FLUX: A pipeline for MEG analysis.

Area of Science:

• Cognitive Neuroscience
• Neuroimaging
• Biophysics

Background:

• Magnetoencephalography (MEG) offers millisecond-timescale human neuronal activity quantification and source localization.
• Existing open-source toolboxes, while powerful, present challenges for research reproducibility and new user onboarding due to numerous analysis options.
• Standardization is needed to streamline MEG data processing and source modeling in cognitive neuroscience.

Purpose of the Study:

• To introduce the FLUX pipeline, a standardized framework for analyzing Magnetoencephalography (MEG) data.
• To enhance reproducibility and accessibility in cognitive neuroscience research by making analysis steps and settings explicit.
• To provide documented code and educational resources for MEG data analysis using MNE Python and FieldTrip.

Main Methods:

• Development of the FLUX pipeline with documented code for MNE Python and FieldTrip.
• Utilization of a visuospatial attention dataset to illustrate pipeline application.
• Implementation of analysis scripts in Jupyter Notebook and MATLAB Live Editor for clear explanations and graphical outputs.

Main Results:

• The FLUX pipeline provides explicit, documented steps for MEG data analysis, focusing on oscillatory brain activity quantification and source localization.
• The pipeline is adaptable for event-related fields and multivariate pattern analysis.
• Explanations, justifications, and graphical outputs are integrated into the provided notebooks for enhanced understanding.

Conclusions:

• The FLUX pipeline promotes standardization in basic MEG analysis steps, aligning approaches across toolboxes and strengthening the field.
• It serves as an educational tool for self-study and workshops, supporting new researchers.
• The pipeline is designed to be dynamic, evolving with toolbox developments and new neuroimaging technologies like Optically Pumped Magnetometers.