  Predicting how the brain can be driven to specific states by means of
internal or external control requires a fundamental understanding of the
relationship between neural connectivity and activity. Network control theory
is a powerful tool from the physical and engineering sciences that can provide
insights regarding that relationship; it formalizes the study of how the
dynamics of a complex system can arise from its underlying structure of
interconnected units. Given the recent use of network control theory in
neuroscience, it is now timely to offer a practical guide to methodological
considerations in the controllability of structural brain networks. Here we
provide a systematic overview of the framework, examine the impact of modeling
choices on frequently studied control metrics, and suggest potentially useful
theoretical extensions. We ground our discussions, numerical demonstrations,
and theoretical advances in a dataset of high-resolution diffusion imaging with
730 diffusion directions acquired over approximately 1 hour of scanning from
ten healthy young adults. Following a didactic introduction of the theory, we
probe how a selection of modeling choices affects four common statistics:
average controllability, modal controllability, minimum control energy, and
optimal control energy. Next, we extend the current state of the art in two
ways: first, by developing an alternative measure of structural connectivity
that accounts for radial propagation of activity through abutting tissue, and
second, by defining a complementary metric quantifying the complexity of the
energy landscape of a system. We close with specific modeling recommendations
and a discussion of methodological constraints.
