ArticleNature protocols2024
A network control theory pipeline for studying the dynamics of the structural connectome.
Article in Nature protocols, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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31 citing papers in PubMed.
- 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
- Neurochemically informed network control theory reveals energetic dysregulation of altered brain state dynamics in obsessive-compulsive disorder.Psychiatry and clinical neurosciences · 2026Article
- Characteristic spatiotemporal features of large-scale functional network architecture in posttraumatic stress disorder.Translational psychiatry · 2026Article
- White matter pathways mediating dorsolateral prefrontal TMS therapy for depression.Nature neuroscience · 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
- Convergent transcriptomic and connectomic controllers of information integration and its anaesthetic breakdown across mammalian brains.Nature human behaviour · 2026Article
- Cognition emerges from phase dynamics of intrinsic coordination.bioRxiv : the preprint server for biology · 2026Article
- Exploring the Role of the Rich Club in Network Control of Neurocognitive States.Human brain mapping · 2026Article
- Brain State Dynamics in Ketamine-Induced Dissociation Resemble Those in Posttraumatic Stress Disorder.Biological psychiatry global open science · 2026Article
- Controlling the human connectome with spatially diffuse input signals.Communications biology · 2026Article
- Controllability of morphometric network colocalize with underlying neurobiology in major depression.Psychological medicine · 2026Article
- Multimodal imaging-based targeting approach for network-level brain stimulation.Frontiers in neuroscience · 2026Article
- Effects of light on brain-state dynamics and energy landscape.Network neuroscience (Cambridge, Mass.) · 2026Article
- Optimal stochastic tracking control for brain network dynamics.Communications biology · 2025Article
- Inferring intrinsic neural timescales using optimal control theory.Nature communications · 2025Article
- The Controllability ofBiomimetics (Basel, Switzerland) · 2025Article
- Multitarget neurostimulation of the deep brain: clinical opportunities, challenges, and emerging technologies.Journal of neural engineering · 2025Review
- 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
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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.
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