NeuroImaging Analysis (NiAnalysis) is an archive-centric NeuroImaging analysis package.

NiAnalysis interacts closely with an archive, storing intermediate outputs, along with the parameters used to derive them, for reuse by subsequent analyses. Archives can either be XNAT repositories or (http://xnat.org) local directories organised by subject and visit, and a BIDS module (http://bids.neuroimaging.io/) is planned as future work.

Analysis workflows are constructed and executed using the NiPype package, and can either be run locally or submitted to high HPC facilities using NiPype’s execution plugins. For a requested analysis output, NiAnalysis determines the required processing steps by querying the archive to check for missing intermediate outputs before constructing the workflow graph. When running in an environment with the modules package installed, NiAnalysis manages the loading and unloading of software modules per pipeline node.

NiAnalysis is designed with an object-oriented philosophy, with the acquired and derived data sets along with the analysis pipelines used to derive the derived data sets encapsulated within "Study" classes.

The NiAnalysis package itself only provides the abstract Study and CombinedStudy base classes, which are designed to be sub-classed by more specific classes representing the analysis that can be performed on different modalities and contrasts (e.g. PetStudy, DiffusionMriStudy, FmriStudy). These contrast/modality classes are intended to be sub-classed and combined into classes that are specific to the a particular PET|MRI study, class (e.g. ASPREE Neuro), and integrate the complete workflow from preprocessing to statistic analysis.

NiAnalysis can be installed using pip:

$ pip install git+https://github.com/mbi-image/nianalysis.git

although for most pipelines you will also need to install the relevant neuro-imaging tools that are called on to the the processing (e.g. FSL, SPM/Matlab, AFNI, MRtrix, etc...).

For automated file format conversion between common neuroimaging formats (e.g. DICOM, NIfTI, MRtrix) the MRtrix (http://mrtrix.org) and/or Dicom2niix (http://github.com/rordenlab/dcm2niix) should be installed. Please see their documentation for instructions.