Vatta Federica, Bruno Paolo, Inchingolo Paolo
Department of Electrotechnics, Electronics and Computer Science, University of Trieste, Via Valerio 10, 34127 Trieste, Italy.
J Clin Neurophysiol. 2002 Jan;19(1):1-15. doi: 10.1097/00004691-200201000-00001.
EEG-based source localization techniques use scalp-potential data to estimate the location of underlying neural activity. EEG source location reconstruction requires the assumption of a source model and the assumption of a conductive head model. Brain lesions can present conductivity values that are dramatically different from those of surrounding normal tissues and have to be included in head models for accurate neural source reconstruction. It is therefore necessary to analyze subjects' anatomic images (using MRI or computed tomography) to identify lesion type and to assign the appropriate conductivity value. Source localization accuracy may be influenced by uncertainties in tissue conductivity assignment during head model construction. The authors present a sensitivity study quantifying the effect of uncertainty in brain lesion conductivity assignment on EEG dipole source localization. They adopted an eccentric-spheres head model in which an eccentric bubble approximated the effects of actual brain lesions. After simulating EEG signal measurement in 64 different pathologic situations, an inverse dipole fitting procedure was carried out, assuming an incorrect lesion conductivity assignment ranging from a half to twice the real value. Incorrect lesion conductivity assignment led to markedly wrong source reconstruction for highly conductive lesions like liquid-filled ones (localization errors as much as 1.7 cm). Conversely, low sensitivity to uncertainties in conductivity assignment was found for lesions with low conductivity like calcified tumors. The authors propose a method based on residual error analysis to improve the lesion conductivity estimate. This procedure can identify lesion tissue conductivity with only a few percent error and guarantees source localization errors less than 5 mm.
基于脑电图的源定位技术利用头皮电位数据来估计潜在神经活动的位置。脑电图源定位重建需要假设一个源模型和一个导电头部模型。脑损伤会呈现出与周围正常组织显著不同的电导率值，为了进行准确的神经源重建，必须将其纳入头部模型。因此，有必要分析受试者的解剖图像（使用磁共振成像或计算机断层扫描）以识别损伤类型并分配适当的电导率值。源定位的准确性可能会受到头部模型构建过程中组织电导率分配不确定性的影响。作者进行了一项敏感性研究，量化了脑损伤电导率分配不确定性对脑电图偶极子源定位的影响。他们采用了一个偏心球头部模型，其中一个偏心气泡近似实际脑损伤的影响。在模拟64种不同病理情况下的脑电图信号测量后，进行了反向偶极子拟合程序，假设损伤电导率分配错误范围为实际值的一半到两倍。对于像充满液体的高导电损伤，错误的损伤电导率分配会导致明显错误的源重建（定位误差高达1.7厘米）。相反，对于像钙化肿瘤这样低导电率的损伤，发现对电导率分配不确定性的敏感性较低。作者提出了一种基于残差分析的方法来改进损伤电导率估计。该程序能够以仅百分之几的误差识别损伤组织电导率，并保证源定位误差小于5毫米。
