Nipype

Nipype
Name	Nipype
Developer	Neuroimaging Informatics Technology Initiative contributors
Released	2009
Programming language	Python (programming language)
Operating system	Linux, macOS, Microsoft Windows
License	BSD license

Nipype Nipype is an open-source Python-based platform for constructing and executing portable neuroimaging analysis workflows. It enables reproducible pipelines by wrapping heterogeneous software from projects such as FSL, SPM (software), AFNI, ANTs (software), and FreeSurfer, and by integrating with computing environments like Docker (software platform), Singularity (software), and SLURM. Nipype is used across laboratories, consortia, and institutions including groups at Harvard University, Massachusetts Institute of Technology, University of Oxford, Stanford University, and University of California, Los Angeles.

Overview

Nipype provides a unified interface to a constellation of neuroimaging packages such as FSL, SPM (software), AFNI, FreeSurfer, ANTs (software), MRtrix, Dipy, Camino (software), ExploreDTI, BrainSuite, SPM12, SPM8, Freesurfer, Caret (software), Connectome Workbench, MRtrix3, Nipy, MNE-Python, BIDS (format), BIDS Apps, OpenNeuro, Human Connectome Project, ADNI, UK Biobank, ENIGMA and others. The project facilitates interoperability among tools developed by teams at McGill University, Johns Hopkins University, UCLA, University College London, Max Planck Society, Karolinska Institutet, Imperial College London, and University of Cambridge. Nipype’s design supports research reproducibility promoted by initiatives like FAIR data principles, ReproNim, INCF, OHBM, Neurostars, and Zenodo.

Architecture and Components

Nipype’s core components include an execution engine, a standardized interface layer, and data provenance capture. The interface layer exposes functionality from projects such as FSL, SPM (software), AFNI, FreeSurfer, ANTs (software), MRtrix3, MNE-Python and Dipy, while the execution engine supports backends including SLURM, Sun Grid Engine, HTCondor, PBS Professional, LSF (software), and local multiprocessing. Provenance and caching integrate with standards and services like BIDS (format), Provenance (computer science), DataLad, Git, Mercurial, DVC (data version control), Zenodo, and Open Science Framework. The architecture accommodates containerization through Docker (software platform), Singularity (software), and orchestration tools such as Kubernetes and Apache Mesos.

Supported Neuroimaging Tools and Interfaces

Nipype wraps a wide range of neuroimaging packages and toolboxes developed at institutions like Massachusetts General Hospital, Montreal Neurological Institute, University of Pennsylvania, University of Washington, University of California, San Diego, Johns Hopkins University, Yale University, Columbia University, Duke University, University of Iowa, National Institutes of Health, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, and European Space Agency. Notable supported packages include FSL, SPM (software), AFNI, FreeSurfer, ANTs (software), MRtrix3, Dipy, MNE-Python, Slicer (software), ITK (software), VTK (software), NiBabel, Nilearn, Nipy, C-PAC (software), fMRIPrep, QSIPrep, MRIQC, pyBIDS, BIDS-validator, XCP-Diff, CPAC, PALM (Permutation Analysis of Linear Models), Freesurfer, SPM12, Eddy (FSL tool), Topup (FSL tool), and BET (FSL tool).

Workflow Construction and Execution

Workflows in Nipype are constructed from modular nodes and connectors that mirror concepts used by projects such as Kepler (software), Taverna, Galaxy (informatics platform), Airflow, Luigi (software), Snakemake, Nextflow, and Cromwell (workflow engine). Developers create interfaces with Python (programming language) classes, compose graphs with nodes analogous to systems at LONI Pipeline, Brainstorm (software), EEGLAB, and FieldTrip, and execute them using plugins for backends like SLURM, HTCondor, PBS Professional, and LSF (software). Execution features include intelligent caching, checkpointing, parallelization, and resource specification for cluster environments used by XSEDE, PRACE, Compute Canada, Amazon Web Services, Google Cloud Platform, and Microsoft Azure.

Use Cases and Applications

Nipype has been applied to task-based fMRI, resting-state fMRI, diffusion MRI, structural MRI, surface-based analyses, and multimodal integration in studies from groups at Harvard Medical School, Massachusetts General Hospital, Stanford University, University of Oxford, Karolinska Institutet, McGill University, University of Toronto, University of Melbourne, Monash University, Peking University, Tsinghua University, Seoul National University, University of Tokyo, Kyoto University, and Riken. It supports pipeline projects like fMRIPrep, QSIPrep, MRIQC, ENIGMA, Human Connectome Project, UK Biobank, and ADNI, and is used in reproducible publications archived on arXiv, bioRxiv, Nature Communications, NeuroImage, Journal of Neuroscience, PLOS ONE, Frontiers in Neuroscience, Science Advances, and Proceedings of the National Academy of Sciences. Researchers integrate Nipype workflows into platforms such as OpenNeuro, CBRAIN, Neurovault, NITRC, XNAT, BrainLife, and DataLad.

Development, Community, and Governance

Development is coordinated by contributors from academic centers and research consortia including ReproNim, INCF, OHBM, Neurostars, Software Carpentry, Mozilla Science Lab, NumFOCUS, Python Software Foundation, SciPy, NumPy, Pandas (software), Matplotlib, scikit-learn, scikit-image, NiBabel, Nilearn, and Dask (software). Governance follows community-driven models similar to projects like TensorFlow, PyTorch, scikit-learn, and Jupyter (software), with contribution workflows via GitHub, GitLab, Git, and continuous integration services like Travis CI, GitHub Actions, CircleCI, Jenkins (software), and AppVeyor. Training and outreach occur at meetings and workshops organized by OHBM, SfN (Society for Neuroscience), SfN, USNeuromatch, NeuroHackademy, Neuroinformatics, CNS (Computational Neuroscience Society), and through tutorials at Neurostars and Zenodo.

Category:Neuroimaging software