Brain Imaging and Behavior

, Volume 8, Issue 2, pp 311–322

The perfect neuroimaging-genetics-computation storm: collision of petabytes of data, millions of hardware devices and thousands of software tools

  • Ivo D. Dinov
  • Petros Petrosyan
  • Zhizhong Liu
  • Paul Eggert
  • Alen Zamanyan
  • Federica Torri
  • Fabio Macciardi
  • Sam Hobel
  • Seok Woo Moon
  • Young Hee Sung
  • Zhiguo Jiang
  • Jennifer Labus
  • Florian Kurth
  • Cody Ashe-McNalley
  • Emeran Mayer
  • Paul M. Vespa
  • John D. Van Horn
  • Arthur W. Toga
  • for the Alzheimer’s Disease Neuroimaging Initiative
SI: Genetic Neuroimaging in Aging and Age-Related Diseases

DOI: 10.1007/s11682-013-9248-x

Cite this article as:
Dinov, I.D., Petrosyan, P., Liu, Z. et al. Brain Imaging and Behavior (2014) 8: 311. doi:10.1007/s11682-013-9248-x

Abstract

The volume, diversity and velocity of biomedical data are exponentially increasing providing petabytes of new neuroimaging and genetics data every year. At the same time, tens-of-thousands of computational algorithms are developed and reported in the literature along with thousands of software tools and services. Users demand intuitive, quick and platform-agnostic access to data, software tools, and infrastructure from millions of hardware devices. This explosion of information, scientific techniques, computational models, and technological advances leads to enormous challenges in data analysis, evidence-based biomedical inference and reproducibility of findings. The Pipeline workflow environment provides a crowd-based distributed solution for consistent management of these heterogeneous resources. The Pipeline allows multiple (local) clients and (remote) servers to connect, exchange protocols, control the execution, monitor the states of different tools or hardware, and share complete protocols as portable XML workflows. In this paper, we demonstrate several advanced computational neuroimaging and genetics case-studies, and end-to-end pipeline solutions. These are implemented as graphical workflow protocols in the context of analyzing imaging (sMRI, fMRI, DTI), phenotypic (demographic, clinical), and genetic (SNP) data.

Keywords

Aging Pipeline Neuroimaging Genetics Computation solutions Workflows IBS Pain Parkinson’s disease Alzheimer’s disease Shape Volume Analysis Big data Visualization 

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Ivo D. Dinov
    • 1
    • 2
  • Petros Petrosyan
    • 1
  • Zhizhong Liu
    • 1
  • Paul Eggert
    • 1
    • 4
  • Alen Zamanyan
    • 1
  • Federica Torri
    • 2
    • 3
  • Fabio Macciardi
    • 2
    • 3
  • Sam Hobel
    • 1
  • Seok Woo Moon
    • 5
  • Young Hee Sung
    • 6
  • Zhiguo Jiang
    • 9
    • 10
  • Jennifer Labus
    • 7
  • Florian Kurth
    • 7
  • Cody Ashe-McNalley
    • 7
  • Emeran Mayer
    • 7
  • Paul M. Vespa
    • 8
  • John D. Van Horn
    • 1
  • Arthur W. Toga
    • 1
    • 2
  • for the Alzheimer’s Disease Neuroimaging Initiative
  1. 1.Laboratory of Neuro Imaging (LONI), David Geffen School of Medicine at UCLAUniversity of California, Los AngelesLos AngelesUSA
  2. 2.Biomedical Informatics Research Network (BIRN), Information Sciences InstituteUniversity of Southern CaliforniaLos AngelesUSA
  3. 3.Department of Psychiatry and Human BehaviorUniversity of California, IrvineIrvineUSA
  4. 4.Department of Computer ScienceUniversity of California, Los AngelesLos AngelesUSA
  5. 5.Department of PsychiatryKonkuk University Chungju HospitalSeoulSouth Korea
  6. 6.Department of NeurologyGachon University, Gil HospitalIncheonSouth Korea
  7. 7.Center for Neurobiology of StressUniversity of California, Los AngelesLos AngelesUSA
  8. 8.Brain Injury Research CenterRonald Reagan UCLA Medical CenterLos AngelesUSA
  9. 9.Human Performance and Engineering LaboratoryKesser Foundation Research CenterWest OrangeUSA
  10. 10.Department of Biomedical EngineeringNew Jersey Institute of TechnologyNewarkUSA

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