Automatic analysis: open, efficient, and versatile neuroimaging workflows in MATLAB
By Tibor Auer, University of Surrey, Guildford, UK
Theme: Open Workflows
Format: Software/process demo
Abstract
Automatic analysis (aa) is a MATLAB-based pipeline system, that provides reproducibly configurable access to major software packages (e.g. SPM, FSL, FreeSurfer) and several other tools (e.g. BrainWavelet, FaceMasking, CoSMoMVPA, EEGLAB, Fieldtrip, hMRI) to process a wide range of modalities (e.g. sMRI, fMRI, dMRI, M/EEG).
You can construct complex workflows with great flexibility and convenience, because aa automatically connects the various steps (called modules in aa) based on its intuitive streaming concept. A recent introduction of toolboxes also aims to reduce the burden of configuring the various tools required by the workflow.
aa also offers an extended QA capability by providing diagnostics for certain modules on several levels. All these diagnostics reports (texts, images, videos) are summarised on interlinked html documents providing both within- and between-subject descriptions for an easier detection of suboptimal/erroneous steps or outlying participants.
aa supports parallel processing on most HPC scheduling systems (e.g. SLURM, Torque, LSF, SGE) with minimum configuration. It can also process anatomical, functional, and diffusion BIDS datasets and can be deployed as a standalone also in the format of BIDS Apps.
Automatic analysis: open, efficient, and versatile neuroimaging workflows in MATLAB
By Tibor Auer, University of Surrey, Guildford, UK
Abstract
Automatic analysis (aa) is a MATLAB-based pipeline system, that provides reproducibly configurable access to major software packages (e.g. SPM, FSL, FreeSurfer) and several other tools (e.g. BrainWavelet, FaceMasking, CoSMoMVPA, EEGLAB, Fieldtrip, hMRI) to process a wide range of modalities (e.g. sMRI, fMRI, dMRI, M/EEG).
You can construct complex workflows with great flexibility and convenience, because aa automatically connects the various steps (called modules in aa) based on its intuitive streaming concept. A recent introduction of toolboxes also aims to reduce the burden of configuring the various tools required by the workflow.
aa also offers an extended QA capability by providing diagnostics for certain modules on several levels. All these diagnostics reports (texts, images, videos) are summarised on interlinked html documents providing both within- and between-subject descriptions for an easier detection of suboptimal/erroneous steps or outlying participants.
aa supports parallel processing on most HPC scheduling systems (e.g. SLURM, Torque, LSF, SGE) with minimum configuration. It can also process anatomical, functional, and diffusion BIDS datasets and can be deployed as a standalone also in the format of BIDS Apps.
Useful Links
https://github.com/automaticanalysis/automaticanalysis https://hub.docker.com/r/bids/aa
Tagging @tiborauer