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ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2020
License: CC BY
Data sources: ZENODO
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The relationship between EEG and fMRI connectomes is reproducible across simultaneous EEG-fMRI studies from 1.5T to 7T

Authors: Wirsich, Jonathan; Jorge, João; Iannotti, Giannina R; Shamshiri, Elhum A; Grouiller, Frédéric; Abreu, Rodolfo; Lazeyras, François; +4 Authors

The relationship between EEG and fMRI connectomes is reproducible across simultaneous EEG-fMRI studies from 1.5T to 7T

Abstract

Connectome dataset for the publication: 'The relationship between EEG and fMRI connectomes is reproducible across simultaneous EEG-fMRI studies from 1.5T to 7T' Wirsich et al. 2021, NeuroImage, doi: 10.1016/j.neuroimage.2021.117864 Data description eeg-fmri_connectomes_$atlas$_scrubbed_$dataset$.mat datasets with filename truncTo4min58_5s hold static connectivities based on timeseries truncated to 4min58.5s. All other datasets are based on static connectivities derived from the total session timeseries. subj: subject subj.name: name of the subject subj.sess: session subj.sess.sess_name: name of the session subj.sess.fMRI: vector of upper triangular of fMRI connectivity subj.sess.EEG: EEG connectomes subj.sess.EEG.name: name of connectivity measure used (imaginary part of the coherency: iCoh, amplitude envelope correlation (orthongonaliyzed): hilb_Ortho, Amplitude envelope correlation (not orthongonaliyzed): hilb_noOrtho) subj.sess.EEG.bands: EEG frequency bands subj.sess.EEG.bands.name: name of frequency band (delta, theta, alpha, beta, gamma) subj.sess.EEG.bands.name.conn: vector of upper triangular of EEG connectivity subj.atlas: Atlas subj.atlas.name: name of atlas used (Desikan or Destrieux) subj.atlas.regions: number of regions Use the following code to convert connectivity vectors to a connectivity matrix for atlas(1): regions = length(atlas(1).labels) mrtx = zeros(regions); count = 1; for r1 = 1:regions-1 for r2 = r1+1:regions mrtx(r1, r2) = subj.sess.fMRI(count); mrtx(r2, r1) = mrtx(r1, r2); count = count + 1; end end atlas_labels.mat atlas.name: atlas Name atlas.labels: labels of each atlas region

We thank Katia Lehongre, Benjamin Morillon (64Ch-3T dataset) and Fani Deligianni and Jonathan Clayden (64Ch-1.5T dataset) for generously sharing their data. We acknowledge support from Swiss National Science Foundation (SNSF, under grants CRSII5_170873, 169198 and 192749 to SV, 188769 to FG and 185909 to JJ). SS acknowledges financial support from NIH R01MH11622601A1 and R21NS10460302. ALG was supported by ERC 260347 – COMPUSLANG and NCCR Evolving Language, SNSF Agreement #51NF40_180888. JJ, FL and RG were supported by Centre d'Imagerie BioMédicale (CIBM) of the UNIL, UNIGE, HUG, CHUV, EPFL and the Leenaards and Jeantet Foundations.

Keywords

resting-state, connectome, EEG-fMRI

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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