Tangential derivative mapping of axial MEG applied to event-related desynchronization research

doi:10.1016/S1388-2457(00)00272-8

Clinical Neurophysiology

Volume 111, Issue 7, 1 July 2000, Pages 1300-1305

https://doi.org/10.1016/S1388-2457(00)00272-8 Get rights and content

Abstract

Objectives: A problem with the topographic mapping of MEG data recorded with axial gradiometers is that field extrema are measured at sensors located at either side of a neuronal generator instead of at sensors directly above the source. This is problematic for the computation of event-related desynchronization (ERD) on MEG data, since ERD relies on a correspondence between the signal maximum and the location of the neuronal generator.

Methods: We present a new method based on computing spatial derivatives of the MEG data. The limitations of this method were investigated by means of forward simulations, and the method was applied to a 150-channel MEG dataset.

Results: The simulations showed that the method has some limitations. (1) Fewer channels reduce accuracy and amplitude. (2) It is less suitable for deep or very extended sources. (3) Multiple sources can only be distinguished if they are not too close to each other. Applying the method in the calculation of ERD on experimental data led to a considerable improvement of the ERD maps.

Conclusions: The proposed method offers a significant advantage over raw MEG signals, both for the topographic mapping of MEG and for the analysis of rhythmic MEG activity by means of ERD.

Introduction

Event-related desynchronization (ERD; Pfurtscheller and Aranibar, 1977) is a technique used to quantify the spatiotemporal evolution of event-related changes in oscillatory EEG activity. ERD has proved to be a sensitive indicator of cortical activity in movement-related brain research as well as in cognitive brain research (cf. Pfurtscheller and Lopez da Silva, 1999, for a comprehensive review of the state of the art of ERD research).

Because of the inherently better spatial resolution of MEG signals as compared to EEG, and because of the differential sensitivity of EEG and MEG for radial and tangential dipoles, it would be desirable to apply the ERD technique to MEG measurements as well. There is, however, a problem with the straightforward computation of ERD on certain types of MEG data. Many modern MEG systems use pickup coils that are sensitive to the component of the magnetic field which is approximately normal to the head surface. As a consequence, a dipole at a given location will produce maximum signal at either side of the dipole, while just above the source the signal will be zero. ERD mapping relies on a reasonable correspondence between the maximum signal and the location of the activated brain area. Since the extrema of the normal components of the magnetic field can be quite distant from the location of the activation, the interpretation can be difficult.

With MEG systems that use so-called planar gradiometers (where the pick-up coil and the compensation coil are in the same plane, which is tangential to the scalp surface) this problem does not arise. Here, a dipole at a given location produces maximal signal at the sensors overlying it. Therefore MEG measured with planar gradiometers is well suited for topographic analyses such as ERD. It has been shown that ERD (or, more precisely, Temporal-Spectral Evolution (TSE; Salmelin and Hari, 1994), a method that uses a slightly different way of quantifying changes in oscillatory activity) can be successfully applied to ‘planar’ MEG data (see Hari and Salmelin, 1997, for a review). This has, amongst others, led to new insights about the nature and the generators of the mu and beta rhythms (e.g. Salmelin et al., 1995a, Salmelin et al., 1995b), and has revealed the existence of 10 Hz rhythmic activity originating from the auditory cortex (Tiihonen et al., 1995, Lehtelä et al., 1997). These findings demonstrate that ERD analyses on MEG data do have an added value compared to ERD analyses on EEG data.

In the present paper we propose a solution to the problem of computing ERD on MEG data recorded with an axial gradiometer system. We have developed a method that is based on computing spatial derivatives of the recorded MEG. ERD can then be computed on the spatial derivatives instead of on the untreated MEG. We will present results from forward simulations that will give us an indication of the limitations and possibilities of the proposed method, and we will apply it in the calculation of ERD on a 150-channel MEG dataset in order to evaluate its usefulness in ERD research.

Section snippets

Computation of MEG derivatives

The normal component of the magnetic field, which is measured by axial gradiometers or magnetometers is considered a scalar field on a surface defined by the sensor array: B(u,v). The local coordinates u and v are locally tangential and mutually orthogonal. The gradient of B denotes the direction and the steepness of the slope of B within the sensor surface: $∇ B(u,v)=(∂B/∂u,∂B/∂v)^{T}$ The steepness of the slope of B(u,v), i.e. the density of contour lines in a contour plot, is the measure we want to

Simulations

A first set of simulations, investigating the influence of the spatial sampling (number of sensors), noise, and source depth on the detectablility of focal sources revealed that both tested sensor configurations were capable of detecting a single dipole. However, lower sampling resolutions decrease the accuracy of the exact peak location. Due to the undersampling with only 64 sensors, the rather sharp peak above the source location cannot be properly reconstructed by the spline interpolation,

Discussion

The simulations showed that the method has some limitations, the most important being the requirement that simultaneously active sources should be at more than 4 cm apart in order to be detected. Furthermore, the method is less suited for deep sources. Finally a dense sensor array increases the localization accuracy and the SNR. Applying the method as a first step in the calculation of ERD on experimental MEG data led to a higher spatial stability, and a more focal ERD and ERS that corresponded

Acknowledgements

The MEG experiment described in this paper was performed in close collaboration with the Department of Medical Informatics from the Technical University of Graz, Austria. The authors wish to thank Professor G. Pfurtscheller and Dr G. Edlinger for their help with the various stages of data processing. The authors further wish to thank Dr G. van Boxtel for his valuable comments on an earlier version of this paper.

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Technical reportTangential derivative mapping of axial MEG applied to event-related desynchronization research

Abstract

Introduction

Section snippets

Computation of MEG derivatives

Simulations

Discussion

Acknowledgements

Trends Neurosci

Neurosci Lett

Electroenceph clin Neurophysiol

Electroenceph clin Neurophysiol

Electroenceph clin Neurophysiol

Neuroscience

Electroenceph clin Neurophysiol

Technical report
Tangential derivative mapping of axial MEG applied to event-related desynchronization research