ArticleProceedings of the National Academy of Sciences of the United States of America2021
Time-evolving controllability of effective connectivity networks during seizure progression.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- Nucleus-specific thalamic involvement in seizure networks differentiates neuromodulation outcomes.medRxiv : the preprint server for health sciences · 2026Article
- Stabilizing fractional dynamical networks suppresses epileptic seizures.Scientific reports · 2026Article
- Functional connectivity-based attractor dynamics of the human brain in rest, task, and disease.eLife · 2026Article
- White matter signals reflect information transmission between brain regions during seizures.Brain : a journal of neurology · 2026Article
- States of dynamic connectivity flow in temporal multiplex networks: A case study in human epilepsy and postictal aphasia.Network neuroscience (Cambridge, Mass.) · 2026Article
- Habit learning is associated with efficiently controlled network dynamics in naive macaque monkeys.Npj complexity · 2026Article
- High energy consumption characterizes abnormal brain state transitions in temporal lobe epilepsy.Neurobiology of disease · 2025Article
- Inferring Neural Communication Dynamics from Field Potentials Using Graph Diffusion Autoregression.bioRxiv : the preprint server for biology · 2025Article
- Intrinsic brain network stability during kainic acid-induced epileptogenesis.Epilepsia open · 2025Article
- Network control theory applied to the human connectome: A study on variability and discriminability of fMRI connectomic features under normal and defective sensorineural conditions.Network neuroscience (Cambridge, Mass.) · 2025Article
- Task-Related Controllability of Functional Connectome During a Working Memory Task in Schizophrenia, Bipolar Disorder, and Major Depressive Disorder.Research (Washington, D.C.) · 2025Article
- A network control theory pipeline for studying the dynamics of the structural connectome.Nature protocols · 2024Article
- Brain age prediction and deviations from normative trajectories in the neonatal connectome.Nature communications · 2024Article
- EEG microstate transition cost correlates with task demands.PLoS computational biology · 2024Article
- Contributions of network structure, chemoarchitecture and diagnostic categories to transitions between cognitive topographies.Nature biomedical engineering · 2024Article
- Parameter estimation in a whole-brain network model of epilepsy: Comparison of parallel global optimization solvers.PLoS computational biology · 2024Article
- Effects of topology on the controllability of brain connectomes through sparsity promoting control.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2024Article
- Functional brain controllability in Parkinson's disease and its association with motor outcomes after deep brain stimulation.Frontiers in neuroscience · 2024Article
- Unearthing the mechanisms of responsive neurostimulation for epilepsy.Communications medicine · 2023Review
- Network models to enhance the translational impact of cross-species studies.Nature reviews. Neuroscience · 2023Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Over one third of the estimated 3 million people with epilepsy in the United States are medication resistant. Responsive neurostimulation from chronically implanted electrodes provides a promising treatment alternative to resective surgery. However, determining optimal personalized stimulation parameters, including when and where to intervene to guarantee a positive patient outcome, is a major open challenge. Network neuroscience and control theory offer useful tools that may guide improvements in parameter selection for control of anomalous neural activity. Here we use a method to characterize dynamic controllability across consecutive effective connectivity (EC) networks based on regularized partial correlations between implanted electrodes during the onset, propagation, and termination regimes of 34 seizures. We estimate regularized partial correlation adjacency matrices from 1-s time windows of intracranial electrocorticography recordings using the Graphical Least Absolute Shrinkage and Selection Operator (GLASSO). Average and modal controllability metrics calculated from each resulting EC network track the time-varying controllability of the brain on an evolving landscape of conditionally dependent network interactions. We show that average controllability increases throughout a seizure and is negatively correlated with modal controllability throughout. Our results support the hypothesis that the energy required to drive the brain to a seizure-free state from an ictal state is smallest during seizure onset, yet we find that applying control energy at electrodes in the seizure onset zone may not always be energetically favorable. Our work suggests that a low-complexity model of time-evolving controllability may offer insights for developing and improving control strategies targeting seizure suppression.
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