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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

基本信息

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
项目类别：
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

项目摘要

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.

总结