...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Neuroscience Methods >Clustering of large cell populations: Method and application to the basal forebrain cholinergic system
【24h】

Clustering of large cell populations: Method and application to the basal forebrain cholinergic system

机译：大细胞群体的聚类：对基底前脑胆能系统的方法和应用

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Functionally related groups of neurons spatially cluster together in the brain. To detect groups of functionally related neurons from 3D histological data, we developed an objective clustering method that provides a description of detected cell clusters that is quantitative and amenable to visual exploration. This method is based on bubble clustering (Gupta and Ghosh, 2008). Our implementation consists of three steps: (i) an initial data exploration for scanning the clustering parameter space; (ii) determination of the optimal clustering parameters; and (iii) final clustering. We designed this algorithm to flexibly detect clusters without assumptions about the underlying cell distribution within a cluster or the number and sizes of clusters. We implemented the clustering function as an integral part of the neuroanatomical data visualization software Virtual RatBrain (http://www.virtualratbrain.org). We applied this algorithm to the basal forebrain cholinergic system, which consists of a diffuse but inhomogeneous population of neurons (Zaborszky, 1992). With this clustering method, we confirmed the inhomogeneity in this system, defined cell clusters, quantified and localized them, and determined the cell density within clusters. Furthermore, by applying the clustering method to multiple specimens from both rat and monkey, we found that cholinergic clusters display remarkable cross-species preservation of cell density within clusters. This method is efficient not only for clustering cell body distributions but may also be used to study other distributed neuronal structural elements, including synapses, receptors, dendritic spines and molecular markers. (C) 2010 Elsevier B.V. All rights reserved.
机译：功能相关的神经元的神经元群在大脑中聚集在一起。为了从3D组织学数据中检测功能相关的神经元的组，我们开发了一种客观聚类方法，该方法提供了对检测到的细胞簇的描述，其是定量的和可视探索的。该方法基于泡沫聚类（Gupta和Ghosh，2008）。我们的实现包括三个步骤：（i）扫描聚类参数空间的初始数据探索; （ii）确定最佳聚类参数; （iii）最终聚类。我们设计了该算法，可以灵活地检测群集，而不会有关集群内的底层小区分布的假设或集群的数量和大小。我们实现了聚类功能作为神经解析数据可视化软件虚拟鼠标虚拟大鼠（http://www.virtualratbrain.org）的组成部分。我们将该算法应用于基础前脑胆能系统，其包括弥漫性但不均匀的神经元（Zaborszky，1992）。利用这种聚类方法，我们确认了该系统中的不均匀性，定义了细胞簇，量化和局部化，并确定了簇内的细胞密度。此外，通过将聚类方法应用于来自大鼠和猴子的多个标本，发现胆碱能簇在簇中显示出显着的跨物种保护细胞密度。该方法不仅有效地用于聚类细胞体分布，而且还可用于研究其他分布的神经元结构元件，包括突触，受体，树突刺和分子标记。 （c）2010年elsevier b.v.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号