Neuroimaging-Based Brain Morphometry in Alzheimer’s Disease

Abstract

Background/Objectives: Alzheimer’s disease (AD) is a leading cause of death worldwide, affecting millions of older Americans and resulting in a substantial economic burden. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) aims to investigate and develop treatments for AD. Methods: This study included 60 participants, divided equally into AD and control cohorts, and utilized magnetic resonance imaging (MRI) scans to detect gray matter volumetric alterations, a key biomarker of AD. The participants’ cortical volume and surface area were quantified using an automated pipeline in MIMICS (Materialise Interactive Medical Imaging Control System). Results: A multivariate regression analysis was conducted to explore the relationship between cortical measurements and potential factors influencing AD susceptibility. The study found that both cortical volume and surface area were statistically significant predictors of AD (p = 0.0004 and p = 0.011, respectively). Age was also a significant factor, with the 65–70 age group showing the strongest association (p < 0.001). The model achieved an accuracy of 0.68 in predicting AD. Conclusions: While voxel-based morphometry (VBM) using MIMICS showed promise, further development of the automated pipeline could enhance accuracy and correlation indices. These findings contribute to our understanding of brain atrophy in AD pathophysiology and highlight the potential of MRI morphometry as a tool for AD biomarker development.