Hyperedge bundling: data, source code, and precautions to modeling-accuracy bias to synchrony estimates

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dc.contributor University of Helsinki, Doctoral Programme Brain & Mind en
dc.contributor University of Helsinki, Neuroscience Center en
dc.contributor University of Helsinki, Neuroscience Center en
dc.contributor University of Helsinki, Doctoral Programme Brain & Mind en
dc.contributor University of Helsinki, Neuroscience Center en
dc.contributor University of Helsinki, Neuroscience Center en
dc.contributor.author Wang, Sheng H.
dc.contributor.author Lobier, Muriel
dc.contributor.author Siebenhühner, Felix
dc.contributor.author Puoliväli, Tuomas
dc.contributor.author Palva, Satu
dc.contributor.author Palva, J. Matias
dc.date.accessioned 2018-08-31T10:00:01Z
dc.date.available 2018-08-31T10:00:01Z
dc.date.issued 2018-06
dc.identifier.citation Wang , S H , Lobier , M , Siebenhühner , F , Puoliväli , T , Palva , S & Palva , J M 2018 , ' Hyperedge bundling: data, source code, and precautions to modeling-accuracy bias to synchrony estimates ' , Data in Brief , vol. 18 , pp. 262-275 . https://doi.org/10.1016/j.dib.2018.03.017 en
dc.identifier.issn 2352-3409
dc.identifier.other PURE: 101158633
dc.identifier.other PURE UUID: 3a8b08e3-8f34-4192-964f-25bec4127721
dc.identifier.other RIS: urn:59B4F25B0454070BF5F1758369C1C7E3
dc.identifier.other Scopus: 85044128784
dc.identifier.other ORCID: /0000-0001-9496-7391/work/47995617
dc.identifier.other ORCID: /0000-0002-2621-9145/work/51990849
dc.identifier.other WOS: 000449815400038
dc.identifier.uri http://hdl.handle.net/10138/240374
dc.description.abstract It has not been well documented that MEG/EEG functional connectivity graphs estimated with zero-lag-free interaction metrics are severely confounded by a multitude of spurious interactions (SI), i.e., the false-positive “ghosts” of true interactions [1,2]. These SI are caused by the multivariate linear mixing between sources, and thus they pose a severe challenge to the validity of connectivity analysis. Due to the complex nature of signal mixing and the SI problem, there is a need to intuitively demonstrate how the SI are discovered and how they can be attenuated using a novel approach that we termed hyperedge bundling. Here we provide a dataset with software with which the readers can perform simulations in order to better understand the theory and the solution to SI. We include the supplementary material of [1] that is not directly relevant to the hyperedge bundling per se but reflects important properties of the MEG source model and the functional connectivity graphs. For example, the gyri of dorsal-lateral cortices are the most accurately modeled areas; the sulci of inferior temporal, frontal and the insula have the least modeling accuracy. Importantly, we found the interaction estimates are heavily biased by the modeling accuracy between regions, which means the estimates cannot be straightforwardly interpreted as the coupling between brain regions. This raise a red flag that the conventional method of thresholding graphs by estimate values is rather suboptimal: because the measured topology of the graph reflects the geometric property of source-model instead of the cortical interactions under investigation. en
dc.format.extent 14
dc.language.iso eng
dc.relation.ispartof Data in Brief
dc.rights en
dc.subject 3112 Neurosciences en
dc.subject SURFACE-BASED ANALYSIS en
dc.subject VISUAL WORKING-MEMORY en
dc.subject RECONSTRUCTION en
dc.subject SEGMENTATION en
dc.subject CAPACITY en
dc.subject BRAIN en
dc.title Hyperedge bundling: data, source code, and precautions to modeling-accuracy bias to synchrony estimates en
dc.type Article
dc.description.version Peer reviewed
dc.identifier.doi https://doi.org/10.1016/j.dib.2018.03.017
dc.type.uri info:eu-repo/semantics/other
dc.type.uri info:eu-repo/semantics/publishedVersion
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