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Updated: Feb 4, 2026
06:04
Functional Mapping with Simultaneous MEG and EEG
Published on: June 14, 2010
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Analysis of Functional Connectivity and Oscillatory Power Using DICS: From Raw MEG Data to Group-Level Statistics in
Marijn van Vliet1, Mia Liljeström1,2, Susanna Aro1
1Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland.
Frontiers in Neuroscience
|October 2, 2018
Summary
This study presents a comprehensive pipeline for analyzing brain connectivity using dynamic imaging of coherent sources (DICS) with magnetoencephalography (MEG) data. The method visualizes functional brain networks by measuring synchronized neural activity between regions.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Signal Processing
Background:
- Brain region communication relies on synchronized oscillatory activity.
- Large-scale functional brain networks can be estimated by measuring synchronicity.
- Magnetoencephalography (MEG) and electroencephalography (EEG) offer high temporal resolution for neural activity.
Purpose of the Study:
- To detail a complete data analysis pipeline for functional connectivity using dynamic imaging of coherent sources (DICS) on MEG data.
- To provide methodological considerations, practical advice, and tradeoffs for analyzing MEG data.
- To guide users through analyzing a specific dataset (ds000117) using MNE-Python.
Main Methods:
- Frequency-domain analysis using dynamic imaging of coherent sources (DICS) beamforming.
- Computation of cross-spectral density (CSD) matrices via wavelet approach across frequency bands (alpha, theta, beta, gamma).
- Stable estimation of all-to-all connectivity using a canonical coherence computation and group-level statistics for visualization.
Main Results:
- A detailed pipeline for DICS analysis of MEG data is presented.
- Cortical mapping of spectral power and visualization of functional brain networks are achieved.
- Significant differences between experimental conditions are identified and visualized at vertex- and parcel-levels.
Conclusions:
- The described pipeline enables robust functional connectivity analysis of MEG data.
- The methodology facilitates the study of cortical sources and synchronization between brain regions.
- The provided code examples and guidance empower researchers to perform advanced MEG data analysis.
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