Publication Date


Document Type


Committee Members

Tarun Goswami, D.Sc. (Advisor); Jaime Ramirez-Vick, Ph.D. (Committee Member); Vic Middleton, Ph.D. (Committee Member)

Degree Name

Master of Science in Biomedical Engineering (MSBME)


Alzheimer’s disease (AD) is the seventh leading cause of death globally with an estimated 6.5 million Americans aged 65 and above living with Alzheimer’s dementia in 2022 and at a projected national cost of $321 billion. AD is characterized by a progressive and irreversible neurodegenerative dysfunction with clinical symptoms such as deterioration in cognition and memory loss. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) is a multi-site, public-private global research initiative that supports both investigation and development of treatments that slow or terminate AD progression. The study included 60 participants, comprising 30 AD and 30 control cohorts respectively. All participants were from the ADNI 1 group with a recorded Functional Activities Questionnaire (FAQ.) and Neuropsychiatric Inventory Questionnaire (NPI-Q) score. Gray matter volumetric alterations detected from magnetic resonance image (MRI) scans have been validated as a crucial AD biomarker. Cortical atrophy was quantified by measuring the volume and surface area of the cortex across the whole brain using an automated pipeline in MIMICS. A stepwise multivariate regression analysis was conducted to investigate the correlation between the participants’ measured cortical volume and surface area with other potential factors that may influence their susceptibility to developing Alzheimer’s disease. The results of the study showed both part volume and surface area generic model fit to be statistically significant at (p = 0.0004) and (p = 0.011) respectively. While the age weighting showed overall significant difference in both part surface area and volume measured by MIMICS at (P = 0.019) and (P = 0.0075), the age group 65 -70 years appear most significant at (P< 0.001). In addition, the performance metrics conducted to evaluate the capability of the model showed an accuracy of 0.68. The study demonstrates the promising utility of voxel-based morphometry using MIMICS, though developing the automated pipeline currently in place, may help improve the accuracy and correlation indices through its use. The results of the study provide further evidence of brain atrophy in the pathophysiology of Alzheimer’s disease and highlight the potential of MRI morphometry for the development of AD biomarkers.

Page Count


Department or Program

Department of Biomedical, Industrial & Human Factors Engineering

Year Degree Awarded