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Optical volumetric brain imaging: speed, depth, and resolution enhancement
Journal of Physics D: Applied Physics ( IF 3.1 ) Pub Date : 2021-06-02 , DOI: 10.1088/1361-6463/abff7b
Shih-Huan Huang , Ninik Irawati , Yu-Feng Chien , Jyun-Yi Lin , Yu-Hsuan Tsai , Po-Yuan Wang , Li-An Chu , Meng-Lin Li , Ann-Shyn Chiang , Kevin Tsia , Shi-Wei Chu

Understanding how the brain functions is one of the grand challenges in modern scientific research. Similar to a computer, a functional brain is composed of hardware and software. The major bottleneck lies in the difficulty to directly observe the brain ‘software’, i.e. the rule and operating information used by the brain that might emerge from pan-neuron/synapse connectome. A recognized strategy for probing the functional connectome is to perform volumetric imaging in brains with high spatiotemporal resolution and deep brain penetration. Among various imaging technologies, optical imaging offers appealing combinations including spatial resolution of sub-micrometer to nanometer, temporal resolution of second to millisecond, penetration depth of millimeter or deeper, and molecular contrast based on the abundant choices of fluorescent indicators. Thus, it is ideal for enabling three-dimensional functional brain mapping of small animal models. In this review, we focus on recent technological advances in optical volumetric imaging, with an emphasis on the tools and methods for enhancing imaging speed, depth, and resolution. The review could serve as a quantitative reference for physicists and biologists to choose the techniques better suited for specific applications, as well as to stimulate novel technical developments to advance brain research.



中文翻译:

光学体积脑成像:速度、深度和分辨率增强

了解大脑如何运作是现代科学研究的重大挑战之一。与计算机类似,功能大脑由硬件和软件组成。主要瓶颈在于难以直接观察大脑“软件”,即可能从泛神经元/突触连接组中出现的大脑使用的规则和操作信息。探测功能性连接组的公认策略是在具有高时空分辨率和大脑深度穿透的大脑中进行体积成像。在各种成像技术中,光学成像提供了有吸引力的组合,包括亚微米到纳米的空间分辨率、秒到毫秒的时间分辨率、毫米或更深的穿透深度以及基于荧光指示剂的丰富选择的分子对比度。因此,它是实现小动物模型的三维功能性大脑映射的理想选择。在这篇综述中,我们关注光学体积成像的最新技术进步,重点是提高成像速度、深度和分辨率的工具和方法。该评论可以作为物理学家和生物学家的定量参考,以选择更适合特定应用的技术,以及刺激新技术发展以推进大脑研究。

更新日期:2021-06-02
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