Studying Dynamic Neural Interactions with MEG

Abstract

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 allows 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.

Cell-Type and State-Dependent Synchronization among Rodent Somatosensory, Visual, Perirhinal Cortex, and Hippocampus CA1

“…The likely influence of volume conduction of hippocampal currents on spike-LFP and WPLI connectivity metrics illustrates that the spatial origin of inter-areal interactions is not necessarily local and must always be carefully interpreted (Nolte et al, 2004 ; Sirota et al, 2008 ; Vinck et al, 2011 ; Schoffelen and Gross, 2014 ).…”

How to detect the Granger-causal flow direction in the presence of additive noise?

“…(Bollimunta et al, 2008;Bosman et al, 2012;Nunez and Srinivasan, 2006;van Kerkoerle et al, 2014). Noise reduction through spatial filtering becomes especially important when multiple distant noise sources that are out of phase are (non-linearly) mixed (Schoffelen and Gross, 2014;Sirota et al, 2008;Vinck et al, 2011).…”

The Role of Spectral Complexity in Connectivity Estimation

“…Further than an estimate of the hidden data x(t), an estimate of the cross-power spectrum can be obtained from y(t). Such estimate can be achieved through a two-step process [22]: i.…”

“…This is the reason why, in the present paper, we focus on the analysis of the cross-power spectrum, which is the mathematical quantity of reference for the computation of most frequency-domain connectivity measures [11,20,21]. From an operational viewpoint, the computation of the cross-power spectrum in the source space typically relies on a two-step procedure: first the neural activity is estimated by applying a regularized inversion method on the recorded time series and then the cross-power spectrum is computed from the Fourier transform of the estimated neural time series [22].…”