title: High-throughput neuroimaging-genetics computational infrastructure.
abstract: 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. Data mining refers to the process of automatically extracting data features, characteristics and associations, which are not readily visible by human exploration of the raw dataset. Result interpretation includes scientific visualization, community validation of findings and reproducible findings. 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. In addition, the institute provides a large number of software tools for image and shape analysis, mathematical modeling, genomic sequence processing, and scientific visualization. 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.
authors.affiliation: Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, University of Southern California Los Angeles, CA, USA ; Biomedical Informatics Research Network, Information Sciences Institute, University of Southern California Los Angeles, CA, USA ; Statistics Online Computational Resource, University of Michigan, UMSN Ann Arbor, MI, USA.
authors.forename: Ivo D
authors.initials: ID
authors.lastname: Dinov
authors.affiliation: Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, University of Southern California Los Angeles, CA, USA.
authors.forename: Petros
authors.initials: P
authors.lastname: Petrosyan
authors.affiliation: Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, University of Southern California Los Angeles, CA, USA.
authors.forename: Zhizhong
authors.initials: Z
authors.lastname: Liu
authors.affiliation: Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, University of Southern California Los Angeles, CA, USA ; Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA.
authors.forename: Paul
authors.initials: P
authors.lastname: Eggert
authors.affiliation: Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, University of Southern California Los Angeles, CA, USA.
authors.forename: Sam
authors.initials: S
authors.lastname: Hobel
authors.affiliation: Brain Injury Research Center, Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
authors.forename: Paul
authors.initials: P
authors.lastname: Vespa
authors.affiliation: Department of Neuropsychiatry, Konkuk University School of Medicine Seoul, Korea.
authors.forename: Seok
authors.initials: S
authors.lastname: Woo Moon
authors.affiliation: Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, University of Southern California Los Angeles, CA, USA.
authors.forename: John D
authors.initials: JD
authors.lastname: Van Horn
authors.affiliation: Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, University of Southern California Los Angeles, CA, USA.
authors.forename: Joseph
authors.initials: J
authors.lastname: Franco
authors.affiliation: Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, University of Southern California Los Angeles, CA, USA ; Biomedical Informatics Research Network, Information Sciences Institute, University of Southern California Los Angeles, CA, USA.
authors.forename: Arthur W
authors.initials: AW
authors.lastname: Toga
keywords: Alzheimer's disease; aging; big data; computation solutions; genetics; neuroimaging; pipeline; visualization