Speaker
Description
MRI is a key tool in translational neuroscience, enabling non-invasive investigation of brain structure and function across species. While human MRI benefits from standardized workflows, comparable tools for rodents remain limited, despite the critical role of mouse models in studying brain function and disease.
To address this gap, we developed AIDA (Atlas-based Imaging Data Analysis), an integrated framework for mouse neuroimaging built around the Allen Mouse Brain Atlas. AIDA combines automated processing, quality control, and functional connectivity analysis with a structured research data management workflow (DOI: 10.1038/s41597-023-02242-8) that organizes imaging data, metadata, and outputs for reproducibility, traceability, and compatibility with open data standards: AIDAmri for automated preprocessing and atlas-based registration (DOI: 10.3389/fninf.2019.00042), AIDAqc for quality control with machine-learning outlier detection (DOI: 10.1162/imag_a_00317), and AIDAconnect for application of graph theory (DOI: 10.1016/j.neuroimage.2022.119110).
Together, AIDA provides a standardized, scalable pipeline for mouse MRI, enhancing reproducibility and the translational impact of preclinical neuroimaging.