andMR

andMR is a term used in neuroimaging and medical research to denote an integrated approach that combines multiple magnetic resonance modalities and computational methods to study brain structure and function. The name signals an emphasis on data fusion across modalities and the extraction of coherent biomarkers from complex data.

Origin and scope: The concept developed as MRI technology expanded to include functional, diffusion, and spectroscopic measurements. Early work focused on combining two modalities, while contemporary andMR workflows aim for seamless multi-modal pipelines, standardized processing, and cross-modal interpretation.

Techniques and data sources: andMR can involve functional MRI (fMRI), diffusion MRI (dMRI), and magnetic resonance spectroscopy (MRS), as well as perfusion or arterial spin labeling in some applications. Analyses combine joint independent component analysis, multi-view learning, Bayesian modeling, and machine learning to fuse signals and identify robust patterns.

Applications: In neuroscience, andMR supports investigations of brain connectivity, development, aging, and disease. In clinical settings, potential uses include presurgical planning, assessment of tumors and stroke, and monitoring therapeutic response. In research, it enables integrative biomarkers that reflect structure, function, and chemistry.

Limitations and challenges: Practical barriers include data acquisition time, cost, and the need for harmonized protocols. Technical issues involve cross-modal registration, differing noise properties, and reproducibility. Ethical and data-sharing considerations also shape how andMR data are collected and used. Advances continue to refine standardization, computational efficiency, and interpretability to broaden adoption across research and clinical practice.