An integrative approach for analyzing hundreds of neurons in task performing mice using wide-field calcium imaging
An integrative approach for analyzing hundreds of neurons in task performing mice using wide-field calcium imaging
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DOI：
10.1038/srep20986
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发表时间：
2016-02-08
影响因子：
4.6
通讯作者：
Han, Xue
中科院分区：
文献类型：
--
作者：
Mohammed, Ali I.;Gritton, Howard J.;Han, Xue
神经技术的进步已成为系统神经科学中神经回路功能研究不可或缺的一部分。高性能荧光传感器和科学 CMOS 相机的最新改进使得神经网络的光学成像规模更大。虽然令人兴奋的技术进步证明了该技术的潜力，但数据采集和分析的进一步改进，特别是那些允许有效处理越来越大的数据集的技术，将极大地促进光学成像在系统神经科学中的应用。在这里，我们展示了广域成像的能力，可以捕获行为小鼠脑组织上数百至数千个神经元的并发动态活动。该系统允许以比以前使用类似功能成像方式实现的更高的空间分辨率可视化形态细节。为了分析广泛的数据集，我们开发了软件来促进快速下游数据处理。使用该系统，我们表明大部分解剖学上不同的海马神经元对与经典条件反射相关的离散环境刺激做出反应，并且观察到的瞬态钙信号的时间动态足以探索大型神经网络的某些时空特征。
Advances in neurotechnology have been integral to the investigation of neural circuit function in systems neuroscience. Recent improvements in high performance fluorescent sensors and scientific CMOS cameras enables optical imaging of neural networks at a much larger scale. While exciting technical advances demonstrate the potential of this technique, further improvement in data acquisition and analysis, especially those that allow effective processing of increasingly larger datasets, would greatly promote the application of optical imaging in systems neuroscience. Here we demonstrate the ability of wide-field imaging to capture the concurrent dynamic activity from hundreds to thousands of neurons over millimeters of brain tissue in behaving mice. This system allows the visualization of morphological details at a higher spatial resolution than has been previously achieved using similar functional imaging modalities. To analyze the expansive data sets, we developed software to facilitate rapid downstream data processing. Using this system, we show that a large fraction of anatomically distinct hippocampal neurons respond to discrete environmental stimuli associated with classical conditioning, and that the observed temporal dynamics of transient calcium signals are sufficient for exploring certain spatiotemporal features of large neural networks.