Author ORCID Identifier


Date of Graduation


Document Type

Thesis (MS)

Program Affiliation

Biomedical Sciences

Degree Name

Masters of Science (MS)

Advisor/Committee Chair

Paul E. Schulz, MD

Committee Member

Khader M. Hasan, Ph.D.

Committee Member

Ines Moreno-Gonzalez, Ph.D.

Committee Member

Cameron Jeter, Ph.D.

Committee Member

Mark D. Pagel, Ph.D.


Modest expansion of the human brain cerebrospinal fluid (CSF)-filled ventricles is normal with aging, and because of this, it can be difficult for physicians to accurately diagnose and treat enlarged ventricles (ventriculomegaly), called hydrocephalus1 (fluid or water in the brain) Ventriculomegaly occurs due to an obstruction (such as a blood clot or tumor), or a change in CSF absorption2. Primary hydrocephalus, also called idiopathic normal pressure hydrocephalus (iNPH), is non-obstructive and may be comorbid with other neurodegenerative diseases such as Alzheimer’s disease (AD) or frontotemporal dementia (FTD). Clinically, it can be difficult to tell whether the pathophysiological changes leading to cognitive impairment also led to the ventriculomegaly, as may occur in AD, versus whether the hydrocephalus itself is driving cognitive and motor impairment, i.e. iNPH. The goal of this thesis project is to investigate the relationship between iNPH and AD in order to better understand how they may contribute to each other, and to help clinicians distinguish between them. To do this, we compared cognitive performance and white matter integrity between patients with “pure” iNPH, “pure” Alzheimer’s disease (AD), and co-morbid iNPH + AD. Our results demonstrated that there are specific periventricular structures in the brain that are associated with cognitive impairment in AD versus iNPH. We conclude that the distribution pattern of AD vs. iNPH may be a valid tool to distinguish between these disorders, and may form the basis for subsequent studies that can further explicate the link between these often-overlapping etiologies.


idiopathic normal hydrocephalus, normal pressure hydrocephalus, diffusion tensor imaging, Alzheimer's disease, machine learning, volumetry, segmentation