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Contextual MEG and EEG Source Estimates Using Spatiotemporal LSTM Networks.

Christoph Dinh1,2,3, John G Samuelsson1,4,5, Alexander Hunold6

  • 1Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States.

Frontiers in Neuroscience
|March 26, 2021
PubMed
Summary

Contextual MNE (CMNE) improves magneto- and electroencephalography (M/EEG) source estimation by using past neural activity. This novel approach enhances spatial fidelity for better brain activity analysis.

Keywords:
EEGLSTMMEGdeep learninggrid-based Markov localizationsource estimationspatial filteringspatiotemporal source estimation

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Area of Science:

  • Neuroscience
  • Biophysics
  • Computational Neuroscience

Background:

  • Magnetoencephalography (M/EEG) source estimation typically analyzes data sample-by-sample, ignoring temporal dependencies.
  • Neuronal activity is inherently context-dependent due to interconnected brain networks, a factor often overlooked in traditional M/EEG analysis.

Purpose of the Study:

  • To introduce a novel method, Contextual MNE (CMNE), that incorporates temporal context into M/EEG source estimation.
  • To enhance the spatial fidelity of M/EEG source estimates by leveraging past activity patterns.

Main Methods:

  • Developed a Long Short-Term Memory (LSTM) network to predict subsequent source estimates based on past M/EEG data.
  • Applied the LSTM network to correct noise-normalized minimum norm estimates (MNE), creating the Contextual MNE (CMNE) method.
  • Validated CMNE using simulated epileptiform activity and real auditory steady-state response (ASSR) data.

Main Results:

  • CMNE demonstrated improved spatial fidelity compared to standard, unfiltered MNE estimates.
  • The method effectively utilized temporal context from past estimates to refine current source localization.
  • Results were consistent across both simulated and empirical M/EEG datasets.

Conclusions:

  • Contextual MNE (CMNE) offers a significant advancement in M/EEG source estimation by integrating temporal dynamics.
  • This LSTM-based approach enhances the accuracy and reliability of brain activity localization.
  • CMNE is a versatile technique applicable to various M/EEG source estimation methods.