Processing workflows for magnetic resonance images of the brain in infants
Project description
Magnetic resonance imaging (MRI) requires a set of preprocessing steps before any statistical analysis. In an effort to standardize preprocessing, we developed fMRIPrep (a preprocessing tool for functional MRI, fMRI), and generalized its standardization approach to other neuroimaging modalities (NiPreps). NiPreps brings standardization and ease of use to the researcher, and effectively limits the methodological variability within preprocessing. fMRIPrep is designed to be used across wide ranges of populations; however it is designed for (and evaluated with) human adult datasets. Infant MRI (i.e., 0-2 years) presents unique challenges due to head size (e.g., reduced SNR and increased partial voluming and rapid shifting in tissue contrast due to myelination. These and other challenges require a more specialized workflow. NiBabies, an open-source pipeline extending from fMRIPrep for infant structural and functional MRI preprocessing, aims to address this need.
The workflow is built atop Nipype and encompases a large set of tools from well-known neuroimaging packages, including FSL, ANTs, FreeSurfer, AFNI, Connectome Workbench, and Nilearn. This pipeline was designed to provide the best software implementation for each state of preprocessing, and will be updated as newer and better neuroimaging software becomes available.
NiBabies performs basic preprocessing steps (coregistration, normalization, unwarping, segmentation, skullstripping etc.) providing outputs that can be easily submitted to a variety of group level analyses, including task-based or resting-state fMRI, graph theory measures, surface or volume-based statistics, etc. NiBabies allows you to easily do the following:
- Take fMRI data from unprocessed (only reconstructed) to ready for analysis.
- Implement tools from different software packages.
- Achieve optimal data processing quality by using the best tools available.
- Generate preprocessing-assessment reports, with which the user can easily identify problems.
- Receive verbose output concerning the stage of preprocessing for each subject, including meaningful errors.
- Automate and parallelize processing steps, which provides a significant speed-up from typical linear, manual processing.
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