A multi-modal human neuroimaging dataset for data integration: simultaneous EEG and fMRI acquisition during a motor imagery neurofeedback task: XP1

uploaded by Claire Cury on 2019-12-02 - almost 2 years ago
last modified on 2019-12-12 - almost 2 years ago
authored by Giulia Lioi, Claire Cury, Lorraine Perronnet, Marsel Mano, Elise Bannier, Anatole Lecuyer, Christian Barillot
322060
We found 4 Warnings in your dataset. You are not required to fix warnings, but doing so will make your dataset more BIDS compliant.

/sub-xp101/eeg/sub-xp101_task-MIpost_eeg.eeg

This file is missing for subject sub-xp101, but is present for at least one other subject.

Subject: sub-xp101; Missing file: sub-xp101_task-MIpost_eeg.eeg

/sub-xp101/eeg/sub-xp101_task-MIpost_eeg.vhdr

This file is missing for subject sub-xp101, but is present for at least one other subject.

Subject: sub-xp101; Missing file: sub-xp101_task-MIpost_eeg.vhdr

/sub-xp101/eeg/sub-xp101_task-MIpost_eeg.vmrk

This file is missing for subject sub-xp101, but is present for at least one other subject.

Subject: sub-xp101; Missing file: sub-xp101_task-MIpost_eeg.vmrk

/sub-xp101/eeg/sub-xp101_task-MIpre_eeg.eeg

This file is missing for subject sub-xp101, but is present for at least one other subject.

Subject: sub-xp101; Missing file: sub-xp101_task-MIpre_eeg.eeg

/sub-xp101/eeg/sub-xp101_task-MIpre_eeg.vhdr

This file is missing for subject sub-xp101, but is present for at least one other subject.

Subject: sub-xp101; Missing file: sub-xp101_task-MIpre_eeg.vhdr

/sub-xp101/eeg/sub-xp101_task-MIpre_eeg.vmrk

This file is missing for subject sub-xp101, but is present for at least one other subject.

Subject: sub-xp101; Missing file: sub-xp101_task-MIpre_eeg.vmrk

/sub-xp101/func/sub-xp101_task-MIpost_bold.json

This file is missing for subject sub-xp101, but is present for at least one other subject.

Subject: sub-xp101; Missing file: sub-xp101_task-MIpost_bold.json

/sub-xp101/func/sub-xp101_task-MIpost_bold.nii.gz

This file is missing for subject sub-xp101, but is present for at least one other subject.

Subject: sub-xp101; Missing file: sub-xp101_task-MIpost_bold.nii.gz

/sub-xp101/func/sub-xp101_task-MIpre_bold.json

This file is missing for subject sub-xp101, but is present for at least one other subject.

Subject: sub-xp101; Missing file: sub-xp101_task-MIpre_bold.json

/sub-xp101/func/sub-xp101_task-MIpre_bold.nii.gz

This file is missing for subject sub-xp101, but is present for at least one other subject.

Subject: sub-xp101; Missing file: sub-xp101_task-MIpre_bold.nii.gz

and 18 more files

/sub-xp101/func/sub-xp101_task-eegNF_bold.nii.gz

The most common set of dimensions is: 106,106,32,210 (voxels), This file has the dimensions: 106,106,32,205 (voxels).

/sub-xp101/func/sub-xp101_task-motorloc_bold.nii.gz

The most common set of dimensions is: 106,106,32,166 (voxels), This file has the dimensions: 106,106,32,163 (voxels).

/sub-xp102/func/sub-xp102_task-MIpre_bold.nii.gz

The most common set of dimensions is: 106,106,32,106 (voxels), This file has the dimensions: 106,106,32,108 (voxels).

/sub-xp106/func/sub-xp106_task-MIpost_bold.nii.gz

The most common set of dimensions is: 106,106,32,106 (voxels), This file has the dimensions: 106,106,32,55 (voxels).

/task-MIpost_channels.tsv

Tabular file contains custom columns not described in a data dictionary

Columns: name type units not defined, please define in: /task-MIpost_channels.json, /channels.json

/task-MIpre_channels.tsv

Tabular file contains custom columns not described in a data dictionary

Columns: name type units not defined, please define in: /task-MIpre_channels.json, /channels.json

/task-eegNF_channels.tsv

Tabular file contains custom columns not described in a data dictionary

Columns: name type units not defined, please define in: /task-eegNF_channels.json, /channels.json

/task-eegfmriNF_channels.tsv

Tabular file contains custom columns not described in a data dictionary

Columns: name type units not defined, please define in: /task-eegfmriNF_channels.json, /channels.json

/task-fmriNF_channels.tsv

Tabular file contains custom columns not described in a data dictionary

Columns: name type units not defined, please define in: /task-fmriNF_channels.json, /channels.json

/task-motorloc_channels.tsv

Tabular file contains custom columns not described in a data dictionary

Columns: name type units not defined, please define in: /task-motorloc_channels.json, /channels.json

/task-MIpost_events.tsv

The onset of the last event is after the total duration of the corresponding scan. This design is suspiciously long.


OpenNeuro Accession Number: ds002336
Files: 325, Size: 16.81GB, Subjects: 10, Session: 1
Available Tasks: eegNF, fmriNF, motorloc, eegfmriNF, MIpre, MIpost
Available Modalities: T1w, eeg, bold

README

———————————————————————————————— ORIGINAL PAPERS ———————————————————————————————— Mano, Marsel, Anatole Lécuyer, Elise Bannier, Lorraine Perronnet, Saman Noorzadeh, and Christian Barillot. 2017. “How to Build a Hybrid Neurofeedback Platform Combining EEG and FMRI.” Frontiers in Neuroscience 11 (140). https://doi.org/10.3389/fnins.2017.00140 Perronnet, Lorraine, L Anatole, Marsel Mano, Elise Bannier, Maureen Clerc, Christian Barillot, Lorraine Perronnet, et al. 2017. “Unimodal Versus Bimodal EEG-FMRI Neurofeedback of a Motor Imagery Task.” Frontiers in Human Neuroscience 11 (193). https://doi.org/10.3389/fnhum.2017.00193.

This dataset named XP1 can be pull together with the dataset XP2 (DOI: 10.18112/openneuro.ds002338.v1.0.0). Data acquisition methods have been described in Perronnet et al. (2017, Frontiers in Human Neuroscience). Simultaneous 64 channels EEG and fMRI during right-hand motor imagery and neurofeedback (NF) were acquired in this study (as well as in XP2). For this study, 10 subjects performed three types of NF runs (bimodal EEG-fMRI NF, unimodal EEG-NF and fMRI-NF).

———————————————————————————————— EXPERIMENTAL PARADIGM ————————————————————————————————
Subjects were instructed to perform a kinaesthetic motor imagery of the right hand and to find their own strategy to control and bring the ball to the target. The experimental protocol consisted of 6 EEG-fMRI runs with a 20s block design alternating rest and task motor localizer run (task-motorloc) - 8 blocks X (20s rest+20 s task) motor imagery run without NF (task-MIpre) -5 blocks X (20s rest+20 s task) three NF runs with different NF conditions (task-eegNF, task-fmriNF, task-eegfmriNF) occurring in random order- 10 blocks X (20s rest+20 s task) motor imagery run without NF (task-MIpost) - 5 blocks X (20s rest+20 s task)

———————————————————————————————— EEG DATA ———————————————————————————————— EEG data was recorded using a 64-channel MR compatible solution from Brain Products (Brain Products GmbH, Gilching, Germany).

RAW EEG DATA

EEG was sampled at 5kHz with FCz as the reference electrode and AFz as the ground electrode, and a resolution of 0.5 microV. Following the BIDs arborescence, raw eeg data for each task can be found for each subject in

XP1/sub-xp1*/eeg

in Brain Vision Recorder format (File Version 1.0). Each raw EEG recording includes three files: the data file (.eeg), the header file (.vhdr) and the marker file (*.vmrk). The header file contains information about acquisition parameters and amplifier setup. For each electrode, the impedance at the beginning of the recording is also specified. For all subjects, channel 32 is the ECG channel. The 63 other channels are EEG channels.

The marker file contains the list of markers assigned to the EEG recordings and their properties (marker type, marker ID and position in data points). Three type of markers are relevant for the EEG processing: R128 (Response): is the fMRI volume marker to correct for the gradient artifact S 99 (Stimulus): is the protocol marker indicating the start of the Rest block S 2 (Stimulus): is the protocol marker indicating the start of the Task (Motor Execution Motor Imagery or Neurofeedback)
Warning : in few EEG data, the first S99 marker might be missing, but can be easily “added” 20 s before the first S 2.

PREPROCESSED EEG DATA

Following the BIDs arborescence, processed eeg data for each task and subject in the pre-processed data folder :

XP1/derivatives/sub-xp1/eeg_pp/*eeg_pp.

and following the Brain Analyzer format. Each processed EEG recording includes three files: the data file (.dat), the header file (.vhdr) and the marker file (*.vmrk), containing information similar to those described for raw data. In the header file of preprocessed data channels location are also specified. In the marker file the location in data points of the identified heart pulse (R marker) are specified as well.

EEG data were pre-processed using BrainVision Analyzer II Software, with the following steps: Automatic gradient artifact correction using the artifact template subtraction method (Sliding average calculation with 21 intervals for sliding average and all channels enabled for correction. Downsampling with factor: 25 (200 Hz) Low Pass FIR Filter:Cut-off Frequency: 50 Hz. Ballistocardiogram (pulse) artifact correction using a semiautomatic procedure (Pulse Template searched between 40 s and 240 s in the ECG channel with the following parameters:Coherence Trigger = 0.5, Minimal Amplitude = 0.5, Maximal Amplitude = 1.3. The identified pulses were marked with R. Segmentation relative to the first block marker (S 99) for all the length of the training protocol (las S 2 + 20 s).

EEG NF SCORES

Neurofeedback scores can be found in the .mat structures in

XP1/derivatives/sub-xp1*/NF_eeg/d_sub*NFeeg_scores.mat

Structures names NF_eeg are composed by the following subfields: ID : Subject ID, for example sub-xp101 lapC3_ERD : a 1x1280 vector of neurofeedback scores. 4 scores per secondes, for the whole session. eeg : a 64x80200 matrix, with the pre-processed EEG signals with the step described above, filtered between 8 and 30 Hz. lapC3_bandpower_8Hz_30Hz : 1x1280 vector. Bandpower of the filtered signal with a laplacian centred on C3, used to estimate the lapC3_ERD. lapC3_filter : 1x64 vector. Laplacian filter centred on C3 channel.

———————————————————————————————— BOLD fMRI DATA ———————————————————————————————— All DICOM files were converted to Nifti-1 and then in BIDs format (version 2.1.4) using the software dcm2niix (version v1.0.20190720 GVV7.4.0)

fMRI acquisitions were performed using echo- planar imaging (EPI) and covering the entire brain with the following parameters

3T Siemens Verio EPI sequence TR=2 s TE=23 ms Resolution 2x2x4 mm3 FOV = 210×210mm2 N of slices: 32 No slice gap

As specified in the relative task event files in XP1\ *events.tsv files onset, the scanner began the EPI pulse sequence two seconds prior to the start of the protocol (first rest block), so the the first two TRs should be discarded. The useful TRs for the runs are therefore

task-motorloc: 320 s (2 to 322) task-MIpre and task-MIpost: 200 s (2 to 202) task-eegNF, task-fmriNF, task-eegfmriNF: 400 s (2 to 402)

In task events files for the different tasks, each column represents:

  • 'onset': onset time (sec) of an event
  • 'duration': duration (sec) of the event
  • 'trial_type': trial (block) type: rest or task (Rest, Task-ME, Task-MI, Task-NF)
  • ''stim_file’: image presented in a stimulus block: during Rest, Motor Imagery (Task-MI) or Motor execution (Task-ME) instructions were presented. On the other hand, during Neurofeedback blocks (Task-NF) the image presented was a ball moving in a square that the subject could control self-regulating his EEG and/or fMRI brain activity.

Following the BIDs arborescence, the functional data and relative metadata are found for each subject in the following directory

XP1/sub-xp1*/func

BOLD-NF SCORES

For each subject and NF session, a matlab structure with BOLD-NF features can be found in

XP1/derivatives/sub-xp1*/NF_bold/

In view of BOLD-NF scores computation, fMRI data were preprocessed using AutoMRI, a software based on spm8 and with the following steps: slice-time correction, spatial realignment and coregistration with the anatomical scan, spatial smoothing with a 6 mm Gaussian kernel and normalization to the Montreal Neurological Institute template For each session, a first level general linear model analysis modeling was then performed. The resulting activation maps (voxel-wise Family-Wise error corrected at p < 0.05) were used to define two ROIs (9x9x3 voxels) around the maximum of activation in the ipsilesional primary motor area (M1) and supplementary motor area (SMA) respectively.

The BOLD-NF scores were calculated as the difference between percentage signal change in the two ROIs (SMA and M1) and a large deep background region (slice 3 out of 16) whose activity is not correlated with the NF task. A smoothed version of the NF scores over the precedent three volumes was also computed.

The NF_boldi structure has the following structure

NF_bold → .m1 → .nf → .smoothnf
→ .roimean (averaged BOLD signal in the ROI) → .bgmean (averaged BOLD signal in the background slice) → .method
NFscores.fmri → .sma→ .nf → .smoothnf
→ .roimean (averaged BOLD signal in the ROI) → .bgmean (averaged BOLD signal in the background slice) → .method

Where the subfield method contains information about the ROI size (.roisize), the background mask (.bgmask) and ROI mask (.roimask).

More details about signal processing and NF calculation can be found in Perronnet et al. 2017 and Perronnet et al. 2018.

———————————————————————————————— ANATOMICAL MRI DATA ———————————————————————————————— As a structural reference for the fMRI analysis, a high resolution 3D T1 MPRAGE sequence was acquired with the following parameters

3T Siemens Verio 3D T1 MPRAGE TR=1.9 s TE=22.6 ms Resolution 1x1x1 mm3 FOV = 256×256 mm2 N of slices: 176

Defacing of MPRAGE T1 images was performed by the submitter using pydeface (https://github.com/poldracklab/pydeface)

Following the BIDs arborescence, the functional data and relative metadata are found for each subject in the following directory

XP1/sub-xp1*/anat

Authors

  • Giulia Lioi
  • Claire Cury
  • Lorraine Perronnet
  • Marsel Mano
  • Elise Bannier
  • Anatole Lecuyer
  • Christian Barillot

Dataset DOI

10.18112/openneuro.ds002336.v2.0.2

License

CC0

Acknowledgements

MRI data acquisition was supported by the Neurinfo MRI research facility at the University Hospital of Rennes, which is granted by the European Union and the French Gouvernment

How to Acknowledge

Funding

  • This work was supported by the French Natinal Research Agency under the reference ANR-10-LABX-07-01

References and Links

  • Perronnet, Lorraine, L Anatole, Marsel Mano, Elise Bannier, Maureen Clerc, Christian Barillot, Lorraine Perronnet, et al. 2017. “Unimodal Versus Bimodal EEG-FMRI Neurofeedback of a Motor Imagery Task.” Frontiers in Human Neuroscience 11 (193). https://doi.org/10.3389/fnhum.2017.00193.
  • Giulia Lioi, Claire Cury, Lorraine Perronnet, Marsel Mano, Elise Bannier, Anatole Lecuyer, Christian Barillot. Simultaneous MRI-EEG during a motor imagery neurofeedback task: an open access brain imaging dataset for multi-modal data integration. bioRxiv 862375; doi: https://doi.org/10.1101/862375

Ethics Approvals

How To Cite

Copy
Giulia Lioi and Claire Cury and Lorraine Perronnet and Marsel Mano and Elise Bannier and Anatole Lecuyer and Christian Barillot (2019). A multi-modal human neuroimaging dataset for data integration: simultaneous EEG and fMRI acquisition during a motor imagery neurofeedback task: XP1. OpenNeuro. [Dataset] doi: 10.18112/openneuro.ds002336.v2.0.2
More citation info

A multi-modal human neuroimaging dataset for data integration: simultaneous EEG and fMRI acquisition during a motor imagery neurofeedback task: XP1

uploaded by Claire Cury on 2019-12-02 - almost 2 years ago
last modified on 2019-12-12 - almost 2 years ago
authored by Giulia Lioi, Claire Cury, Lorraine Perronnet, Marsel Mano, Elise Bannier, Anatole Lecuyer, Christian Barillot
322060

OpenNeuro Accession Number: ds002336
Files: 325, Size: 16.81GB, Subjects: 10, Session: 1
Available Tasks: eegNF, fmriNF, motorloc, eegfmriNF, MIpre, MIpost
Available Modalities: T1w, eeg, bold

README

———————————————————————————————— ORIGINAL PAPERS ———————————————————————————————— Mano, Marsel, Anatole Lécuyer, Elise Bannier, Lorraine Perronnet, Saman Noorzadeh, and Christian Barillot. 2017. “How to Build a Hybrid Neurofeedback Platform Combining EEG and FMRI.” Frontiers in Neuroscience 11 (140). https://doi.org/10.3389/fnins.2017.00140 Perronnet, Lorraine, L Anatole, Marsel Mano, Elise Bannier, Maureen Clerc, Christian Barillot, Lorraine Perronnet, et al. 2017. “Unimodal Versus Bimodal EEG-FMRI Neurofeedback of a Motor Imagery Task.” Frontiers in Human Neuroscience 11 (193). https://doi.org/10.3389/fnhum.2017.00193.

This dataset named XP1 can be pull together with the dataset XP2 (DOI: 10.18112/openneuro.ds002338.v1.0.0). Data acquisition methods have been described in Perronnet et al. (2017, Frontiers in Human Neuroscience). Simultaneous 64 channels EEG and fMRI during right-hand motor imagery and neurofeedback (NF) were acquired in this study (as well as in XP2). For this study, 10 subjects performed three types of NF runs (bimodal EEG-fMRI NF, unimodal EEG-NF and fMRI-NF).

———————————————————————————————— EXPERIMENTAL PARADIGM ————————————————————————————————
Subjects were instructed to perform a kinaesthetic motor imagery of the right hand and to find their own strategy to control and bring the ball to the target. The experimental protocol consisted of 6 EEG-fMRI runs with a 20s block design alternating rest and task motor localizer run (task-motorloc) - 8 blocks X (20s rest+20 s task) motor imagery run without NF (task-MIpre) -5 blocks X (20s rest+20 s task) three NF runs with different NF conditions (task-eegNF, task-fmriNF, task-eegfmriNF) occurring in random order- 10 blocks X (20s rest+20 s task) motor imagery run without NF (task-MIpost) - 5 blocks X (20s rest+20 s task)

———————————————————————————————— EEG DATA ———————————————————————————————— EEG data was recorded using a 64-channel MR compatible solution from Brain Products (Brain Products GmbH, Gilching, Germany).

RAW EEG DATA

EEG was sampled at 5kHz with FCz as the reference electrode and AFz as the ground electrode, and a resolution of 0.5 microV. Following the BIDs arborescence, raw eeg data for each task can be found for each subject in

XP1/sub-xp1*/eeg

in Brain Vision Recorder format (File Version 1.0). Each raw EEG recording includes three files: the data file (.eeg), the header file (.vhdr) and the marker file (*.vmrk). The header file contains information about acquisition parameters and amplifier setup. For each electrode, the impedance at the beginning of the recording is also specified. For all subjects, channel 32 is the ECG channel. The 63 other channels are EEG channels.

The marker file contains the list of markers assigned to the EEG recordings and their properties (marker type, marker ID and position in data points). Three type of markers are relevant for the EEG processing: R128 (Response): is the fMRI volume marker to correct for the gradient artifact S 99 (Stimulus): is the protocol marker indicating the start of the Rest block S 2 (Stimulus): is the protocol marker indicating the start of the Task (Motor Execution Motor Imagery or Neurofeedback)
Warning : in few EEG data, the first S99 marker might be missing, but can be easily “added” 20 s before the first S 2.

PREPROCESSED EEG DATA

Following the BIDs arborescence, processed eeg data for each task and subject in the pre-processed data folder :

XP1/derivatives/sub-xp1/eeg_pp/*eeg_pp.

and following the Brain Analyzer format. Each processed EEG recording includes three files: the data file (.dat), the header file (.vhdr) and the marker file (*.vmrk), containing information similar to those described for raw data. In the header file of preprocessed data channels location are also specified. In the marker file the location in data points of the identified heart pulse (R marker) are specified as well.

EEG data were pre-processed using BrainVision Analyzer II Software, with the following steps: Automatic gradient artifact correction using the artifact template subtraction method (Sliding average calculation with 21 intervals for sliding average and all channels enabled for correction. Downsampling with factor: 25 (200 Hz) Low Pass FIR Filter:Cut-off Frequency: 50 Hz. Ballistocardiogram (pulse) artifact correction using a semiautomatic procedure (Pulse Template searched between 40 s and 240 s in the ECG channel with the following parameters:Coherence Trigger = 0.5, Minimal Amplitude = 0.5, Maximal Amplitude = 1.3. The identified pulses were marked with R. Segmentation relative to the first block marker (S 99) for all the length of the training protocol (las S 2 + 20 s).

EEG NF SCORES

Neurofeedback scores can be found in the .mat structures in

XP1/derivatives/sub-xp1*/NF_eeg/d_sub*NFeeg_scores.mat

Structures names NF_eeg are composed by the following subfields: ID : Subject ID, for example sub-xp101 lapC3_ERD : a 1x1280 vector of neurofeedback scores. 4 scores per secondes, for the whole session. eeg : a 64x80200 matrix, with the pre-processed EEG signals with the step described above, filtered between 8 and 30 Hz. lapC3_bandpower_8Hz_30Hz : 1x1280 vector. Bandpower of the filtered signal with a laplacian centred on C3, used to estimate the lapC3_ERD. lapC3_filter : 1x64 vector. Laplacian filter centred on C3 channel.

———————————————————————————————— BOLD fMRI DATA ———————————————————————————————— All DICOM files were converted to Nifti-1 and then in BIDs format (version 2.1.4) using the software dcm2niix (version v1.0.20190720 GVV7.4.0)

fMRI acquisitions were performed using echo- planar imaging (EPI) and covering the entire brain with the following parameters

3T Siemens Verio EPI sequence TR=2 s TE=23 ms Resolution 2x2x4 mm3 FOV = 210×210mm2 N of slices: 32 No slice gap

As specified in the relative task event files in XP1\ *events.tsv files onset, the scanner began the EPI pulse sequence two seconds prior to the start of the protocol (first rest block), so the the first two TRs should be discarded. The useful TRs for the runs are therefore

task-motorloc: 320 s (2 to 322) task-MIpre and task-MIpost: 200 s (2 to 202) task-eegNF, task-fmriNF, task-eegfmriNF: 400 s (2 to 402)

In task events files for the different tasks, each column represents:

  • 'onset': onset time (sec) of an event
  • 'duration': duration (sec) of the event
  • 'trial_type': trial (block) type: rest or task (Rest, Task-ME, Task-MI, Task-NF)
  • ''stim_file’: image presented in a stimulus block: during Rest, Motor Imagery (Task-MI) or Motor execution (Task-ME) instructions were presented. On the other hand, during Neurofeedback blocks (Task-NF) the image presented was a ball moving in a square that the subject could control self-regulating his EEG and/or fMRI brain activity.

Following the BIDs arborescence, the functional data and relative metadata are found for each subject in the following directory

XP1/sub-xp1*/func

BOLD-NF SCORES

For each subject and NF session, a matlab structure with BOLD-NF features can be found in

XP1/derivatives/sub-xp1*/NF_bold/

In view of BOLD-NF scores computation, fMRI data were preprocessed using AutoMRI, a software based on spm8 and with the following steps: slice-time correction, spatial realignment and coregistration with the anatomical scan, spatial smoothing with a 6 mm Gaussian kernel and normalization to the Montreal Neurological Institute template For each session, a first level general linear model analysis modeling was then performed. The resulting activation maps (voxel-wise Family-Wise error corrected at p < 0.05) were used to define two ROIs (9x9x3 voxels) around the maximum of activation in the ipsilesional primary motor area (M1) and supplementary motor area (SMA) respectively.

The BOLD-NF scores were calculated as the difference between percentage signal change in the two ROIs (SMA and M1) and a large deep background region (slice 3 out of 16) whose activity is not correlated with the NF task. A smoothed version of the NF scores over the precedent three volumes was also computed.

The NF_boldi structure has the following structure

NF_bold → .m1 → .nf → .smoothnf
→ .roimean (averaged BOLD signal in the ROI) → .bgmean (averaged BOLD signal in the background slice) → .method
NFscores.fmri → .sma→ .nf → .smoothnf
→ .roimean (averaged BOLD signal in the ROI) → .bgmean (averaged BOLD signal in the background slice) → .method

Where the subfield method contains information about the ROI size (.roisize), the background mask (.bgmask) and ROI mask (.roimask).

More details about signal processing and NF calculation can be found in Perronnet et al. 2017 and Perronnet et al. 2018.

———————————————————————————————— ANATOMICAL MRI DATA ———————————————————————————————— As a structural reference for the fMRI analysis, a high resolution 3D T1 MPRAGE sequence was acquired with the following parameters

3T Siemens Verio 3D T1 MPRAGE TR=1.9 s TE=22.6 ms Resolution 1x1x1 mm3 FOV = 256×256 mm2 N of slices: 176

Defacing of MPRAGE T1 images was performed by the submitter using pydeface (https://github.com/poldracklab/pydeface)

Following the BIDs arborescence, the functional data and relative metadata are found for each subject in the following directory

XP1/sub-xp1*/anat

Authors

  • Giulia Lioi
  • Claire Cury
  • Lorraine Perronnet
  • Marsel Mano
  • Elise Bannier
  • Anatole Lecuyer
  • Christian Barillot

Dataset DOI

10.18112/openneuro.ds002336.v2.0.2

License

CC0

Acknowledgements

MRI data acquisition was supported by the Neurinfo MRI research facility at the University Hospital of Rennes, which is granted by the European Union and the French Gouvernment

How to Acknowledge

Funding

  • This work was supported by the French Natinal Research Agency under the reference ANR-10-LABX-07-01

References and Links

  • Perronnet, Lorraine, L Anatole, Marsel Mano, Elise Bannier, Maureen Clerc, Christian Barillot, Lorraine Perronnet, et al. 2017. “Unimodal Versus Bimodal EEG-FMRI Neurofeedback of a Motor Imagery Task.” Frontiers in Human Neuroscience 11 (193). https://doi.org/10.3389/fnhum.2017.00193.
  • Giulia Lioi, Claire Cury, Lorraine Perronnet, Marsel Mano, Elise Bannier, Anatole Lecuyer, Christian Barillot. Simultaneous MRI-EEG during a motor imagery neurofeedback task: an open access brain imaging dataset for multi-modal data integration. bioRxiv 862375; doi: https://doi.org/10.1101/862375

Ethics Approvals

How To Cite

Copy
Giulia Lioi and Claire Cury and Lorraine Perronnet and Marsel Mano and Elise Bannier and Anatole Lecuyer and Christian Barillot (2019). A multi-modal human neuroimaging dataset for data integration: simultaneous EEG and fMRI acquisition during a motor imagery neurofeedback task: XP1. OpenNeuro. [Dataset] doi: 10.18112/openneuro.ds002336.v2.0.2
More citation info

Dataset File Tree

Git Hash: 1225f85 

BIDS Validation

We found 4 Warnings in your dataset. You are not required to fix warnings, but doing so will make your dataset more BIDS compliant.

/sub-xp101/eeg/sub-xp101_task-MIpost_eeg.eeg

This file is missing for subject sub-xp101, but is present for at least one other subject.

Subject: sub-xp101; Missing file: sub-xp101_task-MIpost_eeg.eeg

/sub-xp101/eeg/sub-xp101_task-MIpost_eeg.vhdr

This file is missing for subject sub-xp101, but is present for at least one other subject.

Subject: sub-xp101; Missing file: sub-xp101_task-MIpost_eeg.vhdr

/sub-xp101/eeg/sub-xp101_task-MIpost_eeg.vmrk

This file is missing for subject sub-xp101, but is present for at least one other subject.

Subject: sub-xp101; Missing file: sub-xp101_task-MIpost_eeg.vmrk

/sub-xp101/eeg/sub-xp101_task-MIpre_eeg.eeg

This file is missing for subject sub-xp101, but is present for at least one other subject.

Subject: sub-xp101; Missing file: sub-xp101_task-MIpre_eeg.eeg

/sub-xp101/eeg/sub-xp101_task-MIpre_eeg.vhdr

This file is missing for subject sub-xp101, but is present for at least one other subject.

Subject: sub-xp101; Missing file: sub-xp101_task-MIpre_eeg.vhdr

/sub-xp101/eeg/sub-xp101_task-MIpre_eeg.vmrk

This file is missing for subject sub-xp101, but is present for at least one other subject.

Subject: sub-xp101; Missing file: sub-xp101_task-MIpre_eeg.vmrk

/sub-xp101/func/sub-xp101_task-MIpost_bold.json

This file is missing for subject sub-xp101, but is present for at least one other subject.

Subject: sub-xp101; Missing file: sub-xp101_task-MIpost_bold.json

/sub-xp101/func/sub-xp101_task-MIpost_bold.nii.gz

This file is missing for subject sub-xp101, but is present for at least one other subject.

Subject: sub-xp101; Missing file: sub-xp101_task-MIpost_bold.nii.gz

/sub-xp101/func/sub-xp101_task-MIpre_bold.json

This file is missing for subject sub-xp101, but is present for at least one other subject.

Subject: sub-xp101; Missing file: sub-xp101_task-MIpre_bold.json

/sub-xp101/func/sub-xp101_task-MIpre_bold.nii.gz

This file is missing for subject sub-xp101, but is present for at least one other subject.

Subject: sub-xp101; Missing file: sub-xp101_task-MIpre_bold.nii.gz

and 18 more files

/sub-xp101/func/sub-xp101_task-eegNF_bold.nii.gz

The most common set of dimensions is: 106,106,32,210 (voxels), This file has the dimensions: 106,106,32,205 (voxels).

/sub-xp101/func/sub-xp101_task-motorloc_bold.nii.gz

The most common set of dimensions is: 106,106,32,166 (voxels), This file has the dimensions: 106,106,32,163 (voxels).

/sub-xp102/func/sub-xp102_task-MIpre_bold.nii.gz

The most common set of dimensions is: 106,106,32,106 (voxels), This file has the dimensions: 106,106,32,108 (voxels).

/sub-xp106/func/sub-xp106_task-MIpost_bold.nii.gz

The most common set of dimensions is: 106,106,32,106 (voxels), This file has the dimensions: 106,106,32,55 (voxels).

/task-MIpost_channels.tsv

Tabular file contains custom columns not described in a data dictionary

Columns: name type units not defined, please define in: /task-MIpost_channels.json, /channels.json

/task-MIpre_channels.tsv

Tabular file contains custom columns not described in a data dictionary

Columns: name type units not defined, please define in: /task-MIpre_channels.json, /channels.json

/task-eegNF_channels.tsv

Tabular file contains custom columns not described in a data dictionary

Columns: name type units not defined, please define in: /task-eegNF_channels.json, /channels.json

/task-eegfmriNF_channels.tsv

Tabular file contains custom columns not described in a data dictionary

Columns: name type units not defined, please define in: /task-eegfmriNF_channels.json, /channels.json

/task-fmriNF_channels.tsv

Tabular file contains custom columns not described in a data dictionary

Columns: name type units not defined, please define in: /task-fmriNF_channels.json, /channels.json

/task-motorloc_channels.tsv

Tabular file contains custom columns not described in a data dictionary

Columns: name type units not defined, please define in: /task-motorloc_channels.json, /channels.json

/task-MIpost_events.tsv

The onset of the last event is after the total duration of the corresponding scan. This design is suspiciously long.

Dataset File Tree

Git Hash: 1225f85 

Comments

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By arnodelorme@gmail.com - 10 months ago
There is one flat EEG dataset ds002336/sub-xp102/eeg/sub-xp102_task-motorloc_eeg