Stephen Robinson: A Perspective on the Information Content of M/EEG Data

National Institute of Mental Health, USA

Abstract

Current practice for M/EEG analysis data includes time-locked averaging of event-related evoked responses and of event-induced changes in band-limited power (e.g., alpha, beta, gamma power, etc.). These analyses are dependent on synchrony of large ensembles of neurons to achieve significant signal-to-noise ratio to be useful for interpretation. Is synchrony necessary for communication of information between brain regions? This talk will discuss nonlinear dynamical alternatives to conventional analysis for revealing asynchronous brain activity. Using MEG LCMV beamforming and symbolic transfer entropy it will be shown that information about communication between brain regions is present over a very wide range of frequencies. Nonlinear dynamic analyses may lead to new insights into brain function in health and disease.

 

Guests are welcome!

 

Organized by

Vincent Jonany / Lisa Velenosi