Automated differentiation of parkinsonian disorders: an ROI-based analysis of subcortical shape and cortical surface...

The clinical manifestations of early-stage parkinsonian syndromes overlap, making accurate differential diagnosis crucial yet challenging. This study aimed to develop a system for automated differentiation of idiopathic Parkinson's disease (IPD) from progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS). Our sample included clinical data and T1-weighted magnetic resonance imaging from 50 IPD, 47 PSP, and 38 CBS patients. We introduced an atlas-based approach to extract shape features from subcortical regions in each subject's native coordinate image space. The surface thickness and folding parameters were also extracted from cortical regions. A statistical analysis was conducted to identify regions with significant differences in the extracted features, followed by the employment of a feed-forward neural network to distinguish these patients. Significant structural differences were observed in several regions, including the thalamic nuclei, basal ganglia, midbrain, cerebellum, cingulate cortex, and insula. Using only cortical surface features, our diagnostic model outperformed the model that relied solely on subcortical shape features. However, the classifier achieved its best predictive performance when incorporating features from both cortical and subcortical structures, yielding an accuracy of 86.1% in a multi-class classification system and 96.1% for distinguishing IPD from PSP and CBS, as well as an accuracy of 94.2% for classifying CBS versus PSP in a two-class classification system. Our findings underscore the significance of cortical morphological patterns and demonstrate that the proposed methodology could potentially serve as an automated diagnostic system in clinical settings.

The online version contains supplementary material available at 10.1007/s11571-025-10402-2.