Multimodal Neuroimaging for Early Detection of Parkinson’s Disease: Biomarkers, Technologies, and Clinical Translation

Parkinson’s disease affects over 10 million people worldwide, yet diagnosis remains largely clinical and typically delayed by years relative to the onset of underlying neurodegeneration. A growing body of evidence now supports the use of neuroimaging — spanning structural MRI, diffusion imaging, functional MRI, PET, SPECT, and emerging quantitative techniques — to detect, characterise, and track pathological changes before and during early symptomatic disease.

This Research Topic brings together the full landscape of neuroimaging approaches applied to the early detection and characterisation of Parkinson’s disease. It covers the spectrum from at-risk and prodromal individuals (REM sleep behaviour disorder, hyposmia, genetic risk carriers) through to early motor-stage PD, and welcomes work across structural, functional, and molecular imaging modalities.

Key questions addressed include: Which imaging biomarkers most reliably distinguish early PD from healthy ageing and atypical parkinsonisms? How can multimodal imaging combinations improve diagnostic sensitivity and specificity? What is the relationship between imaging changes and clinical, genetic, and fluid biomarkers? How do imaging biomarkers perform across diverse populations and scanner environments? And how close are we to bringing these tools into routine clinical practice?

We welcome original research, systematic reviews, methods papers, and perspectives spanning:

o Structural MRI — neuromelanin-sensitive MRI, free-water diffusion, quantitative susceptibility mapping (QSM), iron-sensitive sequences, volumetry

o Functional MRI — resting-state connectivity, task-based fMRI, functional biomarkers of motor and non-motor circuits

o Molecular imaging — dopaminergic PET/SPECT (DAT, D2/D3), neuroinflammation tracers, tau and α-synuclein PET, cardiac MIBG

o Novel and emerging modalities — ultra-high-field MRI, total-body PET, quantitative MRI metrics, peripheral organ imaging

o AI and computational methods — machine learning for early PD classification, progression modelling, and imaging biomarker discovery — specifically applied to early/prodromal PD

o Multi-site and population studies — harmonisation of imaging protocols, imaging in underrepresented populations, longitudinal cohort imaging (PPMI, PARS, UK Biobank, and others)

o Biomarker integration — combining imaging with genetic, fluid, digital, or clinical markers for prodromal staging and risk stratification

o Clinical translation — diagnostic criteria informed by imaging, regulatory pathways for imaging biomarkers, real-world implementation