Improving source reconstructions by combining bioelectric and biomagnetic data

Abstract

Objectives: A framework for combining bioelectric and biomagnetic data is presented. The data are transformed to signal-to-noise ratios and reconstruction algorithms utilizing a new regularization approach are introduced.

Methods: Extensive simulations are carried out for 19 different EEG and MEG montages with radial and tangential test dipoles at different eccentricities and noise levels. The methods are verified by real SEP/SEF measurements. A common realistic volume conductor is used and the less-well-known in-vivo conductivities are matched by calibration to the magnetic data. Single equivalent dipole fits as well as spatiotemporal source models are presented for single and combined modality evaluations and overlaid to anatomic MR images.

Results: Normalized sensitivity and dipole resolution profiles of these acquisition systems are derived from these synthetic data. The methods are verified by simultaneously measured somasensory data.

Conclusions: Superior spatial resolution of the combined data studies is revealed, which is due to the complementary nature of both modalities and the increased number of sensors. a better understanding of the underlying neironal processes can be acheived, since an improved differentiation between quasi-tangential and quasi-radial sources is possible.

Introduction

Source reconstructions combining bioelectric and biomagnetic data promise to benefit from the advantages of both modalities (Cohen and Cuffin, 1979; Cohen and Cuffin, 1983; Cohen and Cuffin, 1987; Cohen and Cuffin, 1991; Cohen et al., 1990; Lopes da Silva et al., 1991; Mauguiere, 1992). Electroencephalographic (EEG) measurements can be carried out with an optimized electrode arrangement and provide nearly equal sensitivities for tangentially- and radially-oriented sources. Due to this unspecific sensitivity distribution EEG data often suffer from a limited signal-to-noise-ratio (SNR) and exhibit rather complex field structures. Magnetoencephalographic (MEG) gradiometer systems have an increased sensitivity for tangential superficial sources, leading to an improved SNR and a larger specificity for this class of generators. On the other hand this means that MEG sensor arrays are more or less blind to (quasi-)radial neural current components and deep sources, leading to a reduced complexity of the measured field patterns. Therefore, a combination of both complementary methods should be able to reveal radial dipole components and stabilize the reconstruction of tangential sources by an increased information content and an improved overall SNR.

In order to combine both modalities in a unified framework, different problems have to be solved:

• The different measures have to be transformed to a common basis. This is done by referencing each sensor to its individual noise statistics (Greenblatt, 1995; Pflieger et al., 1998). So every measurement channel contributes with its statistical relevance to the ensuing evaluation procedures. One method to automatically determine the noise level of each sensor is to use the standard deviation of a fraction of the smallest signal levels of the total latency range, e.g. the smallest 20%. This method of course requires about 20% signal-free (e.g. pre-trigger) samples in the measurement.

• For unified reconstruction algorithms, a common volume conductor model has to be used. EEG data strongly depend on the head's shape and its conductivities (Cuffin, 1990). For example, in volume conductor models with one compartment only, the measured signals are inversely proportional to the conductivity. MEG signals in the spherical volume conductor approximation are not at all affected by the conductivity. With more realistically-shaped volume conductor models (e.g. boundary element method (BEM) models) the MEG data show only a weak dependence on the electric properties of the compartments. Furthermore the individual real (in-vivo) tissue conductivities are not well known (Geddes and Baker, 1963). We have thus chosen latencies where single dominantly tangential dipoles can explain the measured data very well for both modalities and used them for matching the conductivities of the volume conductor model. Thus the magnetic data are used for calibrating the electric conductivities via a common scaling factor that keeps the relative conductivities of the model compartments (brain, skull, skin) constant.

• MEG reconstructions with realistic volume conductor models tend to overemphasize quasi-radial current components due to their very small gain (Menninghaus and Lütkenhöner, 1995). Therefore, dipole regularization techniques have to be introduced in order to limit or suppress these low-gain components at least in single-modality MEG evaluations. In EEG or combined EEG/MEG examinations, the electric data are expected to reduce these effects due to their nearly isotropic orientational sensitivity distributions (Fuchs et al., 1998b).

For testing the methods described above in view of their spatial resolution, simulations with different electrode and magnetometer/gradiometer set-ups were used with a 3 spherical shells volume conductor model. White noise, uncorrelated across sensors, was added to calculated field distributions to get statistically relevant results for different dipole depths and signal-to-noise-ratios. Equivalent dipolar sources were then fitted with single and combined modalities.

Evoked somatosensory measurements from electric medianus nerve stimulation with simultaneously recorded 31 electrodes EEG (SEP) and 31 channels MEG (SEF) (Buchner et al., 1994) were used to test combined evaluations with real data. The volume conductor was modeled by a BEM consisting of 3 compartments. The head/brain compartments for the BEM model were semi-automatically segmented, and triangulated from magnetic resonance (MR) images (Wagner et al., 1995). Single equivalent dipoles, cortically constrained deviation scans (Fuchs et al., 1994; Fuchs et al., 1998a), and spatiotemporal dipole models (Scherg and von Cramon, 1985; Mosher et al., 1992) were used to compare single modality and merged evaluations.