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Source imaging of high-density visual evoked potentials with multi-scale brain parcellations and connectomes

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Pascucci David, Tourbier Sebastien, Rué-Queralt Joan, Carboni Margherita, Hagmann Patric, Plomp Gijs,
Project Exploring brain communication pathways by combining diffusion based quantitative structural connectivity and EEG source imaging : application to physiological and epileptic networks
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Original article (peer-reviewed)

Journal Scientific Data
Volume (Issue) 9(1)
Page(s) 9 - 9
Title of proceedings Scientific Data
DOI 10.1038/s41597-021-01116-1

Open Access

URL http://doi.org/10.1038/s41597-021-01116-1
Type of Open Access Publisher (Gold Open Access)

Abstract

AbstractWe describe the multimodal neuroimaging dataset VEPCON (OpenNeuro Dataset ds003505). It includes raw data and derivatives of high-density EEG, structural MRI, diffusion weighted images (DWI) and single-trial behavior (accuracy, reaction time). Visual evoked potentials (VEPs) were recorded while participants (n = 20) discriminated briefly presented faces from scrambled faces, or coherently moving stimuli from incoherent ones. EEG and MRI were recorded separately from the same participants. The dataset contains raw EEG and behavioral data, pre-processed EEG of single trials in each condition, structural MRIs, individual brain parcellations at 5 spatial resolutions (83 to 1015 regions), and the corresponding structural connectomes computed from fiber count, fiber density, average fractional anisotropy and mean diffusivity maps. For source imaging, VEPCON provides EEG inverse solutions based on individual anatomy, with Python and Matlab scripts to derive activity time-series in each brain region, for each parcellation level. The BIDS-compatible dataset can contribute to multimodal methods development, studying structure-function relations, and to unimodal optimization of source imaging and graph analyses, among many other possibilities.
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