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      Investigating the effect of electrical brain stimulation using a connectome-based brain network model

      abstract
      1 , 2 , , 1 , 1 , 3 , 4 , 5 , 3 , 4
      BMC Neuroscience
      BioMed Central
      24th Annual Computational Neuroscience Meeting: CNS*2015
      18-23 July 2015

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          Abstract

          Transcranial direct current stimulation (tDCS) leads to positive effects in neurological and psychiatric diseases, such as depression, pain, or stroke, which outlast the treatment itself. Although numerous influencing stimulation parameters and factors are known, the mechanisms behind tDCS remain unclear. To reveal the mechanisms tDCS started to be considered to affect networks while (de)polarizing parts of the brain. We study here the ability of tDCS as a tool to bias functional networks by affecting the connections given the brain structure. We used structural data, that is, a human connectome to construct a large-scale brain network model of 74 cerebral areas, each described by a Jansen and Rit model. The model was designed on the basis of the neuroinformatics platform The Virtual Brain to account for reproducibility of the simulations. The tDCS-induced currents on the cerebral areas were calculated using a finite element method model. Based on the dynamical repertoire of an isolated area [1], we analyzed the brain activity, that is, the spatiotemporal dynamics in terms of rhythms and baseline potentials during rest, during tDCS, and the change between both. We identified the network states during rest and catalogued all states for further modeling studies. During tDCS, increased functional connectivity was found among a set of scalp EEG sensors, as reported in measurements [2], as well as among cerebral cortical areas (see Figure 1). Furthermore, tDCS led to sharpened frequency spectra and increased (anode) or decreased (cathode) power in the respective areas. Figure 1 New functional connections are established during tDCS: among cortical areas, Panel A; and among scalp EEG electrodes, Panel B. This study supports the notion that noninvasive brain stimulation is able to bias brain dynamics by affecting the competitive interplay of functional subnetworks. Our work constitutes a basis for further modeling studies to test target-oriented manipulation of functional networks (e.g. through adapted electrode montages) to improve pertinent treatment conditions. Furthermore, our approach emphasizes the role of structural data such as the network topology in emerging dynamics. Dynamics cannot necessarily be predicted from the structure but we found the structure especially important at transitions of network states.

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          Modulating functional connectivity patterns and topological functional organization of the human brain with transcranial direct current stimulation.

          Transcranial direct current stimulation (tDCS) is a noninvasive brain stimulation technique that alters cortical excitability and activity in a polarity-dependent way. Stimulation for few minutes has been shown to induce plastic alterations of cortical excitability and to improve cognitive performance. These effects might be caused by stimulation-induced alterations of functional cortical network connectivity. We aimed to investigate the impact of tDCS on cortical network function through functional connectivity and graph theoretical analysis. Single recordings in healthy volunteers with 62 electroencephalography channels were acquired before and after 10 min of facilitatory anodal tDCS over the primary motor cortex (M1), combined with inhibitory cathodal tDCS of the contralateral frontopolar cortex, in resting state and during voluntary hand movements. Correlation matrices containing all 62 pairwise electrode combinations were calculated with the synchronization likelihood (SL) method and thresholded to construct undirected graphs for the θ, α, β, low-γ and high-γ frequency bands. SL matrices and undirected graphs were compared before and after tDCS. Functional connectivity patterns significantly increased within premotor, motor, and sensorimotor areas of the stimulated hemisphere during motor activity in the 60-90 Hz frequency range. Additionally, tDCS-induced significant intrahemispheric and interhemispheric connectivity changes in all the studied frequency bands. In summary, we show for the first time evidence for tDCS-induced changes in brain synchronization and topological functional organization. Copyright © 2010 Wiley-Liss, Inc.
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            Bifurcation analysis of neural mass models: Impact of extrinsic inputs and dendritic time constants.

            Neural mass models (NMMs) explain dynamics of neuronal populations and were designed to strike a balance between mathematical simplicity and biological plausibility. They are currently widely used as generative models for noninvasive electrophysiological brain measurements; that is, magneto- and electroencephalography (M/EEG). Here, we systematically describe the oscillatory regimes which a NMM of a single cortical source with extrinsic input from other cortical and subcortical areas to each subpopulation can explain. For this purpose, we used bifurcation analysis to describe qualitative changes in system behavior in response to quantitative input changes. This approach allowed us to describe sequences of oscillatory regimes, given some specific input trajectory. We systematically classified these sequential phenomena and mapped them into parameter space. Our analysis suggests a principled scheme of how complex M/EEG phenomena can be modeled parsimoniously on two time scales: While the system displays fast oscillations, it slowly traverses phase space to another qualitatively different oscillatory regime, depending on the input dynamics. The resulting scheme is useful for applications where one needs to model an ordered sequence of switching between qualitatively different oscillatory regimes, for example, in pharmacological interventions, epilepsy, sleep, or context-induced state changes. Copyright (c) 2009 Elsevier Inc. All rights reserved.
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              Author and article information

              Contributors
              Conference
              BMC Neurosci
              BMC Neurosci
              BMC Neuroscience
              BioMed Central
              1471-2202
              2015
              4 December 2015
              : 16
              : Suppl 1
              : O13
              Affiliations
              [1 ]Institute of Biomedical Engineering and Informatics, Ilmenau University of Technology, Ilmenau, 98693, Germany
              [2 ]Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
              [3 ]Aix-Marseille Université, Institut de Neurosciences des Systèmes, Marseille, France
              [4 ]Institut National de la Santé et de la Recherche Médical, UMR_S 1106, 27 Bd Jean Moulin, 13385, Marseille Cedex 5, France
              [5 ]Centre National de la Recherche Scientifique, 3, rue Michel-Ange, 75794, Paris, France
              Article
              1471-2202-16-S1-O13
              10.1186/1471-2202-16-S1-O13
              4697511
              e146e543-1855-4d99-b572-1b8d87ab7efa
              Copyright © 2015 Kunze et al.

              This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

              24th Annual Computational Neuroscience Meeting: CNS*2015
              Prague, Czech Republic
              18-23 July 2015
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              Neurosciences
              Neurosciences

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