Mihaljević Bojan, Benavides-Piccione Ruth, Bielza Concha, DeFelipe Javier, Larrañaga Pedro
Computational Intelligence Group, Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Boadilla del Monte, Madrid, 28660, Spain,
Neuroinformatics. 2015 Apr;13(2):193-208. doi: 10.1007/s12021-014-9254-1.
An accepted classification of GABAergic interneurons of the cerebral cortex is a major goal in neuroscience. A recently proposed taxonomy based on patterns of axonal arborization promises to be a pragmatic method for achieving this goal. It involves characterizing interneurons according to five axonal arborization features, called F1-F5, and classifying them into a set of predefined types, most of which are established in the literature. Unfortunately, there is little consensus among expert neuroscientists regarding the morphological definitions of some of the proposed types. While supervised classifiers were able to categorize the interneurons in accordance with experts' assignments, their accuracy was limited because they were trained with disputed labels. Thus, here we automatically classify interneuron subsets with different label reliability thresholds (i.e., such that every cell's label is backed by at least a certain (threshold) number of experts). We quantify the cells with parameters of axonal and dendritic morphologies and, in order to predict the type, also with axonal features F1-F4 provided by the experts. Using Bayesian network classifiers, we accurately characterize and classify the interneurons and identify useful predictor variables. In particular, we discriminate among reliable examples of common basket, horse-tail, large basket, and Martinotti cells with up to 89.52% accuracy, and single out the number of branches at 180 μm from the soma, the convex hull 2D area, and the axonal features F1-F4 as especially useful predictors for distinguishing among these types. These results open up new possibilities for an objective and pragmatic classification of interneurons.
对大脑皮质中γ-氨基丁酸能中间神经元进行公认的分类是神经科学的一个主要目标。最近提出的一种基于轴突分支模式的分类法有望成为实现这一目标的实用方法。它涉及根据五个称为F1 - F5的轴突分支特征对中间神经元进行表征，并将它们分类为一组预定义的类型，其中大多数类型在文献中已有记载。不幸的是，神经科学专家对于一些提议类型的形态学定义几乎没有达成共识。虽然监督分类器能够根据专家的分类对中间神经元进行分类，但其准确性有限，因为它们是用有争议的标签进行训练的。因此，在这里我们使用不同的标签可靠性阈值（即每个细胞的标签至少有一定数量（阈值）的专家支持）对中间神经元子集进行自动分类。我们用轴突和树突形态参数对细胞进行量化，并且为了预测类型，还使用专家提供的轴突特征F1 - F4。使用贝叶斯网络分类器，我们准确地表征和分类了中间神经元，并识别出有用的预测变量。特别是，我们以高达89.52%的准确率区分了常见篮状细胞、马尾细胞、大篮状细胞和马丁诺蒂细胞的可靠示例，并指出距胞体180μm处的分支数量、凸包二维面积以及轴突特征F1 - F4是区分这些类型特别有用的预测指标。这些结果为中间神经元的客观实用分类开辟了新的可能性。
