Collaborative Research: ABI Innovation: Towards Computational Exploration of Large-Scale Neuro-Morphological Datasets

分析单个神经元的性质是了解神经系统和大脑工作机制的基础任务。研究神经元形态是分析神经元的一种有效方法，在确定神经元的性质方面起着重要作用。近年来，不断增加的神经元数据库极大地促进了神经元形态学的研究。然而，这些数据的绝对数量和复杂性对计算分析构成了重大挑战，阻碍了这些数据的全部潜力的实现。这个跨学科的项目将寻求新的途径来组装大量的神经元形态，并为神经科学家探索和分析不同类型的神经元提供一个统一的框架。该研究能够解决以往方法难以解决的许多神经科学难题，包括细粒度神经元识别、潜在模式发现和探索等。正在开发的大规模方法将对神经科学的未来特别有益，因为越来越多的神经元被重建并添加到数据库中。所开发的计算方法和工具很可能适用于解决其他生物信息学问题，特别是那些处理大规模数据集的问题。该项目的广泛影响不仅包括对本科研究人员和高中生的教育支持，特别是女性和那些代表性不足的群体，而且还有助于神经科学和其他STEM领域的研究。该项目的长期目标是为神经科学家开发有效的计算方法和工具，以超细粒度的准确性实时交互地探索大规模神经元数据库。这项研究具有很强的多学科组成部分，涉及机器学习，信息检索和神经信息学的联系思想。特别是，新颖的想法将通过整个框架在三个相互关联的组件中实现。第一部分解决了基于深度学习模型的准确高效的神经元重建和跟踪问题。第二部分解决了通过多模态和在线二进制编码方法在大型神经元数据库中有效发现相关实例的问题。第三部分讨论了用于知识发现和挖掘的智能可视化和交互方案，配备了可结合领域专家反馈的交互式编码，以增强查询算法以获得微调结果。与以往的方法和系统相比，该项目将为帮助神经科学家高效、准确、鲁棒地分析和探索大规模神经元数据库开辟一条新的途径。所提出的方法的性能将使用公共神经形态学数据库（例如，NeuroMorpho, BigNeuron）进行验证，并与几个基准进行比较。要开发的工具的有效性将由神经科学家对特定领域的假设驱动应用进行评估。该项目的结果将在以下网站上公布：http://webpages.uncc.edu/~szhang16/和https://github.com/divelab/。

Analyzing single neuron's property is a fundamental task to understand the nervous system and brain working mechanism. Investigating neuron morphology is an effective way to analyze neurons, since it plays a major role in determining neurons' properties. Recently, the ever-increasing neuron databases have greatly facilitated the research of neuron morphology. However, the sheer volume and complexity of these data pose significant challenges for computational analysis, preventing the realization of the full potential of such data. This interdisciplinary project will seek for new avenue to assemble the massive neuron morphologies and provide a unified framework for neuroscientists to explore and analyze different types of neurons. The research is able to tackle many challenges in neuroscience which are hard to solve with previous methods, including fine-grained neuron identification, latent pattern discovery and exploration, etc. The large-scale methods being developed will be particularly beneficial in the future of neuroscience, since more and more neurons are reconstructed and added to the databases. The computational methods and tools developed are very likely to be applicable for solving other bioinformatics problems, especially those dealing with large-scale datasets. The broader impact of this project not only includes educational support for undergraduate researchers and high school students, particularly women and those underrepresented groups, but also contributes to the research of neuroscience and other STEM fields.The long-term goal of this project is to develop effective computational methods and tools for neuroscientists to interactively explore large-scale neuron databases with ultra-fine-grained accuracy, in real-time. This research has a strong multidisciplinary component that involves a nexus ideas from machine learning, information retrieval, and neuroinformatics. Particularly, novel ideas will be implemented in three inter-related components through the whole framework. The first one addresses the accurate and efficient neuron reconstruction and tracing based on deep learning models. The second addresses the efficient discovery of relevant instances among large-size neuron databases via multi-modal and online binary coding methods. The third part addresses intelligent visualization and interaction schemes for knowledge discovery and mining, equipped with interactive coding that can incorporate domain experts' feedback to enhance the query algorithms for fine-tuned results. Compared with previous methods and systems, this project will open a new avenue to assist neuroscientists analyzing and exploring large-scale neuron databases with high efficiency, accuracy, and robustness. The performance of proposed methods will be validated using public neuro-morphological databases (e.g., NeuroMorpho, BigNeuron) and compared with several benchmarks. The effectiveness of the tools to be developed will be evaluated by neuroscientists on domain-specific hypothesis-driven applications. The outcome of the project will be made available at the following websites: http://webpages.uncc.edu/~szhang16/ and https://github.com/divelab/.