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Published December 18, 2023 | Version v1
Dataset Open

Glasser52: A parcellation for MEG-Analysis

Creators

  • 1. ROR icon University of Oxford
  • 2. ROR icon Yale University
  • 3. ROR icon University of Birmingham

Description

Summary

A parcellation with 52 parcels that can be linked to anatomical labels of the HCP-MMP atlas and is suitable for analyses of MEG recordings.

The parcellation was created based on the Human Connectome Project Multimodal Parcellation (HCP-MMP) atlas (Glasser et al., 2016) following the procedure suggested by Tait et al. (2021).

The underlying idea of this procedure is to 1) calculate the influence of each of the 22 regions of the HCP-MMP atlas on MEG recordings obtained from healthy controls and PD volunteers and 2) to merge and divide regions so that 52 parcels with similar influence on the MEG sensor signal are obtained. This approach allows to start from the anatomical HCP-MMP atlas and adapts it according to functional signals obatined from MEG recordings. Thus, it enables for a good compromise between solely anatomical or solely functional derived parcellations. (For more information see the Additional Information.docx).

Contents:

Glasser52_binary_space-MNI152NLin6_res-8x8x8.nii.gz
Parcellation for MEG-Analyses.

Glasser_Merging.xlsx
Excel sheet with detailed information on why parcels were merged or devided.

Labels_short.p
Short Labels of the Glasser52 labels. Best loaded with pickle.load(open("../Labels_short.p","rb")).

Labels.p
Labels of the Glasser52 labels. Best loaded with pickle.load(open("../Labels.p","rb")).

Additional Informatio.docx
Description of the motivation, methods used, and creation of the Glasser52 parcellation.

parcel_names_and_mni_coordinates.docx
Table with MNI-coordinates of Glasser52 parcel centres.

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Additional details

References

  • Glasser, M. F., Coalson, T. S., Robinson, E. C., Hacker, C. D., Harwell, J., Yacoub, E., Ugurbil, K., Andersson, J., Beckmann, C. F., Jenkinson, M., Smith, S. M., & Van Essen, D. C. (2016). A multi-modal parcellation of human cerebral cortex. Nature, 536(7615), 171–178. https://doi.org/10.1038/nature18933
  • Tait, L., Özkan, A., Szul, M. J., & Zhang, J. (2021). A systematic evaluation of source reconstruction of resting MEG of the human brain with a new high‐resolution atlas: Performance, precision, and parcellation. Human Brain Mapping, 42(14), 4685–4707. https://doi.org/10.1002/hbm.25578
  • Lawrence, R. M., Bridgeford, E. W., Myers, P. E., Arvapalli, G. C., Ramachandran, S. C., Pisner, D. A., Frank, P. F., Lemmer, A. D., Nikolaidis, A., & Vogelstein, J. T. (2021). Standardizing human brain parcellations. Scientific Data, 8(1), 78. https://doi.org/10.1038/s41597-021-00849-3
  • Quinn, A. J., Vidaurre, D., Abeysuriya, R., Becker, R., Nobre, A. C., & Woolrich, M. W. (2018). Task-Evoked Dynamic Network Analysis Through Hidden Markov Modeling. Frontiers in Neuroscience, 12, 603. https://doi.org/10.3389/fnins.2018.00603