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Review
. 2022 May 11;9(1):10.
doi: 10.1186/s40708-022-00158-4.

Smart imaging to empower brain-wide neuroscience at single-cell levels

Shuxia Guo #  1 , Jie Xue #  2 , Jian Liu  2 , Xiangqiao Ye  2 , Yichen Guo  2 , Di Liu  2 , Xuan Zhao  2 , Feng Xiong  2 , Xiaofeng Han  3 , Hanchuan Peng  2
Affiliations
Review

Smart imaging to empower brain-wide neuroscience at single-cell levels

Shuxia Guo et al. Brain Inform. .

Abstract

A deep understanding of the neuronal connectivity and networks with detailed cell typing across brain regions is necessary to unravel the mechanisms behind the emotional and memorial functions as well as to find the treatment of brain impairment. Brain-wide imaging with single-cell resolution provides unique advantages to access morphological features of a neuron and to investigate the connectivity of neuron networks, which has led to exciting discoveries over the past years based on animal models, such as rodents. Nonetheless, high-throughput systems are in urgent demand to support studies of neural morphologies at larger scale and more detailed level, as well as to enable research on non-human primates (NHP) and human brains. The advances in artificial intelligence (AI) and computational resources bring great opportunity to 'smart' imaging systems, i.e., to automate, speed up, optimize and upgrade the imaging systems with AI and computational strategies. In this light, we review the important computational techniques that can support smart systems in brain-wide imaging at single-cell resolution.

Keywords: Artificial intelligence; Brain-wide; Neuroscience; Single-cell; Smart imaging.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Key components to build up a smart imaging system in brain-wide neuroscience at single-cell level
Fig. 2
Fig. 2
Different imaging modalities with respective to the spatial resolution
Fig. 3
Fig. 3
Principles of different data reformatting. BigDataViewer: the green blocks in the original space represent the data to be loaded into memory. One slice is read into memory once and cached. TDat: after recursively down-sampling the original data, only a CUBOID is read into memory and split into 3D blocks. Vaa3D-TeraFly: the data is read once and transformed in to multi-resolution (adapted from Ref. [110])
Fig. 4
Fig. 4
Building blocks of a CNNs, b ResNet, and c LSTM
See this image and copyright information in PMC

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