A network control theory pipeline for studying the dynamics of the structural connectome

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 ma...

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Veröffentlicht in:	Nature protocols Jg. 19; H. 12; S. 3721 - 3749
Hauptverfasser:	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., Bassett, Dani S.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 01.12.2024 Nature Publishing Group
Schlagworte:	631/378/116/1925 631/378/116/2392 631/378/116/2393 Analytical Chemistry Biological Techniques Biomedical and Life Sciences Brain - diagnostic imaging Brain - physiology Cognitive ability Computation Computational Biology/Bioinformatics Connectome - methods Control systems Control theory Controllability Developmental stages Dynamic systems theory Dynamical systems Dynamics Graph theory Humans Life Sciences Microarrays Models, Neurological Nerve Net - physiology Network control Network topologies Neural networks Neurosciences Organic Chemistry Protocol Software Software packages Structure-function relationships System theory Topology
ISSN:	1754-2189, 1750-2799, 1750-2799
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Abstract	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.
AbstractList	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.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. 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. 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 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 (nctpy). The procedures are appropriate for users with a background in network neuroscience and experience in dynamical systems theory. 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. 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 pointsWe 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.
Author	Bassett, Dani S. Cieslak, Matthew Parkes, Linden Zhou, Dale Satterthwaite, Theodore D. Kim, Jason Z. Shinohara, Russell T. Gur, Raquel E. Stiso, Jennifer Covitz, Sydney Gur, Ruben C. Pasqualetti, Fabio Brynildsen, Julia K.
AuthorAffiliation	8 Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, Philadelphia, PA 19104, USA 14 Santa Fe Institute, Santa Fe, NM 87501, USA 11 Department of Neurology, Perelman School of Medicine, Philadelphia, PA 19104, USA 12 Department of Electrical and Systems Engineering, University of Pennsylvania, PA 19104, USA 13 Department of Physics and Astronomy, University of Pennsylvania, PA 19104, USA 6 Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA 9 Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA 2 Department of Bioengineering, University of Pennsylvania, PA 19104, USA 1 Department of Psychiatry, Rutgers University, Piscataway, NJ 08854, USA 10 Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA 5
AuthorAffiliation_xml	– name: 1 Department of Psychiatry, Rutgers University, Piscataway, NJ 08854, USA – name: 6 Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA – name: 11 Department of Neurology, Perelman School of Medicine, Philadelphia, PA 19104, USA – name: 14 Santa Fe Institute, Santa Fe, NM 87501, USA – name: 5 Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children’s Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA – name: 13 Department of Physics and Astronomy, University of Pennsylvania, PA 19104, USA – name: 7 Department of Mechanical Engineering, University of California, Riverside, Riverside, CA 92521, USA – name: 9 Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA – name: 2 Department of Bioengineering, University of Pennsylvania, PA 19104, USA – name: 4 Department of Physics, Cornell University, Ithaca, NY 14853, USA – name: 12 Department of Electrical and Systems Engineering, University of Pennsylvania, PA 19104, USA – name: 10 Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA – name: 3 Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA – name: 8 Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, Philadelphia, PA 19104, USA
Author_xml	– sequence: 1 givenname: Linden orcidid: 0000-0002-9329-7207 surname: Parkes fullname: Parkes, Linden email: linden.parkes@rutgers.edu organization: Department of Psychiatry, Rutgers University, Department of Bioengineering, University of Pennsylvania, Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania – sequence: 2 givenname: Jason Z. orcidid: 0000-0002-3970-4561 surname: Kim fullname: Kim, Jason Z. organization: Department of Physics, Cornell University – sequence: 3 givenname: Jennifer surname: Stiso fullname: Stiso, Jennifer organization: Department of Bioengineering, University of Pennsylvania – sequence: 4 givenname: Julia K. surname: Brynildsen fullname: Brynildsen, Julia K. organization: Department of Bioengineering, University of Pennsylvania – sequence: 5 givenname: Matthew orcidid: 0000-0002-1931-4734 surname: Cieslak fullname: Cieslak, Matthew organization: Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children’s Hospital of Philadelphia Research Institute, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania – sequence: 6 givenname: Sydney surname: Covitz fullname: Covitz, Sydney organization: Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children’s Hospital of Philadelphia Research Institute, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania – sequence: 7 givenname: Raquel E. surname: Gur fullname: Gur, Raquel E. organization: Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children’s Hospital of Philadelphia Research Institute, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania – sequence: 8 givenname: Ruben C. orcidid: 0000-0002-4082-8502 surname: Gur fullname: Gur, Ruben C. organization: Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children’s Hospital of Philadelphia Research Institute, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania – sequence: 9 givenname: Fabio surname: Pasqualetti fullname: Pasqualetti, Fabio organization: Department of Mechanical Engineering, University of California, Riverside – sequence: 10 givenname: Russell T. surname: Shinohara fullname: Shinohara, Russell T. organization: Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania – sequence: 11 givenname: Dale surname: Zhou fullname: Zhou, Dale organization: Department of Bioengineering, University of Pennsylvania – sequence: 12 givenname: Theodore D. surname: Satterthwaite fullname: Satterthwaite, Theodore D. organization: Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children’s Hospital of Philadelphia Research Institute, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Center for Biomedical Image Computation and Analytics, University of Pennsylvania – sequence: 13 givenname: Dani S. surname: Bassett fullname: Bassett, Dani S. organization: Department of Bioengineering, University of Pennsylvania, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Department of Neurology, Perelman School of Medicine, Department of Electrical and Systems Engineering, University of Pennsylvania, Department of Physics and Astronomy, University of Pennsylvania, Santa Fe Institute
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/39075309$$D View this record in MEDLINE/PubMed
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DOI	10.1038/s41596-024-01023-w
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Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Writing—reviewing and editing: L.P., J.Z.K, J.S., J.K.B., M.C., S.C., R.E.G., R.C.G., F.P., R.T.S., D.Z., T.D.S, and D.S.B. Data curation: J.K.B., M.C., S.C., R.E.G., R.C.G., R.T.S., D.Z., and T.D.S. Software: L.P., J.Z.K., and J.S. Formal analysis: L.P., and J.Z.K. Visualization: L.P., and J.Z.K. These authors contributed equally Methodology: L.P., J.Z.K., J.S., and D.S.B. Conceptualization: L.P., J.Z.K., T.D.S., and D.S.B. Writing—original draft: L.P., and J.Z.K. Author contributions
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Snippet	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...
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Title	A network control theory pipeline for studying the dynamics of the structural connectome
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