main-contentsVJ.tex

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\begin{document}
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%%  Authorship and Title
\title[TVB]{Integrating neuroinformatics tools in TheVirtualBrain}
\author[Woodman {et~al}]{
        M. Marmaduke Woodman\,$^{1,*}$,  
        Laurent Pezard\,$^{1}$,  
        Lia Domide\,$^{3}$, 
        Stuart Knock\,$^{1}$, 
        Paula Sanz Leon\,$^{1}$, 
        Jochen Mersmann\,$^{2}$,
        Anthony R. McIntosh \,$^{4}$ and  
        Viktor Jirsa\,$^{1}$\footnote{to whom correspondence should be addressed: marmaduke.woodman@univ-amu.fr,
        viktor.jirsa@univ-amu.fr}}

\address{$^{1}$ Institut de Neurosciences des Syst{\`e}mes, Aix-Marseille
    Universit\'e,  27, Bd. Jean Moulin, 13005, Marseille, France.\\
         $^{3}$ Codemart, 13, Petofi Sandor, 400610, Cluj-Napoca, Romania.\\
         $^{2}$ CodeBox GmbH, Hugo Eckener Str. 7, 70184 Stuttgart, Germany.\\
         $^{4}$ Rotman Research Institute at Baycrest, Toronto, M6A 2E1, Ontario, Canada\\
        }

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\begin{abstract}
\section{}

TheVirtualBrain (TVB) is a neuroinformatics Python package representing the
convergence of lines of work in clinical, systems, theoretical neuroscience in
the integration, analysis, visualization and modeling of neural dynamics of the
human brain as well as the imaging modalities through which these dynamics are
measured. Specifically, TVB is composed of a flexible simulator for both neural
dynamics and modalities such as electroencephalography (EEG), magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI), common analysis techniques such
as wavelet decomposition and multiscale sample entropy, interactive visualizers
for replaying cortical timeseries on the 3D surface or editing large-scale
connectivity matrices, and an (optional) user interface accessible through
modern web browsers. Tying together these pieces with persistent data storage,
based on a combination of SQL \& HDF5, is a rich, open-ended system of
datatypes modeling (systems level) neuroscientific data and the relations among
them. This data modeling system in parallel with the so-called adapter pattern
architecture permit the integration of TVB with any other computational system,
including MATLAB, for which support is already available.  Notably, TVB provides
infrastructure for multiple projects and multiple users, possibly participating
under multiple roles: for instance, a clinical researcher may import structural data of a patient obtained from various techniques such as magnetic resonance imaging (MRI) or diffusion spectrum imaging (DSI) \note[sk]{Is this actually true? I'm not really
up to date on this aspect of things, but, the last time I paid attention this 
feature wasn't fully integrated/functional -- If not, then it seems like a bad 
idea to publish that something is possible when it isn't...}\note[vj]{I changed the text. done}, identify 
potential lesion points, and then
share this data with a computational expert who would then enter to contribute
simulation parameter sweeps and analyses, to test which lesion point is most
probably given certain empirical imaging data, et cetera. This is one of many
multi-user use cases supported by TVB.  TVB also drives research forward on
many levels: the simulator itself represents the systematization of several
recent ad-hoc simulations in the modeling literature on human resting state.  In
these ways, TVB serves as an integrating neuroinformatics platform for various tools and disparate expertises in the high level analysis, handling of structural and functional data and modeling of the human brain.  Here we briefly outline the history and motivation for TVB as a unified project
\textit{per se}. We proceed to describe the framework and simulator, giving
usage examples in the web UI and in plain Python scripting.  Finally, we
compare TVB with the nearest neighbors in brain modeling, simulation
performance, recent advances thereupon with native code compilation and GPUs,
and the role of Python and its rich scientific ecosystem in TVB. 

  
\section{Keywords:} large-scale brain network, simulation,  web platform, Python, virtual
brain, connectivity, connectome, neural mass, neural field, time delays,
full-brain network model, GPUs

\end{abstract}

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\note[lp]{Emphasize the process of integrating new tools in TVB? This is not
completely clear for me now.}

\section{Motivations}
\input{motivationVJ.tex}

\section{Architecture}
\input{architecture.tex}

\section{Simulator}
\input{simulator.tex}

\section{User Interaction}
\input{ui.tex}

\section{Future Work}
\input{future.tex}

\section*{Acknowledgments}
 Several authors have also participated in the development of TVB. They are cited in the
\texttt{AUTHORS} file in TVB distribution and deserve also our warm acknowlegments. LP whishes to thank specifically Y.~Manhoun for his implication in the conception and development of the first prototype of the TVB architecture. The research reported herein was supported by the  Brain Network Recovery Group through the James S. McDonnell Foundation and the FP7-ICT BrainScales. PSL is supported by a doctoral fellowship from Ministere de la Recherche.

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