Non-linear dimensionality reduction on extracellular waveforms reveals cell type diversity in premotor cortex.
Non-linear dimensionality reduction on extracellular waveforms reveals cell type diversity in premotor cortex.
复制标题
细胞外波形的非线性降维揭示了前运动皮层的细胞类型多样性。
DOI：
10.7554/elife.67490
复制
发表时间：
2021-08-06
期刊：
影响因子：
7.7
通讯作者：
Chandrasekaran C
中科院分区：
文献类型：
--
作者：
Lee EK;Balasubramanian H;Tsolias A;Anakwe SU;Medalla M;Shenoy KV;Chandrasekaran C
皮质回路被认为包含大量协调产生行为的细胞类型。目前的体内方法依赖于细胞外波形的特定特征的聚类来识别推定的细胞类型，但这些仅捕获少量的变化。在这里，我们开发了一种新的方法（WaveMAP），结合非线性降维与图聚类来识别推定的细胞类型。我们将WaveMAP应用于猕猴背侧运动前区皮层记录的细胞外波形，以执行决策任务。使用WaveMAP，我们稳健地建立了8个波形簇，并表明这些簇概括了先前确定的窄峰和宽峰类型，同时揭示了这些亚型中先前未知的多样性。这八个集群表现出不同的层流分布，特征射击率模式，和决策相关的动态。当使用基于特征的方法时，这种洞察力就比较弱了。因此，WaveMAP提供了对皮层回路中细胞类型动态的更细致的理解。
Cortical circuits are thought to contain a large number of cell types that coordinate to produce behavior. Current in vivo methods rely on clustering of specified features of extracellular waveforms to identify putative cell types, but these capture only a small amount of variation. Here, we develop a new method (WaveMAP) that combines non-linear dimensionality reduction with graph clustering to identify putative cell types. We apply WaveMAP to extracellular waveforms recorded from dorsal premotor cortex of macaque monkeys performing a decision-making task. Using WaveMAP, we robustly establish eight waveform clusters and show that these clusters recapitulate previously identified narrow- and broad-spiking types while revealing previously unknown diversity within these subtypes. The eight clusters exhibited distinct laminar distributions, characteristic firing rate patterns, and decision-related dynamics. Such insights were weaker when using feature-based approaches. WaveMAP therefore provides a more nuanced understanding of the dynamics of cell types in cortical circuits.