Computational Neuroscience In Epilepsy

Epilepsy is a neurological disorder that affects millions of patients worldwide and arises from the concurrent action of multiple pathophysiological processes. The power of mathematical analysis and computational modeling is increasingly utilized in basic and clinical epilepsy research to better understand the relative importance of the multi-faceted, seizure-related changes taking place in the brain during an epileptic seizure. This groundbreaking book is designed to synthesize the current ideas and future directions of the emerging discipline of computational epilepsy research. Chapters address relevant basic questions (e.g., neuronal gain control) as well as long-standing, critically important clinical challenges (e.g., seizure prediction). Computational Neuroscience in Epilepsy should be of high interest to a wide range of readers, including undergraduate and graduate students, postdoctoral fellows and faculty working in the fields of basic or clinical neuroscience, epilepsy research, computational modeling and bioengineering. Covers a wide range of topics from molecular to seizure predictions and brain implants to control seizures Contributors are top experts at the forefront of computational epilepsy research Chapter contents are highly relevant to both basic and clinical epilepsy researchers

Topic Editor Prof. Jorge Alvaro Gonzalez-Martinez has received a consulting grant from Zimmer Biomet. Prof. Stéphan Chabardès has also worked as a consultant for Zimmer Biomet. Prof. Chauvel has declared no competing interests with regards to the Research Topic subject.

A comprehensive Introduction to the world of brain and behavior computational models This book provides a broad collection of articles covering different aspects of computational modeling efforts in psychology and neuroscience. Specifically, it discusses models that span different brain regions (hippocampus, amygdala, basal ganglia, visual cortex), different species (humans, rats, fruit flies), and different modeling methods (neural network, Bayesian, reinforcement learning, data fitting, and Hodgkin-Huxley models, among others). Computational Models of Brain and Behavior is divided into four sections: (a) Models of brain disorders; (b) Neural models of behavioral processes; (c) Models of neural processes, brain regions and neurotransmitters, and (d) Neural modeling approaches. It provides in-depth coverage of models of psychiatric disorders, including depression, posttraumatic stress disorder (PTSD), schizophrenia, and dyslexia; models of neurological disorders, including Alzheimer’s disease, Parkinson’s disease, and epilepsy; early sensory and perceptual processes; models of olfaction; higher/systems level models and low-level models; Pavlovian and instrumental conditioning; linking information theory to neurobiology; and more. Covers computational approximations to intellectual disability in down syndrome Discusses computational models of pharmacological and immunological treatment in Alzheimer's disease Examines neural circuit models of serotonergic system (from microcircuits to cognition) Educates on information theory, memory, prediction, and timing in associative learning Computational Models of Brain and Behavior is written for advanced undergraduate, Master's and PhD-level students—as well as researchers involved in computational neuroscience modeling research.

This book presents the latest research in computational methods for modeling and simulating brain disorders. In particular, it shows how mathematical models can be used to study the relationship between a given disorder and the specific brain structure associated with that disorder. It also describes the emerging field of computational psychiatry, including the study of pathological behavior due to impaired functional connectivity, pathophysiological activity, and/or aberrant decision-making. Further, it discusses the data analysis techniques that will be required to analyze the increasing amount of data being generated about the brain. Lastly, the book offers some tips on the application of computational models in the field of quantitative systems pharmacology. Mainly written for computational scientists eager to discover new application fields for their model, this book also benefits neurologists and psychiatrists wanting to learn about new methods.

Epilepsy, one of the most prevalent neurological disorders, affects approximately 1% (greater than 60 million) of the world's population. In an estimated 20 million of these patients, seizures are not controlled even by multiple anti-seizure drugs, and are extremely difficult to predict. Epilepsy: The Intersection of Neurosciences, Biology, Mathematics, Engineering, and Physics seamlessly brings together the neurosciences, mathematics, computational sciences, engineering, physics, and clinical epileptology to present to readers a highly didactic, integrated, clear and practically useful knowledge base and research directions. Laying out the foundations of signal analysis, data conditioning, linear and non-linear analysis, introduction to dynamical systems and fundamental anatomical and neurophysiological concepts, this book: Introduces non-physicians to language and concepts necessary to establish a meaningful dialog with epileptologists Introduces physicians to dynamical theory and signal processing without which interdisciplinary collaborations would not be productive Mines knowledge from fields devoted to the investigation of aperiodic paroxysmal relaxation phenomena, such as earthquakes, which bear dynamical similarities with epilepsy, so as to lay the proper scientific foundations for epileptology and foster much needed therapeutic advances efficiently Reviews spatiotemporal behavior of seizures, mechanisms of epileptogenesis and ictogenesis as well as of seizure control and ancillary technology Calls attention to nocturnal frontal lobe epilepsy as a potentially fruitful paradigm for advancing seizure prediction. Of all neurological disorders, epilepsy demands of investigators the broadest and deepest knowledge of dynamical, control, and system theories, knowledge that cannot be amassed without possessing a certain level of sophistication in relevant areas of neurosciences, physics, mathematics, and engineering. Narrowing the inescapable cultural chasm that commonly fragments multidisciplinary efforts, this book captures and enriches the burgeoning interdisciplinary synergism in the nascent field of dynamical epileptology.

This volume includes papers presented at the Fifth Annual Computational Neurosci ence meeting (CNS*96) held in Boston, Massachusetts, July 14 - 17, 1996. This collection includes 148 of the 234 papers presented at the meeting. Acceptance for mceting presenta tion was based on the peer review of preliminary papers originally submitted in May of 1996. The papers in this volume represent final versions of this work submitted in January of 1997. As represented by this volume, computational neuroscience continues to expand in quality, size and breadth of focus as increasing numbers of neuroscientists are taking a computational approach to understanding nervous system function. Defining computa tional neuroscience as the exploration of how brains compute, it is clear that there is al most no subject or area of modern neuroscience research that is not appropriate for computational studies. The CNS meetings as well as this volume reflect this scope and di versity.

Trains researchers and graduate students in state-of-the-art statistical and machine learning methods to build models with real-world data.

Part of the Oxford Textbooks in Clinical Neurology (OTCN) series, this volume covers the scientific basis, clinical diagnosis, and treatment of epilepsy and epileptic seizures, and is complemented by an online edition.