A Hierarchical Structure of Cortical Interneuron Electrical Diversity Revealed by Automated Statistical Analysis
A Hierarchical Structure of Cortical Interneuron Electrical Diversity Revealed by Automated Statistical Analysis
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DOI：
10.1093/cercor/bhs290
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发表时间：
2013-12-01
期刊：
影响因子：
3.7
通讯作者：
Segev, Idan
中科院分区：
文献类型：
--
作者：
Druckmann, Shaul;Hill, Sean;Segev, Idan
尽管皮层中间神经元电特性的多样性已经得到了很好的认识，但不同电类型（e型）的数量仍然是一个有争议的问题。最近，多个实验室在Petilla convention （Petilla interneuron Nomenclature Group， PING）提出的主观分类方案的基础上，对中间神经元变异的描述进行了标准化。在这里，我们提出了一个定量的，统计分析数据库的近500个神经元手动注释根据PING命名法。对于每个细胞，从阈上电流刺激的反应中提取38个特征，并进行统计分析，以检查皮质中间神经元是否细分为e型。我们表明，划分为不同的e类型确实是数据可变性的主要组成部分。分析建议将PING e类型分类细化为分层的，这样首先在一个粗的子分区中捕获大多数可变性，然后将其划分为更细的子分区。这种粗糙的划分与众所周知的中间神经元向快速尖峰和适应性细胞的划分相匹配。更细的子分区匹配突发、连续和延迟子类型。此外，我们的分析能够根据它们区分电子类型的能力对特征进行排名。我们表明，我们的定量e型分配的准确率超过90，并设法捕获了几个人为错误。
Although the diversity of cortical interneuron electrical properties is well recognized, the number of distinct electrical types (e-types) is still a matter of debate. Recently, descriptions of interneuron variability were standardized by multiple laboratories on the basis of a subjective classification scheme as set out by the Petilla convention (Petilla Interneuron Nomenclature Group, PING). Here, we present a quantitative, statistical analysis of a database of nearly five hundred neurons manually annotated according to the PING nomenclature. For each cell, 38 features were extracted from responses to suprathreshold current stimuli and statistically analyzed to examine whether cortical interneurons subdivide into e-types. We showed that the partitioning into different e-types is indeed the major component of data variability. The analysis suggests refining the PING e-type classification to be hierarchical, whereby most variability is first captured within a coarse subpartition, and then subsequently divided into finer subpartitions. The coarse partition matches the well-known partitioning of interneurons into fast spiking and adapting cells. Finer subpartitions match the burst, continuous, and delayed subtypes. Additionally, our analysis enabled the ranking of features according to their ability to differentiate among e-types. We showed that our quantitative e-type assignment is more than 90 accurate and manages to catch several human errors.