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Modified MRI Anonymization (De-Facing) for Improved MEG Coregistration.

Ricardo Bruña1,2, Delshad Vaghari3, Andrea Greve4

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A new automated method for de-facing structural MRI data, which preserves the nose, improves head model accuracy for MEG/EEG source localization. This technique balances data sharing needs with scientific reproducibility.

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Area of Science:

  • Neuroimaging
  • Biomedical Engineering
  • Data Science

Background:

  • Accurate source localization of Magnetoencephalography (MEG) and Electroencephalography (EEG) signals necessitates structural Magnetic Resonance Imaging (MRI) for head modeling.
  • Sharing sensitive structural MRI data for reproducibility is often hindered by privacy concerns requiring facial anonymization.
  • Existing automated de-facing methods can remove crucial facial features, compromising the coregistration accuracy between MRI and MEG/EEG data.

Purpose of the Study:

  • To introduce and evaluate a novel automated de-facing technique for structural MRI data that preserves the nose.
  • To assess the impact of this 'face-trimming' method on MRI-MEG/EEG coregistration and head model creation for source localization.
  • To determine if the proposed method compromises individual identification compared to standard de-facing approaches.

Main Methods:

  • Development of an automated de-facing algorithm that specifically retains the nose region of structural MRI scans.
  • Comparison of coregistration accuracy between MRI data processed with the new method and standard de-facing techniques.
  • Evaluation of the 'face-trimming' method's effect on automated segmentation and surface extraction for head model generation.
  • Behavioral assessment to quantify identification risks associated with the 'face-trimming' approach versus standard de-facing.

Main Results:

  • The automated de-facing method preserving the nose demonstrated effective MRI-MEG/EEG coregistration, comparable to methods that remove the entire face.
  • Behavioral data confirmed that 'face-trimming' does not increase identification risk relative to standard de-facing.
  • The proposed method exhibited less impact on automated segmentation and surface extraction processes crucial for head model creation.

Conclusions:

  • The novel 'face-trimming' automated de-facing method offers a viable solution for sharing structural MRI data while maintaining high-quality MEG/EEG source localization.
  • This approach effectively balances the need for data privacy with the requirements for accurate neuroimaging analysis and reproducible research.
  • The 'face-trimming' technique is recommended for structural MRI data intended for forward modeling in MEG/EEG source reconstruction.