As Alzheimer’s disease pathology begins decades before clinical symptoms, early functional brain changes in asymptomatic Alzheimer’s disease (AsymAD) remain poorly characterized, particularly from a longitudinal perspective. Although AsymAD individuals are biomarker-positive for Alzheimer’s pathology, they remain cognitively unimpaired, representing a preclinical stage that is clinically silent yet biologically active. At this stage, conventional MRI markers and cross-sectional analysis often lack sensitivity to detect the subtle functional alterations that precede symptom onset. Consequently, compared to symptomatic Alzheimer’s disease, AsymAD is substantially more difficult to identify using imaging markers alone, necessitating more sensitive, network-level, and longitudinal approaches. Resting-state functional MRI provides a noninvasive framework for probing intrinsic functional brain networks and detecting early network-level disruptions. Since brain networks exhibit a small-world topology, defined by high local clustering (segregation) and short average path lengths (integration), graph-theoretical metrics sensitive to subtle perturbations related to these properties may provide early network-level signatures of AsymAD. In this study, we examine longitudinal changes in functional connectivity in AsymAD compared with cognitively normal controls using complementary connectivity approaches, including region-to-region connectivity (RRC), graph-theoretical metrics, and seed-based connectivity (SBC). General linear models are used to assess between-subject and repeated-measure effects, with primary emphasis on group-by-session interactions.