High-throughput neuroimaging-genetics computational infrastructure - Dinov, Ivo D.; Petrosyan, Petros; Liu, Zhizhong; Eggert, Paul; Hobel, Sam; Vespa, Paul; Woo Moon, Seok; Van Horn, John D.; Franco, Joseph; Toga, Arthur W. (Frontiers in Neuroinformatics, 2014)
Many contemporary neuroscientific investigations face significant challenges in terms of data management, computational processing, data mining and results interpretation. These four pillars define the core infrastructure necessary to plan, organize, orchestrate, validate and disseminate novel scientific methods, computational resources and translational healthcare findings. Data management includes protocols for data acquisition, archival, query, transfer, retrieval and aggregation. Computational processing involves the necessary software, hardware and networking infrastructure required to handle large amounts of heterogeneous neuroimaging, genetics, clinical and phenotypic data and meta-data. In this manuscript we describe the novel high-throughput neuroimaging-genetics computational infrastructure available at the Institute for Neuroimaging and Informatics (INI) and the Laboratory of Neuro Imaging (LONI) at University of Southern California (USC). INI and LONI include ultra-high-field and standard-field MRI brain scanners along with an imaging-genetics database for storing the complete provenance of the raw and derived data and meta-data. A unique feature of this architecture is the Pipeline environment, which integrates the data management, processing, transfer and visualization. Through its client-server architecture, the Pipeline environment provides a graphical user interface for designing, executing, monitoring validating, and disseminating of complex protocols that utilize diverse suites of software tools and web-services. These pipeline workflows are represented as portable XML objects which transfer the execution instructions and user specifications from the client user machine to remote pipeline servers for distributed computing. Using Alzheimer’s and Parkinson’s data, we provide several examples of translational applications using this infrastructure.