Methods ARTICLE

Front. Neuroinform., 22 August 2011 | doi: 10.3389/fninf.2011.00013

Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python

  • 1 Neuroinformatics and Computational Neuroscience Doctoral Training Centre, School of Informatics, University of Edinburgh, Edinburgh, UK
  • 2 Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
  • 3 Department of Psychology, University of California, Berkeley, CA, USA
  • 4 Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
  • 5 Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
  • 6 McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
  • 7 Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA

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.

Keywords: neuroimaging, data processing, workflow, pipeline, reproducible research, Python

Citation: Gorgolewski K, Burns CD, Madison C, Clark D, Halchenko YO, Waskom ML, Ghosh SS (2011) Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python. Front. Neuroinform. 5:13. doi: 10.3389/fninf.2011.00013

Received: 23 June 2011; Accepted: 23 July 2011;
Published online: 22 August 2011.

Edited by:

Andrew P. Davison, CNRS, France

Reviewed by:

Gael Varoquaux, INSERM, France
Ivo Dinov, University of California, USA

Copyright: © 2011 Gorgolewski, Burns, Madison, Clark, Halchenko, Waskom, Ghosh. This is an open-access article subject to a non-exclusive license between the authors and Frontiers Media SA, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and other Frontiers conditions are complied with.

*Correspondence: Krzysztof Gorgolewski, School of Informatics, University of Edinburgh, Informatics Forum, 10 Crichton Street, Edinburgh EH8 9AB, UK. e-mail: krzysztof.gorgolewski@gmail.com

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