Neuroinformatics Research Core

项目总结：神经信息学核心

PROJECT SUMMARY: NEUROINFORMATICS CORE The Neuroinformatics (NI) Core was established at Kansas State University (K-State) with Phase 1 COBRE funding to support the primary projects, programs, and cores across CNAP. In Phase 2, the NI Core will extend our skills in systems, workflow design, and high-performance computing (HPC) to include deep expertise in machine learning and artificial intelligence (ML/AI) models, adding two faculty advisors and two graduate students to the core. The NI Core provides secure, fast, and efficient access for data sharing, analytics, computational modeling, and HPC. This core is particularly important for supporting the management, analysis, and modeling of large data sets collected in neuroscience studies such as neuroimaging (functional magnetic resonance imaging, diffusion tensor imaging), neural recordings (electroencephalography, electrophysiology), auditory learning, transgenic Alzheimer’s Disease modeling, and other quantitative behavioral measurements that are often conducted in real-time. During Phase 2, the NI Core will add support for on-premises and cloud-based sensitive data and analysis in partnership with the K-State Chief Information Security Officer and the Research Information Security Enclave (RISE). RISE is a cloud- based system that uses Microsoft Azure cloud to meet the appropriate standards for sponsored-research efforts. The NI Core is an established core facility that will support all three primary projects, the two other research cores, and all CNAP programs. NI Core access will be available to all primary and affiliated members of CNAP. The provision of HPC and data storage capabilities will continue to promote our ability to attract top researchers (e.g., new faculty at K-State, pilot grant leaders, and additional researchers who will benefit from CNAP resources). Neuroscience researchers are becoming increasingly reliant on tools for working with large data sets and the effective application of machine learning techniques. The NI Core will supply excellent support for executing modern neuroscience techniques that generate, analyze, and distribute large data sets, removing a barrier to CNAP faculty and researchers whose research relies on these techniques currently, or those whose research will do so in the future. Increased tools and staff resources will continue to support extramural funding success across the cores and programs and promote graduation of junior investigators to independent status. This core will continue to directly support the other two CNAP core facilities and will provide an excellent resource for developing new collaborations with Scientific Exchange Network partner institutions and supporting training opportunities for acquiring new machine learning and data analytic techniques. The overarching goal of the NI Core is to promote the ability of CNAP researchers to compete for extramural funding by incorporating cutting-edge technologies, such as ML/AI, that are needed to answer the most challenging questions facing modern neuroscience researchers.