FLUX: a pipeline for MEG analysis

Oscar Ferrante, Ling Liu, Tamas Minarik, Urszula Gorska, Tara Ghafari, Huan Luo, Ole Jensen

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Abstract

Magnetoencephalography (MEG) allows for quantifying modulations of human neuronal activity on a millisecond time scale while also making it possible to estimate the location of the underlying neuronal sources. The technique relies heavily on signal processing and source modelling. To this end, there are several open-source toolboxes developed by the community. While these toolboxes are powerful as they provide a wealth of options for analyses, the many options also pose a challenge for reproducible research as well as for researchers new to the field. The FLUX pipeline aims to make the analyses steps and setting explicit for standard analysis done in cognitive neuroscience. It focuses on quantifying and source localization of oscillatory brain activity, but it can also be used for event-related fields and multivariate pattern analysis. The pipeline is derived from the Cogitate consortium addressing a set of concrete cognitive neuroscience questions. Specifically, the pipeline including documented code is defined for MNE Python (a Python toolbox) and FieldTrip (a Matlab toolbox), and a data set on visuospatial attention is used to illustrate the steps. The scripts are provided as notebooks implemented in Jupyter Notebook and MATLAB Live Editor providing explanations, justifications and graphical outputs for the essential steps. Furthermore, we also provide suggestions for text and parameter settings to be used in registrations and publications to improve replicability and facilitate pre-registrations. The FLUX can be used for education either in self-studies or guided workshops. We expect that the FLUX pipeline will strengthen the field of MEG by providing some standardization on the basic analysis steps and by aligning approaches across toolboxes. Furthermore, we also aim to support new researchers entering the field by providing education and training. The FLUX pipeline is not meant to be static; it will evolve with the development of the toolboxes and with new insights. Furthermore, with the anticipated increase in MEG systems based on the Optically Pumped Magnetometers, the pipeline will also evolve to embrace these developments.
Original languageEnglish
Article number119047
JournalNeuroImage
Volume253
Early online date8 Mar 2022
DOIs
Publication statusPublished - Jun 2022

Bibliographical note

Funding Information:
This publication was made possible through the support of grants from the James S. McDonnell Foundation Understanding Human Cognition Collaborative Award (Grant No. 220020448 ), the Wellcome Trust (Investigator Award in Science Grant No. 207550 ), the BBSRC (Grant No. BB/R018723/1 ), the National Natural Science Foundation of China ( 31930052 ), the Royal Society (Wolfson Research Merit Award) and the Templeton World Charity Foundation, Inc (no. TWCF0389 to L. Melloni, M. Pitts and L. Mudrik). The opinions expressed in this publication are those of the author(s) and do not necessarily reflect the views of Templeton World Charity Foundation, Inc.

Publisher Copyright:
© 2022

Keywords

  • Decoding
  • Event-related fields
  • Magnetoencephalography
  • Multivariate pattern analysis
  • Pre-processing
  • Pre-registration
  • Replicability
  • Source modelling

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

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