[Comp-neuro] Frontiers Research Topic: Information-based methods for Neuroimaging (updated info)

Jesus Cortes jesus.m.cortes at gmail.com
Sat Jan 19 18:29:08 CET 2013


In collaboration with Frontiers in Neuroscience, we are currently
organizing a Research Topic, "Information-based methods for Neuroimaging:
analyzing structure, function and dynamics".

Host Specialty: Frontiers in Neuroinformatics

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

Topic Editor(s): Daniele Marinazzo, Jesus Cortes, Miguel Angel Muñoz

Abstract Submission Deadline: March 01, 2013

Article Submission Deadline: November 01, 2013

Confirmed contributors are:

Daniel Chicharro
Demian Battaglia
Joaquin Goñi
Joseph Lizier
Timothy Mullen
Olaf Sporns
Stefano Panzeri
Michael Wibral


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Description: 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. Thus, for instance, to compute the statistical dependence between two
random variables, the Mutual Information accounts for the information bits
that the two variables are sharing (if it is zero, the two variables are
statistically independent).

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. This is the spirit of the growing-in-popularity
Transfer Entropy (Schreiber 2000), an Information-based method to estimate
directed influence.

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 Consciousness.

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.
Information flow and transfer in the brain can be straightforwardly
associated to consciousness: will the knowledge of the structure and the
dynamics lead us to define consciousness? Do different information
processing pathways exist in different consciousness states, or is simply
the amount of information different? Information based measurements could
help to clarify this issue.

A Research Topic bringing together contributions from researchers from
different backgrounds which are either developing new approaches, or
applying existing methodologies to new data would be an optimal round table
and starting platform for the development and validation of new
Information-based methodologies for the understanding of brain structure,
function, and dynamics.

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About Frontiers Research Topics:
Frontiers Research Topics are designed to be an organized, encyclopedic
coverage of a particular research area, and a forum for discussion and
debate. Contributions can be of different article types (Original Research,
Methods, Hypothesis & Theory, and others).

Our Research Topic has a dedicated homepage on the Frontiers website, where
contributing articles are accumulated and discussions can be easily held.
Once all articles are published, the topic will be compiled into an e-book,
which can be sent to foundations that fund your research, to journalists
and press agencies, and to any number of other organizations. As the
ultimate reference source from leading scientists, Frontiers Research Topic
articles become highly cited.

Frontiers is a Swiss-based, open access publisher. As such an article
accepted for publication incurs a publishing fee, which varies depending on
the article type. The publishing fee for accepted articles is below average
compared to most other open access journals - and lower than
subscription-based journals that apply page and color figure charges.
Moreover, for Research Topic articles, the publishing fee is discounted
quite steeply thanks to the support of the Frontiers Research Foundation.
Details on Frontiers’ fees can be found at
http://www.frontiersin.org/about/PublishingFees.

When published, your article will be freely available to visitors to the
Frontiers site, and will be indexed in PubMed and other academic archives.
As an author in Frontiers, you will retain the copyright to your own paper
and all figures.

For more information about this topic and Frontiers in Neuroinformatics,
please visit:

http://www.frontiersin.org/Neuroinformatics/researchtopics/Information-based_methods_for_/1241

Should you choose to participate, please confirm by sending a quick email
and then your abstract using the following link:
http://www.frontiersin.org/submissioninfo

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Best Regards

Daniele Marinazzo, Jesus M Cortes, Miguel Angel Muñoz


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