NRSA application: Characterizing acetylcholine, noradrenaline, and dopamine diffusion through the extracellular space in three subregions of macaque neocortex

项目摘要 人们已经做了大量的工作来描述神经调节系统的结构基础，以及如何 这些结构特征形成神经调节剂的作用。然而，尽管神经调质主要发出信号， 通过体积传输，这需要它们从释放部位穿过细胞外空间（ECS）， 靶受体，通过ECS的神经调节扩散很少受到关注。我们知道从 计算模型表明，ECS扩散取决于诸如体积分数、组织弯曲度和 ECS几何结构。迄今为止的模拟主要集中在突触扩散和突触溢出 然而，神经调节功能在更大的时空尺度上发挥作用：甚至 例如，在猕猴皮层中，神经调节剂仅通过第四层扩散，需要分子在 从释放位点到靶受体的距离最多为0.5 mm。与组织孔隙度相关的因素成为 在这样的距离上越来越重要，但目前的计算机模型，如通用模拟引擎， MCell仅用于研究纳米尺度的分子扩散。因此，我们需要更新 计算模型能够模拟神经调质在更大空间和 能够纳入与宏观尺度相关的措施的时间范围。为此我 我建议开发一种混合模型，通过结合现有的MCell来实现这一关键功能 能力与大规模的算法从模型的散装扩散。因为ECS扩散被认为是变化的， 到目前为止还没有被系统评估的大脑区域，我将用这个多尺度模型， 模拟乙酰胆碱、去甲肾上腺素和多巴胺在猕猴皮层不同区域扩散， 测试诸如组织粒度或组织各向异性的因素如何影响常见的扩散度量（例如， 浓度、扩散速率、有效扩散系数和扩散张量）。由于神经调节 网络已经与几乎所有的大脑功能联系在一起，了解神经调质的动力学 在大脑中的扩散是理解正常大脑功能的重要一步。此外，因为 在整个发育过程中观察到ECS动力学和神经调节系统的变化， 正常衰老或神经病理学，如中风相关的缺血和痴呆，确定关键参数， 确定信号结果，以及可以修改它们的活动过程，可能是 诊断学和治疗学的进步

PROJECT SUMMARY Much work has been done to characterize the structural underpinnings of neuromodulatory systems and how these architectural features shape neuromodulator action. Yet, although, neuromodulators primarily signal through volume transmission which requires them to traverse the extracellular space (ECS) from release site to target receptor, neuromodulatory diffusion through the ECS has received little attention. We know from computational models that ECS diffusion is dependent on factors such as volume fraction, tissue tortuosity and ECS geometry. Simulations so far have mainly focused on synaptic diffusion and synaptic spillover mechanisms whereas neuromodulation functions at much larger spatiotemporal scales than that: even neuromodulator diffusion through just layer IV in macaque cortex, for example, requires molecules to travel distances up to 0.5 mm from release site to target receptor. Factors related to tissue porosity become increasingly more important at such distances but current computer models like the common simulation engine MCell are only equipped to study molecular diffusion at the nanometer scale. Consequently, we need updated computational models capable of simulating the diffusion of neuromodulators across greater spatial and temporal ranges capable of incorporating measures that become relevant at that macroscale. For this, I propose to develop a hybrid model which will achieve this critical functionality by combining existing MCell capabilities with large-scale algorithms from models of bulk diffusion. Because ECS diffusion is thought to vary with brain regions which to date has not been systematically evaluated, I will then, with this multiscale model, simulate diffusion of acetylcholine, noradrenaline, and dopamine across different regions of macaque cortex to test how factors such as tissue granularity or tissue anisotropy affect common diffusion metrics (e.g., concentrations, diffusion rates, effective diffusion coefficients, and diffusion tensor). Since neuromodulatory networks have been linked to virtually every brain function, understanding the dynamics of neuromodulator diffusion across the brain is an important step in understanding normal brain function. Furthermore, because changes in ECS dynamics and in neuromodulatory systems have been observed throughout development and normal aging or with neuropathology like stroke-related ischemia and dementia, identifying key parameters that determine signaling outcomes, but also the active processes by which they can be modified, may be key for the advancement in diagnostics and therapeutics.