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Multi-subject MEG/EEG source imaging with sparse multi-task regression.

Hicham Janati1, Thomas Bazeille2, Bertrand Thirion2

  • 1Inria Saclay, France; ENSAE, CREST, France.

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|May 22, 2020
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Summary
This summary is machine-generated.

This study introduces Minimum Wasserstein Estimates (MWE), a novel group-wise source localization method for magnetoencephalography and electroencephalography (M/EEG). MWE improves brain source localization accuracy by jointly analyzing multiple subjects, outperforming individual analyses.

Keywords:
BrainEEG / MEG source imagingInverse modeling

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Area of Science:

  • Neuroscience
  • Biophysics
  • Computational Biology

Background:

  • Magnetoencephalography and electroencephalography (M/EEG) measure neural activity via electromagnetic fields.
  • Source localization of M/EEG data is an ill-posed inverse problem due to limited sensor data.
  • Current group studies often analyze subjects independently, limiting precision.

Purpose of the Study:

  • To develop a novel group-wise source localization method for M/EEG data.
  • To improve the accuracy and precision of identifying neural sources across subjects.
  • To better handle inter-subject variability and subject-specific noise levels.

Main Methods:

  • Proposed Minimum Wasserstein Estimates (MWE), a multi-task regularization approach using optimal transport metrics.
  • Jointly regressed source localization across subjects to increase observations and improve problem conditioning.
  • Incorporated inter-subject variability by promoting spatial proximity of activation foci rather than perfect overlap.
  • Estimated subject-specific noise levels to account for varying signal-to-noise ratios.

Main Results:

  • MWE reduced localization error by up to 4 mm per source compared to individual subject analyses in simulations.
  • Experiments on the Cam-CAN dataset demonstrated improved spatial specificity in population imaging.
  • MWE outperformed individual models like dSPM and a state-of-the-art Bayesian group model.
  • Analysis of multimodal data showed MWE reduced the MEG-fMRI gap in brain mapping.

Conclusions:

  • MWE offers a more precise and robust method for group-level M/EEG source localization.
  • The optimal transport-based approach effectively handles inter-subject variability.
  • This method enhances the spatial resolution and accuracy of neuroimaging analyses, particularly in population studies.
  • MWE facilitates better integration of MEG and fMRI data for comprehensive brain mapping.