Alzheimer’s disease (AD) is a severe neurodegenerative disorder that affects a growing worldwide elderly population. Identification of brain functional biomarkers is expected to help determine preclinical stages for targeted mechanistic studies and development of therapeutic interventions to deter disease progression. Connectomic analysis, a graph theory–based methodology used in the analysis of brain-derived connectivity matrices was used in conjunction with percolation theory targeted attack model to investigate the network effects of AD-related amyloid deposition. We used matrices derived from resting-state functional magnetic resonance imaging collected on mice with extracellular amyloidosis (TgCRND8 mice, n = 17) and control littermates ( n = 17). Global, nodal, spatial, and percolation-based analysis was performed comparing AD and control mice. These data indicate a short-term compensatory response to neurodegeneration in the AD brain via a strongly connected core network with highly vulnerable or disconnected hubs. Targeted attacks demonstrated a greater vulnerability of AD brains to all types of attacks and identified progression models to mimic AD brain functional connectivity through betweenness centrality and collective influence metrics. Furthermore, both spatial analysis and percolation theory identified a key disconnect between the anterior brain of the AD mice to the rest of the brain network. Author Summary Accurate biomarkers of Alzheimer’s disease (AD) are needed for early diagnosis and treatments. Connectomic analysis, a graph theory approach, coupled with percolation theory, a network attack approach, were applied here to analyze neuroimaging through a quantitative lens. We report a marker of AD vulnerability, which highlighted a core network disconnected from key hubs, notably within the anterior portion of the brain disconnected. Additionally, preliminary models using targeted attacks provide potential pathways of neurodegeneration from the control state to the diseased state. These findings show key differences in brain connectivity due to AD and provide a potential methodology for identifying biomarkers.