NeuroSpin hMT+ Localizer DATA (MEG & aMRI)

uploaded by Alexandre Gramfort on 2020-11-20 - 10 months ago
last modified on 2020-11-29 - 10 months ago
authored by Nicolas Zilber, Philippe Ciuciu, Alexandre Gramfort, Leila Azizi, Virginie van Wassenhove
01099

OpenNeuro Accession Number: ds003392
Files: 159, Size: 9.61GB, Subjects: 11, Session: 1
Available Tasks: localizer, noise
Available Modalities: T1w, meg, coordsystem, channels, events

README

Dataset description: Magnetoencephalography (MEG) dataset recorded during a hMT+ (human visual motion area) localizer task

Published in: Zilber, N., Ciuciu, P., Gramfort, A., Azizi, L., & Van Wassenhove, V. (2014). Supramodal processing optimizes visual perceptual learning and plasticity. Neuroimage, 93, 32-46.

This MEG dataset was prepared in the Brain Imaging Data Structure (MEG-BIDS, Niso et al. 2018) format using MNE-BIDS (Appelhoff et al. 2019).

The dataset contains 10 of the 12 participants from the vision-only training group. 2 participants were removed, one due to problems with the trigger channel, and one due to different settings in the acquisition preventing us from processing the dataset without prior adjustment.

EXPERIMENT

Participants were presented with a cloud of moving dots, always starting with incoherent movement (up or down result in equal display, due to the incoherence). After 500 ms, the movement became coherent in 50% of the trials (95% coherence, up or down) and remained incoherent in the other 50%, lasting for 1000 ms. Participants were instructed to passively view the stimuli for a total of 120 trials.

Events:

1: coherent / down 2: coherent / up 3: incoherent / down 4: incoherent / up

MEG

Brain magnetic fields were recorded in a MSR using a 306 MEG system (Neuromag Elekta LTD, Helsinki). MEG recordings were sampled at 2 kHz and band-pass filtered between 0.03 and 600 Hz.

Four head position coils (HPI) measured the head position of participants before each block; three fiducial markers (nasion and pre-auricular points) were used for digitization and anatomicalMRI (aMRI) immediately following MEG acquisition.

Electrooculograms (EOG, horizontal and vertical eye movements) and electrocardiogram (ECG) were simultaneously recorded. Prior to the session, 5 min of empty room recordings was acquired for the computation of the noise covariance matrix.

Bad MEG channels were marked manually.

MRI

The T1 weighted aMRI was recorded using a 3-T Siemens Trio MRI scanner. Parameters of the sequence were: voxel size: 1.0 × 1.0 × 1.1 mm; acquisition time: 466 s; repetition time TR = 2300 ms; and echo time TE = 2.98 ms

References

Zilber, N., Ciuciu, P., Gramfort, A., Azizi, L., & Van Wassenhove, V. (2014). Supramodal processing optimizes visual perceptual learning and plasticity. Neuroimage, 93, 32-46.

Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896

Niso, G., Gorgolewski, K. J., Bock, E., Brooks, T. L., Flandin, G., Gramfort, A., Henson, R. N., Jas, M., Litvak, V., Moreau, J., Oostenveld, R., Schoffelen, J., Tadel, F., Wexler, J., Baillet, S. (2018). MEG-BIDS, the brain imaging data structure extended to magnetoencephalography. Scientific Data, 5, 180110. http://doi.org/10.1038/sdata.2018.110

Authors

  • Nicolas Zilber
  • Philippe Ciuciu
  • Alexandre Gramfort
  • Leila Azizi
  • Virginie van Wassenhove

Dataset DOI

10.18112/openneuro.ds003392.v1.0.4

License

CC0

Acknowledgements

We are grateful to the NeuroSpin nursing staff for their help in recruiting and preparing participants for MEG data acquisition, and to Antoine Grigis for help with the anonymization of the MRIs.

How to Acknowledge

Please cite:

Zilber, N., Ciuciu, P., Gramfort, A., Azizi, L., & Van Wassenhove, V. (2014). Supramodal processing optimizes visual perceptual learning and plasticity. Neuroimage, 93, 32-46.

Funding

  • This work was supported by a Marie Curie IRG-249222 and an ERC-StG-263584 to V.vW and an ANR Schubert ANR-0909-JCJC-071 to P.C.

Ethics Approvals

How To Cite

Copy
Nicolas Zilber and Philippe Ciuciu and Alexandre Gramfort and Leila Azizi and Virginie van Wassenhove (2020). NeuroSpin hMT+ Localizer DATA (MEG & aMRI). OpenNeuro. [Dataset] doi: 10.18112/openneuro.ds003392.v1.0.4
More citation info

NeuroSpin hMT+ Localizer DATA (MEG & aMRI)

uploaded by Alexandre Gramfort on 2020-11-20 - 10 months ago
last modified on 2020-11-29 - 10 months ago
authored by Nicolas Zilber, Philippe Ciuciu, Alexandre Gramfort, Leila Azizi, Virginie van Wassenhove
01099

OpenNeuro Accession Number: ds003392
Files: 159, Size: 9.61GB, Subjects: 11, Session: 1
Available Tasks: localizer, noise
Available Modalities: T1w, meg, coordsystem, channels, events

README

Dataset description: Magnetoencephalography (MEG) dataset recorded during a hMT+ (human visual motion area) localizer task

Published in: Zilber, N., Ciuciu, P., Gramfort, A., Azizi, L., & Van Wassenhove, V. (2014). Supramodal processing optimizes visual perceptual learning and plasticity. Neuroimage, 93, 32-46.

This MEG dataset was prepared in the Brain Imaging Data Structure (MEG-BIDS, Niso et al. 2018) format using MNE-BIDS (Appelhoff et al. 2019).

The dataset contains 10 of the 12 participants from the vision-only training group. 2 participants were removed, one due to problems with the trigger channel, and one due to different settings in the acquisition preventing us from processing the dataset without prior adjustment.

EXPERIMENT

Participants were presented with a cloud of moving dots, always starting with incoherent movement (up or down result in equal display, due to the incoherence). After 500 ms, the movement became coherent in 50% of the trials (95% coherence, up or down) and remained incoherent in the other 50%, lasting for 1000 ms. Participants were instructed to passively view the stimuli for a total of 120 trials.

Events:

1: coherent / down 2: coherent / up 3: incoherent / down 4: incoherent / up

MEG

Brain magnetic fields were recorded in a MSR using a 306 MEG system (Neuromag Elekta LTD, Helsinki). MEG recordings were sampled at 2 kHz and band-pass filtered between 0.03 and 600 Hz.

Four head position coils (HPI) measured the head position of participants before each block; three fiducial markers (nasion and pre-auricular points) were used for digitization and anatomicalMRI (aMRI) immediately following MEG acquisition.

Electrooculograms (EOG, horizontal and vertical eye movements) and electrocardiogram (ECG) were simultaneously recorded. Prior to the session, 5 min of empty room recordings was acquired for the computation of the noise covariance matrix.

Bad MEG channels were marked manually.

MRI

The T1 weighted aMRI was recorded using a 3-T Siemens Trio MRI scanner. Parameters of the sequence were: voxel size: 1.0 × 1.0 × 1.1 mm; acquisition time: 466 s; repetition time TR = 2300 ms; and echo time TE = 2.98 ms

References

Zilber, N., Ciuciu, P., Gramfort, A., Azizi, L., & Van Wassenhove, V. (2014). Supramodal processing optimizes visual perceptual learning and plasticity. Neuroimage, 93, 32-46.

Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896

Niso, G., Gorgolewski, K. J., Bock, E., Brooks, T. L., Flandin, G., Gramfort, A., Henson, R. N., Jas, M., Litvak, V., Moreau, J., Oostenveld, R., Schoffelen, J., Tadel, F., Wexler, J., Baillet, S. (2018). MEG-BIDS, the brain imaging data structure extended to magnetoencephalography. Scientific Data, 5, 180110. http://doi.org/10.1038/sdata.2018.110

Authors

  • Nicolas Zilber
  • Philippe Ciuciu
  • Alexandre Gramfort
  • Leila Azizi
  • Virginie van Wassenhove

Dataset DOI

10.18112/openneuro.ds003392.v1.0.4

License

CC0

Acknowledgements

We are grateful to the NeuroSpin nursing staff for their help in recruiting and preparing participants for MEG data acquisition, and to Antoine Grigis for help with the anonymization of the MRIs.

How to Acknowledge

Please cite:

Zilber, N., Ciuciu, P., Gramfort, A., Azizi, L., & Van Wassenhove, V. (2014). Supramodal processing optimizes visual perceptual learning and plasticity. Neuroimage, 93, 32-46.

Funding

  • This work was supported by a Marie Curie IRG-249222 and an ERC-StG-263584 to V.vW and an ANR Schubert ANR-0909-JCJC-071 to P.C.

Ethics Approvals

How To Cite

Copy
Nicolas Zilber and Philippe Ciuciu and Alexandre Gramfort and Leila Azizi and Virginie van Wassenhove (2020). NeuroSpin hMT+ Localizer DATA (MEG & aMRI). OpenNeuro. [Dataset] doi: 10.18112/openneuro.ds003392.v1.0.4
More citation info

Dataset File Tree

Git Hash: 476aa80 

BIDS Validation

Dataset File Tree

Git Hash: 476aa80 

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