Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA.
Neuroscience Institute, Georgia State University, Atlanta, GA, USA.
Brain Struct Funct. 2018 Apr;223(3):1107-1120. doi: 10.1007/s00429-017-1541-9. Epub 2017 Nov 1.
Pairing in vivo imaging and computational modeling of dendritic arborization (da) neurons from the fruit fly larva provides a unique window into neuronal growth and underlying molecular processes. We image, reconstruct, and analyze the morphology of wild-type, RNAi-silenced, and mutant da neurons. We then use local and global rule-based stochastic simulations to generate artificial arbors, and identify the parameters that statistically best approximate the real data. We observe structural homeostasis in all da classes, where an increase in size of one dendritic stem is compensated by a reduction in the other stems of the same neuron. Local rule models show that bifurcation probability is determined by branch order, while branch length depends on path distance from the soma. Global rule simulations suggest that most complex morphologies tend to be constrained by resource optimization, while simpler neuron classes privilege path distance conservation. Genetic manipulations affect both the local and global optimal parameters, demonstrating functional perturbations in growth mechanisms.
对果蝇幼虫树突分支（da）神经元进行体内成像和计算建模的联合研究为研究神经元的生长和潜在的分子过程提供了一个独特的窗口。我们对野生型、RNAi 沉默型和突变型 da 神经元进行成像、重建和形态分析。然后，我们使用基于局部和全局规则的随机模拟来生成人工树突，并确定在统计学上最能近似真实数据的参数。我们观察到所有 da 类别的结构平衡，其中一个树突干的大小增加被同一神经元的其他干的减少所补偿。局部规则模型表明，分支概率由分支顺序决定，而分支长度取决于从体轴的路径距离。全局规则模拟表明，大多数复杂的形态往往受到资源优化的限制，而较简单的神经元类则优先保持路径距离的守恒。遗传操作会影响局部和全局最优参数，这表明生长机制的功能受到干扰。
