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PUMI

Laboratory for Predictive Neuroimaging - University Hospital Essen, Germany

Open in GitHub Codespaces

Status

GitHub GitHub repo size GitHub last commit GitHub Workflow Status GitHub tag (latest by date) GitHub release (latest by date)

Docker

Docker Image Version (latest semver) Docker Image Size (latest semver)

Docker Image Version (latest semver) Docker Image Size (latest semver)

Issues

GitHub issues GitHub closed issues

First steps for developers

Clone this repo locally

git clone git@github.com:pni-lab/PUMI.git

Set up dependencies

Option A: Docker

  • pull the docker image:
    • pnilab/pumi-slim:latest: for a slim image containing the latest PUMI version and only the exact dependencies it needs
    • pnilab/pumi:latest: for the full image, containing all dependnecies but no PUMI source code (useful when integrating new tools, but takes long to download). To work in the full image:
    • - run it: `docker run -ti pnilab/pumi bash`
- get the latest PUMI source by `git clone http://github.com/pni-lab/PUMI.git`
- install PUMI: `cd /PUMI; pip install .`
-
  • set up your IDE to work within the container

Option B: Install all non-python dependencies locally

  • FSL
  • AFNI
  • ANTs
  • Freesurfer

Get test dataset (optional)

cd data_in
export WEBDAV_USERNAME=XXXX
export WEBDAV_PASSWORD=XXXX-XXXX-XXXX-XXXX
datalad install -s git@github.com:pni-data/pumi_test_data.git pumi_test_data
datalad siblings -d pumi_test_data enable -s sciebo.sfb289
datalad get pumi_test_data/*

Contact the developers for webdav credentials.

Coding Conventions

  • name of workflow is the same as the name of the variable that holds it

  • name of node is the same as the name of the variable that holds it

  • qc nodes's name defines the subdir in qc; it should be: <base_wf>_qc

  • avoid "batch-connects" in @PumiPipeline funcions: it is preferred that right after node (or workflow) definition all possible connect statements corresponding to the node are specified

  • for readibility, we use the signature: connect(source_node, source_field, dest_node, dest_field)

  • except, in case there are multiple connections between the same pair of nodes, batch-connect should be used

  • @PumiPipeline funcions' first connect statement(s) is (are) connecting to the inputspec

  • @PumiPipeline funcions' last connect statement(s) is (are) connecting to the outputspec

  • @PumiPipeline funcions' are minimalistic and do NO "housekeeping".

Version incrementing rules

  • increment major if:
    • reverse-compatibility is broken
    • a substantial set of new features are added or a grand milestone is reached in the development
  • increment minor if:
    • the running environment must be changed, i.e. when the docker image pnilab/pumi has been changed
    • new feature is added (e.g. a new preprocessing step is integrated)
  • increment patch for smaller patches, e.g.:
    • changes in existing behavior (new parameter, params renamed)
    • bugfixes
    • typically after merging a pull request

Caution:

Reverse compatibility will not be guaranteed until the major version reaches 1

Incrementing major or minor version:

  • commit the changes
  • tag the commit, deploy the new full docker image locally, push the tag:
git tag <MAJOR>.<MINOR>.<PATCH>
./deploy_full.sh # creates the new full docker image
git push --tag
  • push to your branch
  • open PR A github action automatically creates the new slim docker image.

Incrementing patch version:

  • commit the changes
  • tag the commit, push the tag
git tag <MAJOR>.<MINOR>.<PATCH>
git push --tag
  • push to your branch
  • open PR A github action automatically creates the new slim docker image.

Cite

Nipype

  • 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.

FSL

  • M.W. Woolrich, S. Jbabdi, B. Patenaude, M. Chappell, S. Makni, T. Behrens, C. Beckmann, M. Jenkinson, S.M. Smith. Bayesian analysis of neuroimaging data in FSL. NeuroImage, 45:S173-86, 2009

  • S.M. Smith, M. Jenkinson, M.W. Woolrich, C.F. Beckmann, T.E.J. Behrens, H. Johansen-Berg, P.R. Bannister, M. De Luca, I. Drobnjak, D.E. Flitney, R. Niazy, J. Saunders, J. Vickers, Y. Zhang, N. De Stefano, J.M. Brady, and P.M. Matthews. Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23(S1):208-19, 2004

  • M. Jenkinson, C.F. Beckmann, T.E. Behrens, M.W. Woolrich, S.M. Smith. FSL. NeuroImage, 62:782-90, 2012

ANTs

  • Tustison NJ, Cook PA, Klein A, Song G, Das SR, Duda JT, Kandel BM, van Strien N, Stone JR, Gee JC, Avants BB. Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements. Neuroimage. 2014 Oct 1;99:166-79. doi: 10.1016/j.neuroimage.2014.05.044. Epub 2014 May 29. PMID: 24879923.

  • Avants BB, Tustison NJ, Stauffer M, Song G, Wu B, Gee JC. The Insight ToolKit image registration framework. Front Neuroinform. 2014 Apr 28;8:44. doi: 10.3389/fninf.2014.00044. PMID: 24817849; PMCID: PMC4009425.

  • Avants BB, Tustison NJ, Wu J, Cook PA, Gee JC. An open source multivariate framework for n-tissue segmentation with evaluation on public data. Neuroinformatics. 2011 Dec;9(4):381-400. doi: 10.1007/s12021-011-9109-y. PMID: 21373993; PMCID: PMC3297199.

AFNI

  • Cox RW, Jesmanowicz A (1999). Real-time 3D image registration for functional MRI. Magnetic Resonance in Medicine, 42: 1014-1018.

  • Glen DR, Taylor PA, Buchsbaum BR, Cox RW, Reynolds RC (2020). Beware (Surprisingly Common) Left-Right Flips in Your MRI Data: An Efficient and Robust Method to Check MRI Dataset Consistency Using AFNI. Front. Neuroinformatics 14. doi.org/10.3389/fninf.2020.00018

  • Taylor PA, Chen G, Glen DR, Rajendra JK, Reynolds RC, Cox RW (2018). FMRI processing with AFNI: Some comments and corrections on ‘Exploring the Impact of Analysis Software on Task fMRI Results’. bioRxiv 308643; doi:10.1101/308643

  • Jo HJ, Saad ZS, Simmons WK, Milbury LA, Cox RW. Mapping sources of correlation in resting state FMRI, with artifact detection and removal. Neuroimage. 2010;52(2):571-582. doi:10.1016/j.neuroimage.2010.04.246

HD-BET

  • Isensee F, Schell M, Tursunova I, Brugnara G, Bonekamp D, Neuberger U, Wick A, Schlemmer HP, Heiland S, Wick W, Bendszus M, Maier-Hein KH, Kickingereder P. Automated brain extraction of multi-sequence MRI using artificial neural networks. Hum Brain Mapp. 2019; 1–13. https://doi.org/10.1002/hbm.24750

pydeface

  • Omer Faruk Gulban, Dylan Nielson, Russ Poldrack, john lee, Chris Gorgolewski, Vanessasaurus, & Satrajit Ghosh. (2019). poldracklab/pydeface: v2.0.0 (v2.0.0). Zenodo. https://doi.org/10.5281/zenodo.3524401

Templateflow

  • TemplateFlow: a community archive of imaging templates and atlases for improved consistency in neuroimaging R Ciric, R Lorenz, WH Thompson, M Goncalves, E MacNicol, CJ Markiewicz, YO Halchenko, SS Ghosh, KJ Gorgolewski, RA Poldrack, O Esteban bioRxiv 2021.02.10.430678; doi: 10.1101/2021.02.10.430678 =======