Morphometric characterization of early- and late-onset Parkinson's disease: An ROI-based study of classification and correlation

Introduction

Parkinson's disease (PD) is associated with progressive neurodegeneration, particularly involving cortico-basal ganglia-thalamo-cortical circuits that underlie motor and cognitive functions. We investigated the morphological brain features derived from structural MRI to differentiate early (EOPD) and late-onset PD (LOPD) from age-related healthy controls.

Methods

3D T1-weighted MRI was acquired in 114 subjects (27 EOPD, 32 YHC, 28 LOPD, and 27 OHC). Gray matter volume (GMV), white matter volumes (WMV), fractal dimension (FD), gyrification index (GI), and cortical thickness (CT) were extracted using CAT12 software. Three tasks, (i) identification of statistically significant regions, (ii) automatic diagnosis using machine learning using individual and combined features, and (iii) correlation study were performed to quantify the relationship between morphological features and clinical variables.

Results

EOPD exhibited a reduction in GMV and cortical complexity in frontal, parietal and temporal lobes compared to YHC. We achieved the highest classification accuracy of 89.06% using FD and CT for EOPD vs YHC, 90.91% using GMV, WMV and FD for LOPD vs OHC and 89.29% using WMV and FD for EOPD vs LOPD after data augmentation for class balancing. EOPD revealed a negative correlation of GMV with UPDRS II (in medial frontal cortex, precuneus and supplementary motor cortex), FD with UPDRS III in pericalcarine; GI and UPDRS II in transverse temporal and pars opercularis; CT with UPDRS III in superior frontal regions.

Conclusion

Distinct morphometric changes were observed in patients with EOPD and LOPD in comparison with HC, suggesting the utility of morphological measures in early diagnosis of PD.