Processing Pipeline for Atlas-Based Imaging Data Analysis of Structural and Functional Mouse Brain MRI (AIDAmri)

Area of Science:

• Neuroimaging
• Bioinformatics
• Comparative Neuroscience

Background:

• Magnetic resonance imaging (MRI) is crucial for studying brain connectivity and pathology in animal models.
• Current small animal MRI analysis lacks standardized pre-processing and atlas-based methods, hindering result validation and comparison.
• Existing methods often rely on custom scripts or adapted clinical software, lacking a unified approach.

Purpose of the Study:

• To introduce AIDAmri, a novel Atlas-based Imaging Data Analysis pipeline for mouse brain MRI.
• To provide a standardized, automated, and user-friendly tool for processing structural and functional mouse brain data.
• To facilitate data comparison and validation across pre-clinical studies by integrating with the Allen Mouse Brain Reference Atlas.

Main Methods:

• AIDAmri is a Python-based pipeline with a modular structure for automated pre-processing of MRI data.
• Includes steps for raw data conversion, skull-stripping, bias-field correction, and image registration to the Allen Mouse Brain Reference Atlas (ARA).
• Processes anatomical MRI, diffusion tensor imaging (DTI) for fiber tracking, and resting-state functional MRI (rs-fMRI) for functional connectivity.

Main Results:

• The pipeline automates key pre-processing steps, reducing user input and programming requirements.
• Ensures results are transferred to the ARA coordinate system for accurate region-based analysis.
• Optimized for efficient processing of large datasets and multi-center studies.

Conclusions:

• AIDAmri addresses the need for a standardized, open-access, cross-platform toolbox for mouse brain MRI analysis.
• Facilitates reproducible and comparable pre-clinical research by standardizing data processing and atlas integration.
• Aims to accelerate discoveries in mouse models of brain disease and connectivity.