Old Dominion University is awarded an Early Faculty Career Development grant to support Dr Shuiwang Ji in research leading to a better understanding of the brain. The brain is an enormously complex system, and the analysis of brain data is thus an equally enormous challenge. Human brains contain billions of neurons and trillions of synapses (junctions); and each of them is unique in their basic biochemistry, functions, and dynamics. The brain is also a multi-level system organized across different spatial scales, ranging from genes, synapses, and cells to circuits, brain regions, and systems. Today, brain science is experiencing rapid changes and is expected to achieve major advances in the near future. Recent technological innovations are enabling scientists to capture the gene expression patterns, connectivity, and neuronal activities at increasing speed and resolution. This is generating a deluge of data that capture the brain activities at different levels of organization. To attack the central challenges of analyzing these new data, this project will develop a class of efficient, integrative, multidimensional, predictive, and correlative techniques and use them to analyze large-scale, high-resolution, and multi-modality sets of brain data. Specifically, this project will develop analytics tools to predict the cellular-resolution, brain-wide connectome ("wiring diagram") from genetic transcriptional profiles. This analysis will elucidate the information pathway from genes to connectivity and ultimately, to function. This project will also integrate other brain dimensions by performing multidimensional network correlative analytics. In addition, this project will address the relationship between gene expression, cell types, and brain structures. The success of this project will be a new class of efficient, robust analytics methods that are flexible enough to be adapted for integrating, modeling, and mining current and future brain data.
The results of this project will have an immediate and strong impact on multiple disciplines, including brain data analytics and computational neuroscience, biological image informatics, and big data analytics. A future long term goal is to uncover basic underlying differences between normal and impaired brain functions. The unified treatment of brain data analytics will be readily transformable into new courses for training next-generation computational biologists. The multidisciplinary nature of this project provides unique opportunities for integrating its components into existing curricula. Brain science has been shown to be a valuable resource for inspiring scientific interests in K-12 students. Components of the project will be integrated into an existing high-school student internship program, thereby inspiring future science students. Underrepresented students will be especially encouraged to participate throughout the project. The results of this project will be disseminated in the form of peer-reviewed publications, open-source software, tutorials, seminars, and workshops. All findings, publications, software, and data will be made publicly available at the project website: http://compbio.cs.odu.edu/CAREER/
