The relationship between EEG and fMRI connectomes is reproducible across simultaneous EEG-fMRI studies from 1.5T to 7T
Creators
- 1. EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine of Geneva, Geneva, Switzerland
- 2. Laboratory for Functional and Metabolic Imaging, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Systems Division, Swiss Center for Electronics and Microtechnology (CSEM), Neuchâtel, Switzerland
- 3. Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland
- 4. ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico – Universidade de Lisboa, Lisbon, Portugal; Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
- 5. Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
- 6. Department of Neuroscience, University of Geneva, Geneva, Switzerland
- 7. Laboratory for Functional and Metabolic Imaging, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland; Department of Radiology, University of Lausanne, Lausanne, Switzerland
- 8. Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, USA; Psychology Department, University of Illinois at Urbana-Champaign, Urbana, IL, USA
Description
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
Notes
Files
atlas_labels.zip
Files
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