Chang Xiao, Kim Michael D, Stephens Rachel, Qu Tiange, Chiba Akira, Tsechpenakis Gavriil
Computer and Information Science Department, Indiana University-Purdue University Indianapolis, 723 W, Michigan St., SL 280, Indianapolis, IN 46202, USA.
Molecular and Cellular Pharmacology Department, University of Miami Miller School of Medicine, 1600 NW, 10th Avenue, RMSB 6056, Miami, FL 33136, USA.
Neuroimage. 2014 Apr 15;90:33-42. doi: 10.1016/j.neuroimage.2013.12.023. Epub 2013 Dec 24.
We exploit the morphological stereotypy and relative simplicity of the Drosophila nervous system to model the diverse neuronal morphologies of individual motor neurons and understand underlying principles of synaptic connectivity in a motor circuit. In our analysis, we use images depicting single neurons labeled with green fluorescent protein (GFP) and serially imaged with laser scanning confocal microscopy. We model morphology with a novel formulation of Conditional Random Fields, a hierarchical latent-state CRF, to capture the highly varying compartment-based structure of the neurons (soma-axon-dendrites). In the training phase, we follow two approaches: (i) hierarchical learning, where compartment labels are given, and (ii) latent-state learning, where compartment labels are not given in the samples. We demonstrate the accuracy of our approach using wild-type motor neurons in the larval ventral nerve cord. However, our method can also be used for the identification of motor neuron mutations, as well as the automated annotation of the motor circuitry in wild type and mutant animals. Our method is directly applicable to the recognition of compartment-defined structures.
我们利用果蝇神经系统的形态刻板性和相对简单性，对单个运动神经元的多种神经元形态进行建模，并理解运动回路中突触连接的潜在原理。在我们的分析中，我们使用描绘用绿色荧光蛋白（GFP）标记并通过激光扫描共聚焦显微镜进行连续成像的单个神经元的图像。我们用一种新颖的条件随机场公式（一种分层潜在状态CRF）对形态进行建模，以捕捉神经元基于隔室的高度变化结构（胞体-轴突-树突）。在训练阶段，我们采用两种方法：（i）分层学习，其中隔室标签是给定的；（ii）潜在状态学习，其中样本中不给出隔室标签。我们使用幼虫腹神经索中的野生型运动神经元证明了我们方法的准确性。然而，我们的方法也可用于识别运动神经元突变，以及对野生型和突变动物的运动回路进行自动注释。我们的方法直接适用于识别隔室定义的结构。
