Pathogenic protein accumulation and spread are fundamental principles of neurodegenerative diseases and ultimately account for the atrophy patterns that distinguish these diseases clinically. However, the biological mechanisms that link pathogenic proteins to specific neural network damage patterns have not been defined. We developed computational models for mechanisms of pathogenic protein accumulation, spread and toxic effects in an artificial neural network of cortical columns. By varying simulation parameters we assessed the effects of modelled mechanisms on network breakdown patterns. Our findings suggest that patterns of network breakdown and the convergence of patterns follow rules determined by particular protein parameters. These rules can account for empirical data on pathogenic protein spread in neural networks. This work provides a basis for understanding the effects of pathogenic proteins on neural circuits and predicting progression of neurodegeneration.