ArticleJournal of neural engineering2020
A practical guide to methodological considerations in the controllability of structural brain networks.
Article in Journal of neural engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers.
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Who cites it
70 citing papers in PubMed.
- Mindful attention promotes control of brain network dynamics for self-regulation and discontinues the past from the present.Proceedings of the National Academy of Sciences of the United States of America · 2023Trial
- Language Recovery after Brain Injury: A Structural Network Control Theory Study.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022Trial
- Longitudinal changes in control energy of brain networks in older adults at familial risk for Alzheimer's disease.bioRxiv : the preprint server for biology · 2026Article
- Spatiotemporal asymmetries on brain energy landscape uncover system entrapment related to depression severity.Nature communications · 2026Article
- Brain energetic landscapes shape state dysregulation in major depressive disorder: a morphological network controllability perspective.Translational psychiatry · 2026Article
- Exploring the Role of the Rich Club in Network Control of Neurocognitive States.Human brain mapping · 2026Article
- Controlling the human connectome with spatially diffuse input signals.Communications biology · 2026Article
- The network architecture of general intelligence in the human connectome.Nature communications · 2026Article
- Controllability of morphometric network colocalize with underlying neurobiology in major depression.Psychological medicine · 2026Article
- Active Inference and Functional Parametrisation: Differential Flatness and Smooth Random Realisation.Entropy (Basel, Switzerland) · 2026Article
- HONeD-in on Brain Activity: Deconvolving Passive Diffusion on the Structural Network from Functional Brain Signals.bioRxiv : the preprint server for biology · 2026Article
- Article
- Optimal stochastic tracking control for brain network dynamics.Communications biology · 2025Article
- Inferring intrinsic neural timescales using optimal control theory.Nature communications · 2025Article
- High energy consumption characterizes abnormal brain state transitions in temporal lobe epilepsy.Neurobiology of disease · 2025Article
- Network control theory uncovers aberrant connectome controllability in trigeminal neuralgia.The journal of headache and pain · 2025Article
- Network control energy reductions under DMT relate to serotonin receptors, signal diversity, and subjective experience.Communications biology · 2025Article
- Prefrontal FGF1 Signaling is Required for Accumbal Deep Brain Stimulation Treatment of Addiction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Determining interaction directionality in complex biochemical networks from stationary measurements.Scientific reports · 2025Article
- Low-dimensional controllability of brain networks.PLoS computational biology · 2025Article
10 more citing papers are in PubMed but not listed here.
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7 authors.
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Abstract
objectivePredicting 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
resultsFollowing 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.
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