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Generating BIDS derivatives with (a) Banana

Project Description

Brain imAgiNg Analysis iN Arcana (Banana) is a collection of imaging analysis methods implemented in the Arcana framework, which is proposed as a code-base for collaborative development of neuroimaging workflows. Unlike traditional "linear" workflows, analyses implemented in Arcana are constructed on-the-fly from cascades of modular pipelines that generate derivatives from a mixture of acquired data and prequisite derivatives (similar to Makefiles). Given the "data-centric" architecture of this approach, there should be a natural harmony between it and the ongoing standardisation of BIDS derivatives.

The primary goal of this project is to closely align the analysis methods implemented in Banana with the BIDS standard, in particular BIDS derivatives, in order to make them familiar to new users and interoperable with other packages. Further to this, in cases where a de facto standard for a particular workflow exists (e.g. fmriprep) Banana should aim to mirror this standard by default. The extensibility of Arcana's object-orientated architecture (via class inheritance) could then be utilised to tailor such standard workflows to the needs of specific studies.

There is also plenty of scope to expand the imaging contrasts/modalities supported by Banana, so if you have expertise in a particular area and are interested in implementing it in Banana we can definitely look to do that as well.

Skills required to participate

Any of the following:

  • Python
  • Workflow design (preferably some Nipype but not essential)
  • Detailed knowledge BIDS specification (or part thereof)
  • Domain-specific knowlege of analysis of a particular imaging modality that you would like to see implemented in Banana (e.g. EEG, MEG, etc..)

Integration

  • Neuroinformaticians who are looking to implement and maintain a suite of generic analysis methods
  • PhD students who are looking to design a comprehensive analysis for their thesis
  • Domain-experts who a looking to implement their existing workflows in a portable framework

Try to define intermediate goals (milestones).

Preparation material

Skimming through the Arcana paper to get up to speed on the concepts would be a good idea.

Arcana BioXiv paper (in press Neuroinformatics, to be 10.1007/s12021-019-09430-1)

There is also some online documentation, but the paper is more comprehensive at this stage

arcana docs

Arcana is built on top of Nipype, so if you want to get your hands dirty implementing some analyses understanding its concepts is also important.

nipype docs

Link to your GitHub repo

Banana Github Repo

Communication

I have set up a new channel on the BrainHack mattermost here