How anatomy shapes dynamics: a semi-analytical study of the brain at rest by a simple spin model

Deco, Gustavo; Senden, Mario; Jirsa, Viktor K.

doi:10.3389/fncom.2012.00068

Cited by 134 publications

(148 citation statements)

References 29 publications

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“…neural networks never reach a fully synchronized nor desynchronized state. Instead, the brain network dynamics exhibits large variability, and the amount of global synchrony of network nodes vary over time, indicating transitions from a synchronized to more desynchronized state 11,14 . This metastable state in brain dynamics can be quantified by the standard deviation σ R of the order parameter R(t) 9,36 .…”

Section: Measures Of the Network Dynamicsmentioning

confidence: 99%

Dynamic changes in network synchrony reveal resting-state functional networks

Vuksanovic

Hövel

2015

Chaos

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Experimental fMRI studies have shown that spontaneous brain activity i.e. in the absence of any external input, exhibit complex spatial and temporal patterns of co-activity between segregated brain regions. These so-called large-scale resting-state functional connectivity networks represent dynamically organized neural assemblies interacting with each other in a complex way. It has been suggested that looking at the dynamical properties of complex patterns of brain functional co-activity may reveal neural mechanisms underlying the dynamic changes in functional interactions. Here, we examine how global network dynamics is shaped by different network configurations, derived from realistic brain functional interactions. We focus on two main dynamics measures: synchrony and variations in synchrony. Neural activity and the inferred hemodynamic response of the network nodes are simulated using system of 90 FitzHugh-Nagumo neural models subject to system noise and time-delayed interactions. These models are embedded into the topology of the complex brain functional interactions, whose architecture is additionally reduced to its main structural pathways. In the simulated functional networks, patterns of correlated regional activity clearly arise from dynamical properties that maximize synchrony and variations in synchrony. Our results on the fast changes of the level of the network synchrony also show how flexible changes in the large-scale network dynamics could be. Keywords: network dynamics; whole brain simulations; FitzHugh-Nagumo neural modelExperimental studies of the human brain activity at rest i.e. without any overt-directed behavior have revealed patterns of correlated activity, so called resting-state networks. The neural mechanisms contributing to the formation of these networks are largely unknown. We use modeling approach to interpret these experimental findings, looking at the brain as the dynamical system. We characterize brain network dynamical properties by synchrony and variability in synchrony. We demonstrate that functional brain interactions may arise from the network dynamics which allow flexible changes between different network configurations. We show that these changes reflect almost periodic alternations between network synchronized and desynchronized state.

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Section: Measures Of the Network Dynamicsmentioning

confidence: 99%

Dynamic changes in network synchrony reveal resting-state functional networks

Vuksanovic

Hövel

2015

Chaos

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“…Thereafter, a lot of interest has been devoted to deepening the understanding of how anatomical constraints shape functional connectivity (Honey et al 2010;Breakspear et al 2010;Cabral et al 2011;Deco et al 2012), and how this relationship can be affected by different pathologies (de Kwaasteniet et al 2013;van Schouwenburg et al 2013). In most of these studies, either the dynamics of FC are not taken into account, or it is modeled, but the information coming from the data and used to assess models is deduced with a static approach of FC [e.g., (Deco et al 2013b)].…”

Section: Phases Of (De)synchronization Between Functional and Structumentioning

confidence: 99%

“…Different approaches have been used to tackle this question, such as direct comparison of functional and structural connectivities (Kötter and Sommer 2000;Sporns et al 2000), graph theory (Passingham et al 2002;Bullmore and Sporns 2009), and model-based approaches to explain the link between SC and FC (Koch et al 2002). However, it is only recently that a clear link between SC and FC (Honey et al 2009;van den Heuvel et al 2009) [reviewed in Damoiseaux and Greicius (2009)] has been established, allowing for testable models (Honey et al 2010;Deco et al 2012). Meanwhile, the classical approach of assuming FC as constant during resting-state recordings (Bullmore and Sporns 2009;Friston 2011) has also evolved recently.…”

Section: Introductionmentioning

confidence: 99%

Cerebral functional connectivity periodically (de)synchronizes with anatomical constraints

et al. 2015

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This paper studies the link between restingstate functional connectivity (FC), measured by the correlations of fMRI BOLD time courses, and structural connectivity (SC), estimated through fiber tractography. Instead of a static analysis based on the correlation between SC and FC averaged over the entire fMRI time series, we propose a dynamic analysis, based on the time evolution of the correlation between SC and a suitably windowed FC. Assessing the statistical significance of the time series against random phase permutations, our data show a pronounced peak of significance for time window widths around 20-30 TR (40-60 s). Using the appropriate window width, we show that FC patterns oscillate between phases of high modularity, primarily shaped by anatomy, and phases of low modularity, primarily shaped by inter-network connectivity. Building upon recent results in dynamic FC, this emphasizes the potential role of SC as a transitory architecture between different highly connected restingstate FC patterns. Finally, we show that the regions contributing the most to these whole-brain level fluctuations of FC on the supporting anatomical architecture belong to the default mode and the executive control networks Steven Laureys and Rodolphe Sepulchre contributed equally. Electronic supplementary material 123Brain Struct Funct DOI 10.1007/s00429-015-1083-y suggesting that they could be capturing consciousness-related processes such as mind wandering.

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“…To examine the link between network architecture and functional entropy we adopted the analytically solvable Ising spin glass model from Deco et al (2012). The model, which is isomorphic to the discrete Hopfield net (Hopfield, 1982), studies the characteristics of the attractor landscapes emerging in a spin glass neural model.…”

Section: Spin Glass Modelmentioning

confidence: 99%

“…Computationally driven theories of cognition hypothesize that the brain may achieve integration of subsystems by flexibly arranging cortical areas into temporal functional networks in accordance with goal-related requirements (Baars, 2005;Ghosh et al 2008;Deco et al 2010). The exact nature as well as the size of the set of possible functional network configurations, referred to as the brain"s functional repertoire, has been suggested to relate directly to the structural architecture of the brain Deco et al 2012;Senden et al 2012). Network architectures that involve a scale free topology; meaning that the degree distribution follows a power law function indicating the existence of a small number of high-degree nodes, have been shown to be able to display a particularly diverse number of functional configurations ).…”

Section: Introductionmentioning

confidence: 99%

Rich club organization supports a diverse set of functional network configurations

et al. 2014

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Brain function relies on the flexible integration of a diverse set of segregated cortical modules, with the structural connectivity of the brain being a fundamentally important factor in shaping the brain"s functional dynamics. Following up on macroscopic studies showing the existence of centrally connected nodes in the mammalian brain, combined with the notion that these putative brain hubs may form a dense interconnected "rich club" collective, we hypothesized that brain connectivity might involve a rich club type of architecture to promote a repertoire of different and flexibly accessible brain functions. With the rich club suggested to play an important role in global brain communication, examining the effects of a rich club organization on the functional repertoire of physical systems in general, and the brain in particular, is of keen interest. Here we elucidate these effects using a spin glass model of neural networks for simulating stable configurations of cortical activity. Using simulations, we show that the presence of a rich club increases the set of attractors and hence the diversity of the functional repertoire over and above the effects produced by scale free type topology alone. Within the networks" overall functional repertoire rich nodes are shown to be important for enabling a high level of dynamic integrations of low-degree nodes to form functional networks. This suggests that the rich club serves as an important backbone for numerous coactivation patterns among peripheral nodes of the network. In addition, applying the spin glass model to empirical anatomical data of the human brain, we show that the positive effects on the functional repertoire attributed to the rich club phenomenon can be observed for the brain as well. We conclude that a rich club organization in network architectures may be crucial for the facilitation and integration of a diverse number of segregated functions.

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How anatomy shapes dynamics: a semi-analytical study of the brain at rest by a simple spin model

Cited by 134 publications

References 29 publications

Dynamic changes in network synchrony reveal resting-state functional networks

Dynamic changes in network synchrony reveal resting-state functional networks

Cerebral functional connectivity periodically (de)synchronizes with anatomical constraints

Rich club organization supports a diverse set of functional network configurations

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