Amyloid-Induced Network Resilience and Collapse in Alzheimer's Disease: Insights From Computational Modeling.
- 1Department of Physics, FFCLRP/University of São Paulo, São Paulo, Brazil.
- 2Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille, France.
Abstract
Alzheimer's disease is marked by progressive synaptic loss and cognitive decline, with amyloid-beta accumulation playing a central pathological role. Although the molecular effects of amyloid-beta are well-characterized, the cascade from local synaptic dysfunction to large-scale network collapse remains poorly understood. We present a biophysically grounded computational model that integrates small-world network topology, Izhikevich neuron dynamics, short-term plasticity, and amyloid-beta-induced 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-phase 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-warning 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-world networks showing delayed collapse and greater functional compensation. Finite-size 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-warning indicators of impending network collapse. This theoretical framework offers new mechanistic targets for the early detection and therapeutic monitoring of neurodegenerative diseases.
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BibTeX
@article{nguessap2026amyloidinduced,
title = {{Amyloid-Induced Network Resilience and Collapse in Alzheimer's Disease: Insights From Computational Modeling.}},
author = {Ediline L F Nguessap and Damien Depannemaecker and Fernando F Ferreira},
journal = {The European journal of neuroscience},
year = {2026},
month = oct,
doi = {10.1111/ejn.70713},
url = {https://europepmc.org/article/MED/42899506},
note = {CC BY},
}
RIS
TY - JOUR
TI - Amyloid-Induced Network Resilience and Collapse in Alzheimer's Disease: Insights From Computational Modeling.
AU - Ediline L F Nguessap
AU - Damien Depannemaecker
AU - Fernando F Ferreira
PY - 2026
DA - 2026/10/01
JO - The European journal of neuroscience
DO - 10.1111/ejn.70713
UR - https://europepmc.org/article/MED/42899506
AB - Alzheimer's disease is marked by progressive synaptic loss and cognitive decline, with amyloid-beta accumulation playing a central pathological role. Although the molecular effects of amyloid-beta are well-characterized, the cascade from local synaptic dysfunction to large-scale network collapse remains poorly understood. We present a biophysically grounded computational model that integrates small-world network topology, Izhikevich neuron dynamics, short-term plasticity, and amyloid-beta-induced 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-phase 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-warning 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-world networks showing delayed collapse and greater functional compensation. Finite-size 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-warning indicators of impending network collapse. This theoretical framework offers new mechanistic targets for the early detection and therapeutic monitoring of neurodegenerative diseases.
SN - 0953-816X
SN - 1460-9568
ER -