Using network control theory to study the dynamics of the structural connectome

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Abstract

Network control theory (NCT) is a simple and powerful tool for studying how network topology informs and constrains dynamics. 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 dynamics in a desired way. We have extensively developed and validated the application of NCT to the human structural connectome. Through these efforts, we have studied (i) how different aspects of connectome topology affect neural dynamics, (ii) whether NCT outputs cohere with empirical data on brain function and stimulation, and (iii) how NCT outputs vary across development and correlate with behavior and mental health symptoms. In this protocol, we introduce a framework for applying NCT to structural connectomes following two main pathways. Our primary pathway focuses on computing the control energy associated with transitioning between specific neural activity states. Our second pathway focuses on computing average controllability, which indexes nodes’ general capacity to control dynamics. We also provide recommendations for comparing NCT outputs against null network models. Finally, we support this protocol with a Python-based software package called network control theory for python ( nctpy) .

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Most cited references 144

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The organization of the human cerebral cortex estimated by intrinsic functional connectivity.

B. Yeo, Fenna M. Krienen, Jorge Sepulcre … (2011)

Information processing in the cerebral cortex involves interactions among distributed areas. Anatomical connectivity suggests that certain areas form local hierarchical relations such as within the visual system. Other connectivity patterns, particularly among association areas, suggest the presence of large-scale circuits without clear hierarchical relations. In this study the organization of networks in the human cerebrum was explored using resting-state functional connectivity MRI. Data from 1,000 subjects were registered using surface-based alignment. A clustering approach was employed to identify and replicate networks of functionally coupled regions across the cerebral cortex. The results revealed local networks confined to sensory and motor cortices as well as distributed networks of association regions. Within the sensory and motor cortices, functional connectivity followed topographic representations across adjacent areas. In association cortex, the connectivity patterns often showed abrupt transitions between network boundaries. Focused analyses were performed to better understand properties of network connectivity. A canonical sensory-motor pathway involving primary visual area, putative middle temporal area complex (MT+), lateral intraparietal area, and frontal eye field was analyzed to explore how interactions might arise within and between networks. Results showed that adjacent regions of the MT+ complex demonstrate differential connectivity consistent with a hierarchical pathway that spans networks. The functional connectivity of parietal and prefrontal association cortices was next explored. Distinct connectivity profiles of neighboring regions suggest they participate in distributed networks that, while showing evidence for interactions, are embedded within largely parallel, interdigitated circuits. We conclude by discussing the organization of these large-scale cerebral networks in relation to monkey anatomy and their potential evolutionary expansion in humans to support cognition.

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The Human Connectome Project consortium led by Washington University, University of Minnesota, and Oxford University is undertaking a systematic effort to map macroscopic human brain circuits and their relationship to behavior in a large population of healthy adults. This overview article focuses on progress made during the first half of the 5-year project in refining the methods for data acquisition and analysis. Preliminary analyses based on a finalized set of acquisition and preprocessing protocols demonstrate the exceptionally high quality of the data from each modality. The first quarterly release of imaging and behavioral data via the ConnectomeDB database demonstrates the commitment to making HCP datasets freely accessible. Altogether, the progress to date provides grounds for optimism that the HCP datasets and associated methods and software will become increasingly valuable resources for characterizing human brain connectivity and function, their relationship to behavior, and their heritability and genetic underpinnings. Copyright © 2013 Elsevier Inc. All rights reserved.

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A mesoscale connectome of the mouse brain.

Seung Wook Oh, Julie Harris, Lydia Ng … (2014)

Comprehensive knowledge of the brain's wiring diagram is fundamental for understanding how the nervous system processes information at both local and global scales. However, with the singular exception of the C. elegans microscale connectome, there are no complete connectivity data sets in other species. Here we report a brain-wide, cellular-level, mesoscale connectome for the mouse. The Allen Mouse Brain Connectivity Atlas uses enhanced green fluorescent protein (EGFP)-expressing adeno-associated viral vectors to trace axonal projections from defined regions and cell types, and high-throughput serial two-photon tomography to image the EGFP-labelled axons throughout the brain. This systematic and standardized approach allows spatial registration of individual experiments into a common three dimensional (3D) reference space, resulting in a whole-brain connectivity matrix. A computational model yields insights into connectional strength distribution, symmetry and other network properties. Virtual tractography illustrates 3D topography among interconnected regions. Cortico-thalamic pathway analysis demonstrates segregation and integration of parallel pathways. The Allen Mouse Brain Connectivity Atlas is a freely available, foundational resource for structural and functional investigations into the neural circuits that support behavioural and cognitive processes in health and disease.

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Author and article information

Contributors

Linden Parkes: Role: ConceptualizationRole: MethodologyRole: SoftwareRole: Formal analysisRole: VisualizationRole: Writing—original draftRole: Writing—reviewing and editing

Jason Z. Kim: Role: ConceptualizationRole: MethodologyRole: SoftwareRole: Formal analysisRole: VisualizationRole: Writing—original draftRole: Writing—reviewing and editing

Jennifer Stiso: Role: MethodologyRole: SoftwareRole: Writing—reviewing and editing

Julia K. Brynildsen: Role: Data curationRole: Writing—reviewing and editing

Matthew Cieslak: Role: Data curationRole: Writing—reviewing and editing

Sydney Covitz: Role: Data curationRole: Writing—reviewing and editing

Raquel E. Gur: Role: Data curationRole: Writing—reviewing and editing

Ruben C. Gur: Role: Data curationRole: Writing—reviewing and editing

Fabio Pasqualetti: Role: Writing—reviewing and editing

Russell T. Shinohara: Role: Data curationRole: Writing—reviewing and editing

Dale Zhou: Role: Data curationRole: Writing—reviewing and editing

Theodore D. Satterthwaite: Role: ConceptualizationRole: Data curationRole: Writing—reviewing and editing

Dani S. Bassett: Role: ConceptualizationRole: MethodologyRole: Writing—reviewing and editing

Journal

Journal ID (nlm-ta): bioRxiv

Journal ID (publisher-id): BIORXIV

Title: bioRxiv

Publisher: Cold Spring Harbor Laboratory

Publication date (Electronic): 24 August 2023

Electronic Location Identifier: 2023.08.23.554519

Affiliations

[1 ]Department of Bioengineering, University of Pennsylvania, PA 19104, USA

[2 ]Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA

[3 ]Department of Psychiatry, Rutgers University, Piscataway, NJ 08854, USA

[4 ]Department of Physics, Cornell University, Ithaca, NY 14853, USA

[5 ]Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children’s Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA

[6 ]Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA

[7 ]Department of Mechanical Engineering, University of California, Riverside, Riverside, CA 92521, USA

[8 ]Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, Philadelphia, PA 19104, USA

[9 ]Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, 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

[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

[14 ]Santa Fe Institute, Santa Fe, NM 87501, USA

Author notes

[*]

These authors contributed equally

Article

DOI: 10.1101/2023.08.23.554519

PMC ID: 10473719

PubMed ID: 37662395

SO-VID: 96b4c860-8ca3-42b7-bc66-1f176aac4342

License:

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