MEG/EEG Part 1: Acquisition, Modeling and Localization

MEG and EEG Signals and Their Sources: Insights from Physics, Physiology and Anatomy
Matti Hamalainen, Massachusetts General Hospital

Understanding the biophysics and physiology underlying the generation of detectable extracranial magnetic fields and electric potentials is of prime importance for correct interpretation of magnetoencephalography (MEG) and electroencephalography (EEG) data.

Matti Hamalainen will discuss the neural sources of MEG and EEG, the effects of electromagnetic properties of intervening tissues to MEG and EEG, as well as similarities and differences between MEG and EEG caused by their different sensitivities to sources in the brain. Finally, Hamalainen will demonstrate how this information is useful in achieving a better understanding of measurement data than is possible through reliance on analytical source estimation methods alone.

Exploiting Temporal Dynamics to Study MEG Cortical Activity and Networks 
Robert Oostenveld, Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Netherlands

Electrophysiology allows study of human brain function with an exquisite temporal resolution. MEG extends the possibilities of EEG with its increased spatial resolution due to the reduced overlap on the channels of the underlying cortical activity. This makes MEG, in principle, ideally suited for the noninvasive investigation of dynamics in cortical networks. However, localizing MEG activity is not a trivial task. Mislocalization of activity affects subsequent estimates of cortical dynamics and network interactions.

Robert Oostenveld will explain the interaction between cortical temporal dynamics and MEG source localization methods. He will present established and new signal processing methods to explain how temporal dynamics of cortical activity can be exploited to improve the identification of cortical networks.

MRI- and fMRI-Informed Source Imaging
Anthony Norcia, Stanford University

The availability of magnetic resonance imaging (MRI) and functional MRI (fMRI) data impacts the source localization problem at many levels, from the creation of models of propagation of fields from sources to sensors, to the underlying model of the sources themselves. Anatomically based constraints, be they structural or functionally derived, can significantly improve the accuracy of source estimates at both the individual participant and the group level.

Anthony Norcia will review common and emerging approaches to the use of MRI and fMRI in source imaging, either in formulating constraints on the problem as a means of averaging across participants, or as an integrated, multi-modal approach.