Objective: This study investigates MRI volumetric analysis via VBM as a biomarker for early PD detection, focusing on structural alterations in deep gray matter regions across PD, PPD, and healthy controls (HC).

Background: Parkinson’s Disease (PD) is a neurodegenerative disorder where early detection remains challenging. Structural MRI biomarkers, particularly volumetric changes in basal ganglia and thalamocortical circuits, may enable timely diagnosis. Voxel-Based Morphometry (VBM) offers a computational framework to quantify regional brain atrophy, yet its utility in prodromal PD (PPD) and early-stage PD remains underexplored.

Method: Longitudinal MRI scans (baseline, 12, 36, 48 months) from PD, PPD, and HC cohorts were analyzed using standardized VBM pipelines. Key steps included affine registration to the MNI152 template, Gaussian pyramid sub-sampling, intensity normalization, and iterative optimization (10,000–100 iterations). Hemispheric region-of-interest (ROI) analysis targeted basal ganglia and thalamus.

Results: Significant volumetric reductions were observed in PD and PPD within basal ganglia [putamen, SNc, SNr, GPi, GPe] (p<0.01) and thalamus (p<0.05), alongside increased caudate volume (p<0.005). Longitudinal analysis revealed progressive atrophy in PD-specific regions (thalamus, substantia nigra reticulata, globus pallidus interna), while HC volumes remained stable. Temporal analysis distinguished early PD from HC with 82% accuracy via accelerated gray matter loss.

Conclusion: Computational MRI volumetry detects early PD-related structural changes, highlighting its potential as a non-invasive biomarker. Future clinical data integration could refine correlations with symptom progression, advancing diagnostic protocols and therapeutic monitoring.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/decoding-parkinsons-progression-longitudinal-volumetric-mri-biomarkers-in-prodromal-and-clinical-stages/