A practical guide to methodological considerations in the controllability of structural brain networks
Journal of Neural Engineering, vol. 17, pp. 026031
Abstract
OBJECTIVE: 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. APPROACH: 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 h of scanning from ten healthy young adults. MAIN RESULTS: 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. SIGNIFICANCE: Our hope is that this accessible account will inspire the neuroimaging community to more fully exploit the potential of network control theory in tackling pressing questions in cognitive, developmental, and clinical neuroscience.
Authors 7
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Teresa M. Karrer Aachen Faculty of Medicine Department of Psychiatry, Psychotherapy and Psychosomatics
University of Pennsylvania · RWTH Aachen University
Affiliation as printed
Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, United States of America
Faculty of Medicine, Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen, Germany
University of Pennsylvania, Philadelphia, United States
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Affiliation as printed
Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, United States of America
These two authors contributed equally
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Affiliation as printed
Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
These two authors contributed equally
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Affiliation as printed
Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
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University of California, Riverside
Affiliation as printed
Department of Mechanical Engineering, University of California, Riverside, CA 92521, United States of America
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Forschungszentrum Jülich · Jülich Aachen Research Alliance · RWTH Aachen University
Affiliation as printed
Faculty of Medicine, Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen, Germany
Institute of Neuroscience and Medicine, JARA-Institute Brain Structure Function Relationship (INM 10), Research Center Jülich, Jülich, Germany
JARA-Translational Brain Medicine, Aachen, Germany
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Santa Fe Institute · University of Pennsylvania
Affiliation as printed
Author to whom any correspondence should be addressed
Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, United States of America
Department of Electrical and Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, United States of America
Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
Department of Physics and Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA 19104, United States of America
Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
Santa Fe Institute, Santa Fe, NM 87501, United States of America
#N# * University of Pennsylvania
Cited by 139 stored of 139
Cited by patents worldwide 1 (Lens.org)
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