Background

This article addresses the automatic classification of reconstructed neurons through their morphological features. The purpose was to extend the capabilities of the L-Measure software.

Methods

New morphological features were developed, based on modifications of the conventional Sholl analysis. The lengths of the compartments, as well as their volumes, were added to the features used in the classical analysis in order to improve the results during automatic neuron classification. FSM were used to obtain subsets of lower cardinality from the full feature sets and the usefulness of these subsets was tested through their use in supervised classification tasks. The study was based on two types of neurons belonging to mice: pyramidal and GABAergic interneurons. Furthermore, a set of pyramidal neurons belonging to Later 4 and Layer 5 was analyzed.

Results

RF classifier shown the best performance combined with a Wrapper method.U-WNAD set allowed to obtain higher values than WN, A and D in all cases and better results than LM for the filters and wrappers FSM. U-LM-WNAD set, led to the highest AUC values for all the FSM studied. Similar results for different regions of cortex were obtained. Comparison with Existing Methods The new features exhibited high discriminatory power with which the values of AUC and Acc obtained in the experiments exceeded those obtained using only the features provided by L-Measure.

Conclusions

The highest values of AUC and Acc were obtained from the sets U-WNAD and U-LM-WNAD, evidencing the discriminatory power of the new proposed features.

中文翻译：

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基于Sholl分析的新形态特征，可对跟踪的神经元进行自动分类。

背景

本文介绍了通过形态特征对神经元进行自动分类的方法。目的是扩展L-Measure软件的功能。

方法

基于常规Sholl分析的改进，开发了新的形态特征。为了在自动神经元分类过程中改善结果，将间隔的长度及其体积添加到经典分析中使用的功能中。使用FSM从完整功能集中获取基数较低的子集，并通过在监督分类任务中使用这些子集来测试这些子集的有效性。该研究基于两种属于小鼠的神经元：锥体神经元和GABA能神经元。此外，分析了一组属于后来4和第5层的锥体神经元。

结果

射频分类器结合Wrapper方法表现出最佳性能。在所有情况下，U-WNAD设置均可以获得比WN，A和D高的值，并且对于滤波器和包装FSM而言，其结果优于LM。U-LM-WNAD设置导致所有研究的FSM的最高AUC值。对于皮质的不同区域获得了相似的结果。与现有方法的比较这些新功能具有很高的区分能力，在实验中获得的AUC和Acc值超过了仅使用L-Measure提供的功能获得的值。

结论

从集合U-WNAD和U-LM-WNAD中获得了AUC和Acc的最大值，这证明了新提议功能的区分能力。