CRCNS Research Proposal: A Unified Framework for Unsupervised Sparse-to-dense Brain Image Generation and Neural Circuit Reconstruction

CRCNS 研究提案：无监督稀疏到密集脑图像生成和神经回路重建的统一框架

• 批准号: 2309073
• 负责人: Yan Yan
• 金额: $ 100万
• 依托单位: Illinois Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2028-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2309073&HistoricalAwards=false
• 关键词: CRCNS; Research; Proposal; Unified; Framework

Understanding how information is processed and propagated in neural circuits in the brain, requires the connections among extremely large numbers of densely packed, intermingled neurons to be accurately mapped out. Despite the century-long effort to map brain circuits, dissection of these complex networks remains technically challenging and time-consuming. This project builds on recent advances in genetic, imaging, and computational methods to develop a unified framework for reliable reconstruction of genetically identified neurons and their connections at single-neuron resolution from 3D image data. The resulting computational methods will be applicable to a wide range of problems in biological and biomedical image analysis.The technical aims of the project are divided into four thrusts. The first thrust generates a large number of super-resolution three dimensional (3D) images of whole Drosophila brains, in each of which connected neurons are labeled by multispectral trans-Tango/Bitbow labeling. The second thrust creates computer vision and unsupervised machine learning algorithms to generate neuronal tracing, and software to aid efficient human proofreading and error correction. This will allow the generation of gold-standard neuronal tracing and segmentation from relatively sparse Brainbow labeling as training inputs. The third thrust creates a generative machine learning model to create realistic Bitbow neurons and synthesize images with various labeling densities based on the sparse annotation training inputs. The fourth thrust develops an annotation machine learning model to reconstruct the densely labeled trans-Tango/Bitbow Drosophila brains with the machine-learning-generated Bitbow images and their corresponding ground truth annotations as training inputs. Together, these efforts form a novel computational framework to enable accurate automatic reconstruction of densely labeled neural circuits.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.

要了解信息是如何在大脑的神经回路中处理和传播的，需要准确地绘制出极其大量密集排列、混合的神经元之间的联系。尽管人们花了一个世纪的时间绘制大脑电路图，但对这些复杂网络的剖析在技术上仍然具有挑战性和耗时。该项目建立在遗传、成像和计算方法的最新进展的基础上，以开发一个统一的框架，用于从3D图像数据以单神经元分辨率可靠地重建遗传识别的神经元及其连接。由此产生的计算方法将适用于生物和生物医学图像分析中的广泛问题。该项目的技术目标分为四个方面。第一次推力产生了大量整个果蝇大脑的超分辨率三维(3D)图像，其中每个连接的神经元都被多光谱TransTango/Bitrow标记。第二个推动力是创建计算机视觉和无监督机器学习算法来生成神经元跟踪，以及帮助高效的人类校对和纠错的软件。这将允许从相对稀疏的脑部标记生成金标准神经元跟踪和分割作为训练输入。第三个推力创建了一个生成性机器学习模型，用于创建逼真的位图神经元，并基于稀疏的标注训练输入合成具有不同标记密度的图像。第四个推力开发了一个注释机器学习模型，以机器学习生成的Bitrow图像及其对应的地面事实注释作为训练输入，重建密集标记的Trans-Tango/Bitrow果蝇大脑。总而言之，这些努力形成了一个新的计算框架，能够准确地自动重建密集标记的神经电路。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。