Studying dynamic neural interactions with MEG
Cham : Springer
InSupek, S.; Aine, C.J. (ed.), Magnetoencephalography: From signals to dynamic cortical networks (2nd ed.), pp. 519-541
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PI Group Neurobiology of Language
Supek, S.; Aine, C.J. (ed.), Magnetoencephalography: From signals to dynamic cortical networks (2nd ed.)
Subject110 000 Neurocognition of Language
Interactions between functionally specialized brain regions are crucial for normal brain function. Magnetoencephalography (MEG) is suited to capture these interactions because it provides whole head measurements of brain activity with temporal resolution in the millisecond range. Many different measures of connectivity exist, and in order to take the connectivity analysis results at face value, one should be aware of the strengths and weaknesses of these measures. Next to this, an important challenge in MEG connectivity analysis lies in the fact that more than one sensor picks up the activity of any underlying source. This field spread severely limits the utility of connectivity measures computed directly between sensor recordings. As a consequence, neuronal interactions should be ideally studied on the level of the reconstructed sources. MEG is well suited for this purpose, since its signal properties and high spatial sampling allow for relatively accurate unmixing of the sensor recordings. This chapter provides some necessary background on connectivity analysis in general and proceeds by describing the challenges that are associated with the analysis of MEG-based connectivity at the sensor level. Source-level approaches are described, and some recent advances with respect to MEG-based connectivity during the resting state and graph theoretic approaches are described.
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