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

项目总结 已经做了大量的工作来表征神经调节系统的结构基础以及如何 这些建筑特征塑造了神经调节器的作用。然而，尽管神经调节剂主要是发出信号 通过体积传递，这需要它们从释放部位穿过细胞外空间(ECS) 靶受体，通过ECS的神经调节性扩散几乎没有受到关注。我们从 ECS扩散取决于体积分数、组织弯曲度和 ECS几何学。到目前为止，模拟主要集中在突触扩散和突触溢出 神经调节在比这更大的时空尺度上发挥作用的机制：甚至 例如，神经调节剂仅通过猕猴皮质的第四层扩散，就需要分子旅行 从释放部位到靶标受体的距离可达0.5 mm。与组织孔隙度相关的因素成为 在这样的距离下越来越重要，但目前的计算机模型，如常见的模拟引擎 Mcell只能在纳米尺度上研究分子扩散。因此，我们需要更新 能够模拟神经调节剂在更大空间和更大范围内扩散的计算模型 能够纳入在该宏观尺度上变得相关的措施的时间范围。为此，我 建议开发一种混合模型，该模型将通过组合现有Mcell来实现这一关键功能 具有来自整体扩散模型的大规模算法的能力。因为ECS扩散被认为是不同的 对于到目前为止还没有系统评估过的大脑区域，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质不同区域的扩散 测试诸如组织粒度或组织各向异性等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)。因为神经调节剂 网络几乎与大脑的所有功能联系在一起，了解神经调节剂的动态 大脑的扩散是了解正常大脑功能的重要一步。此外，因为 在整个发育和发育过程中观察到了ECS动力学和神经调节系统的变化 正常衰老或患有中风相关缺血和痴呆等神经病理的患者，确定关键参数 确定信令结果，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展。

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.