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Although the molecular effects of amyloid\u2010beta are well\u2010characterized, the cascade from local synaptic dysfunction to large\u2010scale network collapse remains poorly understood. We present a biophysically grounded computational model that integrates small\u2010world network topology, Izhikevich neuron dynamics, short\u2010term plasticity, and amyloid\u2010beta\u2010induced synaptic pruning to investigate how amyloid burden degrades functional brain connectivity over time. Crucially, we simulate the evolving network dynamics across disease stages, revealing how firing rate, synchrony, variability, and Fano factor evolve in response to progressive structural degradation. Our results demonstrate a two\u2010phase deterioration: Initial compensatory dynamics followed by abrupt disintegration of coordinated activity. This temporal dissociation mirrors clinical observations and offers mechanistic insight into Alzheimer's disease progression. We further identify a critical accumulated amyloid dose beyond which network collapse becomes inevitable, preceded by early\u2010warning indicators such as declining global efficiency and the spectral radius of the network connections. Network topology, synaptic time constants, and repair mechanisms strongly influence resilience, with small\u2010world networks showing delayed collapse and greater functional compensation. Finite\u2010size scaling reveals that larger networks experience earlier and sharper phase transitions. Ultimately, by establishing a mathematical link between microscopic synaptic degradation and macroscopic network failure, our findings suggest that if experimental proxies for the spectral radius can be identified, they would serve as highly sensitive early\u2010warning indicators of impending network collapse. This theoretical framework offers new mechanistic targets for the early detection and therapeutic monitoring of neurodegenerative diseases.<\/jats:p>","DOI":"10.1111\/ejn.70713","type":"journal-article","created":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T11:37:44Z","timestamp":1792496264000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Amyloid\u2010Induced Network Resilience and Collapse in Alzheimer's Disease: Insights From Computational Modeling"],"prefix":"10.1111","volume":"64","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-2854-9794","authenticated-orcid":false,"given":"Ediline L. 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