**Abstract:** A groundbreaking collaboration between the Department of Electronic Engineering at Tsinghua University, the Beijing Academy of Artificial Intelligence, and the IDG/McGovern Institute for Brain Research at Tsinghua University has resulted in the development of a novel full-brain 3D imaging platform called PATTERN (Photoacoustic Tomography with Temporal Encoding Reconstruction). This innovative technology is designed to address the limitations of existing full-brain 3D fluorescence imaging techniques, such as fluorescence micro-optical sectioning tomography (fMOST) and light-sheet fluorescence microscopy (LSFM), particularly in large-scale sample imaging, high-throughput 3D brain imaging, and integrating multi-omics analysis. PATTERN leverages photoacoustic tomography to achieve large field-of-view, rapid, and highly sensitive full-brain fluorescence imaging while preserving the original physical and chemical properties and biological activity of the samples. The platform acts as a highly compatible bridge for cross-modal full-brain 3D analysis, enabling integration with functional magnetic resonance imaging (fMRI), high-precision full-brain fluorescence imaging, and spatial transcriptomics. This integration facilitates personalized, multi-modal data consolidation and joint analysis, offering a robust strategy for other brain analysis techniques. Key innovations of PATTERN include the use of the temporal characteristics of photoacoustic signal bleaching for high-sensitivity recognition of fluorescent proteins, a multi-view fusion imaging approach to achieve isotropic 3D resolution, and the application of neural networks to remove artifacts and enhance signal reliability. PATTERN allows for the visualization of fluorescence expression patterns and neural projection structures across the entire brain without causing damage to the samples, making it particularly suitable for rapid and comprehensive imaging. The researchers demonstrated the effectiveness of PATTERN by imaging brain samples from various animals, including mice, rats, ferrets, and marmosets, and conducting quantitative morphological analysis of brain regions. Notably, PATTERN's large field-of-view capability enabled direct imaging of the entire central nervous system of a mouse, completing the imaging process within 30 minutes. This revealed detailed structural information and fluorescence signal distribution, such as the bidirectional projections from the motor cortex to the spinal cord, which were verified using conventional optical microscopy on sliced samples. Moreover, PATTERN's ability to retain the biological and physiological characteristics of the samples after imaging allows for subsequent physiological and biochemical analyses. The researchers showcased a combined PATTERN imaging and spatial transcriptomics analysis, where they used an adeno-associated virus (AAV) vector to knock down the expression of the early immediate gene c-fos in specific neurons of the mouse hippocampus. By integrating the 3D fluorescence data from PATTERN with the spatial transcriptomics data, they could visualize and analyze the gene expression levels in manipulated and non-manipulated regions before and after learning, providing insights into the impact of spatial distance on memory-related gene expression in different hippocampal subregions. The versatility of PATTERN extends to exploring the relationship between specific molecules and gene expression patterns in neurons at different locations within the brain. This capability offers a new perspective for understanding brain function and disease at multiple scales and in a personalized manner. The research findings were published in the journal *Nature Communications* on May 18, 2024, under the title "Photoacoustic Tomography with Temporal Encoding Reconstruction (PATTERN) for Cross-Modal Individual Analysis of the Whole Brain." The paper's co-first authors are Dr. Yiwén Chen and Yan Luo from the Department of Electronic Engineering at Tsinghua University, and Hao-Yu Yang and Yijun Niu from the School of Life Sciences and the IDG/McGovern Institute for Brain Research at Tsinghua University. The co-corresponding authors are Associate Professor Cheng Ma from the Department of Electronic Engineering and Dr. Bo Lei from the Beijing Academy of Artificial Intelligence. The study also benefited from significant contributions by Professor Songhai Shi and Professor Yi Zhong from the School of Life Sciences at Tsinghua University. The research was supported by the National Natural Science Foundation of China, the National Key Research and Development Program of China, the National Science and Technology Major Project on New Generation Artificial Intelligence, the Tsinghua-Foshan Advanced Manufacturing Institute for Electronics Information Devices and Systems, the Tsinghua University Initiative Research Program, the Tsinghua-Peking Center for Life Sciences, the Tsinghua Institute for Precision Medicine, and the Tsinghua Institute for Smart Healthcare. **Key Points:** - Development of PATTERN, a novel full-brain 3D imaging platform. - Addresses limitations of existing techniques in large-scale, high-throughput, and multi-omics integration. - Utilizes photoacoustic tomography for large field-of-view, rapid, and high-sensitivity imaging. - Compatible with fMRI, high-precision fluorescence imaging, and spatial transcriptomics. - Demonstrated effectiveness in imaging brain samples from multiple species. - Retains sample integrity for subsequent analyses. - Provides a new method for visualizing specific molecular and gene expression patterns in the brain. - Offers a multi-scale, personalized approach to understanding brain function and disease.