The relationship between structural and functional connectivity: Graph theoretical analysis of an EEG neural mass model

Ponten, S.C.; Daffertshofer, Andreas; Hillebrand, Arjan; Stam, Cornelis J.

doi:10.1016/j.neuroimage.2009.10.049

Cited by 89 publications

(80 citation statements)

References 65 publications

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“…Yet it has also been noted that MEG and other available techniques for whole human brain electrophysiological recording will severely undersample electrical activity at a neuronal level. Therefore, the topology of a network derived from surface field recordings will not inevitably reflect the topology of the underlying network of neuronal sources (Antiqueira et al, 2010) or the network of anatomical connections between them (Ponten et al, 2010). Source reconstruction promises greater anatomical resolution of electrical activity but multisource MEG reconstruction is a challenging area of active methodological development and at least some reconstruction algorithms can severely perturb patterns of synchronization or covariance in the sensor data (Bullmore and Bassett, 2011).…”

Section: Methodsmentioning

confidence: 99%

Cognitive Effort Drives Workspace Configuration of Human Brain Functional Networks

et al. 2011

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Effortful cognitive performance is theoretically expected to depend on the formation of a global neuronal workspace. We tested specific predictions of workspace theory, using graph theoretical measures of network topology and physical distance of synchronization, in magnetoencephalographic data recorded from healthy adult volunteers (N ϭ 13) during performance of a working memory task at several levels of difficulty. We found that greater cognitive effort caused emergence of a more globally efficient, less clustered, and less modular network configuration, with more long-distance synchronization between brain regions. This pattern of task-related workspace configuration was more salient in the ␤-band (16 -32 Hz) and ␥-band (32-63 Hz) networks, compared with both lower (␣-band; 8 -16 Hz) and higher (high ␥-band; 63-125 Hz) frequency intervals. Workspace configuration of ␤-band networks was also greater in faster performing participants (with correct response latency less than the sample median) compared with slower performing participants. Processes of workspace formation and relaxation in relation to time-varying demands for cognitive effort could be visualized occurring in the course of task trials lasting Ͻ2 s. These experimental results provide support for workspace theory in terms of complex network metrics and directly demonstrate how cognitive effort breaks modularity to make human brain functional networks transiently adopt a more efficient but less economical configuration.

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Section: Methodsmentioning

confidence: 99%

Cognitive Effort Drives Workspace Configuration of Human Brain Functional Networks

et al. 2011

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“…Several studies were devoted to characterizing brain networks dynamics. Some of them focused on the complexity of a graph [7,8]. Nonetheless, the underlying issue is how to define 'graph complexity'.…”

Section: Introductionmentioning

confidence: 99%

“…Nonetheless, the underlying issue is how to define 'graph complexity'. Several studies suggest that a network with high complexity must stand halfway between segregation and integration [7], whereas others propose that it must be related to the small-world concept (halfway between lattice and random graphs) [8]. All these definitions are very influenced by network topology, so the complementary to traditional graph measures remains in the background.…”

Section: Introductionmentioning

confidence: 99%

Novel measure of the weigh distribution balance on the brain network: Graph complexity applied to schizophrenia

Gómez‐Pilar

Poza

Bachiller

et al. 2016

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Abstract-The aim of this study was to assess brain complexity dynamics in schizophrenia (SCH) patients during an auditory oddball task. For that task, we applied a novel graph measure based on the balance of the node weighs distribution. Previous studies applied complexity parameters that were strongly dependent on network topology. This fact could bias the results besides being necessary correction techniques as surrogating process. In the present study, we applied a novel graph complexity measure from the information theory: Shannon Graph Complexity (SGC). Complexity patterns form electroencephalographic recordings of 20 healthy controls and 20 SCH patients during an auditory oddball task were analyzed. Results showed a significantly more pronounced decrease of SGC for controls than for SCH patients during the cognitive task. These findings suggest an important change in the brain configuration towards more balanced networks, mainly in the connections related to longrange interactions. Since these changes are significantly more pronounced in controls, it implies a deficit in the neural network reorganization in SCH patients. In addition, SGC showed a suitable discrimination ability using a leave-one-out cross-validation: 0.725 accuracy and 0.752 area under receiver operating characteristics curve. The novel complexity measure proposed in this study demonstrated to be independent of network topology and, therefore, it complements traditional graph measures to characterize brain networks.

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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%

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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The relationship between structural and functional connectivity: Graph theoretical analysis of an EEG neural mass model

Cited by 89 publications

References 65 publications

Cognitive Effort Drives Workspace Configuration of Human Brain Functional Networks

Cognitive Effort Drives Workspace Configuration of Human Brain Functional Networks

Novel measure of the weigh distribution balance on the brain network: Graph complexity applied to schizophrenia

The topological realization

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