Abstract
The reliability of scientific results critically depends on reproducible and transparent data processing. Cross-subject and cross-study comparability of imaging data in general, and magnetic resonance imaging (MRI) data in particular, is contingent on the quality of registration to a standard reference space. In small animal MRI this is not adequately provided by currently used processing workflows, which utilize high-level scripts optimized for human data, and adapt animal data to fit the scripts, rather than vice-versa. In this fully reproducible article we showcase a generic workflow optimized for the mouse brain, alongside a standard reference space suited to harmonize data between analysis and operation. We introduce four separate metrics for automated quality control (QC), and a visualization method to aid operator inspection. Benchmarking this workflow against common legacy practices reveals that it performs more consistently, better preserves variance across subjects while minimizing variance across sessions, and improves both volume and smoothness conservation RMSE approximately 2-fold. We propose this open source workflow and the QC metrics as a new standard for small animal MRI registration, ensuring workflow robustness, data comparability, and region assignment validity, all of which are indispensable prerequisites for the comparability of scientific results across experiments and centers.
Highlights
Correspondence of brain areas across individuals is a prerequisite for making generalizable statements regarding brain function and organization
Extreme similarity score maximization is not a desired outcome, if nonlinear transformations are employed, as this may result in image distortions which should be penalized in quality control (QC)
We present a novel registration workflow, entitled SAMRI Generic, which offers several advantages compared to the ad hoc approaches commonly used for small animal magnetic resonance imaging (MRI)
Summary
Correspondence of brain areas across individuals is a prerequisite for making generalizable statements regarding brain function and organization. This is achieved by spatial transformation of brain maps in a study to a population or standard reference template. This process, called registration, is an integral constituent of any neuroimaging workflow attempting to produce results which are both spatially resolved and meaningful at the population level. Registration in human brain imaging benefits from high-level functions (e.g. flirt and fnirt from the FSL package[1], or antsIntroduction.sh from the ANTs package[2]), optimized for the size and spatial features of the human brain. Registration is frequently performed using the selfsame high-level functions from human brain imaging — rendered usable for mouse brain data by adjusting the data to fit the priors and optimized parameters of the functions, rather than vice-versa
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