A practical guide to methodological considerations in the controllability of structural brain networks

摘要

Objective. Predicting how the brain can be driven to specific states by means of internal or externalcontrol requires a fundamental understanding of the relationship between neural connectivityand activity. Network control theory is a powerful tool from the physical and engineering sciencesthat can provide insights regarding that relationship; it formalizes the study of how the dynamicsof a complex system can arise from its underlying structure of interconnected units. Approach.Given the recent use of network control theory in neuroscience, it is now timely to offer a practicalguide to methodological considerations in the controllability of structural brain networks. Herewe provide a systematic overview of the framework, examine the impact of modeling choices onfrequently studied control metrics, and suggest potentially useful theoretical extensions. We groundour discussions, numerical demonstrations, and theoretical advances in a dataset of high-resolutiondiffusion imaging with 730 diffusion directions acquired over approximately 1 h of scanning fromten healthy young adults. Main results. Following a didactic introduction of the theory, we probehow a selection of modeling choices affects four common statistics: average controllability, modalcontrollability, minimum control energy, and optimal control energy. Next, we extend the currentstate-of-the-art in two ways: first, by developing an alternative measure of structural connectivitythat accounts for radial propagation of activity through abutting tissue, and second, by defininga complementary metric quantifying the complexity of the energy landscape of a system. Weclose with specific modeling recommendations and a discussion of methodological constraints.Significance. Our hope is that this accessible account will inspire the neuroimaging community tomore fully exploit the potential of network control theory in tackling pressing questions in cognitive,developmental, and clinical neuroscience.