Identification and clustering of event patterns from in vivo multiphoton optical recordings of neuronal ensembles
Identification and clustering of event patterns from in vivo multiphoton optical recordings of neuronal ensembles
复制标题
DOI：
10.1152/jn.01310.2007
复制
发表时间：
2008-07-01
影响因子：
2.5
通讯作者：
Wang, Samuel S. -H.
中科院分区：
文献类型：
--
作者：
Ozden, Ilker;Lee, H. Megan;Wang, Samuel S. -H.
体内多光子荧光显微镜可以对脑组织中的细胞结构进行数百微米深度的成像，并且当与活性依赖性指示剂染料的使用相结合时，开启了观察完整的、功能正常的神经回路的可能性。我们开发了用于分析体内多光子数据集的工具，以识别单细胞中的响应结构和事件以及神经系统内的活动模式。对小脑浦肯野细胞树突群的数据进行了分析，这些树突产生基于钙的复合动作电位。对于图像分割，使用基于相关性的方法对共变像素进行分组来识别活动树突。从树突荧光信号中提取放电事件，检测率为 95%，假阳性率为 8%。由于有时直到下一帧才检测到在一个电影帧中开始的事件，因此使用基于可能性的校正过程来补偿检测延迟。为了识别倾向于同步放电的树突群，开发了基于 k 均值的程序来分析群体中的成对相关性。由于重复运行 k 均值通常会生成不同的聚类，因此将这些运行组合起来以确定一致的聚类数量和组成。这个过程被称为元 k 均值，它给出的聚类效果与 k 均值的单独运行一样好，独立于随机初始种子，并且允许排除异常值。我们的方法通常可用于分析各种大脑结构中的多细胞活动记录。
In vivo multiphoton fluorescence microscopy allows imaging of cellular structures in brain tissue to depths of hundreds of micrometers and, when combined with the use of activity-dependent indicator dyes, opens the possibility of observing intact, functioning neural circuitry. We have developed tools for analyzing in vivo multiphoton data sets to identify responding structures and events in single cells as well as patterns of activity within the neural ensemble. Data were analyzed from populations of cerebellar Purkinje cell dendrites, which generate calcium-based complex action potentials. For image segmentation, active dendrites were identified using a correlation-based method to group covarying pixels. Firing events were extracted from dendritic fluorescence signals with a 95% detection rate and an 8% false-positive rate. Because an event that begins in one movie frame is sometimes not detected until the next frame, detection delays were compensated using a likelihood-based correction procedure. To identify groups of dendrites that tended to fire synchronously, a k-means-based procedure was developed to analyze pairwise correlations across the population. Because repeated runs of k-means often generated dissimilar clusterings, the runs were combined to determine a consensus cluster number and composition. This procedure, termed meta-k-means, gave clusterings as good as individual runs of k-means, was independent of random initial seeding, and allowed the exclusion of outliers. Our methods should be generally useful for analyzing multicellular activity recordings in a variety of brain structures.