Automated MRI-Based Subcortical Volumetric Analysis for Biomarker Identification in Parkinson’s disease A Case–Control Study

Background Parkinson’s disease (PD) is a progressive neurodegenerative disorder primarily affecting basal ganglia circuits involved in motor control. Despite advances in neuroimaging, reliable structural biomarkers for PD diagnosis remain limited. Magnetic resonance imaging (MRI)-based volumetric analysis offers a non-invasive approach to quantify structural brain changes associated with neurodegeneration. Objective To investigate subcortical brain volume alterations in Parkinson’s disease using automated MRI volumetric analysis and to evaluate their potential as diagnostic biomarkers. Methods A case–control study was conducted including 20 PD patients and 20 healthy controls. High-resolution T1-weighted MRI scans were processed using the volBrain automated segmentation pipeline. Volumes of key subcortical structures, including the putamen, caudate nucleus, pallidum, thalamus, and hippocampus, were extracted. Group differences were assessed using independent samples t-tests, and effect sizes were calculated using Cohen’s d. A composite biomarker score was derived, and diagnostic performance was evaluated using receiver operating characteristic (ROC) analysis. Results Significant reductions in subcortical volumes were observed in PD subjects, particularly in the putamen (p = 0.004, d = 1.10), caudate (p = 0.006, d = 1.00), and pallidum (p = 0.020, d = 0.70). The thalamus showed modest significance (p = 0.030, d = 0.60), while hippocampal volumes were not significantly different (p = 0.120). The composite biomarker demonstrated improved discrimination (p = 0.002, d = 1.15), with ROC analysis yielding an AUC of 0.85. Conclusion Automated MRI volumetry identifies significant basal ganglia alterations in PD, supporting their role as potential imaging biomarkers for diagnosis and disease characterization.