B. R. Cottereau, J. M. Ales, A. M. Norcia


Publication year



Periodical Number

1872-678X (Electronic)



Author Address

Universite de Toulouse, Centre de Recherche Cerveau et Cognition, UPS, France; CNRS UMR 5549, CerCo, Toulouse, France. Electronic address: FAU - Ales, Justin M School of Psychology and Neuroscience, University of St Andrews, St Mary's Quad, South Street, St Andrews KY16 9JP, UK. FAU - Norcia, Anthony M Department of Psychology, Stanford University, Stanford, CA, United States.

Full version

EEG and MEG have excellent temporal resolution, but the estimation of the neural sources that generate the signals recorded by the sensors is a difficult, ill-posed problem. The high spatial resolution of functional MRI makes it an ideal tool to improve the localization of the EEG/MEG sources using data fusion. However, the combination of the two techniques remains challenging, as the neural generators of the EEG/MEG and BOLD signals might in some cases be very different. Here we describe a data fusion approach that was developed by our team over the last decade in which fMRI is used to provide source constraints that are based on functional areas defined individually for each subject. This mini-review describes the different steps that are necessary to perform source estimation using this approach. It also provides a list of pitfalls that should be avoided when doing fMRI-informed EEG/MEG source imaging. Finally, it describes the advantages of using a ROI-based approach for group-level analysis and for the study of sensory systems.