Physical-chemical Aspects Of Cell And Tissue Excitability

为了了解扩散MR信号的生物物理基础，前STBB博士后研究员，现为特拉维夫大学助理教授的Uri Nevo成功构建并测试了一个实验系统，使用扩散MRI方法询问器官型培养脑切片。 这项工作已经显示出有希望的结果，在测量的扩散系数图的变化，在培养的组织所受到的环境条件的变化。从这些研究中出现的一个假设是，在许多不同的长度尺度上发生的活动过程（细胞流动，水流过膜等）。是弥散加权MRI信号中某些信号损失的原因。这种认识促使了一种理论的串联发展，以解释微观流体流动如何影响测量的扩散加权MRI信号和在组织中测量的扩散系数（即，伪扩散）和实验模型系统Rheo-NMR，其中可以产生产生已知量的伪扩散的良好表征的流场。 这些综合研究的重要性在于，如果这种微观运动，如流动，水流过膜等，在扩散加权MRI实验中，这些信号会表现为额外的信号损失，那么我们就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。 我们也一直在与布拉德利罗斯合作，试图检查不同的物理机制，可以利用或用于直接使用MRI检测神经电流。 我们之前研究的一种方法是，由强磁场（如大型临床MRI扫描仪内的磁场）中产生的洛伦兹力引起的小位移是否可以用于使用MRI测量体内神经电流。 我们的计算表明，由洛伦兹力引起的神经诱导位移将无法通过使用现有技术的MRI检测到。 在经颅磁刺激（TMS）领域，里斯本的Pedro米兰达和他的团队与STBB合作，使用有限元方法（FEM）进行了详细的计算，以预测TMS期间大脑中感应的电场和电流密度分布。 以前，我们发现组织异质性和电导率的各向异性（即，电导率张量场）对扭曲感应场，甚至在某些区域产生兴奋性或抑制性热点有显著贡献。 最近，我们一直在开发皮质褶皱的有限元模型，包含脑回和脑沟。我们发现，这种复杂的皮质解剖结构也会显著影响组织内感应电场的分布，以及可能被这种刺激兴奋或抑制的神经细胞的位置和类型。 这些现象可能在解释或推断兴奋区域或部位以及确定神经兴奋来源方面具有重要的临床后果。我们开始将TMS的宏观模型与CNS中神经兴奋性的微观模型结合起来，以便能够预测TMS中的兴奋位点，甚至是兴奋的神经元群体。 最近，我们也一直在应用这些先进的有限元模型来研究潜在的直流激励（DCE）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).