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

## TL;DR

This paper provides a practical overview of network control theory applied to structural brain networks, examining modeling choices, extending metrics, and offering methodological guidance based on high-resolution imaging data.

## Contribution

It offers a systematic review of control metrics, explores the impact of modeling decisions, and introduces new measures for connectivity and energy landscape complexity in brain networks.

## Key findings

- Modeling choices significantly influence control metrics.
- Radial propagation improves structural connectivity measures.
- New metrics enhance understanding of energy landscape complexity.

## Abstract

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.