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

By Krzysztof J. Gorgolewski, Christopher D. Burns, Cindee M. Madison, Dav Clark, Yaroslav O. Halchenko, Michael L. Waskom, Satrajit S. Ghosh. Front. Neuroinform., 2011

2,091 citations recorded by the source corpus.

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.

Subfields: Biophysics, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/63a604e73af861640cbf711cef8b75fc6d5ccd1e

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Citations in the corpus

Mindboggling morphometry of human brains (2016, background): “50186) to promote a modular, flexible design that captures provenance information [57].”
Neuroimaging of the Philadelphia Neurodevelopmental Cohort (2013, positive): “1996), and was completed within the XNAT framework using NiPype (Gorgolewski et al., 2011) and PyXNAT (Schwartz et al.”
Data-Efficient Mutual Information Neural Estimator (2019, methodology): “A.2 Additional Details on Dataset Preprocessing Preprocessing was performed using fMRIPrep [9], a Nipype [19] based tool.”
PANDA: a pipeline toolbox for analyzing brain diffusion images (2013, methodology): “Currently, there have been a few packages such as MIPAV (McAuliffe et al., 2001), JIST (Lucas et al., 2010), Nipype (Gorgolewski et al., 2011), and LONI (Dinov et al., 2009), which aim to facilitate automated processing of neuroimaging dataset.”
From brain maps to cognitive ontologies: informatics and the search for mental structure (2015, background): “…surrounding which cognitive process deserves credit for the effect of a particular experimental task on brain activity is attributable to the impracticality of using dozens of different tasks in every study in order to isolate a specific process by converging operations (Garner et al. 1956).”
Assessing and tuning brain decoders: Cross‐validation, caveats, and guidelines (2016, methodology): “The preprocessing pipeline was orchestrated through the Nipype processing infrastructure (Gorgolewski et al., 2011).”
Data sharing in neuroimaging research (2012, methodology): “The ﬁrst trials could be performed with Nipype (Gorgolewski et al., 2011) and PyXNAT (Schwartz et al., submitted).”
A voxel-wise encoding model for early visual areas decodes mental images of remembered scenes (2014, positive): “This motion correction and registration pipeline was implemented using the nipype Python library (Gorgolewski et al., 2011).”
Analyzing Neuroimaging Data Through Recurrent Deep Learning Models (2018, positive): “We convolved these predictors with a canonical hemodynamic response function (HRF) [Glover, 1999], as implemented in NiPy 0.4.1 [Gorgolewski et al., 2011], to generate the model predictors.”
Nmrglue: an open source Python package for the analysis of multidimensional NMR data (2013, background): “2011), neuroimaging (Gorgolewski et al. 2011) and statistics (Seabold and Perktold 2010).”
How open science helps researchers succeed (2016, background): “, the NIPY group of projects in neuroimaging 381 [138].”