Generating BIDS derivatives with (a) Banana

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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, and 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 could then be utilised to tailor such standard workflows to the needs of specific studies (via class inheritance).

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

• Python programmers and workflow designers who are looking to implement and maintain a suite of generic analysis methods should be able to help extend existing classes and implement new ones for different imaging contrast/modalities not currently covered.
• Domain-experts (e.g. EEG, MEG, pre-clinical MRI) who a interested implementing existing workflows within in a portable, extensible framework could help to guide the implementation, check the derivatives they create are correct, etc...
• 1st and 2nd year PhD students who are planning the analysis for their thesis, could look to create their own customised "study" classes that extend from the generic base classes in Banana to integrate all their analysis in the same code-base (and re-use common derivatives/QC, maintain provenance records).

Preparation material

Skim through the Arcana paper for the basic concepts,

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

There is also some online documentation,

arcana docs

Arcana is built on top of Nipype so understanding Nipype concepts would also be useful,

nipype docs

Link to your GitHub repo

Banana Github Repo

Communication

There is a new channel on the BrainHack mattermost here