ArticleBrain stimulation
Investigating brain network dynamics in state-dependent stimulation: A concurrent electroencephalography and transcranial magnetic stimulation study using hidden Markov models.
Article in Brain stimulation. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- What Static Connectivity Misses: Dynamic Alpha-Band Brain Network States in First-Episode Psychosis.Brain topography · 2026Article
- Same movies, different stories: aberrant brain state dynamics during naturalistic emotional stimuli in depression.Journal of translational medicine · 2025Article
- State-Dependent Transcranial Magnetic Stimulation Synchronized with Electroencephalography: Mechanisms, Applications, and Future Directions.Brain sciences · 2025Review
- Canonical Hidden Markov Model Networks for studying M/EEG.Imaging neuroscience (Cambridge, Mass.)Article
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSystems neuroscience studies have shown that baseline brain activity can be categorized into large-scale networks (resting-state-networks, RNSs), with influence on cognitive abilities and clinical symptoms. These insights have guided millimeter-precise selection of brain stimulation targets based on RSNs. Concurrently, Transcranial Magnetic Stimulation (TMS) studies revealed that baseline brain states, measured by EEG signal power or phase, affect stimulation outcomes. However, EEG dynamics in these studies are mostly limited to single regions or channels, lacking the spatial resolution needed for accurate network-level characterization.
objectiveWe aim at mapping brain networks with high spatial and temporal precision and to assess whether the occurrence of specific network-level-states impact TMS outcome. To this end, we will identify large-scale brain networks and explore how their dynamics relates to corticospinal excitability.
methodsThis study leverages Hidden Markov Models to identify large-scale brain states from pre-stimulus source space high-density-EEG data collected during TMS targeting the left primary motor cortex in twenty healthy subjects. The association between states and fMRI-defined RSNs was explored using the Yeo atlas, and the trial-by-trial relation between states and corticospinal excitability was examined.
resultsWe extracted fast-dynamic large-scale brain states with unique spatiotemporal and spectral features resembling major RSNs. The engagement of different networks significantly influences corticospinal excitability, with larger motor evoked potentials when baseline activity was dominated by the sensorimotor network.
conclusionsThese findings represent a step forward towards characterizing brain network in EEG-TMS with both high spatial and temporal resolution and underscore the importance of incorporating large-scale network dynamics into TMS experiments.
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