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Related Experiment Video

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Adaptive cortical parcellations for source reconstructed EEG/MEG connectomes.

Seyedeh-Rezvan Farahibozorg1, Richard N Henson1, Olaf Hauk1

  • 1MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.

Neuroimage
|September 13, 2017
PubMed
Summary

This study introduces adaptive brain parcellations to improve Electro- and Magnetoencephalography (EEG/MEG) connectome reconstruction. Optimized parcels reduce leakage, leading to more accurate brain network analysis.

Keywords:
Adaptive parcellationCross-talk functionsFunctional connectomeMEG/EEGSource reconstructionWhole-brain connectivity

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Area of Science:

  • Neuroscience
  • Biophysics
  • Computational Biology

Background:

  • Electro- and Magnetoencephalography (EEG/MEG) offer rich temporal and spectral brain activity data.
  • Source reconstruction from EEG/MEG is challenged by signal leakage, complicating functional connectome analysis.
  • Standard anatomical parcellations may not match EEG/MEG's spatial resolution, leading to inaccurate connectivity.

Purpose of the Study:

  • To develop and evaluate methods for optimizing brain parcellations to minimize leakage in EEG/MEG source reconstruction.
  • To compare adaptive parcellation strategies against standard anatomical approaches for improved functional connectome accuracy.

Main Methods:

  • Utilized cross-talk functions (CTFs) to quantify leakage for specific sensor configurations and reconstruction methods.
  • Compared a split-and-merge (SaM) algorithm on anatomical parcellations with a region growing (RG) algorithm on all brain vertices.
  • Applied minimum-norm reconstructions to real EEG/MEG data and simulated realistic networks.

Main Results:

  • Both SaM and RG algorithms converged to approximately 70 parcels, suggesting a resolution limit for the tested EEG/MEG configuration.
  • Adaptive parcellations demonstrated superior sensitivity and distinguishability compared to standard anatomical parcellations.
  • Simulations showed significant improvements in network reconstruction accuracy, notably reducing false connections due to leakage.

Conclusions:

  • Adaptive parcellations optimize the number and boundaries of brain regions to minimize leakage.
  • This approach enables more accurate reconstruction of functional brain connectomes from EEG/MEG data.
  • Optimized parcellations enhance the reliability of brain network analysis using EEG/MEG.