MEG to BIDS and Anonymization

BIDS formatting

M/EEG data will be formatted according to the Brain Imaging Data Structure (BIDS) for MEG and EEG, respectively (Niso et al., 2018; Pernet et al., 2019), using the MNE-BIDS python package (Appelhoff et al., 2019). The BIDS structure imposes a hierarchical folder structure for raw MEG and EEG data and standardized file naming, for empty room, resting state, and task-related M/EEG, and anatomical MRI recordings.

Accompanying information such as event files, participant and recording information (age, date, system calibration, etc.) are stored along with the data in human- and machine-readable format. As the BIDS standard imposes no data format, the M/EEG data will remain in the format generated during recording. Currently, no standard formatting exists for preprocessed M/EEG data, which are stored in a 'derivatives' folder.

Volumetric segmentation and cortical surface reconstruction of participants' anatomical MRI images will be performed using the FreeSurfer software (Fischl, 2012). Coordinate alignment between M/EEG and MRI recordings will be performed manually using MNE python's co-registration tools. Then the MRI recordings along with the anatomical coordinates and coordinate transformation files will be written to the corresponding folders. MRI data are stored in 'Nifti' format (.nii).

Anonymization

All data shared among collaborators or publicly will be fully anonymized. To this end, an inbuilt procedure in MNE-BIDS will be used to remove all information that could link the data to an individual participant (recording date, participant birth date or age, participant identifiers). The MRI will be 'defaced' by identifying all image values that would depict the participant's facial features according to the provided anatomical landmarks and replacing them with a mask of zeros.

Since the co-registration of M/EEG and MRI data requires to work with non-anonymized data to observe the location of the anatomical landmarks (nasion, left and right peri-auricular points), a first non-anonymized BIDS repository will be created locally on a protected CEA-server. Once the transformation file is obtained the data can be fully anonymized and shared.

References

Appelhoff, S., Sanderson, M., Brooks, T. L., Vliet, M. van, Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A. P., Larson, E., Gramfort, A., & Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software, 4(44), 1896. https://doi.org/10.21105/joss.01896

Fischl, B. (2012). FreeSurfer. NeuroImage, 62(2), 774--781. https://doi.org/10.1016/j.neuroimage.2012.01.021

Niso, G., Gorgolewski, K. J., Bock, E., Brooks, T. L., Flandin, G., Gramfort, A., Henson, R. N., Jas, M., Litvak, V., T. Moreau, J., Oostenveld, R., Schoffelen, J.-M., Tadel, F., Wexler, J., & Baillet, S. (2018). MEG-BIDS, the brain imaging data structure extended to magnetoencephalography. Scientific Data, 5(1), 180110. https://doi.org/10.1038/sdata.2018.110

Pernet, C., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., & Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6(1), 103. https://doi.org/10.1038/s41597-019-0104-8