Literature DB >> 12880830

The LONI Pipeline Processing Environment.

David E Rex1, Jeffrey Q Ma, Arthur W Toga.   

Abstract

The analysis of raw data in neuroimaging has become a computationally entrenched process with many intricate steps run on increasingly larger datasets. Many software packages exist that provide either complete analyses or specific steps in an analysis. These packages often possess diverse input and output requirements, utilize different file formats, run in particular environments, and have limited abilities with certain types of data. The combination of these packages to achieve more sensitive and accurate results has become a common tactic in brain mapping studies but requires much work to ensure valid interoperation between programs. The handling, organization, and storage of intermediate data can prove difficult as well. The LONI Pipeline Processing Environment is a simple, efficient, and distributed computing solution to these problems enabling software inclusion from different laboratories in different environments. It is used here to derive a T1-weighted MRI atlas of the human brain from 452 normal young adult subjects with fully automated processing. The LONI Pipeline Processing Environment's parallel processing efficiency using an integrated client/server dataflow model was 80.9% when running the atlas generation pipeline from a PC client (Acer TravelMate 340T) on 48 dedicated server processors (Silicon Graphics Inc. Origin 3000). The environment was 97.5% efficient when the same analysis was run on eight dedicated processors.

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Year:  2003        PMID: 12880830     DOI: 10.1016/s1053-8119(03)00185-x

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  144 in total

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3.  Deficits in axonal transport in hippocampal-based circuitry and the visual pathway in APP knock-out animals witnessed by manganese enhanced MRI.

Authors:  Joseph J Gallagher; Xiaowei Zhang; Gregory J Ziomek; Russell E Jacobs; Elaine L Bearer
Journal:  Neuroimage       Date:  2012-02-10       Impact factor: 6.556

4.  The development of the corpus callosum in the healthy human brain.

Authors:  Eileen Luders; Paul M Thompson; Arthur W Toga
Journal:  J Neurosci       Date:  2010-08-18       Impact factor: 6.167

Review 5.  Structural brain atlases: design, rationale, and applications in normal and pathological cohorts.

Authors:  Pravat K Mandal; Rashima Mahajan; Ivo D Dinov
Journal:  J Alzheimers Dis       Date:  2012       Impact factor: 4.472

6.  Social status modulates neural activity in the mentalizing network.

Authors:  Keely A Muscatell; Sylvia A Morelli; Emily B Falk; Baldwin M Way; Jennifer H Pfeifer; Adam D Galinsky; Matthew D Lieberman; Mirella Dapretto; Naomi I Eisenberger
Journal:  Neuroimage       Date:  2012-01-25       Impact factor: 6.556

7.  Multimodal, multidimensional models of mouse brain.

Authors:  Allan J Mackenzie-Graham; Erh-Fang Lee; Ivo D Dinov; Heng Yuan; Russell E Jacobs; Arthur W Toga
Journal:  Epilepsia       Date:  2007       Impact factor: 5.864

8.  Online resource for validation of brain segmentation methods.

Authors:  David W Shattuck; Gautam Prasad; Mubeena Mirza; Katherine L Narr; Arthur W Toga
Journal:  Neuroimage       Date:  2008-11-25       Impact factor: 6.556

9.  Hippocampal to basal forebrain transport of Mn2+ is impaired by deletion of KLC1, a subunit of the conventional kinesin microtubule-based motor.

Authors:  Christopher S Medina; Octavian Biris; Tomas L Falzone; Xiaowei Zhang; Amber J Zimmerman; Elaine L Bearer
Journal:  Neuroimage       Date:  2016-10-14       Impact factor: 6.556

10.  White matter microstructure in subjects with attention-deficit/hyperactivity disorder and their siblings.

Authors:  Katherine E Lawrence; Jennifer G Levitt; Sandra K Loo; Ronald Ly; Victor Yee; Joseph O'Neill; Jeffry Alger; Katherine L Narr
Journal:  J Am Acad Child Adolesc Psychiatry       Date:  2013-03-07       Impact factor: 8.829

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