Name
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.
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Year | DOI | Venue |
|---|---|---|
2022 | 10.1186/s40708-022-00158-4 | Brain Informatics |
Keywords | DocType | Volume |
Smart imaging, Neuroscience, Artificial intelligence, Brain-wide, Single-cell | Journal | 9 |
Issue | ISSN | Citations |
1 | 2198-4018 | 0 |
PageRank | References | Authors |
0.34 | 41 | 10 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuxia Guo | 1 | 0 | 0.34 |
| Jie Xue | 2 | 0 | 0.34 |
| Jian Liu | 3 | 0 | 0.34 |
| Xiangqiao Ye | 4 | 0 | 0.34 |
| Yichen Guo | 5 | 0 | 0.34 |
| Di Liu | 6 | 0 | 0.34 |
| Xuan Zhao | 7 | 0 | 0.34 |
| Feng Xiong | 8 | 0 | 0.34 |
| Xiaofeng Han | 9 | 0 | 0.34 |
| Hanchuan Peng | 10 | 3930 | 182.27 |