Departamento de Inteligencia Artificial, Facultad de Informatica, Universidad Politécnica de Madrid, Spain.
Dev Neurobiol. 2011 Jan 1;71(1):71-82. doi: 10.1002/dneu.20809.
In the study of neural circuits, it becomes essential to discern the different neuronal cell types that build the circuit. Traditionally, neuronal cell types have been classified using qualitative descriptors. More recently, several attempts have been made to classify neurons quantitatively, using unsupervised clustering methods. While useful, these algorithms do not take advantage of previous information known to the investigator, which could improve the classification task. For neocortical GABAergic interneurons, the problem to discern among different cell types is particularly difficult and better methods are needed to perform objective classifications. Here we explore the use of supervised classification algorithms to classify neurons based on their morphological features, using a database of 128 pyramidal cells and 199 interneurons from mouse neocortex. To evaluate the performance of different algorithms we used, as a "benchmark," the test to automatically distinguish between pyramidal cells and interneurons, defining "ground truth" by the presence or absence of an apical dendrite. We compared hierarchical clustering with a battery of different supervised classification algorithms, finding that supervised classifications outperformed hierarchical clustering. In addition, the selection of subsets of distinguishing features enhanced the classification accuracy for both sets of algorithms. The analysis of selected variables indicates that dendritic features were most useful to distinguish pyramidal cells from interneurons when compared with somatic and axonal morphological variables. We conclude that supervised classification algorithms are better matched to the general problem of distinguishing neuronal cell types when some information on these cell groups, in our case being pyramidal or interneuron, is known a priori. As a spin-off of this methodological study, we provide several methods to automatically distinguish neocortical pyramidal cells from interneurons, based on their morphologies.
在神经回路的研究中，辨别构建回路的不同神经元细胞类型变得至关重要。传统上，神经元细胞类型是使用定性描述符来分类的。最近，人们已经尝试使用无监督聚类方法对神经元进行定量分类。虽然这些算法很有用，但它们没有利用研究人员已知的先前信息，这可以提高分类任务的效率。对于新皮层 GABA 能中间神经元，辨别不同细胞类型的问题特别困难，需要更好的方法来进行客观分类。在这里，我们探索了使用监督分类算法根据神经元的形态特征对其进行分类的方法，使用了来自小鼠新皮层的 128 个锥体神经元和 199 个中间神经元的数据库。为了评估不同算法的性能，我们使用了自动区分锥体神经元和中间神经元的测试作为“基准”，通过是否存在顶树突来定义“真实情况”。我们将层次聚类与一系列不同的监督分类算法进行了比较，发现监督分类优于层次聚类。此外，选择区分特征子集可提高两种算法集的分类准确性。对选定变量的分析表明，与体部和轴突形态变量相比，树突特征最有助于将锥体神经元与中间神经元区分开来。我们得出的结论是，当预先知道有关这些细胞群的一些信息（在我们的案例中是锥体神经元或中间神经元）时，监督分类算法更适合于一般的神经元细胞类型辨别问题。作为这项方法学研究的一个副产品，我们提供了几种基于形态学自动区分新皮层锥体神经元和中间神经元的方法。
