Linear inverse source estimate of combined EEG and MEG data related to voluntary movements

Abstract

A method for the modeling of human movement-related cortical activity from combined electroencephalography (EEG) and magnetoencephalography (MEG) data is proposed. This method includes a subject's multi-compartment head model (scalp, skull, dura mater, cortex) constructed from magnetic resonance images, multi-dipole source model, and a regularized linear inverse source estimate based on boundary element mathematics. Linear inverse source estimates of cortical activity were regularized by taking into account the covariance of background EG and MEG sensor noise. EEG (121 sensors) and MEG (43 sensors) data were recorded in separate sessions whereas normal subjects executed voluntary right one-digit movements. Linear inverse source solution of EEG, MEG, and EEG-MEG data were quantitatively evaluated by using three performance indexes. The first two indexes (Dipole Localization Error [DLE] and Spatial Dispersion [SDis]) were used to compute the localization power for the source solutions obtained. Such indexes were based on the information provided by the column of the resolution matrix (i.e., impulse response). Ideal DLE values tend to zero (the source current was correctly retrieved by the procedure). In contrast, high DLE values suggest severe mislocalization in the source reconstruction. A high value of SDis at a source space point mean that such a source will be retrieved by a large area with the linear inverse source estimation. The remaining performance index assessed the quality of the source solution based on the information provided by the rows of the resolution matrix R, i.e., resolution kernels. The i-th resolution kernels of the matrix R describe how the estimation of the i-th source is distorted by the concomitant activity of all other sources. A statistically significant lower dipole localization error was observed and lower spatial dispersion in source solutions produced by combined EEG-MEG data than from EEG and MEG data considered separately (P < 0.05). These effects were not due to an increased number of sensors in the combined EEG-MEG solutions. They result from the independence of source information conveyed by the multimodal measurements. From a physiological point of view, the linear inverse source solution of EEG-MEG data suggested a contralaterally preponderant bilateral activation of primary sensorimotor cortex from the preparation to the execution of the movement. This activation was associated with that of the supplementary motor area. The activation of bilateral primary sensorimotor cortical areas was greater during the processing of afferent information related to the ongoing movement than in the preparation for the motor act. In conclusion, the linear inverse source estimate of combined MEG and EEG data improves the estimate of movement-related cortical activity.

Figure 1

Realistic magnetic resonance (MR)‐constructed head model of Subject 1. The structures modeling the scalp, skull, dura mater and cerebral cortex are presented. On the visible modeled scalp surface, electrode density can be appreciated (spatial sampling: 128 electrodes). The inset shows the modeled central sulcus and the left primary sensorimotor area contralateral to the side of the movement (right middle finger extension).

Figure 2

Disposition of the electric (up) and magnetic (bottom) sensors for the recording of EEG and MEG data related to unilateral unaimed right‐middle finger voluntary movements (separate recording sessions). Averaged MEG and EEG time‐series (wave forms) recorded from two selected magnetic (M1 and M2) and electric (E1 and E2) sensors are shown on the right of the figure. These sensors overlay the primary sensorimotor cortex contralateral to the movement.

Figure 3

Mean values of DLE (upper diagram), SDis (central diagram) and RI (lower diagram) indexes computed from the linear inverse source estimates of EEG, MEG and combined EEG‐MEG data, for each ROI (left and right M1 and S1) as well as for the whole cortical source space (All). In particular, the index values were computed by surface data relative to 43 magnetic sensors, 61 electric sensors, 104 sensors (61 electric and 43 magnetic ones), 121 electric sensors and 164 sensors (121 electric and 43 magnetic ones). Acronyms: S1, primary somatosensory area; M1, primary motor area; SMA, supplementary motor area.

Figure 4

Cortical current source density estimates of EEG, MEG, and combined EEG‐MEG data. The estimates of the cortical current strengths are represented on the realistic MR‐constructed subject's head model. Percent color scale is normalized with reference to the maximum amplitude calculated for each map. Maximum negativity (−100%) is coded in red and maximum positivity (+100%) is coded in violet.

Figure 5

Time series of cortical current density estimates modeling activity of each ROI during the movement preparation and execution. These estimates reflect the potentials/fields recorded from EEG (121 electric sensors; upper diagram), MEG (43 magnetic sensors; central diagram) and EEG‐MEG (121 electric and 43 magnetic sensors, lower diagram) data. Time is relative to the onset (zerotime) of the electromyographic (EMG) responses recorded from the operating muscle. Each colored waveform refers to the time varying current activity in a particular region of interest.