Efficient, Distributed and Interactive Neuroimaging Data Analysis Using the LONI Pipeline

Abstract

The LONI Pipeline is a graphical environment for construction, validation and execution of advanced neuroimaging data analysis protocols (Rex et al., 2003). It enables automated data format conversion, allows Grid utilization, facilitates data provenance, and provides a significant library of computational tools. There are two main advantages of the LONI Pipeline over other graphical analysis workflow architectures. It is built as a distributed Grid computing environment and permits efficient tool integration, protocol validation and broad resource distribution. To integrate existing data and computational tools within the LONI Pipeline environment, no modification of the resources themselves is required. The LONI Pipeline provides several types of process submissions based on the underlying server hardware infrastructure. Only workflow instructions and references to data, executable scripts and binary instructions are stored within the LONI Pipeline environment. This makes it portable, computationally efficient, distributed and independent of the individual binary processes involved in pipeline data-analysis workflows. We have expanded the LONI Pipeline (V.4.2) to include server-to-server (peer-to-peer) communication and a 3-tier failover infrastructure (Grid hardware, Sun Grid Engine/Distributed Resource Management Application API middleware, and the Pipeline server). Additionally, the LONI Pipeline provides three layers of background-server executions for all users/sites/systems. These new LONI Pipeline features facilitate resource-interoperability, decentralized computing, construction and validation of efficient and robust neuroimaging data-analysis workflows. Using brain imaging data from the Alzheimer's Disease Neuroimaging Initiative (Mueller et al., 2005), we demonstrate integration of disparate resources, graphical construction of complex neuroimaging analysis protocols and distributed parallel computing. The LONI Pipeline, its features, specifications, documentation and usage are available online (http://Pipeline.loni.ucla.edu).

Keywords: LONI Pipeline; data provenance; neuroimaging; resources; software tools; tool integration; tool interoperability; workflows.

Figure 1

LONI Pipeline intelligence component uses key-words to automatically generate a hierarchical interface and the complete analysis workflow, which represents the proposed study protocol, using semantic natural language processing of language grammar. The image insert shows the graphical user interface invoking the pipeline intelligence grammar view.

Figure 2

Using a robust executable entitled Brain Parser (Tu et al., 2008), LONI Pipeline can be used to extract 56 predefined ROI masks from any input brain image volume (inserts).

Figure 3

LONI Pipeline workflow for constructing a population-based whole brain anatomical atlas in Alzheimer's Disease patients (insets).

Figure 4

An Alzheimer's Disease (AD) Pipeline workflow. The left-panel contains some predefined Pipeline module definitions including some complete workflows. The central-panel shows the main six steps of the data analysis – data conversion, pre-processing, automated extraction of regions of interest, shape processing, global shape analysis and automated cortical surface extraction. Each of these steps is itself a nested collection of modules, a pipeline, which contains a series of processing steps. The insert-figure illustrates the 3-level deep nested processing part of the Global Shape Analysis node (see the top-level tabs of the insert).

Figure 5

NC shape-curvature measure for 5 ROI's at two times (T1, baseline, blue, and T2, 12-month follow-up, green). L_Caudate and R_Caudate, left and right caudate, L_Hippo and R_Hippo, left and right hippocampus, R_SupFrontalGyrus, right superior frontal gyrus.

Figure 6

Comparison of the four volume and shape measures for both times (baseline and 12-month follow up) across the three cohorts for one region of interest – the Right Superior Frontal Gyrus. T1 and T1 labels represent baseline and follow-up time scans. The statistics signature vector includes MeanCurv, average global shape mean curvature; MeanSurf, total shape surface area; MeanFract, mean global shape fractal dimension; and MeanVol, volume of the inside region of the shape. The three different cohorts, NC, MCI and AD, are colored in blue, green and red, respectively.