Effi cient , 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).

[1]  Marieke Langen,et al.  Caudate Nucleus Is Enlarged in High-Functioning Medication-Naive Subjects with Autism , 2007, Biological Psychiatry.

[2]  PlaleBeth,et al.  A survey of data provenance in e-science , 2005 .

[3]  Arthur W Toga,et al.  The LONI Pipeline Processing Environment , 2003, NeuroImage.

[4]  R. Woods,et al.  Gender effects on cortical thickness and the influence of scaling , 2006, Human brain mapping.

[5]  Arthur W. Toga,et al.  Neuroimaging Data Provenance Using the LONI Pipeline Workflow Environment , 2008, IPAW.

[6]  BitterIngmar,et al.  Comparison of Four Freely Available Frameworks for Image Processing and Visualization That Use ITK , 2007 .

[7]  Gregor von Laszewski,et al.  A Collaborative Informatics Infrastructure for Multi-Scale Science , 2004, Proceedings of the Second International Workshop on Challenges of Large Applications in Distributed Environments, 2004. CLADE 2004..

[8]  Kiralee M. Hayashi,et al.  Dynamics of Gray Matter Loss in Alzheimer's Disease , 2003, The Journal of Neuroscience.

[9]  Ian T. Foster,et al.  Accelerating Medical Research using the Swift Workflow System , 2007, HealthGrid.

[10]  Kiralee M. Hayashi,et al.  Dynamic mapping of normal human hippocampal development , 2006, Hippocampus.

[11]  Carole A. Goble,et al.  Mining Taverna's semantic web of provenance , 2008, Concurr. Comput. Pract. Exp..

[12]  C. Jack,et al.  Ways toward an early diagnosis in Alzheimer’s disease: The Alzheimer’s Disease Neuroimaging Initiative (ADNI) , 2005, Alzheimer's & Dementia.

[13]  Paul M. Thompson,et al.  Brain Anatomical Structure Segmentation by Hybrid Discriminative/Generative Models , 2008, IEEE Transactions on Medical Imaging.

[14]  Arthur W. Toga,et al.  Cerebellar cortical atrophy in experimental autoimmune encephalomyelitis , 2006, NeuroImage.

[15]  Charles DeCarli,et al.  Sex, apolipoprotein E epsilon 4 status, and hippocampal volume in mild cognitive impairment. , 2005, Archives of neurology.

[16]  Bertram Ludäscher,et al.  CONCURRENCY AND COMPUTATION : PRACTICE AND EXPERIENCE Concurrency Computat , 2008 .

[17]  R. Woods,et al.  Sex differences in cortical thickness mapped in 176 healthy individuals between 7 and 87 years of age. , 2007, Cerebral cortex.

[18]  Jan-Martin Kuhnigk,et al.  Comparison of Four Freely Available Frameworks for Image Processing and Visualization That Use ITK , 2007, IEEE Transactions on Visualization and Computer Graphics.

[19]  Cláudio T. Silva,et al.  VisTrails: visualization meets data management , 2006, SIGMOD Conference.

[20]  Arthur W. Toga,et al.  A meta-algorithm for brain extraction in MRI , 2004, NeuroImage.

[21]  Jason Maassen,et al.  Programming Scientific and Distributed Workflow with Triana Services , 2004 .

[22]  Martin Senger,et al.  BioMoby extensions to the Taverna workflow management and enactment software , 2006, BMC Bioinformatics.

[23]  Paul M. Thompson,et al.  Functional MRI BOLD response to Tower of London performance of first-episode schizophrenia patients using cortical pattern matching , 2005, NeuroImage.

[24]  Paul M. Thompson,et al.  Asymmetries of cortical shape: Effects of handedness, sex and schizophrenia , 2007, NeuroImage.

[25]  Scott T. Grafton,et al.  Automated image registration: I. General methods and intrasubject, intramodality validation. , 1998, Journal of computer assisted tomography.

[26]  Paul M. Thompson,et al.  3 D pattern of brain atrophy in HIV / AIDS visualized using tensor-based morphometry , 2006 .

[27]  W. Drevets.  Neuroimaging and neuropathological studies of depression: implications for the cognitive-emotional features of mood disorders , 2001, Current Opinion in Neurobiology.

[28]  Paul T. Groth,et al.  The Requirements of Using Provenance in e-Science Experiments , 2007, Journal of Grid Computing.

[29]  Arthur W. Toga,et al.  Provenance in neuroimaging , 2008, NeuroImage.

[30]  Rohit Bakshi,et al.  Multiple Sclerosis Medical Image Analysis and Information Management , 2005, Journal of neuroimaging : official journal of the American Society of Neuroimaging.

[31]  Paul M. Thompson,et al.  What is where and why it is important , 2007, NeuroImage.

[32]  Edward A. Lee,et al.  CONCURRENCY AND COMPUTATION: PRACTICE AND EXPERIENCE Concurrency Computat.: Pract. Exper. 2000; 00:1–7 Prepared using cpeauth.cls [Version: 2002/09/19 v2.02] Taverna: Lessons in creating , 2022 .

[33]  Arthur W. Toga,et al.  iTools: A Framework for Classification, Categorization and Integration of Computational Biology Resources , 2008, PloS one.

[34]  Edward A. Lee,et al.  Scientific workflow management and the Kepler system , 2006, Concurr. Comput. Pract. Exp..