Information-based methods for neuroimaging: analyzing structure, function and dynamics (Record no. 64255)

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fixed length control field 04239naaaa2200385uu 4500
001 - CONTROL NUMBER
control field https://directory.doabooks.org/handle/20.500.12854/50228
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220220040927.0
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 978-2-88919-502-2
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9782889195022
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.3389/978-2-88919-502-2
Terms of availability doi
041 0# - LANGUAGE CODE
Language code of text/sound track or separate title English
042 ## - AUTHENTICATION CODE
Authentication code dc
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Daniele Marinazzo
Relationship auth
245 10 - TITLE STATEMENT
Title Information-based methods for neuroimaging: analyzing structure, function and dynamics
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Name of publisher, distributor, etc. Frontiers Media SA
Date of publication, distribution, etc. 2015
300 ## - PHYSICAL DESCRIPTION
Extent 1 electronic resource (191 p.)
506 0# - RESTRICTIONS ON ACCESS NOTE
Terms governing access Open Access
Source of term star
Standardized terminology for access restriction Unrestricted online access
520 ## - SUMMARY, ETC.
Summary, etc. 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.
540 ## - TERMS GOVERNING USE AND REPRODUCTION NOTE
Terms governing use and reproduction Creative Commons
Use and reproduction rights https://creativecommons.org/licenses/by/4.0/
Source of term cc
-- https://creativecommons.org/licenses/by/4.0/
546 ## - LANGUAGE NOTE
Language note English
653 ## - INDEX TERM--UNCONTROLLED
Uncontrolled term brain connectivity
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Uncontrolled term Information Theory
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Uncontrolled term neuroinformatics
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Uncontrolled term transfer entropy
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Uncontrolled term network theory
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Uncontrolled term mutual information
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Uncontrolled term computational neuroscience
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Uncontrolled term functional connectome
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Uncontrolled term Granger causality
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Uncontrolled term structural connectome
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Miguel Angel Munoz
Relationship auth
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Jesus M. Cortes
Relationship auth
856 40 - ELECTRONIC LOCATION AND ACCESS
Host name www.oapen.org
Uniform Resource Identifier <a href="http://journal.frontiersin.org/researchtopic/1241/information-based-methods-for-neuroimaging-analyzing-structure-function-and-dynamics">http://journal.frontiersin.org/researchtopic/1241/information-based-methods-for-neuroimaging-analyzing-structure-function-and-dynamics</a>
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Public note DOAB: download the publication
856 40 - ELECTRONIC LOCATION AND ACCESS
Host name www.oapen.org
Uniform Resource Identifier <a href="https://directory.doabooks.org/handle/20.500.12854/50228">https://directory.doabooks.org/handle/20.500.12854/50228</a>
Access status 0
Public note DOAB: description of the publication

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