Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil.
Department of Computer Science, Federal University of São Carlos, São Carlos, Brazil.
Neuroinformatics. 2019 Jan;17(1):147-161. doi: 10.1007/s12021-018-9388-7.
The shape of a neuron can reveal interesting properties about its function. Therefore, morphological neuron characterization can contribute to a better understanding of how the brain works. However, one of the great challenges of neuroanatomy is the definition of morphological properties that can be used for categorizing neurons. This paper proposes a new methodology for neuron morphological analysis by considering different hierarchies of the dendritic tree for characterizing and categorizing neuronal cells. The methodology consists in using different strategies for decomposing the dendritic tree along its hierarchies, allowing the identification of relevant parts (possibly related to specific neuronal functions) for classification tasks. A set of more than 5000 neurons corresponding to 10 classes were examined with supervised classification algorithms based on this strategy. It was found that classification accuracies similar to those obtained by using whole neurons can be achieved by considering only parts of the neurons. Branches close to the soma were found to be particularly relevant for classification.
神经元的形状可以揭示其功能的有趣性质。因此，形态神经元特征可以有助于更好地理解大脑的工作原理。然而，神经解剖学的一个巨大挑战是定义可用于对神经元进行分类的形态属性。本文提出了一种新的神经元形态分析方法，通过考虑树突的不同层次来对神经元细胞进行特征描述和分类。该方法包括使用不同的策略沿着树突的层次结构对其进行分解，从而可以识别出对分类任务具有重要意义的部分（可能与特定神经元功能相关）。基于这一策略，使用基于监督的分类算法对超过 5000 个神经元（对应 10 个类别）进行了检查。结果发现，仅考虑神经元的一部分就可以达到与使用整个神经元类似的分类精度。研究发现，靠近胞体的分支对分类特别重要。
