Show simple item record

Depositordc.contributorSmith, Keith
Funderdc.contributor.otherEPSRC - Engineering and Physical Sciences Research Councilen_UK
Time Perioddc.coverage.temporalstart=2015-11-01; end=2016-11-01; scheme=W3C-DTFen
Data Creatordc.creatorSmith, Keith
Data Creatordc.creatorEscudero, Javier
Date Accessioneddc.date.accessioned2016-11-07T13:05:31Z
Date Availabledc.date.available2017-01-01T05:15:08Z
Citationdc.identifier.citationSmith, Keith; Escudero Rodriguez, Javier. (2017). The Complex Hierarchical Topology of EEG Functional Connectivity, 2015-2016 [software]. University of Edinburgh. School of Engineering. Institute for Digital Communications. https://doi.org/10.7488/ds/1520.en
Persistent Identifierdc.identifier.urihttps://hdl.handle.net/10283/2149
Persistent Identifierdc.identifier.urihttps://doi.org/10.7488/ds/1520
Dataset Description (abstract)dc.description.abstractUnderstanding the complex hierarchical topology of functional brain networks is a key aspect of functional connectivity research. Such topics are obscured by the widespread use of sparse binary network models which are fundamentally different to the complete weighted networks derived from functional connectivity. We introduce two techniques to probe the hierarchical complexity of topologies. Firstly, a new metric to measure hierarchical complexity; secondly, a Weighted Complex Hierarchy (WCH) model. To thoroughly evaluate our techniques, we generalise sparse binary network archetypes to weighted forms and explore the main topological features of brain networks- integration, regularity and modularity- using curves over density. By controlling the parameters of our model, the highest complexity is found to arise between a random topology and a strict 'class-based' topology. Further, the model has equivalent complexity to EEG phase-lag networks at peak performance. Hierarchical complexity attains greater magnitude and range of differences between different networks than the previous commonly used complexity metric and our WCH model offers a much broader range of network topology than the standard scale-free and small-world models at a full range of densities. Our metric and model provide a rigorous characterisation of hierarchical complexity. Importantly, our framework shows a scale of complexity arising between 'all nodes are equal' topologies at one extreme and 'strict class-based' topologies at the other.en_UK
Languagedc.language.isoengen_UK
Publisherdc.publisherUniversity of Edinburgh. School of Engineering. Institute for Digital Communicationsen_UK
Relation (Is Referenced By)dc.relation.isreferencedbyhttps://doi.org/10.1016/j.jneumeth.2016.11.003
Relation (Is Referenced By)dc.relation.isreferencedbySmith, K & Escudero, J 2017, 'The Complex Hierarchical Topology of EEG Functional Connectivity' Journal of Neuroscience Methods, vol 276, pp. 1-12. DOI: 10.1016/j.jneumeth.2016.11.003
Relation (Is Referenced By)dc.relation.isreferencedbyhttp://arxiv.org/abs/1604.01680
Rightsdc.rightsCreative Commons Attribution 4.0 International Public Licenseen
Subjectdc.subjectEEG Functional Connectivityen_UK
Subjectdc.subjectComplexityen_UK
Subjectdc.subjectHierarchyen_UK
Subjectdc.subjectBrain Networksen_UK
Subjectdc.subjectGraph Topologyen_UK
Subject Classificationdc.subject.classificationEngineeringen_UK
Titledc.titleThe Complex Hierarchical Topology of EEG Functional Connectivityen_UK
Typedc.typesoftwareen_UK

Download All
zip file MD5 Checksum: 9d00ee60ab17bc6a1a06be0fe4953789

Files in this item

Thumbnail
Thumbnail
Thumbnail
Thumbnail
Thumbnail
Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record