PUMI

Laboratory for Predictive Neuroimaging - University Hospital Essen, Germany

Status

Docker

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 =======