Personalized whole-brain activity patterns predict human corticospinal tract activation in real-time

Uttara U Khatri; Kristen Pulliam; Muskan Manesiya; Melanie Vieyra Cortez; José Del R Millán; Sara J Hussain

doi:10.1016/j.brs.2024.12.1193

Personalized whole-brain activity patterns predict human corticospinal tract activation in real-time

Brain Stimul. 2024 Dec 21;18(1):64-76. doi: 10.1016/j.brs.2024.12.1193. Online ahead of print.

Authors

Uttara U Khatri¹, Kristen Pulliam¹, Muskan Manesiya¹, Melanie Vieyra Cortez¹, José Del R Millán², Sara J Hussain³

Affiliations

¹ Movement and Cognitive Rehabilitation Science Program, Department of Kinesiology and Health Education, The University of Texas at Austin, Austin, TX, USA.
² Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA; Department of Neurology, The University of Texas at Austin, Austin, TX, USA; Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA.
³ Movement and Cognitive Rehabilitation Science Program, Department of Kinesiology and Health Education, The University of Texas at Austin, Austin, TX, USA. Electronic address: [email protected].

PMID: 39716573
DOI: 10.1016/j.brs.2024.12.1193

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

Keywords: Brain stimulation; Corticospinal tract; Electroencephalography; Machine learning; Motor cortex; Transcranial magnetic stimulation.

Abstract

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