Morphogeometric variation analysis of cortical surfaces in Alzheimer's Disease. A novel approach for prognostic biomarker discovery

BACKGROUND AND OBJECTIVE: Longitudinal morphogeometric analysis is essential to understand neurodegenerative progression in Alzheimer's disease (AD). This research evaluates external and internal cortical surfaces' morphology extracted from MRI scans to characterize structural changes in AD. METHODS: MRI scans from 22 patients with AD across multiple imaging sessions were segmented to generate 3D cortical reconstructions. A novel sectional analysis method was applied to sweep coronal planes at 1mm. intervals along the posterior-anterior axis. Morphogeometric indices were calculated for each section to generate sectional curves, and mathematical curve descriptors were computed as potential biomarkers. Statistical evaluation of these descriptors included both an effectiveness metric and a Linear Mixed Model (LMM) analysis to assess longitudinal trends and determine statistical significance. RESULTS: Curve descriptors showed greater effectiveness to detect morphological changes than traditional whole-brain geometric metrics. The external cortical surface volume curve achieved 87.88 % effectiveness, surpassing whole-brain volume (84.85 %). The internal cortical surface area sectional curve reached 81.82 %, outperforming traditional measures (75.78 %). The novel IECN index achieved 72.73 %, highlighting its biomarker potential. CONCLUSIONS: Novel morphogeometric indices and sectional curve descriptors complement traditional biomarkers, improving AD detection and monitoring. The employed methodology is sensitive to local cortical changes that may be overlooked in whole-brain assessments.