research.

In the course of its normal function, the brain produces toxic substances that accumulate and are transported from the space between brain cells. If these substances are not cleared, their accumulation is thought to yield crippling results such as Alzheimer's disease and migraines. The mechanics of this clearance is poorly understood, so this research project aim to study and characterize this process. Experimental techniques and computational approaches are being combined to produce a predictive clearance model based on fundamental mechanics principles of fluid flow and diffusion. The experimental study is being conducted in vivo, which will allow for a physiologically-relevant match between brain function and the corresponding deformation of brain tissue and the associated flow of the fluid in-between cells. This study is relevant for advancing the state of the art in neurophysiology and for future development of therapeutic interventions, both pharmacological and surgical, for addressing pathologies including Alzheimer's disease, hydrocephalus, and migraine. This project has an educational component aiming at training graduate and undergraduate students in advanced neuroscience research and in biomedical engineering. Specifically, the researchers and developing and offering a level-appropriate laboratory and computational projects for undergraduates with a focus on the merging of experimental techniques and mechanics in neuroscience.

This project focuses on delivering the first mechanics-based model of the effects of neurovasculature coupling on transport in the brain. A theoretical and computational framework is being created to model multiple concurrent transport mechanisms in a computational framework that integrates empirical in vivo observations of the brain micromechanical neurovascular response to chosen stimuli. The biomedical problem motivating the proposed research is the comparative assessment of convective and diffusive mechanisms for toxic metabolite clearance from the brain interstitium. Buildup of these compounds can be strongly neurotoxic and can trigger neuronal functional instabilities with severe, if not lethal, consequences---from spreading depolarization to epilepsy to Alzheimer's disease to mental illnesses. While vital for brain function, metabolite transport and clearance remains poorly understood. The specific project goals are: 1) To model brain tissue as a deformable porous medium with embedded vasculature, and to apply a numerical scheme developed by the PIs for predicting transport driven by blood vasodilation; 2) To identify sets of relevant physiological conditions from the experiments, and, from these, to define corresponding metabolite transport boundary value problems. Pulsation (heart-gated blood vessel dilation) and functional hyperemia (neurovascular coupling driven vessel dilation) will be considered. Anatomical, material, and loading parameters will be inferred using in vivo two-photon microscopy in the brains of living mice with cranial windows. Fluorescence-based digital image correlation will deliver microscale deformation maps of brain tissue. Fluid flow in the brain will be visualized by infusing fluorescent dyes; 3) To numerically solve the problems in goal 2 to determine interstitial fluid flow and metabolite transport through deformable tissue with convection and diffusion as concurrent mechanisms. Ranges of physiological conditions and constitutive parameters are being tested, and fluid-structure interaction between tissue and fluid-filled paravascular space are being explicitly modeled. The high selectivity of the blood-brain barrier remains a major challenge in developing effective drug delivery methods for brain cancer, dementia, spreading depolarization, and epilepsy. By focusing on metabolite transport in brain, this research project will contribute to advancing pharmacological and surgical therapies for many brain pathologies.

Abstract There is a several decade history demonstrating that electrical polarization of neurons can modulate neuronal firing, and that such polarization can suppress (or excite) spiking activity and seizures. We have demonstrated seizure control using both open- and closed-loop stimulation strategies (J Neurophysiol, 76:4202-4205,1996; J Neurosci, 21:590-600, 2001). With past NIMH and CRCNS support (R01MH50006, 1R01EB014641) ? we discovered a unification in the computational biophysics of spikes, seizures, and spreading depression (J Neurosci, 34:11733-11743, 2014). These findings demonstrate that the repertoire of the dynamics of the neuronal membrane encompasses a broad range of dynamics ranging from normal to pathological, and that seizures and spreading depression are manifestations of the inherent properties of those membranes. Recently we achieved a major experimental verification of key predictions from the unification predictions in in vivo epilepsy. Most recently, we achieved the experimental goal of the most recent CRCNS project, ?Model-Based Control of Spreading Depression?, by demonstrating that neuronal polarization can suppress (or enhance), block, or prevent spreading depression, the physiological underpinning of migraine auras. Remarkably, this suppression requires the opposite polarity as that required to suppress spikes and seizures, and is fully consistent with the computational biophysical models of spreading depression. Further surprising findings from these experiments was that suppression of spreading depression does not appear to generate seizures, and vice versa, that when the brain is in seizure activity suppression does not generate spreading depression. The implications of the above is that in controlling brain dynamics from different states of the brain, that there can be state dependent control which is qualitatively very different from that required in other states. Furthermore, the control algorithms required to maintain a given steady state (e.g. normal spiking) may differ from that required to guide a system from a pathological state back into a steady state. We propose the hypothesis that there is an entirely new framework for feedback control of neuronal circuitry ? State Dependent Control. This is a model-based framework, wherein neuronal systems are sensed through electrical or optical sensors, and the data assimilated into a biophysical computational model of the possible states. Feedback control is then applied based upon the state, and the trajectory of the system through state space is continually observed. Working out state dependent control for brain activity has health implications for not only epilepsy and migraine, but more broadly in intensive care settings because of the harmful effects of spreading depression waves in traumatic brain injury, stroke, and subarachnoid hemorrhage.

Project Summary The Cross-Disciplinary Neural Engineering (CDNE) predoctoral training program will train the future research leaders able to bridge across the disciplinary boundaries of engineering, sciences and mathematics to neurosciences and the treatment of human brain health. Such leaders are necessary to produce lasting and relevant engineering, scientific and medical findings that will lead to improved human health. The CDNE program build from the strengths of the affiliated graduate programs and Penn State?s Center for Neural Engineering to create a new training program focused on cross- disciplinary training for graduate students. Trainees coming from a range of disciplinary programs including engineering, physics, mathematics and neuroscience graduate programs will complete a common neuroscience course core; be co-mentored by at least two trainers across disciplinary realms of Materials and Devices, Theory and Computation, Brain Physiology, and Human Brain Health; and participate in a range of activities designed to enhance cross disciplinary communication and collaboration, quantitative approaches, and scientific rigor. The CDNE will augment existing graduate programs in Engineering Science and Mechanics, Mechanical Engineering, Electrical Engineering, Mathematics, Physics, Anthropology, Neuroscience, and Biomedical Engineering, to provide specialized training in neural engineering. The CDNE program will fund the training of a total of 15 graduate students during the five years by a combination the NIH (10) and PSU (5) funded fellowships. Each trainee will be supported for a two-year period starting in their 3rd year. Trainees will gain experience working across disciplines and able to be both experts in one domain, and able to understand the language, science needs of other domains and thereby make significant contributions bridging across both.