Can structure predict function in the human brain?

Honey, Christopher J.; Thivierge, Jean-Philippe; Sporns, Olaf

doi:10.1016/j.neuroimage.2010.01.071

Cited by 588 publications

(476 citation statements)

References 148 publications

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“…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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Section: Introductionmentioning

confidence: 99%

Cerebral functional connectivity periodically (de)synchronizes with anatomical constraints

et al. 2015

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“…This in effect means that if we understand that certain functions are results of particular network topology we can also infer required anatomical features from that topology, we can infer from it even the number of neurons or the number of edges (connections) (Alexander-Bloch et al 2012;Bressler 1995;Honey et al 2010;Hutchison et al 2013;Ponten et al 2010;Sporns, Honey and Kötter 2007). But as opposed to mechanistic and semantic approaches the topological properties that are the realization base are not defined at the local level, as we have seen in the example of small-world topology and the spread of infectious disease in the Watts and Strogatz (1998) model: the topology that realizes the faster spread stands at the global level.…”

Section: Basics Of Topological Approach In Cognitive Neurosciencementioning

confidence: 99%

“…In network neuroscience the brain is represented as a system of interacting networks at different scales Bassett and Siebenhühner 2013;Sporns 2010Sporns , 2012Seung, 2012). At the micro-scale individual neurons are connected to one another through synapses and they form networks in which information flows as electrical impulses which are called action potentials (Bassett and Muldoon 2016, p. 1).…”

Section: Basics Of Topological Approach In Cognitive Neurosciencementioning

confidence: 99%

The topological realization

Kostić

2016

Synthese

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In this paper, I argue that the newly developed network approach in neuroscience and biology provides a basis for formulating a unique type of realization, which I call topological realization. Some of its features and its relation to one of the dominant paradigms of realization and explanation in sciences, i.e. the mechanistic one, are already being discussed in the literature. But the detailed features of topological realization, its explanatory power and its relation to another prominent view of realization, namely the semantic one, have not yet been discussed. I argue that topological realization is distinct from mechanistic and semantic ones because the realization base in this framework is not based on local realisers, regardless of the scale (because the local vs global distinction can be applied at any scale) but on global realizers. In mechanistic approach, the realization base is always at the local level, in both ontic (Craver 2007(Craver , 2013) and representational accounts (Bechtel and Richardson 2010). The explanatory power of realization relation in mechanistic approach comes directly from the realization relation-either by showing how a model is mapped onto a mechanism, or by describing some ontic relations that are explanatory in themselves. Similarly, the semantic approach requires that concepts at different scales logically satisfy microphysical descriptions, which are at the local level. In topological framework the realization base can be found at different scales, but whatever the scale the realization base is global, within that scale, and not local. Furthermore, topological realization enables us to answer the "why" questions, which according to Polger (2010) make it explanatory. The explanatoriness of topological realization stems from understanding mathematical consequences of different topologies, not from the mere fact that a system realizes them.

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“…Functional connectivity is constrained by structural connectivity (Honey et al, 2010;Cabral et al, 2012). In other words, when there is functional connectivity, there is often structural connectivity, although structural connectivity is not a necessary requirement for functional connectivity (Honey et al, 2009).…”

Section: Validation Of Structural Connectivity Estimatesmentioning

confidence: 99%

Bayesian inference of structural brain networks

et al. 2013

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Structural brain networks are used to model white-matter connectivity between spatially segregated brain regions. The presence, location and orientation of these white matter tracts can be derived using diffusion-weighted magnetic resonance imaging in combination with probabilistic tractography. Unfortunately, as of yet, none of the existing approaches provide an undisputed way of inferring brain networks from the streamline distributions which tractography produces. Stateof-the-art methods rely on an arbitrary threshold or, alternatively, yield weighted results that are difficult to interpret. In this paper, we provide a generative model that explicitly describes how structural brain networks lead to observed streamline distributions. This allows us to draw principled conclusions about brain networks, which we validate using simultaneously acquired resting-state functional MRI data. Inference may be further informed by means of a prior which combines connectivity estimates from multiple subjects. Based on this prior, we obtain networks that significantly improve on the conventional approach.

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Can structure predict function in the human brain?

Cited by 588 publications

References 148 publications

Cerebral functional connectivity periodically (de)synchronizes with anatomical constraints

Cerebral functional connectivity periodically (de)synchronizes with anatomical constraints

The topological realization

Bayesian inference of structural brain networks

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