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

IF 7.6 1区 医学 Q1 CLINICAL NEUROLOGY Brain Stimulation Pub Date : 2025-01-01 DOI:10.1016/j.brs.2024.12.1193
Uttara U. Khatri , Kristen Pulliam , Muskan Manesiya , Melanie Vieyra Cortez , José del R. Millán , Sara J. Hussain
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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.
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个性化的全脑活动模式实时预测人类皮质脊髓束的激活。
背景:经颅磁刺激(TMS)干预可以治疗脑卒中相关的运动障碍,但其效果是高度可变的。脑状态依赖的经颅磁刺激方法是解决这一问题的一个很有希望的方法,但病变位置和振荡动力学的个体间差异使得将它们转化为脑卒中后的大脑具有挑战性。个性化的大脑状态依赖方法需要专门设计来解决这些挑战。方法:作为实现这一目标的第一步,我们测试了一种新的基于机器学习的EEG-TMS系统,该系统可以实时识别健康成年人的个性化大脑活动模式,反映皮质脊髓束(CST)的强弱激活(强和弱CST状态)。参与者完成了一个单次的研究,包括获取TMS- eeg - emg训练数据集,个性化分类器训练,以及在分类器预测的个性化CST状态下实时的eeg通知单脉冲TMS。结果:在分类器预测的个性化强CST状态下,实时触发的MEP振幅显著大于相应的弱CST和随机CST状态下触发的MEP振幅。在分类器预测的个性化强CST状态下实时触发的MEP振幅也显著小于相应的弱CST状态下触发的MEP振幅。个性化的CST状态每次持续~ 1-2秒,连续的相似状态之间间隔~ 1秒。个体参与者在分类器预测的个性化强和弱CST状态之间表现出独特的频谱空间脑电图模式差异。结论:我们的研究结果首次表明,个性化的全脑脑电图活动模式可以实时预测健康人的CST激活。这些发现代表了使用个性化脑状态依赖的经颅磁刺激干预来促进脑卒中后CST功能的关键一步。
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来源期刊
Brain Stimulation
Brain Stimulation 医学-临床神经学
CiteScore
13.10
自引率
9.10%
发文量
256
审稿时长
72 days
期刊介绍: Brain Stimulation publishes on the entire field of brain stimulation, including noninvasive and invasive techniques and technologies that alter brain function through the use of electrical, magnetic, radiowave, or focally targeted pharmacologic stimulation. Brain Stimulation aims to be the premier journal for publication of original research in the field of neuromodulation. The journal includes: a) Original articles; b) Short Communications; c) Invited and original reviews; d) Technology and methodological perspectives (reviews of new devices, description of new methods, etc.); and e) Letters to the Editor. Special issues of the journal will be considered based on scientific merit.
期刊最新文献
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