Dr. Steven Schiff: Model-based Observation and Control for the Brain: From Control of Seizures and Migraines, to Reducing Infant Brain Infections in Africa
Brush Chair Professor of Engineering in the departments of Neurosurgery, Engineering Science & Mechanics, Physics, and BioE, Pennsylvania State University
Bio: Steven J. Schiff, Brush Chair Professor of Engineering and Director of the Penn State Center for Neural Engineering, is a faculty member in the Departments of Neurosurgery, Engineering Science and Mechanics, and Physics. A Pediatric Neurosurgeon with particular interests in Epilepsy, Hydrocephalus, Sustainable Health Engineering and Global Health, he holds an S.B. degree from MIT, and a Ph.D. in Physiology and M.D. from Duke University School of Medicine. His book, Neural Control Engineering, was published by the MIT Press in 2012. Dr. Schiff has been listed in the Consumer’s Research Council of America’s guides to top physicians and surgeons, serves on the editorial boards of multiple journals, and is a Fellow of the American Physical Society, American College of Surgeons, American Association of Neurological Surgeons, and the American Association for the Advancement of Science. In 2015 he received the NIH Director's Pioneer Award. He plays the viola in the Nittany Valley Symphony in an out of tune manner.
Abstract: Since the 1950s, we have developed mature theories of modern control theory and computational neuroscience with little interaction between these disciplines. With the advent of computationally efficient nonlinear Kalman filtering techniques (developed in robotics and weather prediction), along with improved neuroscience models that provide increasingly accurate reconstruction of dynamics in a variety normal and disease states in the brain, the prospects for synergistic interaction between these fields are now strong. I will show recent examples of the use of nonlinear control theory for the assimilation and control of single neuron and network dynamics, a control framework for Parkinson’s disease, and the potential for unification in control of spreading depression and seizures. Recent results help explain why the subtle and deep intersection of symmetry, in brains and models, is important to take into account in this transdisciplinary fusion of computational models of the computational brain with real-time control. Lastly, I will describe how such symmetries apply to network optimization and control for the prevention of infant brain infections in Africa.
Dr. Kristen Harris: Analytical challenges to understanding subcellular resource allocation for synaptic plasticity and homeostasis
Professor of Neuroscience, University of Texas at Austin
Bio: Kristen Harris is Professor of Neuroscience and Fellow in the Center for Learning and Memory at the University of Texas at Austin. For more than two decades, her laboratory has pursued understanding of structural synaptic plasticity in the developing and mature nervous system. They have been among the first to develop computer-assisted approaches to analyze synapses in three-dimensions through serial section electron microscopy (3DEM) under a variety of experimental and natural conditions. This powerful set of techniques has led to new understanding of synaptic structure under normal conditions as well as in response to experimental conditions such as long-term potentiation, a cellular mechanism of learning and memory. Dr. Harris received training in neuroscience earning her M.S. from the University of Illinois, Ph.D. from Northeastern Ohio University's College of Medicine, and postdoctoral training at Massachusetts General Hospital. She then served on the faculty of the Harvard Medical School, Boston University, and the Medical College of Georgia, where she was Director of the Synapses & Cognitive Neuroscience Center. Read More.
Abstract: Smooth endoplasmic reticulum (SER) forms a membranous network that extends throughout neurons. SER regulates intracellular calcium and the posttranslational modification and trafficking of membrane and proteins. Greater SER volume, folding, or branching reduces the movement of membrane cargo and local delivery of resources increases in the vicinity of complex SER (Spacek and Harri, 1997; Cui-Wang et al., 2012). Long-term potentiation by theta-burst stimulation (TBS-LTP) was induced to discern SER remodeling underlying synaptic plasticity. After TBS-LTP, the spine apparatus composed of highly folded SER appeared in more spines as synapses enlarged. In dendritic segments surrounding a spine apparatus the SER in the dendritic shaft was more branched and local spine number was maintained. In contrast, dendritic segments without SER-containing spines had less highly branched shaft SER and fewer neighboring spines were maintained following TBS-LTP than under control conditions. Thus, the benefits of a spine apparatus appear to preserve neighboring but not more distant dendritic spines as the dendrites rebalanced resource allocation towards the enlarged synapses. These findings from 3DEM provide subcellular structure that could underlie differences in capacity for basic synaptic transmission and plasticity across dendritic spine clusters. Given the small dimensions and diversity in shape of the SER and other critical subcellular resources, such as polyribosomes and endosomes, the challenge for next generation analyses is to provide tools that reliably identify, track, and measure the dimensions of ultrastructural components in 3D. I will share obstacles faced by humans performing these analyses in the hope they will inform future reconstructions and informatics.
Spacek, J. & Harris, K. M. Three-dimensional organization of smooth endoplasmic reticulum in hippocampal CA1 dendrites and dendritic spines of the immature and mature rat. J Neurosci 17, 190–203 (1997).
Cui-Wang T, Hanus C, Cui T, Helton T, Bourne JN, Watson DJ, Harris KM and Ehlers MD (2012). Local zones of endoplasmic reticulum complexity confine cargo in neuronal dendrites. Cell 148(1-2):309-21.
Dr. Giulio Tononi: Consciousness: From Theory to Practice
Professor of Psychiatry, Distinguished Professor in Consciousness Science, and the David P. White Chair in Sleep Medicine, University of Wisconsin – Madison
Bio: Giulio Tononi received his medical degree from the University of Pisa, Italy, where he specialized in Psychiatry. After serving as a medical officer in the Army, he obtained a Ph.D. in neuroscience as a fellow of the Scuola Superiore for his work on sleep regulation. From 1990 to 2000, he was a member of The Neurosciences Institute, first in New York and then in San Diego. He is currently Professor of Psychiatry, Distinguished Professor in Consciousness Science, and the David P. White Chair in Sleep Medicine at the University of Wisconsin, Madison. In 2005 he received the NIH Director’s Pioneer Award for his work on sleep. His laboratory studies consciousness and its disorders as well as the mechanisms and functions of sleep. Read More.
Abstract: Neuroscience has made great progress in relating the behavioral and neural correlates of consciousness. Yet it has proven hard to establish which neural structures and modes of activity are necessary and sufficient for being conscious. Moreover, empirical studies are inadequate to assess the presence and quality of consciousness in difficult cases, such as certain unresponsive patients, newborn infants, animals with behaviors and brains unlike ours, or machines that approximate our cognitive abilities. To make headway, empirical studies must be complemented by a fundamental theory of what consciousness is and what it takes to have it. Integrated information theory (IIT) starts from the essential properties of consciousness and translates them into requirements that any physical system must satisfy to be conscious. It goes on to show that the physical substrate of consciousness (PSC) must be a maximum of intrinsic, irreducible cause-effect power, and provides a calculus to determine, in principle, both the quality and the quantity of an experience. Applied to the brain, the principles of IIT imply that the PSC is constituted of those neural elements that together compose a maximum of intrinsic cause-effect power, and that such maximum can shrink, move, split and disintegrate depending on various anatomical and physiological parameters. Similarly, IIT predicts that the spatial grain of the neural elements constituting the PSC, the temporal grain at which they do so, and the relevant neural states, are again those that maximize intrinsic cause-effect power. These predictions are in principle testable with stimulation and recording experiments at the systems and cellular levels. The theory can explain parsimoniously many known facts about the relationship between consciousness and the brain, including its association with certain cortical structures, its breakdown in deep sleep, anesthesia and seizures, and its return in dreams. Finally, the theory has motivated the development of promising new tests for the practical assessment of consciousness in non-communicative subjects.
Dr. Bob Jacobs: Cortical neuromorphology beyond rodents and primates: A personal journey
Professor of Psychology, Colorado College
Bio: Bob Jacobs received his Ph.D. in Applied Linguistics at UCLA, where he worked with John Schumann and Arnold B. Scheibel. He has lived for extended periods of time in Germany, Japan, and China. He became a member of the Colorado College Psychology department in 1993 and developed the Neuroscience major in 1996. His research interests include language acquisition, non-human animal communication, cognitive neuroethology, and comparative neuroanatomy. Read More.
Abstract: This talk provides an overview of three decades of quantitative morphological research on neocortical neurons (~3,000) using the Golgi method. Early research was focused on establishing a quantitative map of regional variation in pyramidal neurons across the human cerebral cortex. In general, cortical regions involved in the early stages of processing (e.g., primary sensory areas) exhibit less complex dendritic/spine systems than those regions involved in the latter stages of information processing (e.g., prefrontal cortex). More recent research has expanded the investigative scope by attempting to qualitatively and quantitatively document the variety of neocortical neurons in a broad collection of large brained mammals that have seldom, if ever, been examined. To this end, this talk provides a brief overview of neuronal types in several species: African elephant, Florida manatee, Cetaceans (e.g., humpback whale), giraffe, Siberian tiger, and clouded leopard. Comparisons across these species reveal some similarities in neuronal types, but also some striking species differences, both qualitatively and quantitatively. Although pyramidal neurons remain the dominant cortical neuron, neuromorphological variation exists across species, suggesting there is more than one way to wire an intelligent brain. In particular, neurons in the African elephant neocortex are the most distinctive, representing a fundamentally different organizational architecture than observed in primates or rodents. This talk is dedicated with gratitude to Dr. Arnold B. Scheibel (now 93 years of age), a pioneer in the exploration of neural structure-function relationships, and my mentor at UCLA.
Dr. Partha Mitra: Neuron Trees in the Brain Jungle: Mapping Brainwide Connectivity
Professor, Cold Spring Harbor Laboratory
Bio: Partha Mitra received his PhD in theoretical physics from Harvard in 1993. He worked in quantitative neuroscience and theoretical engineering at Bell Laboratories from 1993-2003 and as an Assistant Professor in Theoretical Physics at Caltech in 1996 before moving to Cold Spring Harbor Laboratory in 2003, where he is currently Crick-Clay Professor of Biomathematics. He is interested in developing an integrative understanding of complex biological systems from a "theoretical engineering" perspective, and has organized meetings and symposia on engineering or design principles in biological systems. His research currently combines experimental, theoretical and informatics approaches to gain an understanding of how brains work. Read More.
Abstract: Mapping circuit connectivity is fundamental to understanding how brains work, how they fail in neurological disorders, and how the underlying principles could be used in modern machine intelligence technologies. After a century of effort we only have limited knowledge of the circuits that form our brains or of any mammal or vertebrate. The challenges are technical: the only way to know the ground truth about these circuits, is to physically or optically section the brain into very thin sections, and trace labeled neurons or groups of labeled neurons. This gives rise to large data sets (~petabytes at the light microscope scale) which until recently posed intractable challenges for data storage, handling and analysis (apart from the technical challenges for physical processing or imaging).
However, this has now changed, and large-scale circuit mapping projects are now being undertaken. Currently, for larger mammalian brains (rodents, primates), the only feasible method that can address circuit architecture at the level of whole brains is to utilize injections of neuronal tracers. These tracers are actively transported in the anterograde or retrograde direction along neurons and therefore allow the mapping of long-range connectivity between brain regions. This approach maps the connectivity architecture at the Mesoscopic Scale, which is defined as the transitional scale from a microscopic scale at which individual variation is prominent, to a larger scale which species-typical patterns, characterized classically in neuroanatomical atlases, are evident. This talk will report on two mesoscale connectivity-mapping projects, in the mouse and in the marmoset monkey.
An interesting aspect is that the data are fundamentally geometrical in nature, and recently developed tools from computational geometry and topology can be brought to bear to understand the circuit architecture. Such tools apply both at the level of individual neurons to extract and characterize neuronal morphology, but also to groups of neurons labeled by tracer injections.
Dr. Paola Pergami: Big Data and Advanced Imaging in Clinical Decision Making: Are We There Yet?
Associate Professor, Pedriatic Neurology, George Washington University
Bio: Paola Pergami recently moved to Children’s National Medical Center. She is a pediatric neurologist with interest in stroke and neuroplasticity. She is an associate Professor at George Washington University. She received her MD from University of Pavia, Italy, and her PhD in Neurotoxicology from the University of Milan, Italy, working on prion diseases. After a Fellowship at the NIH, she moved to WVU, and then to UPMC, to complete her training in Pediatric Neurology. At WVU she established the Pediatric Stroke Center and the Pediatric Neuroimaging Laboratory, where she use advanced imaging techniques to support understanding of brain plasticity and mechanisms of recovery following stroke. She is interested in identifying biomarkers for early diagnosis and quantification of ischemic brain injury in neonates and children using DTI and resting state fMRI. She is studying the role of white matter lesions and abnormal networks in the development of motor and cognitive delay in children with ischemic brain injury in order to understand how rehabilitation can modify the abnormal brain connectivity resulting from a stroke. Read More.
Abstract: Over the last 20 years the exponential development of in vivo neuroimaging has allowed for a multimodal approach in the examination of the brain with simultaneous acquisition of structural, functional and connectivity data. Diffusion tensor imaging (DTI) progressed from reconstruction of fiber orientation from measured diffusion along 6 directions to methods able to resolve up to 512 fiber directions. Resting-state Functional MRI (rs-fMRI) enables exploration of brain at rest, and identification of specific networks sub-serving different cognitive functions. Multiple approaches have attempted to integrate functional and structural data either in relation to limited local activation patterns, or for global connectivity between regions and systems. Despite the complexity and large volume of data, most analyses are based on a snapshot in time of constantly changing intra and inter-regional activation and communication.
Additionally, the assumption that white matter (WM) tracts are static structural correlates of the brain's different functional states is debatable, as indeed WM tracts can be differentially engaged for specific tasks in specific areas. As the richness of brain data sets continues to grow, and new acquisition and analysis approaches continue to be developed, more issues have to be considered. We are attempting to translate this expanding knowledge into information of clinical significance in order to support a wide range of medical applications including urgent care decision, patient selections for specific treatment, disease surveillance, and outcome prediction.
The specific application to children adds an additional layer of complexity, in order to avoid meaningless inference in the rapidly changing and especially diverse developing brain. Further challenges are expected from the integration with clinical data such as genetic information, electrophysiological data and continuous monitoring parameters.
We will discuss potential healthcare applications in pediatric neurology such as management of status epilepticus, identification of salvageable penumbra in stroke, and emergency neonatal intervention.