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ShareScore release 0.9.0
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12 results for “EEG-fMRI”
A dataset recording joint EEG-fMRI during affective music listening
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Simultaneous EEG-fMRI - Confidence in perceptual decisions
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Dataset to 'Altered correlation of concurrently recorded EEG-fMRI connectomes in temporal lobe epilepsy '
<p>Dataset to 'Altered correlation of concurrently recorded EEG-fMRI connectomes in temporal lobe epilepsy '</p> <div> <div>For the linked publication see: <a href="https://doi.org/10.1162/netn_a_00362" target="_blank" rel="noopener">https://doi.org/10.1162/netn_a_00362</a></div> <div> </div> </div> <p><em>eeg-fmri_$dataset$_$group$_connectomes_desikan_scrubbed.mat</em><br>datasets with filename truncTo5min hold static connectivities based on timeseries truncated<br>to 5min. All other datasets are based on static connectivities derived from the total session<br>timeseries.</p> <p><br>subj: subject<br>subj.name: name of the subject<br>subj.sess: session<br>subj.sess.sess_name: name of the session<br>subj.sess.fMRI: vector of upper triangular of fMRI connectivity<br>subj.sess.EEG: EEG connectomes<br>subj.sess.EEG.name: name of connectivity measure used (corrected imaginary part of the coherency: iCoh)<br>subj.sess.EEG.bands: EEG frequency bands<br>subj.sess.EEG.bands.name: name of frequency band (delta, theta, alpha, beta, gamma)<br>subj.sess.EEG.bands.name.conn: vector of upper triangular of EEG connectivity<br>subj.atlas: Atlas<br>subj.atlas.name: name of atlas used (Desikan)<br>subj.atlas.regions: number of regions</p> <p><em>$dataset$_$group$_</em>particpants.tsv: Subject metadata</p> <p><em>$dataset$_$group$_spikes.mat: Interictal epileptoform discharges (IEDs) marked for each session of TLE patients</em></p> <p><em>aparc_aseg_yeoR7_68reg_eeg_nosubc_cmfg2dan.mat: mapping of Desikan regions to Yeo7-networks (Yeo et al. 2011, JNP)</em></p> <p><em>desi_coord_68.txt: MNI coordinates of region centers of the Desikan atlas<br></em></p> <p><em>For the related code to this dataset please clone: </em>https://github.com/jwirsich/eeg-fmri-tle or use the code provided in 'eeg-fmri-tle-code.zip'</p>
Spatial–temporal dynamics of gesture–speech integration: a simultaneous EEG-fMRI study
<p>The semantic integration between gesture and speech (GSI) is mediated by the left posterior temporal sulcus/middle temporal gyrus (pSTS/MTG) and the left inferior frontal gyrus (IFG). Evidence from electroencephalography (EEG) suggests that oscillations in the alpha and beta bands may support processes at different stages of GSI. In the present study, we investigated the relationship between electrophysiological oscillations and blood-oxygen-level-dependent (BOLD) activity during GSI. In a simultaneous EEG-fMRI study, German participants (n = 19) were presented with videos of an actor either performing meaningful gestures in the context of a comprehensible German (GG) or incomprehensible Russian sentence (GR), or just speaking a German sentence (SG). EEG results revealed reduced alpha and beta power for the GG vs. SG conditions, while fMRI analyses showed BOLD increase in the left pSTS/MTG for GG > GR ∩ GG > SG. In time-window-based EEG-informed fMRI analyses, we further found a positive correlation between single-trial alpha power and BOLD signal in the left pSTS/MTG, the left IFG, and several sub-cortical regions. Moreover, the alpha-pSTS/MTG correlation was observed in an earlier time window in comparison to the alpha-IFG correlation, thus supporting a two-stage processing model of GSI. Our study shows that EEG-informed fMRI implies multiple roles of alpha oscillations during GSI, and that the method is a best candidate for multidimensional investigations on complex cognitive functions such as GSI.</p>
EEG-fMRI Experiments During Anesthesia Induction With Propofol
ClinicalTrials.gov study NCT06179719. IPD Sharing: Not stated. Countries: 1. Publications: 4.
The relationship between EEG and fMRI connectomes is reproducible across simultaneous EEG-fMRI studies from 1.5T to 7T
<p><strong>Connectome dataset for the publication: 'The relationship between EEG and fMRI connectomes is reproducible across simultaneous EEG-fMRI studies from 1.5T to 7T'<br> Wirsich et al. 2021, NeuroImage, doi: </strong>10.1016/j.neuroimage.2021.117864</p> <p><strong>Data description</strong></p> <p><em>eeg-fmri_connectomes_$atlas$_scrubbed_$dataset$.mat</em><br> datasets with filename truncTo4min58_5s hold static connectivities based on timeseries truncated<br> to 4min58.5s. All other datasets are based on static connectivities derived from the total session<br> timeseries.</p> <p><br> subj: subject<br> subj.name: name of the subject<br> subj.sess: session<br> subj.sess.sess_name: name of the session<br> subj.sess.fMRI: vector of upper triangular of fMRI connectivity<br> subj.sess.EEG: EEG connectomes<br> subj.sess.EEG.name: name of connectivity measure used (imaginary part of the coherency: iCoh,<br> amplitude envelope correlation (orthongonaliyzed): hilb_Ortho, Amplitude envelope correlation<br> (not orthongonaliyzed): hilb_noOrtho)<br> subj.sess.EEG.bands: EEG frequency bands<br> subj.sess.EEG.bands.name: name of frequency band (delta, theta, alpha, beta, gamma)<br> subj.sess.EEG.bands.name.conn: vector of upper triangular of EEG connectivity<br> subj.atlas: Atlas<br> subj.atlas.name: name of atlas used (Desikan or Destrieux)<br> subj.atlas.regions: number of regions</p> <p><br> Use the following code to convert connectivity vectors to a connectivity matrix for atlas(1):</p> <pre><code>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</code></pre> <p><br> <em>atlas_labels.mat</em></p> <p>atlas.name: atlas Name</p> <p>atlas.labels: labels of each atlas region</p> <p><br> </p>
Data from: Deactivation in the posterior mid-cingulate cortex reflects perceptual transitions during binocular rivalry: evidence from simultaneous EEG-fMRI
Binocular rivalry is a phenomenon in which perception spontaneously shifts between two different images that are dichoptically presented to the viewer. By elucidating the cortical networks responsible for these stochastic fluctuations in perception, we can potentially learn much about the neural correlates of visual awareness. We obtained concurrent EEG-fMRI data for a group of 20 healthy human subjects during the continuous presentation of dichoptic visual stimuli. The two eyes' images were tagged with different temporal frequencies so that eye specific steady-state visual evoked potential (SSVEP) signals could be extracted from the EEG data for direct comparison with changes in fMRI BOLD activity associated with binocular rivalry. We additionally included a smooth replay condition that emulated the perceptual transitions experienced during binocular rivalry as a control stimulus. We evaluated a novel SSVEP-informed fMRI analysis in this study in order to delineate the temporal dynamics of rivalry-related BOLD activity from both an electrophysiological and behavioral perspective. In this manner, we assessed BOLD activity during rivalry that was directly correlated with peaks and crosses of the two rivaling, frequency-tagged SSVEP signals, for comparison with BOLD activity associated with subject reported perceptual transitions. Our findings point to a critical role of a right lateralized fronto-parietal network in the processing of bistable stimuli, given that BOLD activity in the right superior/inferior parietal lobules was significantly elevated throughout binocular rivalry and in particular during perceptual transitions, compared with the replay condition. Based on the SSVEP-informed analysis, rivalry was further associated with significantly enhanced BOLD suppression in the posterior mid-cingulate cortex during perceptual transitions, compared with SSVEP crosses. Overall, this work points to a careful interplay between early visual areas, the right posterior parietal cortex and the mid-cingulate cortex in mediating the spontaneous perceptual changes associated with binocular rivalry and has significant implications for future multimodal imaging studies of perception and awareness.
Data from: Deactivation in the posterior mid-cingulate cortex reflects perceptual transitions during binocular rivalry: evidence from simultaneous EEG-fMRI
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Stroke Recovery Research Based on EEG-fMRI
ClinicalTrials.gov study NCT05427422. IPD Sharing: Not stated. Countries: 1. Publications: 0.
National Study on the Interest of EEG-fMRI in the Presurgical Evaluation of Partial Epilepsies Drug
ClinicalTrials.gov study NCT03278210. IPD Sharing: NO. Countries: 1. Publications: 0.
EEG-fMRI: Towards a Useful Clinical Tool in Epilepsy
ClinicalTrials.gov study NCT02410889. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Relationship and Central Mechanism Between Diabetes and Cognitive Impairment Based on Simultaneous EEG-fMRI Approach and Peripheral Neuropathology Biomarkers Assay
ClinicalTrials.gov study NCT05545657. IPD Sharing: YES. Countries: 1. Publications: 0.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.