Identification of MRI-Derived Structural Biomarkers in Female Alzheimer’s Disease Subjects Using CAT12 and Mimics: Effects of Voxel Geometry on Biomarker Estimation

Abstract

Alzheimer’s disease (AD) is the leading cause of dementia and disproportionately affects women, who experience a higher lifetime risk and more rapid structural brain changes than men. Reliable imaging biomarkers are essential for detecting these changes, although their estimation may be influenced by voxel geometry, image resolution, and segmentation methodology. In this study, magnetic resonance imaging (MRI) scans from 40 female participants with AD obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database were analyzed using the Computational Anatomy Toolbox 12 (CAT12), implemented within Statistical Parametric Mapping 12 (SPM12), and Materialise Mimics to identify structural biomarkers associated with neurodegeneration. Cortical thickness, gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), and total intracranial volume (TIV) were quantified, while Brain Parenchymal Volume (BPV) and Brain Parenchymal Fraction (BPF) were calculated to assess global brain tissue preservation. The analyses demonstrated characteristic AD-related changes, including cortical thinning, GM loss, ventricular enlargement, and increased CSF volume. Significant correlations among cortical thickness, tissue volumes, BPV, and BPF further supported their complementary role in characterizing disease-related structural changes. Overall, these findings suggest that MRI-derived measures of cortical thickness, tissue volumes, BPV, and BPF provide useful structural biomarkers of AD and emphasize the importance of considering voxel geometry and segmentation methodology when evaluating neuroimaging biomarkers.