ArticleBrain stimulation
Personalized whole-brain activity patterns predict human corticospinal tract activation in real-time.
Article in Brain stimulation. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06365086 (Targeting Personalized Brain States Reflecting Strong and Weak Corticospinal Tract Output in Real-time), which is not on this map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Targeting Personalized Brain States Reflecting Strong and Weak Corticospinal Tract Output in Real-time
Who cites it
2 citing papers in PubMed.
- From Oscillations to Brain States: Real-Time EEG-TMS for Adaptive Neuromodulation.Bioengineering (Basel, Switzerland) · 2026Review
- Real-time TMS-EEG for brain state-controlled research and precision treatment: a narrative review and guide.Journal of neural engineering · 2024Review
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- Update of
Authors and funding
6 authors.
Funding
Abstract
backgroundTranscranial magnetic stimulation (TMS) interventions could feasibly treat stroke-related motor impairments, but their effects are highly variable. Brain state-dependent TMS approaches are a promising solution to this problem, but inter-individual variation in lesion location and oscillatory dynamics can make translating them to the poststroke brain challenging. Personalized brain state-dependent approaches specifically designed to address these challenges are needed.
methodsAs a first step towards this goal, we tested a novel machine learning-based EEG-TMS system that identifies personalized brain activity patterns reflecting strong and weak corticospinal tract (CST) activation (strong and weak CST states) in healthy adults in real-time. Participants completed a single-session study that included the acquisition of a TMS-EEG-EMG training dataset, personalized classifier training, and real-time EEG-informed single-pulse TMS during classifier-predicted personalized CST states.
resultsMEP amplitudes elicited in real-time during classifier-predicted personalized strong CST states were significantly larger than those elicited during corresponding weak and random CST states. MEP amplitudes elicited in real-time during classifier-predicted personalized strong CST states were also significantly less variable than those elicited during corresponding weak CST states. Personalized CST states lasted for ∼1-2 s at a time and ∼1 s elapsed between consecutive similar states. Individual participants exhibited unique differences in spectro-spatial EEG patterns between classifier-predicted personalized strong and weak CST states.
conclusionOur results show for the first time that personalized whole-brain EEG activity patterns predict CST activation in real-time in healthy humans. These findings represent a pivotal step towards using personalized brain state-dependent TMS interventions to promote poststroke CST function.
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