A practical guide to methodological considerations in the controllability of structural brain networks
Teresa M Karrer1,2, Jason Z Kim2,3, Jennifer Stiso4,3
1Faculty of Medicine, Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen, Germany.
Journal of Neural Engineering
|January 23, 2020
Summary
Network control theory offers a framework for understanding brain activity and structure. This guide details methodological choices for analyzing brain network controllability, enhancing neuroscience research.
Area of Science:
- Neuroscience
- Network Science
- Control Theory
Background:
- Understanding brain states requires linking neural connectivity and activity.
- Network control theory provides a framework to study how system dynamics emerge from structure.
Purpose of the Study:
- To provide a practical guide to methodological considerations in applying network control theory to structural brain networks.
- To examine the impact of modeling choices on controllability metrics.
- To suggest theoretical extensions for network control theory in neuroscience.
Main Methods:
- Systematic overview of network control theory applied to neuroscience.
- Analysis of a high-resolution diffusion imaging dataset (730 directions, 10 healthy adults).
- Examination of modeling choices on average controllability, modal controllability, minimum control energy, and optimal control energy.
Main Results:
- Demonstrated the impact of modeling choices on controllability metrics.
- Introduced an alternative measure of structural connectivity accounting for radial activity propagation.
- Defined a new metric for quantifying the complexity of a system's energy landscape.
Conclusions:
- Offers specific modeling recommendations and discusses methodological constraints.
- Aims to inspire the neuroimaging community to utilize network control theory for cognitive, developmental, and clinical neuroscience questions.
