A network control theory pipeline for studying the dynamics of the structural connectome - Parkes, Linden; Kim, Jason Z.; Stiso, Jennifer; Brynildsen, Julia K.; Cieslak, Matthew; Covitz, Sydney; Gur, Raquel E.; Gur, Ruben C.; Pasqualetti, Fabio; Shinohara, Russell T.; Zhou, Dale; Satterthwaite, Theodore D.; et al. (Nature protocols, 2024)
Network control theory (NCT) is a simple and powerful tool for studying how network topology informs and constrains the dynamics of a system. Compared to other structure–function coupling approaches, the strength of NCT lies in its capacity to predict the patterns of external control signals that may alter the dynamics of a system in a desired way. An interesting development for NCT in the neuroscience field is its application to study behavior and mental health symptoms. To date, NCT has been validated to study different aspects of the human structural connectome. NCT outputs can be monitored throughout developmental stages to study the effects of connectome topology on neural dynamics and, separately, to test the coherence of empirical datasets with brain function and stimulation. Here, we provide a comprehensive pipeline for applying NCT to structural connectomes by following two procedures. The main procedure focuses on computing the control energy associated with the transitions between specific neural activity states. The second procedure focuses on computing average controllability, which indexes nodes’ general capacity to control the dynamics of the system. We provide recommendations for comparing NCT outputs against null network models, and we further support this approach with a Python-based software package called ‘network control theory for python’. The procedures in this protocol are appropriate for users with a background in network neuroscience and experience in dynamical systems theory. Key points We present a protocol on how to model the dynamics of neural connectivity states using network control theory (NCT) via a software package written in Python to compute the control energy associated with the transitions between states and the average controllability of the network’s dynamics. NCT complements biophysical models of neuronal communication and graph-theoretical measures of internodal communication. This protocol describes a comprehensive framework for applying network control theory to the human structural connectome to study its topology and show how that topology affects the dynamics of neural activity states, using a software package written in Python.