EEG and MEG coherence: measures of functional connectivity at distinct spatial scales of neocortical dynamics.

We contrasted coherence estimates obtained with EEG, Laplacian, and MEG measures of synaptic activity using simulations with head models and simultaneous recordings of EEG and MEG. EEG coherence is often used to assess functional connectivity in human cortex. However, moderate to large EEG coherence can also arise simply by the volume conduction of current through the tissues of the head. We estimated this effect using simulated brain sources and a model of head tissues (cerebrospinal fluid (CSF), skull, and scalp) derived from MRI. We found that volume conduction can elevate EEG coherence at all frequencies for moderately separated (<10 cm) electrodes; a smaller levation is observed with widely separated (>20 cm) electrodes. This volume conduction effect was readily observed in experimental EEG at high frequencies (40-50 Hz). Cortical sources generating spontaneous EEG in this band are apparently uncorrelated. In contrast, lower frequency EEG coherence appears to result from a mixture of volume conduction effects and genuine source coherence. Surface Laplacian EEG methods minimize the effect of volume conduction on coherence estimates by emphasizing sources at smaller spatial scales than unprocessed potentials (EEG). MEG coherence estimates are inflated at all frequencies by the field spread across the large distance between sources and sensors. This effect is most apparent at sensors separated by less than 15 cm in tangential directions along a surface passing through the sensors. In comparison to long-range (>20 cm) volume conduction effects in EEG, widely spaced MEG sensors show smaller field-spread effects, which is a potentially significant advantage. However, MEG coherence estimates reflect fewer sources at a smaller scale than EEG coherence and may only partially overlap EEG coherence. EEG, Laplacian, and MEG coherence emphasize different spatial scales and orientations of sources.

Comparison of the effect of volume conduction on EEG coherence with the effect of field spread on MEG coherence.

We analyzed models of volume conduction and magnetic field spread to account for aspects of spatial structure in electroencephalographic (EEG) and magnetoencephalographic (MEG) coherence. The head volume conduction model consisted of three confocal ellipsoids, representing three layers (brain, skull, and scalp) with different tissue conductivities, while the magnetic field model follows from the Biot-Savart law in a spherically symmetric medium. Source models were constructed based on magnetic resonance imaging data from three subjects, approximating neocortical current source distributions as dipoles oriented perpendicular to the local cortical surface. Assuming that every source is uncorrelated to every other source, coherence between sensors due to volume conduction and field-spread effects was estimated. Spatial properties of the model coherences were then compared with simultaneously recorded spontaneous EEG and MEG. In both models and experimental data, EEG and MEG coherence was elevated between closely spaced channels. At very large channel separations, the field-spread effect on MEG coherence appears smaller than the volume conduction effect on EEG coherence. In EEG coherence studies, surface Laplacian methods can be used to remove volume conduction effects. With single-coil magnetometers, MEG coherences are free of field effects only for sensor pairs separated by more than 20 cm. Model coherences resemble most high-frequency (e.g. >20 Hz) data; volume conduction and field-spread effects are independent of frequency, suggesting mostly uncorrelated sources in these bands. High-frequency EEG and MEG coherence can evidently serve as an estimate of coherence effects due to volume conduction and field effects, when source and head models are not available for individual subjects.

Source analysis of EEG oscillations using high-resolution EEG and MEG.

We investigated spatial properties of the source distributions that generate scalp electroencephalographic (EEG) oscillations. The inherent complexity of the spatio-temporal dynamics of EEG oscillations indicates that conceptual models that view source activity as consisting only of a few "equivalent dipoles" are inadequate. We present an approach that uses volume conduction models to characterize the distinct spatial filtering of cortical source activity by average reference EEG, high-resolution EEG, and magnetoencephalography (MEG). By comparing these three measures, we can make inferences about the sources of EEG oscillations without having to make prior assumptions about the sources. We apply this approach to spontaneous EEG oscillations observed with eyes closed at rest. Both EEG and MEG recordings show robust alpha rhythms over posterior regions of the cortex; however, the dominant frequency of these rhythms varies between EEG and MEG recordings. Frontal alpha and theta rhythms are generated almost exclusively by superficial radial dipole layers that generate robust EEG signals but very little MEG signals; these sources are presumably mainly in the gyral crowns of frontal cortex. MEG and high-resolution EEG estimates of alpha rhythms provide evidence of local tangential and radial sources in the posterior cortex, lying mainly on sulcal and gyral surfaces. Despite the detailed information about local radial and tangential sources potentially afforded by high-resolution EEG and MEG, it is also evident that the alpha and theta rhythms receive contributions from non-local source activity, for instance large dipole layers distributed over lobeal or (potentially) even larger spatial scales.