Computing Sciences Unit, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland.
Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.
J Neural Eng. 2023 Mar 8;20(2). doi: 10.1088/1741-2552/acbdc1.
This study focuses on the effects of dynamical vascular modeling on source localization errors in electroencephalography (EEG). Our aim of thisstudy is to (a) find out the effects of cerebral circulation on the accuracy of EEG source localization estimates, and (b) evaluate its relevance with respect to measurement noise and interpatient variation.We employ a four-dimensional (3D + T) statistical atlas of the electrical properties of the human head with a cerebral circulation model to generate virtual patients with different cerebral circulatory conditions for EEG source localization analysis. As source reconstruction techniques, we use the linearly constraint minimum variance (LCMV) beamformer, standardized low-resolution brain electromagnetic tomography (sLORETA), and the dipole scan (DS).Results indicate that arterial blood flow affects source localization at different depths and with varying significance. The average flow rate plays an important role in source localization performance, while the pulsatility effects are very small. In cases where a personalized model of the head is available, blood circulation mismodeling causes localization errors, especially in the deep structures of the brain where the main cerebral arteries are located. When interpatient variations are considered, the results show differences up to 15 mm for sLORETA and LCMV beamformer and 10 mm for DS in the brainstem and entorhinal cortices regions. In regions far from the main arteries vessels, the discrepancies are smaller than 3 mm. When measurement noise is added and interpatient differences are considered in a deep dipolar source, the results indicate that the effects of conductivity mismatch are detectable even for moderate measurement noise. The signal-to-noise ratio limit for sLORETA and LCMV beamformer is 15 dB, while the limit is under 30 dB for DS.Localization of the brain activity via EEG constitutes an ill-posed inverse problem, where any modeling uncertainty, e.g. a slight amount of noise in the data or material parameter discrepancies, can lead to a significant deviation of the estimated activity, especially in the deep structures of the brain. Proper modeling of the conductivity distribution is necessary in order to obtain an appropriate source localization. In this study, we show that the conductivity of the deep brain structures is particularly impacted by blood flow-induced changes in conductivity because large arteries and veins access the brain through that region.
本研究专注于动力学血管建模对脑电图（EEG）源定位误差的影响。我们的研究目的是：（a）发现脑循环对 EEG 源定位估计准确性的影响，以及（b）评估其与测量噪声和个体间变异性的相关性。我们采用具有脑循环模型的人类头部电特性的四维（3D+T）统计图谱，为 EEG 源定位分析生成具有不同脑循环条件的虚拟患者。作为源重建技术，我们使用线性约束最小方差（LCMV）波束形成器、标准化低分辨率脑电磁层析成像（sLORETA）和偶极子扫描（DS）。结果表明，动脉血流在不同深度和不同显著程度上影响源定位。平均流速在源定位性能中起着重要作用，而脉动效应非常小。在可获得头部个性化模型的情况下，血液循环失配会导致定位误差，特别是在大脑主要动脉所在的大脑深部结构中。当考虑个体间变异性时，结果显示 sLORETA 和 LCMV 波束形成器在脑干和内嗅皮质区域的差异高达 15 毫米，DS 为 10 毫米。在远离主要动脉的区域，差异小于 3 毫米。当添加测量噪声并考虑深部偶极子源的个体间差异时，结果表明，即使在中等测量噪声下，也可以检测到电导率失配的影响。sLORETA 和 LCMV 波束形成器的信噪比极限为 15dB，而 DS 的极限小于 30dB。通过 EEG 对大脑活动进行定位是一个不适定的逆问题，任何建模不确定性，例如数据中的少量噪声或材料参数差异，都可能导致估计活动的显著偏差，尤其是在大脑的深部结构中。为了获得适当的源定位，有必要对电导率分布进行适当的建模。在这项研究中，我们表明，由于大动脉和静脉通过该区域进入大脑，深部脑结构的电导率特别受到血流诱导的电导率变化的影响。
