Ji Na, Freeman Jeremy, Smith Spencer L
Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA.
Department of Cell Biology and Physiology, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA.
Nat Neurosci. 2016 Aug 26;19(9):1154-64. doi: 10.1038/nn.4358.
Neural circuitry has evolved to form distributed networks that act dynamically across large volumes. Conventional microscopy collects data from individual planes and 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 processing and analyzing volume imaging data of increasing size and complexity. Together, optical and computational advances are providing a broader view of neural circuit dynamics and helping elucidate how brain regions work in concert to support behavior.
神经回路已经进化形成了分布式网络,这些网络在大量区域动态运作。传统显微镜从单个平面收集数据,无法以与神经回路功能和行为相关的时间分辨率对大量区域的回路进行采样。在此,我们综述用于神经回路快速体成像的新兴技术。我们关注两个关键挑战:限制图像速度的光学系统惯性,以及限制图像范围的像差。光学采样时间必须足够长以确保高保真测量,但优化的采样策略和点扩散函数工程可以在这一限制内促进神经活动的快速体成像。我们还讨论了用于处理和分析尺寸和复杂度不断增加的体成像数据的新计算策略。光学和计算方面的进展共同为神经回路动力学提供了更广阔的视角,并有助于阐明脑区如何协同工作以支持行为。