Babiloni Fabio, Babiloni Claudio, Carducci Filippo, Romani Gian Luca, Rossini Paolo M, Angelone Leonardo M, Cincotti Febo
Dipartimento di Fisiologia Umana e Farmacologia, Università di Roma "La Sapienza," Roma, Italy.
Hum Brain Mapp. 2004 May;22(1):52-62. doi: 10.1002/hbm.20011.
Previous simulation studies have stressed the importance of the multimodal integration of electroencephalography (EEG) and magnetoencephalography (MEG) data in the estimation of cortical current density. In such studies, no systematic variations of the signal-to-noise ratio (SNR) and of the number of sensors were explicitly taken into account in the estimation process. We investigated effects of variable SNR and number of sensors on the accuracy of current density estimate by using multimodal EEG and MEG data. This was done by using as the dependent variable both the correlation coefficient (CC) and the relative error (RE) between imposed and estimated waveforms at the level of cortical region of interests (ROI). A realistic head and cortical surface model was used. Factors used in the simulations were: (1). the SNR of the simulated scalp data (with seven levels: infinite, 30, 20, 10, 5, 3, 1); (2). the particular inverse operator used to estimate the cortical source activity from the simulated scalp data (INVERSE, with two levels, including minimum norm and weighted minimum norm); and (3). the number of EEG or MEG sensors employed in the analysis (SENSORS, with three levels: 128, 61, 29 for EEG and 153, 61, or 38 in MEG). Analysis of variance demonstrated that all the considered factors significantly affect the CC and the RE indexes. Combined EEG-MEG data produced statistically significant lower RE and higher CC in source current density reconstructions compared to that estimated by the EEG and MEG data considered separately. These observations hold for the range of SNR values presented by the analyzed data. The superiority of current density estimation by multimodal integration of EEG and MEG was not due to differences in number of sensors between unimodal (EEG, MEG) and combined (EEG-MEG) inverse estimates. In fact, the current density estimate relative to the EEG-MEG multimodal integration involved 61 EEG plus 63 MEG sensors, whereas estimations carried out with the single modalities alone involved 128 sensors for EEG and 153 sensors for MEG. The results of the simulations also suggest that the use of simultaneous 29 EEG sensors during the MEG measurements carried out with full sensor arrangements (153 sensors) returned an accuracy of the cortical source estimate statistically similar to that obtained by combining 64 EEG and 153 MEG sensors.
先前的模拟研究强调了脑电图（EEG）和脑磁图（MEG）数据的多模态整合在估计皮质电流密度中的重要性。在这类研究中，估计过程中没有明确考虑信噪比（SNR）和传感器数量的系统变化。我们通过使用多模态EEG和MEG数据，研究了可变SNR和传感器数量对电流密度估计准确性的影响。这是通过将感兴趣的皮质区域（ROI）水平上施加的波形与估计的波形之间的相关系数（CC）和相对误差（RE）作为因变量来实现的。使用了逼真的头部和皮质表面模型。模拟中使用的因素有：（1）模拟头皮数据的SNR（有七个水平：无穷大、30、20、10、5、3、1）；（2）用于从模拟头皮数据估计皮质源活动的特定逆算子（INVERSE，有两个水平，包括最小范数和加权最小范数）；以及（3）分析中使用的EEG或MEG传感器数量（SENSORS，有三个水平：EEG为128、61、29，MEG为153、61或38）。方差分析表明，所有考虑的因素均显著影响CC和RE指标。与单独考虑的EEG和MEG数据估计相比，联合EEG-MEG数据在源电流密度重建中产生了统计学上显著更低的RE和更高的CC。这些观察结果适用于分析数据呈现的SNR值范围。EEG和MEG多模态整合进行电流密度估计的优越性并非由于单模态（EEG、MEG）和联合（EEG-MEG）逆估计之间传感器数量的差异。实际上，相对于EEG-MEG多模态整合的电流密度估计涉及61个EEG加上63个MEG传感器，而单独使用单模态进行的估计中，EEG涉及128个传感器，MEG涉及’153个传感器。模拟结果还表明，在使用完整传感器阵列（153个传感器）进行MEG测量期间同时使用29个EEG传感器，其皮质源估计的准确性在统计学上与组合64个EEG和153个MEG传感器所获得的准确性相似。
