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© 2014 Springer-Verlag Berlin Heidelberg. All rights are reserved.Researchers are beginning to appreciate the brain as more than a mere collection of loosely connected, highly specialised components. While there is clear specialisation among regions of the cortex, the true power of the brain appears to arise from the ability of those regions to work together across a range of spatial scales as a richly interconnected and complex network. On all levels, the study of brain connectivity seeks to understand how different regions of the cortex communicate, what the emerging networks signify functionally, and why these are important for normal behaviour. The use of MEG in this endeavour is an attempt to understand these processes on the broad, interregional scale, and in that respect MEG is an ideal tool. It has a good deal of spatial resolution, enough to distinguish between brain areas ∼1 cm apart, and exquisite temporal resolution, enough to record even the fastest electrical oscillations the brain can generate. This chapter begins with a brief overview of the history of electrophysiological measures and their application to the study of brain connectivity. We then describe some of the core theory underlying the measurement of magnetic fields generated by the brain and practical considerations of measuring correlated activity with MEG. Some notable applications of MEG to the study of brain networks will then be described and a comparison will be made between MEG to other methods such as ECoG. The chapter will also explore some of the principal mathematical techniques used by researchers to probe different aspects of connectivity ranging from simple correlational approaches to more involved concepts such as multivariate autoregressive models (MAR). Finally, we will discuss limitations of using MEG to study connectivity and also give some insight into the exciting prospects the future might hold for MEG connectivity research.

Original publication

DOI

10.1007/978-3-642-33045-2_16

Type

Chapter

Book title

Magnetoencephalography: From Signals to Dynamic Cortical Networks

Publication Date

01/07/2014

Volume

9783642330452

Pages

321 - 358