Personalized Brain Network Models (BNMs) are a computational tool that simulate a specific individual’s brain activity based on measured structural brain connections. These models have been shown to be sensitive to individual differences in brain network structure and allow one to perform in silico experiments in order to make predictions about the effects of stimulation, disease progression, or drug treatment at the level of a specific individual. I will describe how one builds such computational models from neuroimaging data and describe work using personalized BNMs to explore individual differences in brain structure and function.
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