Physical-chemical Aspects Of Cell And Tissue Excitability

为了了解扩散磁共振信号的生物物理基础，前STBB博士后研究员、现任特拉维夫大学助理教授的乌里·内沃成功地构建并测试了一个使用扩散磁共振方法询问器官型培养脑片的实验系统。这项工作已经显示出令人振奋的结果，将测量的扩散系数图的变化与培养组织所受环境条件的变化联系起来。从这些研究中得出的一个假说是，活跃的过程发生在许多不同的长度尺度上(细胞流动，水穿过细胞膜等)。对扩散加权MRI信号中的一些信号损失负责。这一见解促使了解释微观流体流动如何影响测量的扩散加权磁共振信号和组织中测量的扩散系数(即伪扩散)的理论和实验模型系统Rheo-核磁共振的相继发展，在该系统中，可以产生表征良好的流场，从而产生已知量的伪扩散。这些联合研究的重要性在于，如果这种微观运动，如流动、跨膜水流等，在扩散加权磁共振实验中表现为额外的信号损失，那么我们可以使用关于这种额外信号损失的信息来推断细胞功能和活力的不同方面，包括兴奋性。 我们还一直在与Bradley Roth合作，试图检查不同的物理机制，这些机制可以被利用或用于直接使用MRI来检测神经电流。我们之前研究的一种方法是，是否可以使用强磁场(如大型临床MRI扫描仪中的那些)中产生的洛伦兹力引起的微小位移来使用MRI测量体内的神经电流。我们的计算表明，使用现有技术的核磁共振无法检测到洛伦兹力引起的神经诱导位移。 在经颅磁刺激(TMS)领域，里斯本的Pedro Miranda和他的团队与STBB合作，使用有限元方法(FEM)进行了详细的计算，以预测TMS期间在大脑中感应的电场和电流密度分布。以前，我们发现组织的异质性和电导率的各向异性(即电导张量场)都会显著地扭曲感应场，甚至在某些区域产生兴奋或抑制的热点。最近，我们一直在开发包含脑回和脑沟的皮质褶皱的有限元模型。我们发现，这种复杂的大脑皮层解剖结构也显著影响了组织内感应电场的分布，以及这种刺激可以兴奋或抑制的神经细胞的位置和类型。这些现象在解释或推断兴奋区域或位置以及确定神经兴奋的来源方面都可能产生重大的临床后果。我们开始将我们的TMS宏观模型与中枢神经系统神经兴奋性的微观模型结合起来，以能够预测TMS中的兴奋位置，甚至是被兴奋的神经元群体。最近，我们也一直在应用这些先进的有限元模型来研究直流励磁(DCE)可能的物理机制。

To understand the biophysical basis of the diffusion MR signal, Uri Nevo, a former STBB post-doctoral fellow, and now an Assistant Professor at Tel Aviv University, has successfully constructed and tested an experimental system to interrogate organotypic cultured brain slices using diffusion MRI methods. This work has already shown promising results relating changes in the measured diffusion coefficient map to changes in environmental conditions to which the cultured tissue is subjected. One hypothesis that emerged from these studies is that active processes occurring at many different length scales (cell streaming, water flow across membranes, etc.) are responsible for some signal loss in the diffusion weighted MRI signal. This insight prompted the tandem development of a theory to explain how microscopic fluid flows affect the measured diffusion weighted MRI signal and the diffusion coefficient measured in tissues (i.e., pseudo-diffusion) and an experimental model system, the Rheo-NMR, in which well-characterized flow fields can be produced that create known amounts of pseudo-diffusion. The importance of these combined studies is that if such microscopic motions, like streaming, water flow across membranes, etc., manifest themselves as additional signal loss in diffusion weighted MRI experiments, then we can use information about this additional signal loss to infer different aspects of cell function and vitality, including excitability. We have also been collaborating with Bradley Roth to try to examine different physical mechanisms that could be exploited or used to detect neural currents directly using MRI. One approach we examined previously was whether small displacements caused by Lorentz forces produced in strong magnetic fields (like those within a large clinical MRI scanner) could be employed to measure neural currents in vivo using MRI. Our calculations showed that the induced displacements of nerves caused by Lorentz forces would not be detectable by MRI using existing technology. In the area of Transcranial Magnetic Stimulation (TMS), Pedro Miranda and his group in Lisbon, in association with STBB, has performed detailed calculations using finite element methods (FEM), to predict the electric field and current density distributions induced in the brain during TMS. Previously, we found that both tissue heterogeneity and anisotropy of the electrical conductivity (i.e., the conductivity tensor field) contribute significantly to distort the induced fields, and even to create excitatory or inhibitory hot spots in some regions. More recently, we have been developing FEM models of cortical folds, containing gyri and sulci. We showed that this complicated cortical anatomy also significantly affects the distribution of induced electric fields within the tissue, and the location and types of nerve cells that could be excited or depressed by such stimuli. These phenomena could have significant clinical consequences both in interpreting or inferring the region or locus of excitation and in determining the source of nerve excitation. We are beginning to marry our macroscopic models of TMS with microscopic models of nerve excitability in the CNS to be able to predict the locus of excitation in TMS and even the populations of neurons that are excited. Recently, we have also been applying these advance FEM models to study the possible physical mechanism underlying Direct Current Excitation (DCE).