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Recent advances and the future generation of neuroinformatics infrastructure

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889196777 Year: Pages: 388 DOI: 10.3389/978-2-88919-677-7 Language: English
Publisher: Frontiers Media SA
Subject: Neurology --- Science (General)
Added to DOAB on : 2016-04-07 11:22:02
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The huge volume of multi-modal neuroimaging data across different neuroscience communities has posed a daunting challenge to traditional methods of data sharing, data archiving, data processing and data analysis. Neuroinformatics plays a crucial role in creating advanced methodologies and tools for the handling of varied and heterogeneous datasets in order to better understand the structure and function of the brain. These tools and methodologies not only enhance data collection, analysis, integration, interpretation, modeling, and dissemination of data, but also promote data sharing and collaboration. This Neuroinformatics Research Topic aims to summarize the state-of-art of the current achievements and explores the directions for the future generation of neuroinformatics infrastructure. The publications present solutions for data archiving, data processing and workflow, data mining, and system integration methodologies. Some of the systems presented are large in scale, geographically distributed, and already have a well-established user community. Some discuss opportunities and methodologies that facilitate large-scale parallel data processing tasks under a heterogeneous computational environment. We wish to stimulate on-going discussions at the level of the neuroinformatics infrastructure including the common challenges, new technologies of maximum benefit, key features of next generation infrastructure, etc. We have asked leading research groups from different research areas of neuroscience/neuroimaging to provide their thoughts on the development of a state of the art and highly-efficient neuroinformatics infrastructure. Such discussions will inspire and help guide the development of a state of the art, highly-efficient neuroinformatics infrastructure.

Information-based methods for neuroimaging: analyzing structure, function and dynamics

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889195022 Year: Pages: 191 DOI: 10.3389/978-2-88919-502-2 Language: English
Publisher: Frontiers Media SA
Subject: Neurology --- Science (General)
Added to DOAB on : 2015-12-03 13:02:24
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The aim of this Research Topic is to discuss the state of the art on the use of Information-based methods in the analysis of neuroimaging data. Information-based methods, typically built as extensions of the Shannon Entropy, are at the basis of model-free approaches which, being based on probability distributions rather than on specific expectations, can account for all possible non-linearities present in the data in a model-independent fashion.Mutual Information-like methods can also be applied on interacting dynamical variables described by time-series, thus addressing the uncertainty reduction (or information) in one variable by conditioning on another set of variables.In the last years, different Information-based methods have been shown to be flexible and powerful tools to analyze neuroimaging data, with a wide range of different methodologies, including formulations-based on bivariate vs multivariate representations, frequency vs time domains, etc. Apart from methodological issues, the information bit as a common unit represents a convenient way to open the road for comparison and integration between different measurements of neuroimaging data in three complementary contexts: Structural Connectivity, Dynamical (Functional and Effective) Connectivity, and Modelling of brain activity. Applications are ubiquitous, starting from resting state in healthy subjects to modulations of consciousness and other aspects of pathophysiology.Mutual Information-based methods have provided new insights about common-principles in brain organization, showing the existence of an active default network when the brain is at rest. It is not clear, however, how this default network is generated, the different modules are intra-interacting, or disappearing in the presence of stimulation. Some of these open-questions at the functional level might find their mechanisms on their structural correlates. A key question is the link between structure and function and the use of structural priors for the understanding of the functional connectivity measures. As effective connectivity is concerned, recently a common framework has been proposed for Transfer Entropy and Granger Causality, a well-established methodology originally based on autoregressive models. This framework can open the way to new theories and applications.This Research Topic brings together contributions from researchers from different backgrounds which are either developing new approaches, or applying existing methodologies to new data, and we hope it will set the basis for discussing the development and validation of new Information-based methodologies for the understanding of brain structure, function, and dynamics.

Producing and Analyzing Macro-Connectomes: Current State and Challenges

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889199815 Year: Pages: 139 DOI: 10.3389/978-2-88919-981-5 Language: English
Publisher: Frontiers Media SA
Subject: Science (General) --- Neurology
Added to DOAB on : 2016-01-19 14:05:46
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Construction of comprehensive and detailed brain regions neuroanatomical connections matrices (macro-connectomes) is necessary to understand how the nervous system is organized and to elucidate how its different parts interact. Macro-connectomes also are the structural foundation of any finer granularity approaches at the neuron classes and types (meso-connectomes) or individual neuron (micro-connectomes) levels. The advent of novel neuroanatomical methods, as well as combinations of classic techniques, form the basis of several large scale projects with the ultimate goal of producing publicly available connectomes at different levels. A parallel approach, that of systematic and comprehensive collation of connectivity data from the published literature and from publicly accessible neuroinformatics platforms, has produced macro-connectomes of different parts of the central nervous system (CNS) in several mammalian species. The emergence of these public platforms that allow for the manipulation of rich connectivity data sets and enable the construction of CNS macro-connectomes in different species may have significant and long lasting implications. Moreover, when these efforts are leveraged by novel statistical methods, they may influence our way of thinking about the brain. Hence, the present brain region-centric paradigm may be challenged by a network-centric one. Ultimately, these projects will provide the information and knowledge for understanding how different neuronal parts communicate and function, developing novel approaches to diseases and disorders, and facilitating translational efforts in neurosciences. With this Research Topic we bring together the current state of macro-connectome related projects including the large scale production of thousands of publicly available neuronatomical experiments, databases with tens of thousands of connectivity records collated from the published literature, and the newest methods for displaying and analyzing this information. This topic also includes a wide range of challenges and how they are addressed - from platforms designed to integrate connectivity data across different sources, species and CNS levels of organization, to languages specifically designed to use these data in models at different scales of resolution, to efforts of 3D reconstruction and data integration, and to approaches for extraction and representation of this knowledge. Finally, we address the present state of different efforts of meso-connectomes construction, and of computational modeling in the context of the

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