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Treffer: Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python

Title:
Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python
Authors:
Gorgolewski, Krzysztof, Burns, Christopher D., Madison, Cindee, Clark, Dav, Halchenko, Yaroslav O., Waskom, Michael L., Ghosh, Satrajit S
Contributors:
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology. Research Laboratory of Electronics, McGovern Institute for Brain Research at MIT, Waskom, Michael L., Ghosh, Satrajit S
Source:
Frontiers
Publisher Information:
Frontiers Research Foundation
Publication Year:
2011
Collection:
DSpace@MIT (Massachusetts Institute of Technology)
Document Type:
Fachzeitschrift article in journal/newspaper
File Description:
application/pdf
Language:
English
Relation:
http://dx.doi.org/10.3389/fninf.2011.00013; Frontiers in Neuroinformatics; http://hdl.handle.net/1721.1/106998; Gorgolewski, Krzysztof et al. “Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python.” Frontiers in Neuroinformatics 5 (2011): n. pag.
Availability:
http://hdl.handle.net/1721.1/106998
Rights:
Creative Commons Attribution 4.0 International License ; http://creativecommons.org/licenses/by-nc-sa/4.0/
Accession Number:
edsbas.8749CAA7
Database:
BASE

Weitere Informationen

Current neuroimaging software offer users an incredible opportunity to analyze their data in different ways, with different underlying assumptions. Several sophisticated software packages (e.g., AFNI, BrainVoyager, FSL, FreeSurfer, Nipy, R, SPM) are used to process and analyze large and often diverse (highly multi-dimensional) data. However, this heterogeneous collection of specialized applications creates several issues that hinder replicable, efficient, and optimal use of neuroimaging analysis approaches: (1) No uniform access to neuroimaging analysis software and usage information; (2) No framework for comparative algorithm development and dissemination; (3) Personnel turnover in laboratories often limits methodological continuity and training new personnel takes time; (4) Neuroimaging software packages do not address computational efficiency; and (5) Methods sections in journal articles are inadequate for reproducing results. To address these issues, we present Nipype (Neuroimaging in Python: Pipelines and Interfaces; http://nipy.org/nipype), an open-source, community-developed, software package, and scriptable library. Nipype solves the issues by providing Interfaces to existing neuroimaging software with uniform usage semantics and by facilitating interaction between these packages using Workflows. Nipype provides an environment that encourages interactive exploration of algorithms, eases the design of Workflows within and between packages, allows rapid comparative development of algorithms and reduces the learning curve necessary to use different packages. Nipype supports both local and remote execution on multi-core machines and clusters, without additional scripting. Nipype is Berkeley Software Distribution licensed, allowing anyone unrestricted usage. An open, community-driven development philosophy allows the software to quickly adapt and address the varied needs of the evolving neuroimaging community, especially in the context of increasing demand for reproducible research. ; National Institute for ...