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

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.

Figure 1

Examples of classes of tools available in the Pipeline computational library.

Figure 2

Traumatic brain injury (TBI) studies demonstrate the diversity of the neuroimaging data in clinical applications. Imaging modalities included in many TBI studies include: TSE: Turbo-Spin-Echo magnetic resonance imaging (MRI); FLAIR: Fluid Attenuated Inversion Recovery MRI; GRE: Gradient-Recalled-Echo (MRI); T2 Haste: Half-Fourier Acquisition Single-Shot Turbo Spin-Echo MRI; MP RAGE: Magnetization-Prepared Rapid Acquisition with Gradient Echo (MRI); T2: T₂-weighted MRI; SWI: Susceptibility Weighted Imaging (MRI); CT: Computed Tomography; FDG: Fludeoxyglucose Positron Emission Tomography (PET); FDG Maps: Statistical maps of Fludeoxyglucose; FDDNP: 2-(1-{6-[(2-[F-18]fluoroethyl)(methyl)amino]-2-naphthyl}ethylidene)malononitrile PET imaging.

Figure 3

Early Onset (EO) ADNI Imaging-Genetics GWAS Study using the pipeline environment.

Figure 4

Example of using the pipeline environment to complete a neuroimaging genetics study of FKBP5 gene (rs1360780) association with attention, measured through behavioral response (dot probe task) and hippocampal morphometrics. The superior and inferior vies of the hippocampal surface map illustrate the vertex locations, on the mean left hippocampus, where FKBP5 carriers (group 1) and non-carriers (group 2) showed significant shape differences.

Figure 5

Analyzing IBS/NC regional differences: (Left) raw sMRI data, (Middle) GSA workflow including data processing, surface reconstruction, 3D parcellation, and statistical analysis, (Right) Statistically significant ROI between-differences rendered as 3D scenes (left cuneus is green, and right angular gyrus is gray; the red cingulate gyrus and the blue insula are shown for orientation only).