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

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http://hdl.handle.net/10138/240374

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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

Title: Hyperedge bundling: data, source code, and precautions to modeling-accuracy bias to synchrony estimates
Author: Wang, Sheng H.; Lobier, Muriel; Siebenhühner, Felix; Puoliväli, Tuomas; Palva, Satu; Palva, J. Matias
Contributor: University of Helsinki, Doctoral Programme Brain & Mind
University of Helsinki, Neuroscience Center
University of Helsinki, Neuroscience Center
University of Helsinki, Doctoral Programme Brain & Mind
University of Helsinki, Neuroscience Center
University of Helsinki, Neuroscience Center
Date: 2018-06
Language: eng
Number of pages: 14
Belongs to series: Data in Brief
ISSN: 2352-3409
URI: http://hdl.handle.net/10138/240374
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.
Subject: 3112 Neurosciences
SURFACE-BASED ANALYSIS
VISUAL WORKING-MEMORY
RECONSTRUCTION
SEGMENTATION
CAPACITY
BRAIN
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