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首页> 外文期刊>Journal of Neuroscience Methods >Computational processing of optical measurements of neuronal and synaptic activity in networks.
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Computational processing of optical measurements of neuronal and synaptic activity in networks.

机译：网络中神经元和突触活动的光学测量的计算处理。

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Imaging of optical reporters of neural activity across large populations of neurones is a widely used approach for investigating the function of neural circuits in slices and in vivo. Major challenges in analysing such experiments include the automatic identification of neurones and synapses, extraction of dynamic signals, and assessing the temporal and spatial relationships between active units in relation to the gross structure of the circuit. We have developed an integrated set of software tools, named SARFIA, by which these aspects of dynamic imaging experiments can be analysed semi-automatically. Key features are image-based detection of structures of interest using the Laplace operator, determining the positions of units in a layered network, clustering algorithms to classify units with similar functional responses, and a database to store, exchange and analyse results across experiments. We demonstrate the use of these tools to analyse synaptic activity in the retina of live zebrafish by multi-photon imaging of SyGCaMP2, a genetically encoded synaptically localised calcium reporter. By simultaneously recording activity across tens of bipolar cell terminals distributed throughout the IPL we made a functional map of the ON and OFF signalling channels and found that these were only partially separated. The automated detection of signals across many neurones in the retina allowed the reliable detection of small populations of neurones generating "ectopic" signals in the "ON" and "OFF" sublaminae. This software should be generally applicable for the analysis of dynamic imaging experiments across hundreds of responding units.
机译：跨大量神经元的神经活动的光学报道分子的成像是一种广泛用于研究切片和体内神经回路功能的方法。分析此类实验的主要挑战包括神经元和突触的自动识别，动态信号的提取以及评估有源单元之间相对于电路总结构的时间和空间关系。我们已经开发了一套集成的软件工具，称为SARFIA，可以通过这些工具半自动地分析动态成像实验的这些方面。关键功能包括使用Laplace算子对感兴趣的结构进行基于图像的检测，确定分层网络中单元的位置，用于对具有相似功能响应的单元进行分类的聚类算法以及用于存储，交换和分析实验结果的数据库。我们演示了使用这些工具通过多光子成像的SyGCaMP2，遗传编码的突触定位的钙记者的多光子成像来分析活斑马鱼的视网膜中的突触活动。通过同时记录分布在整个IPL上的数十个双极小区终端的活动，我们绘制了ON和OFF信号通道的功能图，发现这些信号通道仅部分分离。跨视网膜中许多神经元的信号的自动检测可以可靠地检测少量神经元，在“ ON”和“ OFF”子层中产生“异位”信号。该软件通常应适用于数百个响应单元之间的动态成像实验分析。

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