Modeling the Resilience of Neural Topologies: A Computational Approach to Understanding Cognitive Reserve in Alzheimer's Disease

The concept of cognitive reserve has emerged as a critical framework in understanding resilience to neurodegenerative diseases like Alzheimer’s. Cognitive reserve hypothesises that individuals with more complex neural networks can delay the onset of disease symptoms due to the robustness of their neural architecture. To test this theory, we developed a computational model that simulates progressive neuronal loss, mimicking the degenerative processes observed in Alzheimer’s disease. Starting with a randomly generated neural topology, our model systematically removes neurons while assessing the integrity of the original network structure. Simulations were conducted across varying initial network sizes to evaluate the relationship between network complexity and resilience to neuronal degradation. Results indicate that larger and more intricate neural networks exhibit greater resilience to structural breakdown, reinforcing the cognitive reserve hypothesis. This computational framework provides valuable insights into the mechanisms underlying delayed symptom onset in neurodegenerative disorders and offers a novel avenue for exploring potential therapeutic interventions.