Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python
Krzysztof J. Gorgolewski,Christopher Burns,Cindee Madison,Dav Clark,Yaroslav O. Halchenko,Michael Waskom,Satrajit S. Ghosh +6 more
TLDR
Nipype solves issues by providing Interfaces to existing neuroimaging software with uniform usage semantics and by facilitating interaction between these packages using Workflows, and provides an environment that encourages interactive exploration of algorithms, eases the design of Workflows within and between packages, and reduces the learning Curve.Abstract:
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 BSD 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.read more
Citations
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Journal ArticleDOI
3D Slicer as an image computing platform for the Quantitative Imaging Network.
Andriy Fedorov,Reinhard Beichel,Jayashree Kalpathy-Cramer,Julien Finet,Jean-Christophe Fillion-Robin,Sonia Pujol,Christian Bauer,Dominique Jennings,Fiona M. Fennessy,Milan Sonka,John M. Buatti,Stephen R. Aylward,James V. Miller,Steve Pieper,Ron Kikinis +14 more
TL;DR: An overview of 3D Slicer is presented as a platform for prototyping, development and evaluation of image analysis tools for clinical research applications and the utility of the platform in the scope of QIN is illustrated.
Journal ArticleDOI
MEG and EEG data analysis with MNE-Python
Alexandre Gramfort,Martin Luessi,Eric B. Larson,Denis A. Engemann,Daniel Strohmeier,Christian Brodbeck,Roman Goj,Mainak Jas,Teon L Brooks,Lauri Parkkonen,Matti Hämäläinen +10 more
TL;DR: MNE-Python as discussed by the authors is an open-source software package that provides state-of-the-art algorithms implemented in Python that cover multiple methods of data preprocessing, source localization, statistical analysis, and estimation of functional connectivity between distributed brain regions.
Journal ArticleDOI
fMRIPrep: a robust preprocessing pipeline for functional MRI
Oscar Esteban,Christopher J. Markiewicz,Ross Blair,Craig A. Moodie,Ayse Ilkay Isik,Asier Erramuzpe,James D. Kent,Mathias Goncalves,Elizabeth DuPre,Snyder M,Hiroyuki Oya,Satrajit S. Ghosh,Satrajit S. Ghosh,Jessey Wright,Joke Durnez,Russell A. Poldrack,Krzysztof J. Gorgolewski +16 more
TL;DR: fMRIPrep is a robust and easy-to-use pipeline for preprocessing of diverse fMRI data that dispenses of manual intervention, thereby ensuring the reproducibility of the results.
Journal ArticleDOI
MNE software for processing MEG and EEG data
Alexandre Gramfort,Martin Luessi,Eric B. Larson,Denis A. Engemann,Daniel Strohmeier,Christian Brodbeck,Lauri Parkkonen,Matti Hämäläinen +7 more
TL;DR: Detailed information about the MNE package is given and typical use cases are described while also warning about potential caveats in analysis.
Journal ArticleDOI
Machine learning for neuroimaging with scikit-learn.
Alexandre Abraham,Alexandre Abraham,Fabian Pedregosa,Fabian Pedregosa,Michael Eickenberg,Michael Eickenberg,Philippe Gervais,Philippe Gervais,Andreas Mueller,Jean Kossaifi,Alexandre Gramfort,Alexandre Gramfort,Alexandre Gramfort,Bertrand Thirion,Bertrand Thirion,Gaël Varoquaux,Gaël Varoquaux +16 more
TL;DR: It is illustrated how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps and its application to neuroimaging data provides a versatile tool to study the brain.
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