A Multi-Region Framework for Alzheimer's Disease Classification Based on Displacement Vector Field Statistics and Jacobian Determinants

This study proposes a classification framework for Alzheimer's Disease (AD) using statistical features from displacement vector fields and Jacobian determinants computed from structural magnetic resonance imaging (MRI). Images from 542 cognitively normal (CN), 341 mild cognitive impairment (MCI), and 245 AD individuals were analyzed. Groupwise registration and deformable coregistration quantified spatial deformations and local volumetric changes. Statistical moments from displacement vector fields and Jacobian determinants enabled regionspecific analysis. Stratified by sex, CN vs. AD classification achieved an AUC of 0.93 and 88.68 % accuracy for males, and an AUC of 0.94 with 87.89 % accuracy for females, demonstrating the efficacy of deformation-based biomarkers for AD diagnosis.