Abstract
Small-mammal neuroimaging offers incredible opportunities to investigate structural and functional aspects of the brain. Many tools have been developed in the last decade to analyse small animal data, but current softwares are less mature than the available tools that process human brain data. The Python package Sammba-MRI (SmAll-MaMmal BrAin MRI in Python; http://sammba-mri.github.io) allows flexible and efficient use of existing methods and enables fluent scriptable analysis workflows, from raw data conversion to multimodal processing.
Highlights
The use of magnetic resonance imaging (MRI) methods in animals provides considerable benefits for improving our understanding of brain structure and function in health and diseases
The SAMRI (Small Animal Magnetic Resonance Imaging) package provides fMRI preprocessing, metadata parsing, and data analysis functions optimized for mouse brains (Ioanas et al, 2020)
Sammba-MRI and the examples provided in its manual depends on the following libraries: Nipype ≥ 1.0.4; Nilearn ≥ 0.4.0; Numpy ≥ 1.14; SciPy ≥ 0.19; Nibabel ≥ 2.0.2; Sklearn ≥ 0.19; matplotlib ≥ 1.5.1; nose ≥ 1.2.1; doctest-ignoreunicode; DICOM ToolKit package as well as FSL, AFNI, ANTs, and RATS
Summary
The use of magnetic resonance imaging (MRI) methods in animals provides considerable benefits for improving our understanding of brain structure and function in health and diseases. The greatest advantages of preclinical MRI include group homogeneity and the opportunity to acquire a high amount of information repeated as needed. This added value, together with practical and ethical considerations, resulted in an increase of the use of small-mammal MRI in research. The SAMRI (Small Animal Magnetic Resonance Imaging) package provides fMRI preprocessing, metadata parsing, and data analysis functions optimized for mouse brains (Ioanas et al, 2020).
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