NCS-FO: Analyzing Synapses, Motifs and Neural Networks for Large-Scale Connectomics

NCS-FO：分析大规模连接组学的突触、基序和神经网络

• 批准号: 1835231
• 负责人: Hanspeter Pfister
• 金额: $ 99.96万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-11-01 至 2021-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1835231&HistoricalAwards=false
• 关键词: NCS; FO; Analyzing; Synapses; Motifs

High-resolution analysis of the brain's connectivity, which reveals the actual wiring diagram connecting nerve cells of the brain, provides insights unattainable any other way into the way the healthy brain works and what goes awry in diseases and disorders of the nervous system. The primary challenge of this approach is that at present there are no reliable, robust and powerful computer-based techniques to analyze the extraordinarily large and vastly complicated networks of brain cells to detect connectional motifs in their highly branching and connected structure. Nor are there visualization tools that allow neuroscientists to explore the brain network patterns effectively. This work will analyze large brain networks from electron microscopy datasets in young and old mammalian brain samples. These data sets each contains hundreds of thousands of nerve cells and billions of synapses that interconnect them. The proposal aims to develop new methods and tools to analyze these vast brain networks at the synapse, motif, and network levels. If successful, the project will provide data and analysis tools for the development of new theories of how the brain works.Recent advances in image acquisition using multi-beam serial-section electron microscopy (sSEM) and automated segmentation methods have enabled data collection for large tissue samples in a variety of animals. These data will be used to curate large-scale datasets with one million labeled synapses with synaptic cleft locations, pre- and postsynaptic polarity predictions, and excitatory and inhibitory type predictions. This has not been accomplished previously given the enormous amount of data. The aim is to discover synaptic motifs by subdividing complex neural networks into quantifiable and meaningful subgraphs. Automatic generation of candidates for motifs will be created by developing an efficient neurite-centric wiring-diagram reconstruction method and subgraph detection algorithm to find common patterns. These data will be used to quantify and compare reconstructed neural networks from different specimens at different spatial and temporal scales and build a visualization platform to assist neuroscientists to analyze these networks as they seek to ask and answer fundamental questions related to neural circuits in the brain.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

对大脑连接的高分辨率分析揭示了连接大脑神经细胞的实际布线图，提供了任何其他方式都无法获得的见解，以了解健康大脑的工作方式以及神经系统疾病和紊乱的原因。这种方法的主要挑战是，目前还没有可靠的，强大的和强大的基于计算机的技术来分析非常大和非常复杂的脑细胞网络，以检测其高度分支和连接结构中的连接基序。也没有可视化工具可以让神经科学家有效地探索大脑网络模式。这项工作将分析来自年轻和老年哺乳动物大脑样本电子显微镜数据集的大型大脑网络。这些数据集每个都包含数十万个神经细胞和数十亿个连接它们的突触。该提案旨在开发新的方法和工具，以在突触，基序和网络水平上分析这些庞大的大脑网络。如果成功，该项目将提供数据和分析工具，为大脑如何工作的新理论的发展，在图像采集使用多束连续切片电子显微镜（sSEM）和自动分割方法的最新进展，使各种动物的大型组织样本的数据收集。这些数据将用于管理大规模数据集，其中包含100万个标记的突触，包括突触间隙位置，突触前和突触后极性预测以及兴奋性和抑制性类型预测。由于数据量巨大，以前没有做到这一点。其目的是通过将复杂的神经网络细分为可量化和有意义的子图来发现突触基序。自动生成的候选图案将通过开发一个有效的神经元为中心的拓扑图重建方法和子图检测算法，以找到共同的模式。这些数据将用于量化和比较不同时空尺度下不同样本的重建神经网络，并建立一个可视化平台，以帮助神经科学家分析这些网络，因为他们试图提出和回答与大脑神经回路相关的基本问题。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值进行评估来支持和更广泛的影响审查标准。

项目成果

AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regions

• DOI: 10.1007/978-3-030-87193-2_17
• 发表时间: 2021-07
• 期刊: ArXiv
• 作者: D. Wei;Kisuk Lee;Hanyu Li;R. Lu;J. A. Bae;Zequan Liu;Lifu Zhang;M'arcia dos Santos;Zudi Lin;T. Uram;Xueying Wang;Ignacio Arganda-Carreras;Brian Matejek;N. Kasthuri;J. Lichtman;H. Pfister

Two-Stream Active Query Suggestion for Large-Scale Object Detection in Connectomics

连接组学中大规模对象检测的双流主动查询建议

• 发表时间: 2020
• 期刊: European Conference Computer Vision (ECCV
• 作者: Lin, Zudi;Wei, Donglai;Jang, Won-Dong;Zhou, Siyan;Chen, Xupeng;Wang, Xueying;Schalek, Richard;Berger, Daniel;Metejek, Brian;Kamentsky, Lee
• 通讯作者: Kamentsky, Lee

Synapse-Aware Skeleton Generation for Neural Circuits

神经回路的突触感知骨架生成

• DOI: 10.1007/978-3-030-32239-7_26
• 发表时间: 2019
• 期刊: Medical Image Computing and Computer Assisted Intervention
• 作者: Matejek, Brian;Wei, Donglai;Wang, Xueying;Zhao, Jinglin;Palagyi, Kalman;Pfister, Hanspeter
• 通讯作者: Pfister, Hanspeter

VICE: Visual Identification and Correction of Neural Circuit Errors

VICE：神经回路错误的视觉识别和纠正

• DOI: 10.1111/cgf.14320
• 发表时间: 2021
• 期刊: Eurographics Conference on Visualization (EuroVis
• 作者: Gonda, F;Wang, S;Beyer, J;Lichtman, J;Pfister, H
• 通讯作者: Pfister, H

MitoEM Dataset: Large-scale 3D Mitochondria Instance Segmentation from EM Images.

• DOI: 10.1007/978-3-030-59722-1_7
• 发表时间: 2020-10
• 期刊: Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
• 作者: Wei D;Lin Z;Franco-Barranco D;Wendt N;Liu X;Yin W;Huang X;Gupta A;Jang WD;Wang X;Arganda-Carreras I;Lichtman JW;Pfister H
• 通讯作者: Pfister H