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This article is part of the supplement: Twentieth Annual Computational Neuroscience Meeting: CNS*2011

Open Access Poster presentation

A neural field model using advanced anatomical connectivity information

Christopher Koch12*, Manh Nguyen Trong12, Andreas Spiegler12 and Thomas R Knösche1

Author Affiliations

1 Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

2 Institute for Biomedical Engineering and Informatics, Ilmenau University of Technology, Ilmenau, Germany

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BMC Neuroscience 2011, 12(Suppl 1):P174  doi:10.1186/1471-2202-12-S1-P174


The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2202/12/S1/P174


Published:18 July 2011

© 2011 Koch et al; licensee BioMed Central Ltd.

This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Poster presentation

We propose a mathematical framework for a neural field model that can accommodate empirical information on connectivity strength between different parts of the brain, and axonal caliber information of these connections. Furthermore, we use integro-differential equations to describe the mean dynamics (i.e., firing rate and mean membrane potential) [1]. We demonstrate the framework at the example of the rat brain.

Here, we specify the propagation velocity distributions by a linear relationship using empirical, position-variant, axonal diameter distributions of myelinated and unmyelinated callosal axons [2]. We approximate the experimentally estimated histograms of axonal diameters using alpha functions. By interpolating these alpha functions in space, weighted by the fiber densities of the myelinated and unmyelinated axons, we compute the velocity probability density (see Figure 1B). Diffusion tensor imaging is used to reconstruct axonal projections through the white matter. We use an atlas-based parcellation of the rat brain [3] to allocate the reconstructed projections to specific brain regions, yielding a connectome (see Figure 1A). The structures that are most strongly interconnected are the hippocampus, the thalamus, the motor and the sensory cortices. A simulation of the electrocorticogram demonstrates the impact of distal over local connections on brain function (see Figure 1C).

thumbnailFigure 1. A. Connectome B. Velocity probability density C. Electrocorticogram

References

  1. Atay F, Hutt A: Stability and bifurcations in neural fields with finite propagation speed and general connectivity.

    SIAM J. Appl. Math 2005, 65(2):644-666. Publisher Full Text OpenURL

  2. Partadiredja G, Miller R, Oorschot DE: The number, size, and type of axons in rat subcortical white matter on left and right sides: A stereological, ultrastructural study.

    Journal of Neurocytology 2003, 32:1165-1179. PubMed Abstract | Publisher Full Text OpenURL

  3. Paxinos G, Watson C: The Rat Brain in Stereotaxic Coordinates. 6th edition. San Diego, Academic Pres; 2007.