Technologies for imaging neural activity in large volumes.
Technologies for imaging neural activity in large volumes.
复制标题
大量成像神经活动的技术。
DOI：
10.1038/nn.4358
复制
发表时间：
2016-08-26
影响因子：
25
通讯作者：
Smith SL
中科院分区：
文献类型：
--
作者：
Ji N;Freeman J;Smith SL
神经电路已经发展成为分布式网络，可以在大容量上动态运行。从各个平面收集数据时，传统显微镜无法以与神经回路功能和行为相关的时间分辨率对大体积的电路进行采样。在这里，我们回顾了神经回路快速体积成像的新兴技术。我们专注于两个关键挑战：光学系统的惯性（限制图像速度）和像差（限制图像体积）。光学采样时间必须足够长才能确保高保真测量，但优化的采样策略和点扩散函数工程可以促进在此限制内的神经活动的快速体积成像。我们还讨论了处理和分析规模和复杂性不断增加的体积成像数据的新计算策略。光学和计算的进步共同为神经回路动力学提供了更广阔的视野，并有助于阐明大脑区域如何协同工作以支持行为。
Neural circuitry has evolved to form distributed networks that act dynamically across large volumes. Collecting data from individual planes, conventional microscopy cannot sample circuitry across large volumes at the temporal resolution relevant to neural circuit function and behaviors. Here, we review emerging technologies for rapid volume imaging of neural circuitry. We focus on two critical challenges: the inertia of optical systems, which limits image speed, and aberrations, which restrict the image volume. Optical sampling time must be long enough to ensure high-fidelity measurements, but optimized sampling strategies and point spread function engineering can facilitate rapid volume imaging of neural activity within this constraint. We also discuss new computational strategies for the processing and analysis of volume imaging data of increasing size and complexity. Together, optical and computational advances are providing a broader view of neural circuit dynamics, and help elucidate how brain regions work in concert to support behavior.