High Capacity Storage System for Computational Biology

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DESCRIPTION (provided by applicant): Innovations in imaging technologies and in the field of computational neuroscience have resulted in the generation of massive amounts of image and statistical data concerning the human brain. Additionally, recent advancements in the acquisition of genetic and other biomedical data have created novel interdisciplinary studies in the field of neuroscience and biomedical informatics. These advances have resulted in the need for high performance computing and novel investigative paradigms for the meaningful analysis of the available data. The Laboratory of Neuro Imaging has been a frontrunner in the adoption of cutting-edge technology to understand dynamic changes such as the development and degeneration of the human brain in health and disease. Our group has gained worldwide recognition for the development of innovative research methodologies, computational algorithms and high-order mathematical approaches to the investigation of brain registration, the analysis of variance between and within populations, and the visualization of these data. These techniques, however, now must not only accommodate four dimensions, as ongoing projects at LONI and elsewhere generate time-varying, multidimensional statistical fields but also be able to reasonably process data that are increasingly more complex. We now routinely apply these computationally demanding methods to population studies in order to achieve sensitivity to potentially subtle differences. In response to these computational challenges, a group of neuro-, biomedical and computer scientists with common interests and computational needs have come together to seek funding to greatly increase the storage capacity and bandwidth of a dedicated shared high performance computing facility. The increasing storage demands placed on this system by four-dimensional and high angular resolution imaging sets, used for intricate surface extraction, nonlinear warping and the multi-modal integration of complex is surfaces with data from disparate sources have clearly identified storage bandwidth limitations and gross storage capacity concerns that limit the progress of large imaging studies. The requested storage upgrade would eliminate capacity bandwidth contention and allow for the optimal utilization of the computational resource by LONI investigators and collaborators. An administrative plan is already in place by which the equipment can be managed equitably. Technical and management personnel also are part of the funded group of participants. Ongoing collaborations and the common programmatic requirements will enable sharing of computer code, analytic procedures, computational strategies and infrastructural capabilities. The requested instrument will enhance the productivity of ongoing computational biomedical research at LONI and collaborating sites in schizophrenia, HIV/AIDS and Alzheimer's disease, among others, and foster the development of leading edge technology and applications for a diverse array of collaborators and multidisciplinary investigators.