School of Electronic Information, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China.
Department of Biomedical Engineering, University of Houston, 3605 Cullen Blvd, Houston, TX, 77004, USA.
Med Biol Eng Comput. 2018 Aug;56(8):1325-1332. doi: 10.1007/s11517-018-1845-9. Epub 2018 Jun 1.
The conductivity of brain tissues is not only essential for electromagnetic source estimation (ESI), but also a key reflector of the brain functional changes. Different from the other brain tissues, the conductivity of whiter matter (WM) is highly anisotropic and a tensor is needed to describe it. The traditional electrical property imaging methods, such as electrical impedance tomography (EIT) and magnetic resonance electrical impedance tomography (MREIT), usually fail to image the anisotropic conductivity tensor of WM with high spatial resolution. The diffusion tensor imaging (DTI) is a newly developed technique that can fulfill this purpose. This paper reviews the existing anisotropic conductivity models of WM based on the DTI and discusses their advantages and disadvantages, as well as identifies opportunities for future research on this subject. It is crucial to obtain the linear conversion coefficient between the eigenvalues of anisotropic conductivity tensor and diffusion tensor, since they share the same eigenvectors. We conclude that the electrochemical model is suitable for ESI analysis because the conversion coefficient can be directly obtained from the concentration of ions in extracellular liquid and that the volume fraction model is appropriate to study the influence of WM structural changes on electrical conductivity. Graphical abstract ᅟ.
脑组织结构的电导率不仅对电磁源估计（ESI）至关重要，还是大脑功能变化的关键反映。与其他脑组织不同，白质（WM）的电导率具有各向异性，需要张量来描述。传统的电特性成像方法，如电阻抗断层成像（EIT）和磁共振电阻抗断层成像（MREIT），通常无法以高空间分辨率对 WM 的各向异性电导率张量进行成像。扩散张量成像（DTI）是一种新开发的技术，可以实现这一目的。本文基于 DTI 回顾了现有的 WM 各向异性电导率模型，并讨论了它们的优缺点，以及确定了这一主题未来研究的机会。获得各向异性电导率张量和扩散张量特征值之间的线性转换系数至关重要，因为它们具有相同的特征向量。我们得出结论，电化学模型适用于 ESI 分析，因为转换系数可以直接从细胞外液中离子的浓度中获得，而体积分数模型适合研究 WM 结构变化对电导率的影响。
