The objective of the proposed research is to harness the capabilities of spatiotemporal analysis methods to understand the progression of Alzheimer's disease using functional brain dynamics. Central to our study is the investigation of changes in global signal brain state phase coupling as the disease progresses. Building on recent advancements in modeling dynamic spatiotemporal patterns from functional brain scans, we explore the significance of 'quasi-periodic patterns' (QPPs). These patterns manifest as alterations in blood oxygen-level-dependent (BOLD) signal between the task-positive network and default mode network producing a widely observed network level anti-correlation, potentially serving as clinical indicators of early disease pathology detection. Firstly, we seek to explore the fidelity of QPPs as a reliable method under, (1) various repetition times (TR) for fMRI scans, (2) canonical brain atlases for region of interest delineation, and (3) preprocessing bandpass frequency ranges. Our in-depth evaluation of QPP capabilities promises to shed light on the intricate dynamics of disease progression. Secondly, using a substantial archive of open-access Alzheimer's disease functional brain scans spanning over a decade, we aim to, (1) determine how stable diagnostic conditions (sNC, sMCI, sDAT) are represented by QPPs, and (2) analyze how spatiotemporal dynamics differ before and after significant milestones in Alzheimer's disease progression (uNC and pMCI). Lastly, we explore the potential of the global averaged BOLD signal, known as the global signal, as a phase-coupled marker for recurrent brain state activation. This approach, recently observed in rodent studies, is now being explored in human dynamic models. Our findings aim to enhance the comprehension of spatiotemporal analyses in functional brain imaging and bolster foundational knowledge for early Alzheimer's disease prevention.