Multimodal MRI and Machine Learning Sharpen Parkinson's Disease Biomarker Profiling

An editorial says multimodal MRI and artificial intelligence/machine learning improve differential diagnosis of Parkinson's disease versus atypical parkinsonian syndromes; clinical use needs standardized protocols and reporting.

• Advanced MRI techniques highlighted include quantitative susceptibility mapping for iron, neuromelanin-sensitive MRI, diffusion tensor imaging free water, metabolic spectroscopy, and functional network mapping.
• Integrated multimodal biomarkers define overlapping biological dimensions—dopaminergic integrity, microstructure, glymphatic function, vascular changes, and large-scale network reorganization—rather than a single marker.
• Clinical translation depends on standardized acquisition, automated post-processing, and accessible reporting frameworks.

Automated summary

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