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

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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/.