Plis Sergey M, George John S, Jun Sung C, Ranken Doug M, Volegov Petr L, Schmidt David M
MS-D454, Applied Modern Physics Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
Phys Med Biol. 2007 Sep 7;52(17):5309-27. doi: 10.1088/0031-9155/52/17/014. Epub 2007 Aug 16.
Source localization by electroencephalography (EEG) requires an accurate model of head geometry and tissue conductivity. The estimation of source time courses from EEG or from EEG in conjunction with magnetoencephalography (MEG) requires a forward model consistent with true activity for the best outcome. Although MRI provides an excellent description of soft tissue anatomy, a high resolution model of the skull (the dominant resistive component of the head) requires CT, which is not justified for routine physiological studies. Although a number of techniques have been employed to estimate tissue conductivity, no present techniques provide the noninvasive 3D tomographic mapping of conductivity that would be desirable. We introduce a formalism for probabilistic forward modeling that allows the propagation of uncertainties in model parameters into possible errors in source localization. We consider uncertainties in the conductivity profile of the skull, but the approach is general and can be extended to other kinds of uncertainties in the forward model. We and others have previously suggested the possibility of extracting conductivity of the skull from measured electroencephalography data by simultaneously optimizing over dipole parameters and the conductivity values required by the forward model. Using Cramer-Rao bounds, we demonstrate that this approach does not improve localization results nor does it produce reliable conductivity estimates. We conclude that the conductivity of the skull has to be either accurately measured by an independent technique, or that the uncertainties in the conductivity values should be reflected in uncertainty in the source location estimates.
通过脑电图(EEG)进行源定位需要精确的头部几何形状和组织电导率模型。从脑电图或结合脑磁图(MEG)的脑电图估计源时间进程需要一个与真实活动一致的正向模型,以获得最佳结果。虽然磁共振成像(MRI)能很好地描述软组织解剖结构,但颅骨(头部主要的电阻性成分)的高分辨率模型需要计算机断层扫描(CT),而这对于常规生理研究来说并不合理。尽管已经采用了多种技术来估计组织电导率,但目前没有技术能提供理想的非侵入性三维电导率断层成像。我们引入一种概率正向建模形式,它能将模型参数的不确定性传播到源定位的可能误差中。我们考虑了颅骨电导率分布的不确定性,但该方法具有通用性,可扩展到正向模型中的其他类型不确定性。我们和其他人之前曾提出,通过同时优化偶极子参数和正向模型所需的电导率值,从测量的脑电图数据中提取颅骨电导率的可能性。使用克拉美 - 罗界,我们证明这种方法既不能改善定位结果,也不能产生可靠的电导率估计。我们得出结论,颅骨电导率必须通过独立技术准确测量,或者电导率值的不确定性应反映在源位置估计的不确定性中。