Widespread Spatiotemporal Patterns of Functional Brain Networks in Longitudinal Progression of Alzheimer's Disease.

Alzheimer's Disease (AD) is characterized by progressive functional network disruptions that precede cognitive decline, yet traditional functional connectivity analyses often fail to capture transient network instabilities critical for early diagnosis. This study investigates the role of Quasi-Periodic Patterns (QPPs) in identifying disease-related connectivity changes across longitudinal stable disease stages (sNC, sMCI, sDAT) and transitioning (uNC, pMCI) AD cohorts using resting-state fMRI data from the Alzheimer's Disease Neuroimaging Initiative. By integrating QPP occurrences with intrinsic connectivity networks (ICNs), we assessed network integrity across disease stages, with statistical significance evaluated using the Kruskal-Wallis test and Dunn's test for post-hoc analysis. Results revealed a progressive decline in functional connectivity integrity, with early impairments in subcortical and executive function networks in stable groups, followed by widespread disconnection in higher cognition, sensorimotor, and visual networks at later stages. Transitioning AD groups exhibited earlier disruptions in visual and cerebellar networks, suggesting their potential as early biomarkers for disease onset. The occurrence of QPPs decreased significantly with disease progression, indicating an increase in functional disconnection. These findings highlight the synergy between QPPs and ICNs as a dynamic and sensitive biomarker framework for AD progression. Future research should further explore this integration within multimodal imaging and clinical diagnostic frameworks to enhance early detection and intervention strategies.