Automatic analysis (aa) is a framework for medical image analysis designed to allow users to achieve an efficient analysis workflow, whether analyzing a single dataset or creating a complex pipeline with many thousands of acquisitions.
aa uses Matlab, and brings together many of the best tools for fMRI analysis (e.g., from SPM5/8/12, FSL and Freesurfer), and MEG/EEG (EEGlab).
Why use it?
- Automatic. Group-level univariate statistics, MVPA, or morphometry using a off-the-shelf recipes with minimal coding
- Restartable. If aa stops for any reason, restarting it will make it begin at the stage where it left off.
- Parallel processing. Where multiple machines are available, aa jobs can seamlessly be distributed across them.
- Flexible control. New neuroimagers are directed to what is essential, more experienced users can readily change many settings, while advanced users can simply add new components to the system.
- Time saving. Structurals and field maps (if you have them) are automatically detected.
- Easy to maintain The code components are stored in a github repository, such that changes and new components can be easily be released and updated.
- Record keeping. Unlike SPM used from the GUI, aa records all parameters used, and allows easy recreation of a dataset from the raw data at a later date.
- Modular, with simple interface. Matlab programmers can easily write new modules and incorporate them into the processing stream.
Cusack R, Vicente-Grabovetsky A, Mitchell DJ, Wild CJ, Auer T, Linke AC, Peelle JE (2015) Automatic analysis (aa): Efficient neuroimaging workflows and parallel processing using Matlab and XML. Frontiers in Neuroinformatics 8:90. http://journal.frontiersin.org/Journal/10.3389/fninf.2014.00090/abstract