BRAIN CONNECTS: Rapid and Cost‐effective Connectomics with Intelligent Image Acquisition, Reconstruction, and Querying

大脑连接：具有智能图像采集、重建和查询功能的快速且经济有效的连接组学

• 批准号: 10663654
• 负责人: Jeff W Lichtman
• 金额: $ 209.59万
• 依托单位: HARVARD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10663654
• 关键词: Acceleration; Affect; Algorithms; Architecture; Artificial Intelligence; Behavior; Brain; Communities; Computer Hardware; Computer software; Data; Data Collection; Data Set; Databases; Detection; Devices; Electron Microscope; Endowment; Engineering; Failure; Generations; Human; Image; Individual; Infrastructure; Intelligence; Machine Learning; Maps; Methods; Mus; Neurons; Neurosciences; Noise; Performance; Process; Research; Research Institute; Research Personnel; Resolution; Resources; Retina; Running; Sampling; Scanning; Services; Signal Transduction; Slice; Speed; Structure; Synapses; System; Time; Validation; broadening participation research; cloud based; comparative; computer infrastructure; computerized data processing; connectome data; cost; cost effective; data analysis pipeline; data ecosystem; data ingestion; data visualization; design; image processing; imaging Segmentation; imaging system; improved; intelligent algorithm; nanoscale; neural; neural circuit; nonhuman primate; programs; reconstruction; scale up; tool

SUMMARY High-throughput connectomics is needed to generate the TB-, PB- and EB-scale wiring diagrams of mammalian brains, but is limited to the few research institutes (e.g., Janelia, Allen, Max Planck) with sufficient infrastructure. As resource-rich as these institutes are, none are able to do a whole brain at nanometer scale on their own. The failure to broaden participation to a larger community is an obstacle to scaling connectomics. We propose a new and more affordable imaging strategy that will allow many more teams to engage in connectomics. High-speed electron microscopes for connectomics – e.g., multibeam SEMs – are rare and prohibitively ex- pensive. More common single-beam SEMs have sufficiently high spatial resolution, but are prohibitively slow for connectomics. We plan to increase the speed of single-beam SEM systems to the speed of multibeam SEMs without substantially increasing cost. Our strategy adds artificial intelligence to SEM architecture to re- duce the number and dwell time of pixels that need to be imaged at high-resolution without adversely affecting “segmentability”. With new software and standard computer hardware, we can turn single-beam SEMs into intel- ligent, powerful devices at negligible cost. We demonstrated a proof-of-concept of a smart scanning system that we engineered into a single-beam SEM. The modified SEM acquires a low-resolution/low-dwell time image of a brain slice at high speed. It then uses ultrafast ML algorithms to extract most of the wiring from these images, while at the same time identifying in real time those salient pixels that should be rescanned to improve signal-to noise in the final wiring diagram. We have achieved >10-fold speedup in image acquisition, and plan to increase the rate significantly more. A significant scale-up in the rate of connectomics demands comparable improvements in image processing (stitching, alignment, and segmentation). We have built computationally more efficient methods for aligning and segmenting connectome datasets. We will integrate these methods into a cloud-based platform that will allow researchers without significant computational infrastructure or expertise to process connectomics datasets. All data products and capabilities will be publicly accessible through BossDB. In summary, this integrated research program will scale connectomics to a much larger neuroscience community.

概括 需要高通量连接组学来生成哺乳动物的 TB、PB 和 EB 规模接线图 脑，但仅限于少数拥有足够基础设施的研究机构（例如 Janelia、Allen、Max Planck）。 尽管这些机构资源丰富，但没有一家机构能够独自在纳米尺度上制造出整个大脑。 未能扩大参与范围是扩大连接组学的一个障碍。 以及更实惠的成像策略，将使更多的团队能够参与连接组学研究。 用于连接组学的高速电子显微镜（例如多束 SEM）非常罕见，而且令人望而却步。 更常见的单束 SEM 具有足够高的空间分辨率，但速度却慢得令人望而却步。 我们计划将单光束 SEM 系统的速度提高到多光束的速度。 我们的策略将人工智能添加到 SEM 架构中，以重新构建 SEM，而无需大幅增加成本。 减少需要以高分辨率成像的像素的数量和停留时间，而不会产生不利影响 借助新的软件和标准计算机硬件，我们可以将单束 SEM 转变为智能型。 我们展示了智能扫描系统的概念验证，该系统的成本可以忽略不计。 我们设计了一个单束 SEM，改进后的 SEM 可以获取低分辨率/低停留时间的图像。 然后，它使用超快的机器学习算法从这些图像中提取大部分线路， 同时实时识别那些应重新扫描以改善信号传输的显着像素 我们已经在图像采集方面实现了超过 10 倍的加速，并计划进一步提高。 率明显更高。 连接组学速率的显着提高需要图像处理方面的相应改进 （缝合、对齐和分割）我们建立了计算上更有效的对齐和分割方法。 我们将把这些方法集成到一个基于云的平台中，该平台将允许 没有重要计算基础设施或专业知识来处理连接组学数据集的研究人员。 数据产品和功能将通过 BossDB 公开访问。 总之，这个综合研究计划将把连接组学扩展到更大的神经科学领域 社区。