Abstract
Magnetic resonance imaging (MRI) is a key technology in multimodal animal studies of brain connectivity and disease pathology. In vivo MRI provides non-invasive, whole brain macroscopic images containing structural and functional information, thereby complementing invasive in vivo high-resolution microscopy and ex vivo molecular techniques. Brain mapping, the correlation of corresponding regions between multiple brains in a standard brain atlas system, is widely used in human MRI. For small animal MRI, however, there is no scientific consensus on pre-processing strategies and atlas-based neuroinformatics. Thus, it remains difficult to compare and validate results from different pre-clinical studies which were processed using custom-made code or individual adjustments of clinical MRI software and without a standard brain reference atlas. Here, we describe AIDAmri, a novel Atlas-based Imaging Data Analysis pipeline to process structural and functional mouse brain data including anatomical MRI, fiber tracking using diffusion tensor imaging (DTI) and functional connectivity analysis using resting-state functional MRI (rs-fMRI). The AIDAmri pipeline includes automated pre-processing steps, such as raw data conversion, skull-stripping and bias-field correction as well as image registration with the Allen Mouse Brain Reference Atlas (ARA). Following a modular structure developed in Python scripting language, the pipeline integrates established and newly developed algorithms. Each processing step was optimized for efficient data processing requiring minimal user-input and user programming skills. The raw data is analyzed and results transferred to the ARA coordinate system in order to allow an efficient and highly-accurate region-based analysis. AIDAmri is intended to fill the gap of a missing open-access and cross-platform toolbox for the most relevant mouse brain MRI sequences thereby facilitating data processing in large cohorts and multi-center studies.
Highlights
Understanding brain function in health and disease at different hierarchical levels requires collaborative interdisciplinary efforts using multiple experimental methods
In order to achieve similar routine atlas-based neuroinformatics of mouse brain magnetic resonance imaging (MRI), several challenges need to be overcome: (1) the image signal-to-noise ratio (SNR) is dramatically reduced due to image voxels in mice which are 10–15-fold smaller in all dimensions (Nieman et al, 2005); (2) scanner hardware consisting of gradients, coils as well as the animal fixation and anesthesia need to be miniaturized and adapted to the mouse body and physiology (Driehuys et al, 2008); (3) human MRI processing tools usually do not work with mouse brain data due to the striking differences in voxel size; and (4) a common 3D MRI-compatible brain atlas with a detailed segmentation is needed to facilitate atlas-based neuroinformatics at different scales
11www.fmrib.ox.ac.uk/fsl processing pipeline, that extracts the structural and functional information from T2w, diffusion tensor imaging (DTI) and resting-state functional MRI (rs-fMRI) data, and which enables a region-by-region analysis of preclinical MRI data based on the Allen Brain Reference Atlas (ARA)
Summary
Understanding brain function in health and disease at different hierarchical levels requires collaborative interdisciplinary efforts using multiple experimental methods. Existing software pipelines (Supplementary Table S1) require commercial software, use different MRI atlases or do not incorporate algorithms for both, structural and functional MRI (Budin et al, 2013; Koch et al, 2019). We developed a novel the Atlas-based Imaging Data Analysis Pipeline, AIDAmri, for structural and functional MRI of the mouse brain using the ARA coordinate system. AIDAmri provides an automated, efficient and highly accurate region-based analysis of multi-parametric MRI, such as anatomical T2-weighted MRI, diffusion tensor imaging (DTI) and resting-state functional MRI (rs-fMRI). Each processing step of the pipeline was validated with qualitative and quantitative measures on mouse brain MRI data acquired at 7.0, 9.4 and 11.7T using different mouse strains and experimental stroke models. Stroke was chosen as an example, as lesions result in dynamic brain deformations due to tissue swelling and atrophy, which presents a major challenge for all automated processing and atlas registration algorithms
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