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The mechanisms of brain physiological functions and pathological mechanisms are crucial for us to understand how the brain works in the normal function such as memory, information processing and attentional perception, or in pathological conditions such as epilepsy, Parkinson's disease, and Alzheimer's diseases. These brain physiological functions and pathological mechanisms generally involve multiple spatial scales of brains, ranging from micro molecules, cellular channels, and meso-scale neuronal networks to the brain regions. To comprehensively understand the neural mechanisms of brain physiological functions and pathological mechanisms, multiple-scale investigations are essential to carry, involving neuronal circuit modeling, neural field modeling, large-scale modeling, data-driven complex network modeling, etc.
How does the motor cortex enable mammals to generate accurate, complex, and purposeful movements? A cubic millimeter of motor cortex contains roughly ̃10̂5 cells, an amazing ̃4 Km of axons and ̃0.4 Km of dendrites, somehow wired together with ̃10̂9 synapses. Corticospinal neurons (a.k.a. Betz cells, upper motor neurons) are a key cell type, monosynaptically conveying the output of the cortical circuit to the spinal cord circuits and lower motor neurons. But corticospinal neurons are greatly outnumbered by all the other kinds of neurons in motor cortex, which presumably also contribute crucially to the computational operations carried out for planning, executing, and guiding actions. Determining the wiring patterns, the dynamics of signaling, and how these relate to movement at the level of specific excitatory and inhibitory cell types is critically important for a mechanistic understanding of the input-output organization of motor cortex. While there is a predictive microcircuit hypothesis that relates motor learning to the operation of the cerebellar cortex, we lack such a microcircuit understanding in motor cortex and we consider microcircuits as a central research topic in the field. This Research Topic covers any issues relating to the microcircuit-level analysis of motor cortex. Contributions are welcomed from neuroscientists at all levels of investigation, from in vivo physiology and imaging in humans and monkeys, to rodent models, in vitro anatomy, electrophysiology, electroanatomy, cellular imaging, molecular biology, disease models, computational modeling, and more.
This book focuses on our current understanding of brain dynamics in various brain disorders (e.g. epilepsy, Alzheimer’s and Parkinson’s disease) and how the multi-scale, multi-level tools of computational neuroscience can enhance this understanding. In recent years, there have been significant advances in the study of the dynamics of the disordered brain at both the microscopic and the macroscopic levels. This understanding can be furthered by the application of multi-scale computational models as integrative principles that may link single neuron dynamics and the dynamics of local and distant brain regions observed using human EEG, ERPs, MEG, LFPs and fMRI. Focusing on the computational models that are used to study movement, memory and cognitive disorders as well as epilepsy and consciousness related diseases, the book brings together physiologists and anatomists investigating cortical circuits; cognitive neuroscientists studying brain dynamics and behavior by means of EEG and functional magnetic resonance imaging (fMRI); and computational neuroscientists using neural modeling techniques to explore local and large-scale disordered brain dynamics. Covering topics that have a significant impact on the field of medicine, neuroscience and computer science, the book appeals to a diverse group of investigators.
Multiscale Biomechanical Modeling of the Brain discusses the constitutive modeling of the brain at various length scales (nanoscale, microscale, mesoscale, macroscale and structural scale). In each scale, the book describes the state-of-the- experimental and computational tools used to quantify critical deformational information at each length scale. Then, at the structural scale, several user-based constitutive material models are presented, along with real-world boundary value problems. Lastly, design and optimization concepts are presented for use in occupant-centric design frameworks. This book is useful for both academia and industry applications that cover basic science aspects or applied research in head and brain protection.The multiscale approach to this topic is unique, and not found in other books. It includes meticulously selected materials that aim to connect the mechanistic analysis of the brain tissue at size scales ranging from subcellular to organ levels. - Presents concepts in a theoretical and thermodynamic framework for each length scale - Teaches readers not only how to use an existing multiscale model for each brain but also how to develop a new multiscale model - Takes an integrated experimental-computational approach and gives structured multiscale coverage of the problems
Cancer is a complex disease process that spans multiple scales in space and time. Driven by cutting-edge mathematical and computational techniques, in silico biology provides powerful tools to investigate the mechanistic relationships of genes, cells, and tissues. It enables the creation of experimentally testable hypotheses, the integration of dat
Neuronal Networks in Brain Function, CNS Disorders, and Therapeutics, edited by two leaders in the field, offers a current and complete review of what we know about neural networks. How the brain accomplishes many of its more complex tasks can only be understood via study of neuronal network control and network interactions. Large networks can undergo major functional changes, resulting in substantially different brain function and affecting everything from learning to the potential for epilepsy. With chapters authored by experts in each topic, this book advances the understanding of: - How the brain carries out important tasks via networks - How these networks interact in normal brain function - Major mechanisms that control network function - The interaction of the normal networks to produce more complex behaviors - How brain disorders can result from abnormal interactions - How therapy of disorders can be advanced through this network approach This book will benefit neuroscience researchers and graduate students with an interest in networks, as well as clinicians in neuroscience, pharmacology, and psychiatry dealing with neurobiological disorders. - Utilizes perspectives and tools from various neuroscience subdisciplines (cellular, systems, physiologic), making the volume broadly relevant - Chapters explore normal network function and control mechanisms, with an eye to improving therapies for brain disorders - Reflects predominant disciplinary shift from an anatomical to a functional perspective of the brain - Edited work with chapters authored by leaders in the field around the globe – the broadest, most expert coverage available
How we raise young children is one of today's most highly personalized and sharply politicized issues, in part because each of us can claim some level of "expertise." The debate has intensified as discoveries about our development-in the womb and in the first months and years-have reached the popular media. How can we use our burgeoning knowledge to assure the well-being of all young children, for their own sake as well as for the sake of our nation? Drawing from new findings, this book presents important conclusions about nature-versus-nurture, the impact of being born into a working family, the effect of politics on programs for children, the costs and benefits of intervention, and other issues. The committee issues a series of challenges to decision makers regarding the quality of child care, issues of racial and ethnic diversity, the integration of children's cognitive and emotional development, and more. Authoritative yet accessible, From Neurons to Neighborhoods presents the evidence about "brain wiring" and how kids learn to speak, think, and regulate their behavior. It examines the effect of the climate-family, child care, community-within which the child grows.
Since 2003, when spontaneous activity in cortical slices was first found to follow scale-free statistical distributions in size and duration, increasing experimental evidences and theoretical models have been reported in the literature supporting the emergence of evidence of scale invariance in the cortex. Although strongly debated, such results refer to many different in vitro and in vivo preparations (awake monkeys, anesthetized rats and cats, in vitro slices and dissociated cultures), suggesting that power law distributions and scale free correlations are a very general and robust feature of cortical activity that has been conserved across species as specific substrate for information storage, transmission and processing. Equally important is that the features reminiscent of scale invariance and criticality are observed at scale spanning from the level of interacting arrays of neurons all the way up to correlations across the entire brain. Thus, if we accept that the brain operates near a critical point, little is known about the causes and/or consequences of a loss of criticality and its relation with brain diseases (e.g. epilepsy). The study of how pathogenetical mechanisms are related to the critical/non-critical behavior of neuronal networks would likely provide new insights into the cellular and synaptic determinants of the emergence of critical-like dynamics and structures in neural systems. At the same time, the relation between the impaired behavior and the disruption of criticality would help clarify its role in normal brain function. The main objective of this Research Topic is to investigate the emergence/disruption of the emergent critical-like states in healthy/impaired neural systems.
Computational Psychiatry: Mathematical Modeling of Mental Illness is the first systematic effort to bring together leading scholars in the fields of psychiatry and computational neuroscience who have conducted the most impactful research and scholarship in this area. It includes an introduction outlining the challenges and opportunities facing the field of psychiatry that is followed by a detailed treatment of computational methods used in the service of understanding neuropsychiatric symptoms, improving diagnosis and guiding treatments. This book provides a vital resource for the clinical neuroscience community with an in-depth treatment of various computational neuroscience approaches geared towards understanding psychiatric phenomena. Its most valuable feature is a comprehensive survey of work from leaders in this field. - Offers an in-depth overview of the rapidly evolving field of computational psychiatry - Written for academics, researchers, advanced students and clinicians in the fields of computational neuroscience, clinical neuroscience, psychiatry, clinical psychology, neurology and cognitive neuroscience - Provides a comprehensive survey of work from leaders in this field and a presentation of a range of computational psychiatry methods and approaches geared towards a broad array of psychiatric problems
Experimental and theoretical approaches to global brain dynamics that draw on the latest research in the field. The consideration of time or dynamics is fundamental for all aspects of mental activity—perception, cognition, and emotion—because the main feature of brain activity is the continuous change of the underlying brain states even in a constant environment. The application of nonlinear dynamics to the study of brain activity began to flourish in the 1990s when combined with empirical observations from modern morphological and physiological observations. This book offers perspectives on brain dynamics that draw on the latest advances in research in the field. It includes contributions from both theoreticians and experimentalists, offering an eclectic treatment of fundamental issues. Topics addressed range from experimental and computational approaches to transient brain dynamics to the free-energy principle as a global brain theory. The book concludes with a short but rigorous guide to modern nonlinear dynamics and their application to neural dynamics.