The New Jersey Institute of Technology's
Electronic Theses & Dissertations Project

Title:	Comparison of longitudinal changes in resting state functional magnetic resonance imaging between alzheimer’s and healthy controls
Author:	Yilmaz, Berk Can
View Online:	njit-etd2020-049 (xiii, 64 pages ~ 2.5 MB pdf)
Department:	Department of Biomedical Engineering
Degree:	Master of Science
Program:	Biomedical Engineering
Document Type:	Thesis
Advisory Committee:	Biswal, Bharat (Committee chair) Adamovich, Sergei (Committee member) Li, Xiaobo (Committee member) Di, Xin (Committee member)
Date:	2020-08
Keywords:	Longitudinal analysis Regional homogenitygenty (reho) Amplitude of low fluctuation(alff) Alzheimer's disease Resting state functional magnetic resonance imaging (rs-fmri) Functional connectivity
Availability:	Unrestricted
Abstract:	Resting State Functional Magnetic Resonance Imaging (rs-fMRI) is a technique that is widely used for analyzing brain function using different approaches and methods. This study involves rs-fMRI analysis of Blood Oxygenation Level Dependent (BOLD) signals acquired from Alzheimer's disease (AD) Patients and Healthy Controls (HC). Each subject in the study had both functional and anatomical images with at least one rs-fMRI scan with their Anatomical (T1) scans. Previous rs-fMRI studies have demonstrated that AD shows differences in Amplitude of Low Frequency (<0.1 Hz) Fluctuations (ALFF), and Regional Homogeneity (ReHo) measures according to HCs. The aim of the study is to investigate individual and group level differences using ReHo and mALFF related measures in a longitudinal analysis. The hypothesis is that with the age and group (AD or HC) of the subject, it is possible to separate AD and HC subjects from each other using 3 different ROIs (DMN – MT – MV), These regions are known to show abnormalities in AD patients but clinical wise never been identified as neuroimaging biomarkers. This study tries to check these ROIs to see if there are significant differences between the AD patients and HCs using 3 different features.