New approaches to the analysis of coordinated neuromagnetic source activity

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Historically, neuroscientists investigated which brain area is involved in specific functions of perception, cognition. However, generally the whole brain is engaged when we speak, think, or move. Thus current research focuses on how brain areas are connected. We can measure brain activity noninvasively with multiple electrodes attached to the scalp in electroencephalography (EEG) and many magnetic field sensors arranged around the head in magnetoencephalography (MEG). We compare the signals at two electrodes or MEG sensors and search for oscillations with common rhythms. Our hypothesis is that synchronous oscillation at two sensors indicates that the underlying neural populations interact with each other. With MEG we can localize the neural activity and apply the analysis of synchrony to neuromagnetic source activity. The results indicate with much higher precision, which brain areas are functional connected than the analysis of sensor data can provide. MEG source analysis is not always perfect, and the cortical sources cannot be separated entirely, specifically if they are located in close vicinity. Because of such limitation, the source signals may show apparent synchrony, which is not related to functional connectivity. To overcome the effects of spurious synchrony, we will develop a series of data analysis techniques, which indicate true functional coordination and are insensitive to effects of spreading source activity. From simulation studies we will learn about the properties of synchrony measures in MEG source data. We will analyze MEG data in studies of perception and cognition. For all studies we develop a solid hypothesis about the functionally relevant brain activity. Based on the physiological properties of the source signals we design our analysis strategy. Specifically we will employ measures like temporal changes in synchrony, the imaginary part of coherence, synchrony between spectral components of the brain activity, statistics of phase resets, changes in stimulus-induced oscillations, and measures from nonlinear dynamics, which are not affected by limitations in the source analysis. The results will allow new and improved applications in basic research and clinical diagnostics.