  The relationship between brain structure and function has been probed using a
variety of approaches, but how the underlying structural connectivity of the
human brain drives behavior is far from understood. To investigate the effect
of anatomical brain organization on human task performance, we use a
data-driven computational modeling approach and explore the functional effects
of naturally occurring structural differences in brain networks. We construct
personalized brain network models by combining anatomical connectivity
estimated from diffusion spectrum imaging of individual subjects with a
nonlinear model of brain dynamics. By performing computational experiments in
which we measure the excitability of the global brain network and spread of
synchronization following a targeted computational stimulation, we quantify how
individual variation in the underlying connectivity impacts both local and
global brain dynamics. We further relate the computational results to
individual variability in the subjects' performance of three language-demanding
tasks both before and after transcranial magnetic stimulation to the
left-inferior frontal gyrus. Our results show that task performance correlates
with either local or global measures of functional activity, depending on the
complexity of the task. By emphasizing differences in the underlying structural
connectivity, our model serves as a powerful tool to predict individual
differences in task performances, to dissociate the effect of targeted
stimulation in tasks that differ in cognitive complexity, and to pave the way
for the development of personalized therapeutics.
