计算机科学
神经影像学
人工智能
模式识别(心理学)
计算机视觉
可视化
神经科学
生物
作者
Yuwen Chen,Haoyu Yang,Yan Luo,Yijun Niu,Myeong Sang Yu,Shanjun Deng,Xuanhao Wang,Handi Deng,Haichao Chen,Lixia Gao,Xinjian Li,Pingyong Xu,Fudong Xue,Jing Miao,Song‐Hai Shi,Yi Zhong,Cheng Ma,Bo Lei
标识
DOI:10.1038/s41467-024-48393-z
摘要
Cross-modal analysis of the same whole brain is an ideal strategy to uncover brain function and dysfunction. However, it remains challenging due to the slow speed and destructiveness of traditional whole-brain optical imaging techniques. Here we develop a new platform, termed Photoacoustic Tomography with Temporal Encoding Reconstruction (PATTERN), for non-destructive, high-speed, 3D imaging of ex vivo rodent, ferret, and non-human primate brains. Using an optimally designed image acquisition scheme and an accompanying machine-learning algorithm, PATTERN extracts signals of genetically-encoded probes from photobleaching-based temporal modulation and enables reliable visualization of neural projection in the whole central nervous system with 3D isotropic resolution. Without structural and biological perturbation to the sample, PATTERN can be combined with other whole-brain imaging modalities to acquire the whole-brain image with both high resolution and morphological fidelity. Furthermore, cross-modal transcriptome analysis of an individual brain is achieved by PATTERN imaging. Together, PATTERN provides a compatible and versatile strategy for brain-wide cross-modal analysis at the individual level.
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