51. The Perfect Neuroimaging-Genetics-Computation Storm: Collision of Petabytes of Data, Millions of Hardware Devices and Thousands of Software Tools
- Author
-
Arthur W. Toga, Zhizhong Liu, Ivo D. Dinov, Alen Zamanyan, Seok Woo Moon, Federica Torri, Florian Kurth, Paul M. Vespa, Petros Petrosyan, Jennifer S. Labus, Sam Hobel, Paul Eggert, Emeran A. Mayer, Young Hee Sung, Zhiguo Jiang, Fabio Macciardi, John D. Van Horn, and Cody Ashe-McNalley
- Subjects
Male ,Computer science ,Cognitive Neuroscience ,Big data ,Context (language use) ,Neuroimaging ,Article ,Workflow ,Irritable Bowel Syndrome ,Behavioral Neuroscience ,Cellular and Molecular Neuroscience ,Software ,Alzheimer Disease ,Server ,Humans ,Radiology, Nuclear Medicine and imaging ,Computational model ,Internet ,business.industry ,Information Dissemination ,Brain ,Computational Biology ,Genomics ,Middle Aged ,Pipeline (software) ,Psychiatry and Mental health ,Data access ,Neurology ,Brain Injuries ,Female ,Neurology (clinical) ,Chronic Pain ,business ,Computer hardware ,Algorithms ,Genome-Wide Association Study - 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.
- Published
- 2014