138
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      The Virtual Brain: a simulator of primate brain network dynamics

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          We present The Virtual Brain (TVB), a neuroinformatics platform for full brain network simulations using biologically realistic connectivity. This simulation environment enables the model-based inference of neurophysiological mechanisms across different brain scales that underlie the generation of macroscopic neuroimaging signals including functional MRI (fMRI), EEG and MEG. Researchers from different backgrounds can benefit from an integrative software platform including a supporting framework for data management (generation, organization, storage, integration and sharing) and a simulation core written in Python. TVB allows the reproduction and evaluation of personalized configurations of the brain by using individual subject data. This personalization facilitates an exploration of the consequences of pathological changes in the system, permitting to investigate potential ways to counteract such unfavorable processes. The architecture of TVB supports interaction with MATLAB packages, for example, the well known Brain Connectivity Toolbox. TVB can be used in a client-server configuration, such that it can be remotely accessed through the Internet thanks to its web-based HTML5, JS, and WebGL graphical user interface. TVB is also accessible as a standalone cross-platform Python library and application, and users can interact with the scientific core through the scripting interface IDLE, enabling easy modeling, development and debugging of the scientific kernel. This second interface makes TVB extensible by combining it with other libraries and modules developed by the Python scientific community. In this article, we describe the theoretical background and foundations that led to the development of TVB, the architecture and features of its major software components as well as potential neuroscience applications.

          Related collections

          Most cited references89

          • Record: found
          • Abstract: found
          • Article: not found

          Impulses and Physiological States in Theoretical Models of Nerve Membrane

          Van der Pol's equation for a relaxation oscillator is generalized by the addition of terms to produce a pair of non-linear differential equations with either a stable singular point or a limit cycle. The resulting "BVP model" has two variables of state, representing excitability and refractoriness, and qualitatively resembles Bonhoeffer's theoretical model for the iron wire model of nerve. This BVP model serves as a simple representative of a class of excitable-oscillatory systems including the Hodgkin-Huxley (HH) model of the squid giant axon. The BVP phase plane can be divided into regions corresponding to the physiological states of nerve fiber (resting, active, refractory, enhanced, depressed, etc.) to form a "physiological state diagram," with the help of which many physiological phenomena can be summarized. A properly chosen projection from the 4-dimensional HH phase space onto a plane produces a similar diagram which shows the underlying relationship between the two models. Impulse trains occur in the BVP and HH models for a range of constant applied currents which make the singular point representing the resting state unstable.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Separate visual pathways for perception and action.

            Accumulating neuropsychological, electrophysiological and behavioural evidence suggests that the neural substrates of visual perception may be quite distinct from those underlying the visual control of actions. In other words, the set of object descriptions that permit identification and recognition may be computed independently of the set of descriptions that allow an observer to shape the hand appropriately to pick up an object. We propose that the ventral stream of projections from the striate cortex to the inferotemporal cortex plays the major role in the perceptual identification of objects, while the dorsal stream projecting from the striate cortex to the posterior parietal region mediates the required sensorimotor transformations for visually guided actions directed at such objects.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Dynamic causal modelling.

              In this paper we present an approach to the identification of nonlinear input-state-output systems. By using a bilinear approximation to the dynamics of interactions among states, the parameters of the implicit causal model reduce to three sets. These comprise (1) parameters that mediate the influence of extrinsic inputs on the states, (2) parameters that mediate intrinsic coupling among the states, and (3) [bilinear] parameters that allow the inputs to modulate that coupling. Identification proceeds in a Bayesian framework given known, deterministic inputs and the observed responses of the system. We developed this approach for the analysis of effective connectivity using experimentally designed inputs and fMRI responses. In this context, the coupling parameters correspond to effective connectivity and the bilinear parameters reflect the changes in connectivity induced by inputs. The ensuing framework allows one to characterise fMRI experiments, conceptually, as an experimental manipulation of integration among brain regions (by contextual or trial-free inputs, like time or attentional set) that is revealed using evoked responses (to perturbations or trial-bound inputs, like stimuli). As with previous analyses of effective connectivity, the focus is on experimentally induced changes in coupling (cf., psychophysiologic interactions). However, unlike previous approaches in neuroimaging, the causal model ascribes responses to designed deterministic inputs, as opposed to treating inputs as unknown and stochastic.
                Bookmark

                Author and article information

                Journal
                Front Neuroinform
                Front Neuroinform
                Front. Neuroinform.
                Frontiers in Neuroinformatics
                Frontiers Media S.A.
                1662-5196
                11 June 2013
                2013
                : 7
                : 10
                Affiliations
                [1] 1Institut de Neurosciences des Systèmes, Aix Marseille Université Marseille, France
                [2] 2Department of Neurology, BrainModes Group, Charité University of Medicine Berlin, Germany
                [3] 3Codemart Cluj-Napoca, Romania
                [4] 4CodeBox GmbH Stuttgart, Germany
                [5] 5Rotman Research Institute at Baycrest Toronto, ON, Canada
                Author notes

                Edited by: Daniele Marinazzo, University of Gent, Belgium

                Reviewed by: Ingo Bojak, University of Reading, UK; Hans Ekkehard Plesser, Norwegian University of Life Sciences, Norway; Laurent U. Perrinet, Centre National de la Recherche Scientifique, France

                *Correspondence: Paula Sanz Leon and Viktor Jirsa, Institut de Neurosciences des Systèmes, Aix Marseille Université, 27, Bd. Jean Moulin, 13005 Marseille, France e-mail: paula.sanz-leon@ 123456univ-amu.fr ; viktor.jirsa@ 123456univ-amu.fr
                Article
                10.3389/fninf.2013.00010
                3678125
                23781198
                bdc3883a-9fd3-4ec4-8151-e643e429ed88
                Copyright © 2013 Sanz Leon, Knock, Woodman, Domide, Mersmann, McIntosh and Jirsa.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.

                History
                : 01 March 2013
                : 22 May 2013
                Page count
                Figures: 12, Tables: 2, Equations: 0, References: 129, Pages: 23, Words: 15732
                Categories
                Neuroscience
                Methods Article

                Neurosciences
                connectome,neural masses,time delays,full-brain network model,virtual brain,large-scale simulation,web platform,python

                Comments

                Comment on this article