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Over the last decade, the study of complex networks has expanded across diverse scientific fields. Increasingly, science is concerned with the structure, behavior, and evolution of complex systems ranging from cells to ecosystems. In Networks of the Brain, Olaf Sporns describes how the integrative nature of brain function can be illuminated from a complex network perspective. Highlighting the many emerging points of contact between neuroscience and network science, the book serves to introduce network theory to neuroscientists and neuroscience to those working on theoretical network models. Sporns emphasizes how networks connect levels of organization in the brain and how they link structure to function, offering an informal and nonmathematical treatment of the subject. Networks of the Brain provides a synthesis of the sciences of complex networks and the brain that will be an essential foundation for future research.
Crucial to understanding how the brain works is connectivity, and the centerpiece ofbrain connectivity is the connectome, a comprehensive description of how neuronsand brain regions are connected. The human brain is a network of extraordinary complexity -- anetwork not by way of metaphor, but in a precise and mathematical sense: an intricate web ofbillions of neurons connected by trillions of synapses. How this network is connected is importantfor virtually all facets of the brain's integrative function. In this book, Olaf Sporns surveyscurrent efforts to chart these connections -- to map the human connectome. Sporns, a pioneer in thefield who was the first to define and use the term "connectome," argues that the nascent field ofconnectomics has already begun to influence the way many neuroscientists collect, analyze, and thinkabout their data. Moreover, the idea of mapping the connections of the human brain in their entiretyhas captured the imaginations of researchers across several disciplines including human cognition,brain and mental disorders, and complex systems and networks. Sporns describes the biological andconceptual foundations of the connectome; the many research challenges it faces; the manycutting-edge empirical strategies, from electron microscopy to magnetic resonance imaging, deployedto map brain connectivity; the relationship between structure and function; and the wide array ofnetwork computational approaches to connectomics. Discovering the HumanConnectome offers the first comprehensive overview of current empirical and computationalapproaches in this rapidly developing field.
Recounts the early days of split-brain research and updates it with new information on the separate modules within the brain that transform random stimuli into a distinct sense of consciousness
This ground breaking book is unique in bringing together two perspectives on learning - sociocultural theory and neuroscience. Drawing on both perspectives, it foregrounds important developments in our understanding of what learning is, where and how learning occurs and what we can do to understand learning as an everyday process. Leading experts from both disciplines demonstrate how sociocultural ideas (such as the relevance of experience, opportunity to learn, environment, personal histories, meaning, participation, memory, and feelings of belonging) align with and reflect upon new understandings emerging from neuroscience concerning plasticity and neural networks. Among the themes critically examined are the following: Mind and brain Culture Ability and talent Success and failure Memory Language Emotion Aimed at and accessible to a broad audience and drawing on both schools of thought, Networks of Mind employs case studies, vignettes and real life examples to demonstrate that, though the language of sociocultural theory and that of neuroscience appear very different, ultimately the concepts of both perspectives align and converge around some key ideas. The book shows where both perspectives overlap, collide and diverge in their assumptions and understanding of fundamental aspects of human flourishing. It shows how neuroscience confirms some of the key messages already well established by sociocultural theory, specifically the importance of opportunity to learn. It also argues that the ascendency of neuroscience may result in the marginalization of sociocultural science, though the latter, it argues, has enormous explanatory power for understanding and promoting learning, and for understanding how learning is afforded and constrained.
Highlights: We verified that only the deep needing stimulation could get Deqi sensation. The acupuncture of Deqi had a greatly effect in the brain network. The default mode network and pain matrix play an important role in the effect of deep needling. Limbic-paralimbic-neocortical network (LPNN) may be the core of the effect of deep needling. Abstract: Objective: Acupuncture is a therapeutic treatment defined as the insertion of needles into the body at specific points (i.e., acupoints). The acupuncture sensation of Deqi is an important component of acupuncture, but the functional brain responses of Deqi have not been entirely supported by the results of functional magnetic resonance imaging (fMRI) studies. The aims of this study were to test the conditions that would generate a Deqi sensation and to investigate the effect of Deqi and the response of acupuncture at different depths and intensities on brain fMRI blood oxygen level-dependent (BOLD) signals. Design/setting: Healthy subjects ( n = 16) completed two resting-state fMRI (rs-fMRI) scans, once during shallow needling (2 mm) and once during deep needling (10–20 mm) pseudorandomly, at the acupoint BL40. Results: When undergoing shallow needling, 16 subjects had a mild stabbing pain sensation, and no one had a composite Deqi sensation; when undergoing deep needling, 14 subjects had a composite Deqi sensation, and only 2 subjects had a sharp pain feeling. Composite deep needling of Deqi sensation modulated neural activity at multiple levels of the brain and cerebellum, decreased functional connectivity in the default mode networks (DMN) and the pain matrix networks, and increased connectivity in the right posterior cerebellar lobe, left parahippocampal gyrus, thalamus, and supplementary motor area ( P
To many scientists the gap between the nineteenth century views of consciousness proposed by the psychologist William James and that developed by the inventor of psychophysics Gustav Fechner has never seemed wider. However the twentieth century concept of collective/cooperative behavior within the brain has partially reconciled these diverging perspectives suggesting the notion of consciousness as a physical phenomenon. A kernel of twenty-first century investigators bases their investigations on physiological fluctuations experiments. These fluctuations, although apparently erratic, when analyzed with advanced methods of fractal statistical analysis reveal the emergence of complex behavior, intermediate between complete order and total randomness, a property usually referred to as temporal complexity. Others, with the help of modern technologies, such MRI, establish a more direct analysis of brain dynamics, and focus on the brain’s topological complexity. Consequently the two groups adopt different approaches, the former being based on phenomenological and macroscopic considerations, and the latter resting on the crucial role of neuron interactions. The neurophysiology research work has an increasing overlap with the emerging field of complex networks, whereas the behavior psychology experiments have until recently ignored the complex cooperative dynamics that are proved by increasing experimental evidence to characterize the brain function. It is crucial to examine both the experimental and theoretical studies that support and those that challenge the view that it is an emergent collective property that allows the healthy brain to function. What needs to be discussed are new ways to understand the transport of information through complex networks sharing the same dynamical properties as the brain. In addition we need to understand information transfer between complex networks, say between the brain and a controlled experimental stimulus. Experiments suggest that brain excitation is described by inverse power-law distributions and recent studies in network dynamics indicate that this distribution is the result of phase transitions due to neuron network dynamics. It is important to stress that the development of dynamic networking establishes a connection between topological and temporal complexity, establishing that a scale-free distribution of links is generated by the dynamic correlation between dynamic elements located at very large Euclidean distances from one another. Dynamic networking and dynamics networks suggest a new way to transfer information: the long-distance communication through local cooperative interaction. It is anticipated that the contributed discussions will clarify how the global intelligence of a complex network emerges from the local cooperation of units and the role played by critical phase transitions in the observed persistence of this cooperation.
An important aspect of neuroscience is to characterize the underlying connectivity patterns of the human brain (i.e., human connectomics). Over the past few years, researchers have demonstrated that by combining a variety of different neuroimaging technologies (e.g., structural MRI, diffusion MRI and functional MRI) with sophisticated analytic strategies such as graph theory, it is possible to noninvasively map the patterns of structural and functional connectivity of human whole-brain networks. With these novel approaches, many studies have shown that human brain networks have nonrandom properties such as modularity, small-worldness and highly connected hubs. Importantly, these quantifiable network properties change with age, learning and disease. Moreover, there is growing evidence for behavioral and genetic correlates. Network analysis of neuroimaging data is opening up a new avenue of research into the understanding of the organizational principles of the brain that will be of interest for all basic scientists and clinical researchers. Such approaches are powerful but there are a number of challenging issues when extracting reliable brain networks from various imaging modalities and analyzing the topological properties, e.g., definitions of network nodes and edges and reproducibility of network analysis. We assembled contributions related to the state-of-the-art methodologies of brain connectivity and the applications involving development, aging and neuropsychiatric disorders such as Alzheimer’s disease, schizophrenia, attention deficit hyperactivity disorder and mood and anxiety disorders. It is anticipated that the articles in this Research Topic will provide a greater range and depth of provision for the field of imaging connectomics.

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