Neuroimaging study designs, computational analyses and data provenance using the LONI pipeline.

Modern computational neuroscience employs diverse software tools and multidisciplinary expertise to analyze heterogeneous brain data. The classical problems of gathering meaningful data, fitting specific models, and discovering appropriate analysis and visualization tools give way to a new class of computational challenges--management of large and incongruous data, integration and interoperability of computational resources, and data provenance. We designed, implemented and validated a new paradigm for addressing these challenges in the neuroimaging field. Our solution is based on the LONI Pipeline environment [3], [4], a graphical workflow environment for constructing and executing complex data processing protocols. We developed study-design, database and visual language programming functionalities within the LONI Pipeline that enable the construction of complete, elaborate and robust graphical workflows for analyzing neuroimaging and other data. These workflows facilitate open sharing and communication of data and metadata, concrete processing protocols, result validation, and study replication among different investigators and research groups. The LONI Pipeline features include distributed grid-enabled infrastructure, virtualized execution environment, efficient integration, data provenance, validation and distribution of new computational tools, automated data format conversion, and an intuitive graphical user interface. We demonstrate the new LONI Pipeline features using large scale neuroimaging studies based on data from the International Consortium for Brain Mapping [5] and the Alzheimer's Disease Neuroimaging Initiative [6]. User guides, forums, instructions and downloads of the LONI Pipeline environment are available at http://pipeline.loni.ucla.edu

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

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)

Neuroimaging study designs, computational analyses and data provenance using the LONI pipeline.

Modern computational neuroscience employs diverse software tools and multidisciplinary expertise to analyze heterogeneous brain data. The classical problems of gathering meaningful data, fitting specific models, and discovering appropriate analysis and visualization tools give way to a new class of computational challenges--management of large and incongruous data, integration and interoperability of computational resources, and data provenance. We designed, implemented and validated a new paradigm for addressing these challenges in the neuroimaging field. Our solution is based on the LONI Pipeline environment [3], [4], a graphical workflow environment for constructing and executing complex data processing protocols. We developed study-design, database and visual language programming functionalities within the LONI Pipeline that enable the construction of complete, elaborate and robust graphical workflows for analyzing neuroimaging and other data. These workflows facilitate open sharing and communication of data and metadata, concrete processing protocols, result validation, and study replication among different investigators and research groups. The LONI Pipeline features include distributed grid-enabled infrastructure, virtualized execution environment, efficient integration, data provenance, validation and distribution of new computational tools, automated data format conversion, and an intuitive graphical user interface. We demonstrate the new LONI Pipeline features using large scale neuroimaging studies based on data from the International Consortium for Brain Mapping [5] and the Alzheimer's Disease Neuroimaging Initiative [6]. User guides, forums, instructions and downloads of the LONI Pipeline environment are available at http://pipeline.loni.ucla.edu

Pipeline meta-algorithm developments.

<p>(<i>Top Panel</i>) This figure shows the LONI Pipeline implementation of the image registration meta-algorithm (IRMA). The four different registration algorithms AIR Linear, AIR Warp, FLIRT and MINC Tracc are present as individual nodes in this pipeline. Parameter sets specifying altogether 186 different runs for these algorithms are stored as editable lists within the LONI Pipeline environment. (<i>Bottom Panel</i>) Parallel-coordinates plot showing the rank-transformed metrics for one input image. Eleven distance metrics were employed to evaluate all 186 alignment instances. The results of the IRMA analysis illustrate that the FLIRT family performed consistently better than the other alignment methods for this input volume. The EDI (Entropy of Difference of Intensities) and the Woo (Woods' coefficient) metrics disagree somewhat with the other 9 metrics.</p

Examples of the Pipeline Database Plug-in Infrastructure using the Imaging Data Archive (IDA) database (left) and the XNAT database (right).

<p>Secure user-authentication provides an appropriate data-access level. The user then selects a location for local/remote storage of the data for the computational Pipeline processing and a format for the data representation (e.g., study-design). The data-download progress monitor provides information about the status of the transfer. At the end a study-design, or a data-source module is constructed, which allows the stratification of the population into groups (Male/female, in this case).</p

Pipeline Study-Design Architecture.

<p>Imaging and non-imaging meta-data for 104 subjects is used to stratify the entire population into 3 distinct cohorts – asymptomatic normal controls (NC), Alzheimer's disease (AD) patients, and Mild Cognitive Impairment (MCI) subjects. The nested inserts show the search and selection grouping criteria, cohort sizes and an instance of an XML meta-data file for one subject. The meta-data can be manually entered, automatically parsed from spreadsheets, databases or clinical charts, or fed in as results of the pipeline workflow calculations (derived data).</p

The global shape analysis (GSA) pipeline workflow.

<p>This workflow illustrates the protocol for construction of study-designs, automated ROI parsing, volumetric and shape measure calculations and between cohorts statistical analysis. 134 ADNI <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0013070#pone.0013070-Jack1" target="_blank">[1]</a> subjects are used in this study representing three independent cohorts classified by their Mini Mental State Exam (MMSE) scores - 18 Alzheimer's disease (AD) patients, 49 Mild Cognitive Impairment (MCI) subjects and 61 asymptomatic normals (NC). The workflow computes 6 global shape measures for each of 56 automatically extracted regions of interest (ROIs) for all subjects <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0013070#pone.0013070-Tu1" target="_blank">[2]</a>. Then, between-cohort statistics of these shape measures are calculated for each region of interest. The output of this workflow includes 18 3D scenes (3 possible group comparisons and 6 different shape measures). Each 3D scene contains only the ROI models of the regions where of the pair of cohorts showed statistically significant global shape differences. In this study-design, there are a total of 18 3D scene outputs reflecting the 3 possible pairs of group analyses (contrasts) comparing two cohorts (AD-NC, NC-MCI, AD-MCI) and the 6 different shape measures (mean-curvature, surface area, volume, shape-index, curvedness and fractal dimension). This workflow completed in about 46 hours on a small 56-node cluster and included a total of 3,209 jobs. The insert image only shows the 3D scene result for the ROIs which had statistically significant difference in mean-curvature between AD and NC subjects. For this comparison and shape measure, the resulting ROIs and their (labels) included right insular cortex (102), right middle orbitofrontal gyrus (30), right postcentral gyrus (42), left cingulated gyrus (121), left gyrus rectus (33) and left postcentral gyrus (41).</p

Pipeline environment as a visual programming language.

<p>Top panel (<b>Figure 3.A</b>) shows an example of a conditional flow of control (if-else), which splits a group of subjects into 3 cohorts, processes each cohort separately and finally maps statistical differences between the 3 groups. The bottom panel (<b>Figure 3.B</b>) demonstrates the Global Shape Analysis (GSA) pipeline workflow with efficient loop-group iterations – see the 3 loop-group modules on the top, one for each for the 3 population cohorts (AD, MCI and NC subjects).</p