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The relationship between brain structure and function has been probed using avariety of approaches, but how the underlying structural connectivity of thehuman brain drives behavior is far from understood. To investigate the effectof anatomical brain organization on human task performance, we use adata-driven computational modeling approach and explore the functional effectsof naturally occurring structural differences in brain networks. We constructpersonalized brain network models by combining anatomical connectivityestimated from diffusion spectrum imaging of individual subjects with anonlinear model of brain dynamics. By performing computational experiments inwhich we measure the excitability of the global brain network and spread ofsynchronization following a targeted computational stimulation, we quantify howindividual variation in the underlying connectivity impacts both local andglobal brain dynamics. We further relate the computational results toindividual variability in the subjects' performance of three language-demandingtasks both before and after transcranial magnetic stimulation to theleft-inferior frontal gyrus. Our results show that task performance correlateswith either local or global measures of functional activity, depending on thecomplexity of the task. By emphasizing differences in the underlying structuralconnectivity, our model serves as a powerful tool to predict individualdifferences in task performances, to dissociate the effect of targetedstimulation in tasks that differ in cognitive complexity, and to pave the wayfor the development of personalized therapeutics.

Data-driven brain network models predict individual variability in behavior