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.

译文

提出了一种根据脑电图 (EEG) 和脑磁图 (MEG) 数据对人体运动相关皮质活动进行建模的方法。该方法包括由磁共振图像构建的受试者的多隔室头部模型 (头皮,头骨,硬脑膜,皮质),多偶极子源模型以及基于边界元数学的正则化线性逆源估计。通过考虑背景EG和MEG传感器噪声的协方差,对皮质活动的线性逆源估计进行了正则化。EEG (121传感器) 和MEG (43传感器) 数据在单独的会话中记录,而正常受试者执行自愿的右一位运动。通过使用三个性能指标定量评估EEG,MEG和eeg-meg数据的线性逆源解。前两个指标 (偶极定位误差 [DLE] 和空间色散 [SDis]) 用于计算获得的源解的定位能力。此类索引基于分辨率矩阵列提供的信息 (即冲动响应)。理想的DLE值趋于零 (通过该过程正确检索了源电流)。相反,较高的DLE值表明源重建过程中存在严重的错误定位。在源空间点处的高SDis值意味着该源将通过线性逆源估计被大面积检索。剩余性能指标根据解析矩阵R的行 (即解析内核) 提供的信息评估源解决方案的质量。矩阵R的第i个分辨率内核描述了第i个源的估计如何因所有其他源的伴随活动而失真。与单独考虑的EEG和MEG数据相比,在由组合eeg-meg数据产生的源溶液中,观察到具有统计学意义的较低偶极子定位误差和较低的空间色散 (P <0.05)。这些影响不是由于组合eeg-meg溶液中传感器数量的增加所致。它们是由多模式测量所传达的源信息的独立性引起的。从生理学的角度来看,eeg-meg数据的线性逆源解决方案表明,从准备到执行运动,主要感觉运动皮层的双向激活对侧优势。这种激活与补充运动区的激活有关。在处理与正在进行的运动有关的传入信息时,双侧初级感觉运动皮层区域的激活要比准备运动动作时更大。总之,结合MEG和EEG数据的线性逆源估计改善了与运动相关的皮层活动的估计。

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