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Role of structural inhomogeneities in resting-state brain dynamics

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Abstract

Brain imaging methods allow a non-invasive assessment of both structural and functional connectivity. However, the mechanism of how functional connectivity arises in a structured network of interacting neural populations is as yet poorly understood. Here we use a modeling approach to explore the way in which functional correlations arise from underlying structural connections taking into account inhomogeneities in the interactions between the brain regions of interest. The local dynamics of a neural population is assumed to be of phase-oscillator type. The considered structural connectivity patterns describe long-range anatomical connections between interacting neural elements. We find a dependence of the simulated functional connectivity patterns on the parameters governing the dynamics. We calculate graph-theoretic measures of the functional network topology obtained from numerical simulations. The effect of structural inhomogeneities in the coupling term on the observed network state is quantified by examining the relation between simulated and empirical functional connectivity. Importantly, we show that simulated and empirical functional connectivity agree for a narrow range of coupling strengths. We conclude that identification of functional connectivity during rest requires an analysis of the network dynamics.

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Notes

  1. Technical details on the numerical analysis. The simulations are carried out using the PYTHON module SCIPY. The algorithm used is based on the Bogacki-Shampine method (Boggio et al. 2009) with an adaptive step size for the numerical integration of Eq. (1). This is similar to the ODE23 routine implemented also in MATLAB (Shampine and Reichelt 1997). Initially, at \(t=0\), all phases \(\theta _{i}, i=1,\dots ,N\), are randomly chosen from a uniform distribution.

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Acknowledgments

This work was supported by BMBF (grant no. 01Q1001B) in the framework of BCCN Berlin (Project B7). We thank John-Dylan Haynes and his group for helpful discussions concerning the fMRI data processing and Yasser Iturria-Medina for sharing the dMRI data used in the study.

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Authors and Affiliations

  1. Bernstein Center for Computational Neuroscience Berlin, Humboldt Universität zu Berlin, Philippstr. 13, 10115, Berlin, Germany

    Vesna Vuksanović & Philipp Hövel

  2. Institut für Theoretische Physik, Tecnische Universität Berlin, Hardenbergstr. 36, 10623, Berlin, Germany

    Vesna Vuksanović & Philipp Hövel

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  1. Vesna Vuksanović
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  2. Philipp Hövel
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Correspondence to Vesna Vuksanović.

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Vuksanović, V., Hövel, P. Role of structural inhomogeneities in resting-state brain dynamics. Cogn Neurodyn 10, 361–365 (2016). https://doi.org/10.1007/s11571-016-9381-5

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