Journal article
Personalized whole-brain activity patterns predict human corticospinal tract activation in real-time
Brain stimulation, Vol.18(1), pp.64-76
01/2025
DOI: 10.1016/j.brs.2024.12.1193
PMCID: PMC11867860
PMID: 39716573
Abstract
Transcranial 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.
As 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.
MEP 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.
Our 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.
•We tested machine learning-driven real-time brain state-dependent TMS in healthy adults.•Our system targeted personalized strong and weak corticospinal tract (CST) activation states.•CST activation was largest and least variable during personalized strong CST states in real-time.•Personalized CST states lasted for ∼1–2 s and ∼1 s elapsed between them.•Future interventional studies using this technique may promote poststroke CST function.
Details
- Title: Subtitle
- Personalized whole-brain activity patterns predict human corticospinal tract activation in real-time
- Creators
- Uttara U. Khatri - The University of Texas at AustinKristen Pulliam - The University of Texas at AustinMuskan Manesiya - The University of Texas at AustinMelanie Vieyra Cortez - The University of Texas at AustinJosé del R. Millán - The University of Texas at AustinSara J. Hussain - The University of Texas at Austin
- Resource Type
- Journal article
- Publication Details
- Brain stimulation, Vol.18(1), pp.64-76
- DOI
- 10.1016/j.brs.2024.12.1193
- PMID
- 39716573
- PMCID
- PMC11867860
- NLM abbreviation
- Brain Stimul
- ISSN
- 1935-861X
- eISSN
- 1876-4754
- Publisher
- Elsevier Inc
- Number of pages
- 13
- Language
- English
- Date published
- 01/2025
- Academic Unit
- Health, Sport, and Human Physiology
- Record Identifier
- 9984948042502771
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