Electrophysiological Profiling of Neocortical Neural Subtypes: A Semi-Supervised Method Applied to in vivo Whole-Cell Patch-Clamp Data
Electrophysiological Profiling of Neocortical Neural Subtypes: A Semi-Supervised Method Applied to in vivo Whole-Cell Patch-Clamp Data
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DOI：
10.3389/fnins.2018.00823
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发表时间：
2018-11-13
影响因子：
4.3
通讯作者：
Safari, Mir-Shahram
中科院分区：
文献类型：
--
作者：
Ghaderi, Parviz;Marateb, Hamid Reza;Safari, Mir-Shahram
人们已经做了很多努力来了解新皮层回路的结构和功能。事实上，了解皮层回路功能的一个有希望的方法是根据它们不同的特性对神经类型进行分类。最近的研究集中在应用现代计算方法来分类神经元的基础上分子，形态，生理，或这些标准的混合。虽然在文献中有研究在体外/体内细胞外或在体外细胞内记录，在体内全细胞膜片钳记录的神经元类型的分类研究仍然缺乏。因此，我们提出了一种新的半监督分类方法的基础上的波形形状的神经元的棘波在体内全细胞膜片钳记录。我们，首先，检测到了尖峰候选者。然后利用离散余弦变换从棘波的时间样本中提取鉴别特征。然后利用模糊c均值聚类提取聚类中心，最后利用最小距离分类器对神经元进行分类。在小鼠初级视皮层的第II/III层，我们发现了三种类型的神经元：兴奋性锥体细胞（Pyr）和两种类型的抑制性神经元：GABA能-小清蛋白阳性（PV）和生长抑素阳性（SST）非锥体细胞。我们在研究中使用了10倍交叉验证。PV、Pyr和SST的分类准确度分别为91.59 +/- 1.69、97.47 +/- 0.67和89.06 +/- 1.99。总体而言，该算法正确分类了92.67 +/- 0.54%的细胞，证实了判别函数的相对稳健性。通过使用来自艾伦研究所细胞类型数据库的50个神经元的库（5种主要的神经元亚型：Pyr、PV、SST、5 HT 3a和血管活性肠肽（VIP）细胞）在体外记录上进一步评估该方法的性能。使用交叉验证框架，其在该数据集上的总体准确度为84.13 +/- 0.81%。因此，该算法是一个很有前途的新工具，在识别细胞的类型与高精度在实验室使用在体内/体外全细胞膜片钳记录技术。开发的程序和整个数据集可在线提供给感兴趣的读者。
A lot of efforts have been made to understand the structure and function of neocortical circuits. In fact, a promising way to understand the functions of cortical circuits is the classification of the neural types, based on their different properties. Recent studies focused on applying modern computational methods to classify neurons based on molecular, morphological, physiological, or mixed of these criteria. Although there are studies in the literature on in vitro/vivo extracellular or in vitro intracellular recordings, a study on the classification of neuronal types using in vivo whole-cell patch-clamp recordings is still lacking. We thus proposed a novel semi-supervised classification method based on waveform shape of neurons' spikes using in vivo whole-cell patch-clamp recordings. We, first, detected spike candidates. Then discriminative features were extracted from the time samples of the spikes using discrete cosine transform. We then extracted the center of clusters using fuzzy c-mean clustering and finally, the neurons were classified using the minimum distance classifier. We distinguished three types of neurons: excitatory pyramidal cells (Pyr) and two types of inhibitory neurons: GABAergic-parvalbumin positive (PV), and somatostatin positive (SST) non-pyramidal cells in layer II/III of the mice primary visual cortex. We used 10-fold cross validation in our study. The classification accuracy for PV, Pyr, and SST was 91.59 +/- 1.69, 97.47 +/- 0.67, and 89.06 +/- 1.99, respectively. Overall, the algorithm correctly classified 92.67 +/- 0.54% of the cells, confirming the relative robustness of the discriminant functions. The performance of the method was further assessed on in vitro recordings by using a pool of 50 neurons from Allen institute Cell Types Database (5 major subtypes of neurons: Pyr, PV, SST, 5HT3a, and vasoactive intestinal peptide (VIP) cells). Its overall accuracy was 84.13 +/- 0.81% on this data set using cross validation framework. The proposed algorithm is thus a promising new tool in recognizing cell's type with high accuracy in laboratories using in vivo/vitro whole-cell patch-clamp recording technique. The developed programs and the entire dataset are available online to interested readers.